258 results on '"John D. Hazle"'
Search Results
2. Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolization
- Author
-
Ahmed W. Moawad, David Fuentes, Ahmed M. Khalaf, Katherine J. Blair, Janio Szklaruk, Aliya Qayyum, John D. Hazle, and Khaled M. Elsayes
- Subjects
volumetric RECIST ,hepatocellular carcinoma ,TACE ,automated segmentation ,tumor response ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy and the leading cause of death in patients with cirrhosis. Various treatments for HCC are available, including transarterial chemoembolization (TACE), which is the commonest intervention performed in HCC. Radiologic tumor response following TACE is an important prognostic factor for patients with HCC. We hypothesized that, for large HCC tumors, assessment of treatment response made with automated volumetric response evaluation criteria in solid tumors (RECIST) might correlate with the assessment made with the more time- and labor-intensive unidimensional modified RECIST (mRECIST) and manual volumetric RECIST (M-vRECIST) criteria. Accordingly, we undertook this retrospective study to compare automated volumetric RECIST (A-vRECIST) with M-vRECIST and mRESIST for the assessment of large HCC tumors' responses to TACE.Methods:We selected 42 pairs of contrast-enhanced computed tomography (CT) images of large HCCs. Images were taken before and after TACE, and in each of the images, the HCC was segmented using both a manual contouring tool and a convolutional neural network. Three experienced radiologists assessed tumor response to TACE using mRECIST criteria. The intra-class correlation coefficient was used to assess inter-reader reliability in the mRECIST measurements, while the Pearson correlation coefficient was used to assess correlation between the volumetric and mRECIST measurements.Results:Volumetric tumor assessment using automated and manual segmentation tools showed good correlation with mRECIST measurements. For A-vRECIST and M-vRECIST, respectively, r = 0.597 vs. 0.622 in the baseline studies; 0.648 vs. 0.748 in the follow-up studies; and 0.774 vs. 0.766 in the response assessment (P < 0.001 for all). The A-vRECIST evaluation showed high correlation with the M-vRECIST evaluation (r = 0.967, 0.937, and 0.826 in baseline studies, follow-up studies, and response assessment, respectively, P < 0.001 for all).Conclusion:Volumetric RECIST measurements are likely to provide an early marker for TACE monitoring, and automated measurements made with a convolutional neural network may be good substitutes for manual volumetric measurements.
- Published
- 2020
- Full Text
- View/download PDF
3. Prospective analysis of in vivo landmark point-based MRI geometric distortion in head and neck cancer patients scanned in immobilized radiation treatment position: Results of a prospective quality assurance protocol
- Author
-
Abdallah S.R. Mohamed, Chase Hansen, Joseph Weygand, Yao Ding, Stephen J. Frank, David I. Rosenthal, Ken-Pin Hwang, John D. Hazle, Clifton D. Fuller, and Jihong Wang
- Subjects
Medical physics. Medical radiology. Nuclear medicine ,R895-920 ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Purpose: Uncertainties related to geometric distortion are a major obstacle for effectively utilizing MRI in radiation oncology. We aim to quantify the geometric distortion in patient images by comparing their in-treatment position MRIs with the corresponding planning CTs, using CT as the non-distorted gold standard. Methods: Twenty-one head and neck cancer patients were imaged with MRI as part of a prospective Institutional Review Board approved study. MR images were acquired with a T2 SE sequence (0.5 Ã 0.5 Ã 2.5 mm voxel size) in the same immobilization position as in the CTs. MRI to CT rigid registration was then done and geometric distortion comparison was assessed by measuring the corresponding anatomical landmarks on both the MRI and the CT images. Several landmark measurements were obtained including; skin to skin (STS), bone to bone, and soft tissue to soft tissue at specific levels in horizontal and vertical planes of both scans. Inter-observer variability was assessed and interclass correlation (ICC) was calculated. Results: A total of 430 landmark measurements were obtained. The median distortion for all landmarks in all scans was 1.06 mm (IQR 0.6â1.98). For each patient 48% of the measurements were done in the right-left direction and 52% were done in the anteroposterior direction. The measured geometric distortion was not statistically different in the right-left direction compared to the anteroposterior direction (1.5 ± 1.6 vs. 1.6 ± 1.7 mm, respectively, p = 0.4). The magnitude of distortion was higher in the STS peripheral landmarks compared to the more central landmarks (2.0 ± 1.9 vs. 1.2 ± 1.3 mm, pÂ
- Published
- 2017
- Full Text
- View/download PDF
4. IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies
- Author
-
Ai-Ling Hsu, Ping Hou, Jason M. Johnson, Changwei W. Wu, Kyle R. Noll, Sujit S. Prabhu, Sherise D. Ferguson, Vinodh A. Kumar, Donald F. Schomer, John D. Hazle, Jyh-Horng Chen, and Ho-Ling Liu
- Subjects
functional magnetic resonance imaging (fMRI) ,presurgical mapping ,preoperative mapping ,resting state ,cerebrovascular reactivity ,software ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Task-evoked and resting-state (rs) functional magnetic resonance imaging (fMRI) techniques have been applied to the clinical management of neurological diseases, exemplified by presurgical localization of eloquent cortex, to assist neurosurgeons in maximizing resection while preserving brain functions. In addition, recent studies have recommended incorporating cerebrovascular reactivity (CVR) imaging into clinical fMRI to evaluate the risk of lesion-induced neurovascular uncoupling (NVU). Although each of these imaging techniques possesses its own advantage for presurgical mapping, a specialized clinical software that integrates the three complementary techniques and promptly outputs the analyzed results to radiology and surgical navigation systems in a clinical format is still lacking. We developed the Integrated fMRI for Clinical Research (IClinfMRI) software to facilitate these needs. Beyond the independent processing of task-fMRI, rs-fMRI, and CVR mapping, IClinfMRI encompasses three unique functions: (1) supporting the interactive rs-fMRI mapping while visualizing task-fMRI results (or results from published meta-analysis) as a guidance map, (2) indicating/visualizing the NVU potential on analyzed fMRI maps, and (3) exporting these advanced mapping results in a Digital Imaging and Communications in Medicine (DICOM) format that are ready to export to a picture archiving and communication system (PACS) and a surgical navigation system. In summary, IClinfMRI has the merits of efficiently translating and integrating state-of-the-art imaging techniques for presurgical functional mapping and clinical fMRI studies.
- Published
- 2018
- Full Text
- View/download PDF
5. Advanced magnetic resonance imaging based algorithm for local grading of glioma.
- Author
-
Evan D. H. Gates, Jonathan S. Lin, Jeffrey S. Weinberg, Sujit S. Prabhu, Jackson Hamilton, John D. Hazle, Gregory N. Fuller, Veera Baladandayuthapani, David T. Fuentes, and Dawid Schellingerhout
- Published
- 2020
- Full Text
- View/download PDF
6. Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic
- Author
-
Mai-Lan Ho, Corey W. Arnold, Summer J. Decker, John D. Hazle, Elizabeth A. Krupinski, and David A. Mankoff
- Subjects
Radiology, Nuclear Medicine and imaging - Published
- 2023
7. One-Pot, One-Step Synthesis of Drug-Loaded Magnetic Multimicelle Aggregates
- Author
-
Chang Soo Kim, Dmitry Nevozhay, Rebeca Romero Aburto, Ashok Pehere, Lan Pang, Rebecca Dillard, Ziqiu Wang, Clayton Smith, Kelsey Boitnott Mathieu, Marie Zhang, John D. Hazle, Robert C. Bast, and Konstantin Sokolov
- Subjects
Pharmacology ,Drug Delivery Systems ,Pharmaceutical Preparations ,Magnetic Phenomena ,Organic Chemistry ,Biomedical Engineering ,Pharmaceutical Science ,Nanoparticles ,Reproducibility of Results ,Bioengineering ,Lipids ,Biotechnology - Abstract
Lipid-based formulations provide a nanotechnology platform that is widely used in a variety of biomedical applications because it has several advantageous properties including biocompatibility, reduced toxicity, relative ease of surface modifications, and the possibility for efficient loading of drugs, biologics, and nanoparticles. A combination of lipid-based formulations with magnetic nanoparticles such as iron oxide was shown to be highly advantageous in a growing number of applications including magnet-mediated drug delivery and image-guided therapy. Currently, lipid-based formulations are prepared by multistep protocols. Simplification of the current multistep procedures can lead to a number of important technological advantages including significantly decreased processing time, higher reaction yield, better product reproducibility, and improved quality. Here, we introduce a one-pot, single-step synthesis of drug-loaded magnetic multimicelle aggregates (MaMAs), which is based on controlled flow infusion of an iron oxide nanoparticle/lipid mixture into an aqueous drug solution under ultrasonication. Furthermore, we prepared molecular-targeted MaMAs by directional antibody conjugation through an Fc moiety using Cu-free click chemistry. Fluorescence imaging and quantification confirmed that antibody-conjugated MaMAs showed high cell-specific targeting that was enhanced by magnetic delivery.
- Published
- 2023
8. Data from Kinetic Modeling and Constrained Reconstruction of Hyperpolarized [1-13C]-Pyruvate Offers Improved Metabolic Imaging of Tumors
- Author
-
John D. Hazle, Stephen Y. Lai, A. Dean Sherry, Craig Malloy, Charles A. Conrad, Dawid Schellingerhout, Mong-Hong Lee, Sai-Ching J. Yeung, Arvind Rao, Ping-Chieh Chou, Liem Phan, Yunyun Chen, Vlad C. Sandulache, Jaehyuk Lee, Matthew E. Merritt, David Fuentes, Wolfgang Stefan, Marc S. Ramirez, Christopher M. Walker, and James A. Bankson
- Abstract
Hyperpolarized [1-13C]-pyruvate has shown tremendous promise as an agent for imaging tumor metabolism with unprecedented sensitivity and specificity. Imaging hyperpolarized substrates by magnetic resonance is unlike traditional MRI because signals are highly transient and their spatial distribution varies continuously over their observable lifetime. Therefore, new imaging approaches are needed to ensure optimal measurement under these circumstances. Constrained reconstruction algorithms can integrate prior information, including biophysical models of the substrate/target interaction, to reduce the amount of data that is required for image analysis and reconstruction. In this study, we show that metabolic MRI with hyperpolarized pyruvate is biased by tumor perfusion and present a new pharmacokinetic model for hyperpolarized substrates that accounts for these effects. The suitability of this model is confirmed by statistical comparison with alternates using data from 55 dynamic spectroscopic measurements in normal animals and murine models of anaplastic thyroid cancer, glioblastoma, and triple-negative breast cancer. The kinetic model was then integrated into a constrained reconstruction algorithm and feasibility was tested using significantly undersampled imaging data from tumor-bearing animals. Compared with naïve image reconstruction, this approach requires far fewer signal-depleting excitations and focuses analysis and reconstruction on new information that is uniquely available from hyperpolarized pyruvate and its metabolites, thus improving the reproducibility and accuracy of metabolic imaging measurements. Cancer Res; 75(22); 4708–17. ©2015 AACR.
- Published
- 2023
9. Supplementary Table from Kinetic Modeling and Constrained Reconstruction of Hyperpolarized [1-13C]-Pyruvate Offers Improved Metabolic Imaging of Tumors
- Author
-
John D. Hazle, Stephen Y. Lai, A. Dean Sherry, Craig Malloy, Charles A. Conrad, Dawid Schellingerhout, Mong-Hong Lee, Sai-Ching J. Yeung, Arvind Rao, Ping-Chieh Chou, Liem Phan, Yunyun Chen, Vlad C. Sandulache, Jaehyuk Lee, Matthew E. Merritt, David Fuentes, Wolfgang Stefan, Marc S. Ramirez, Christopher M. Walker, and James A. Bankson
- Abstract
Summary statistics for kpl, the apparent rate of conversion for HP Pyruvate to lactate, under various measurement conditions and parameter constraints due to differing prior information.
- Published
- 2023
10. Supplementary Video from Kinetic Modeling and Constrained Reconstruction of Hyperpolarized [1-13C]-Pyruvate Offers Improved Metabolic Imaging of Tumors
- Author
-
John D. Hazle, Stephen Y. Lai, A. Dean Sherry, Craig Malloy, Charles A. Conrad, Dawid Schellingerhout, Mong-Hong Lee, Sai-Ching J. Yeung, Arvind Rao, Ping-Chieh Chou, Liem Phan, Yunyun Chen, Vlad C. Sandulache, Jaehyuk Lee, Matthew E. Merritt, David Fuentes, Wolfgang Stefan, Marc S. Ramirez, Christopher M. Walker, and James A. Bankson
- Abstract
Animation of the dynamic conversion of hyperpolarized pyruvate into lactate in a murine model of anaplastic thyroid cancer. Heat maps for (left) HP pyruvate and (right) HP lactate overlay T2-weighted MRI. This animation can be estimated with arbitrary temporal resolution following model-based reconstruction from highly undersampled data.
- Published
- 2023
11. Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma
- Author
-
Jorge E. Jimenez, Dong Dai, Guofan Xu, Ruiyang Zhao, Tengfei Li, Tinsu Pan, Linghua Wang, Yingyan Lin, Zhangyang Wang, David Jaffray, John D. Hazle, Homer A. Macapinlac, Jia Wu, and Yang Lu
- Subjects
Lymphoma ,Piperidines ,Fluorodeoxyglucose F18 ,Adenine ,Positron Emission Tomography Computed Tomography ,Humans ,Radiology, Nuclear Medicine and imaging ,General Medicine ,Article ,Retrospective Studies - Abstract
PURPOSE: To develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients. MATERIALS AND METHODS: One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy (18)F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib were determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: maximum standard uptake value (SUV(max)), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). RESULTS: The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUV(max) (ROC AUC, 0.519; P = 0.823), MTV (ROC AUC, 0.579; P = 0.412), TLG (ROC AUC, 0.576; P = 0.199), and a composite model built using all three (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model’s performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomics model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001). CONCLUSIONS: We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients.
- Published
- 2023
12. Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with 90Y Microspheres Using Dose Voxel Kernels
- Author
-
Trifon Spyridonidis, Konstantinos A. Mountris, Dimitris Visvikis, John D. Hazle, Panagiotis Papadimitroulas, Dimitris Plachouris, Dimitris J. Apostolopoulos, George C. Kagadis, Konstantinos Katsanos, and Nikolaos D. Papathanasiou
- Subjects
Pharmacology ,Cancer Research ,Computer science ,Monte Carlo method ,General Medicine ,computer.software_genre ,90y microspheres ,Oncology ,Voxel ,Dosimetry ,Radiology, Nuclear Medicine and imaging ,Internal dosimetry ,Radiation treatment planning ,computer ,Clinical evaluation ,Biomedical engineering - Abstract
Background: The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioemboli...
- Published
- 2021
13. A deep‐learning‐based prediction model for the biodistribution of 90 Y microspheres in liver radioembolization
- Author
-
Nikolaos D. Papathanasiou, John D. Hazle, George C. Kagadis, Ilias Gatos, Panagiotis Papadimitroulas, Dimitris J. Apostolopoulos, Dimitris Visvikis, Trifon Spyridonidis, Dimitris Plachouris, Kerasia Maria Plachouri, and Ioannis Tzolas
- Subjects
Biodistribution ,business.industry ,General Medicine ,computer.software_genre ,medicine.disease ,Microsphere ,Voxel ,Absorbed dose ,Hepatocellular carcinoma ,medicine ,Distribution (pharmacology) ,Nuclear medicine ,business ,Liver cancer ,Radiation treatment planning ,computer - Abstract
Background Radioembolization with 90 Y microspheres is a treatment approach for liver cancer. Currently employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient and tissue characteristics. Purpose The purpose of the present study was to employ DL algorithms to differentiate patterns of pretreatment distribution of 99m Tc-macroaggregated albumin on SPECT/CT and posttreatment distribution of 90 Y microspheres on PET/CT and to accurately predict how the 90 Y-microspheres will be distributed in the liver tissue by radioembolization therapy. Methods Data for 19 patients with liver cancer (10 with hepatocellular carcinoma, 5 with intrahepatic cholangiocarcinoma, 4 with liver metastases) who underwent radioembolization with 90 Y microspheres were used for DL training. We developed a 3D voxel-based variation of the Pix2Pix model, which is a special type of conditional GANs designed to perform image-to-image translation. SPECT and CT scans along with the clinical target volume for each patient were used as inputs, as were their corresponding posttreatment PET scans. The real and predicted absorbed PET doses for the tumor and the whole liver area were compared. Our model was evaluated using the leave-one-out method, and the dose calculations were measured using a tissue-specific dose voxel kernel. Results The comparison of the real and predicted PET/CT scans showed an average absorbed dose difference of 5.42% ± 19.31% and 0.44% ± 1.64% for the tumor and the liver area, respectively. The average absorbed dose differences were 7.98 ± 31.39 Gy and 0.03 ± 0.25 Gy for the tumor and the non-tumor liver parenchyma, respectively. Our model had a general tendency to underpredict the dosimetric results; the largest differences were noticed in one case, where the model underestimated the dose to the tumor area by 56.75% or 72.82 Gy. Conclusions The proposed deep-learning-based pretreatment planning method for liver radioembolization accurately predicted 90 Y microsphere biodistribution. Its combination with a rapid and accurate 3D dosimetry method will render it clinically suitable and could improve patient-specific pretreatment planning. This article is protected by copyright. All rights reserved.
- Published
- 2021
14. Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles.
- Author
-
Lynn Bi, Javad Sovizi, Kelsey Mathieu, Wolfgang Stefan, Sara L. Thrower, John D. Hazle, and David Fuentes 0001
- Published
- 2018
- Full Text
- View/download PDF
15. Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans
- Author
-
Ahmed W Moawad, Mouhammed Amir Habra, David Fuentes, Ayahallah A. Ahmed, John D. Hazle, and Khaled M. Elsayes
- Subjects
Radiological and Ultrasound Technology ,Receiver operating characteristic ,business.industry ,Urology ,Feature extraction ,Gastroenterology ,Confusion matrix ,medicine.disease ,Machine learning ,computer.software_genre ,030218 nuclear medicine & medical imaging ,Random forest ,Pheochromocytoma ,03 medical and health sciences ,0302 clinical medicine ,Binary classification ,Discriminative model ,Feature (computer vision) ,030220 oncology & carcinogenesis ,Medicine ,Radiology, Nuclear Medicine and imaging ,Artificial intelligence ,business ,computer - Abstract
To evaluate the ability of radiomic feature extraction and a machine learning algorithm to differentiate between benign and malignant indeterminate adrenal lesions on contrast-enhanced computed tomography (CT) studies. Adrenal “incidentalomas” are adrenal lesions that are accidentally discovered during workup not related to the adrenal glands; they have an incidence as high as 5%. Small adrenal incidentalomas ( 10 HU) need further evaluation to calculate the absolute percentage of washout (APW). If the APW is 10 HU, and APW
- Published
- 2021
16. Using Cyber-Infrastructure for Dynamic Data Driven Laser Treatment of Cancer.
- Author
-
Chandrajit L. Bajaj, J. Tinsley Oden, Kenneth R. Diller, James C. Browne, John D. Hazle, Ivo Babuska, Jon Bass, L. Bidaut, Leszek F. Demkowicz, Andrew Elliott, Yusheng Feng, David Fuentes 0001, Boseob Kwon, Serge Prudhomme, R. Jason Stafford, and Yongjie Zhang 0001
- Published
- 2007
- Full Text
- View/download PDF
17. Development of a Computational Paradigm for Laser Treatment of Cancer.
- Author
-
J. Tinsley Oden, Kenneth R. Diller, Chandrajit L. Bajaj, James C. Browne, John D. Hazle, Ivo Babuska, Jon Bass, Leszek F. Demkowicz, Yusheng Feng, David Fuentes 0001, Serge Prudhomme, Marissa Nichole Rylander, R. Jason Stafford, and Yongjie Zhang 0001
- Published
- 2006
- Full Text
- View/download PDF
18. AAPM Report 373: The content, structure, and value of the Professional Doctorate in Medical Physics (DMP)
- Author
-
Jay W. Burmeister, Charles W. Coffey, John D. Hazle, Neil Kirby, Yu Kuang, Michael A. Lamba, Brian Loughery, and Niko Papanikolaou
- Subjects
Research Report ,Radiation ,Education, Medical, Graduate ,Humans ,Internship and Residency ,Radiology, Nuclear Medicine and imaging ,Instrumentation ,United States ,Health Physics ,Accreditation - Abstract
The Professional Doctorate in Medical Physics (DMP) was originally conceived as a solution to the shortage of medical physics residency training positions. While this shortage has now been largely satisfied through conventional residency training positions, the DMP has expanded to multiple institutions and grown into an educational pathway that provides specialized clinical training and extends well beyond the creation of additional training spots. As such, it is important to reevaluate the purpose and the value of the DMP. Additionally, it is important to outline the defining characteristics of the DMP to assure that all existing and future programs provide this anticipated value. Since the formation and subsequent accreditation of the first DMP program in 2009-2010, four additional programs have been created and accredited. However, no guidelines have yet been recommended by the American Association of Physicists in Medicine. CAMPEP accreditation of these programs has thus far been based only on the respective graduate and residency program standards. This allows the development and operation of DMP programs which contain only the requisite Master of Science (MS) coursework and a 2-year clinical training program. Since the MS plus 2-year residency pathway already exists, this form of DMP does not provide added value, and one may question why this existing pathway should be considered a doctorate. Not only do we, as a profession, need to outline the defining characteristics of the DMP, we need to carefully evaluate the potential advantages and disadvantages of this pathway within our education and training infrastructure. The aims of this report from the Working Group on the Professional Doctorate Degree for Medical Physicists (WGPDMP) are to (1) describe the current state of the DMP within the profession, (2) make recommendations on the structure and content of the DMP for existing and new DMP programs, and (3) evaluate the value of the DMP to the profession of medical physics.
- Published
- 2022
19. The Calcium Versus Hemorrhage Trial
- Author
-
Ken Ping Hwang, Donald F. Schomer, Megan C. Jacobsen, Lucia Le Roux, Jason M. Johnson, Dawid Schellingerhout, Veerabhadran Baladandayuthapani, John D. Hazle, and Dianna D. Cody
- Subjects
medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Hemorrhage ,Quantitative susceptibility mapping ,Magnetic resonance imaging ,Dual-Energy Computed Tomography ,General Medicine ,Gold standard (test) ,Institutional review board ,030218 nuclear medicine & medical imaging ,Clinical trial ,03 medical and health sciences ,0302 clinical medicine ,McNemar's test ,Hounsfield scale ,Humans ,Medicine ,Calcium ,Radiology, Nuclear Medicine and imaging ,Prospective Studies ,Radiology ,Tomography, X-Ray Computed ,business ,030217 neurology & neurosurgery - Abstract
PURPOSE Chronic susceptibility lesions in the brain can be either hemorrhagic (potentially dangerous) or calcific (usually not dangerous) but are difficult to discriminate on routine imaging. We proposed to develop quantitative diagnostic criteria for single-energy computed tomography (SECT), dual-energy computed tomography (DECT), and quantitative susceptibility mapping (QSM) to distinguish hemorrhage from calcium. MATERIALS AND METHODS Patients with positive susceptibility lesions on routine T2*-weighted magnetic resonance of the brain were recruited into this prospective imaging clinical trial, under institutional review board approval and with informed consent. The SECT, DECT, and QSM images were obtained, the lesions were identified, and the regions of interest were defined, with the mean values recorded. Criteria for quantitative interpretation were developed on the first 50 patients, and then applied to the next 45 patients. Contingency tables, scatter plots, and McNemar test were applied to compare classifiers. RESULTS There were 95 evaluable patients, divided into a training set of 50 patients (328 lesions) and a validation set of 45 patients (281 lesions). We found the following classifiers to best differentiate hemorrhagic from calcific lesions: less than 68 Hounsfield units for SECT, calcium level of less than 15 mg/mL (material decomposition value) for DECT, and greater than 38 ppb for QSM. There was general mutual agreement among the proposed criteria. The proposed criteria outperformed the current published criteria. CONCLUSIONS We provide the updated criteria for the classification of chronic positive susceptibility brain lesions as hemorrhagic versus calcific for each major clinically available imaging modality. These proposed criteria have greater internal consistency than the current criteria and should likely replace it as gold standard.
- Published
- 2021
20. Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering
- Author
-
Pingjun Chen, Siba El Hussein, Fuyong Xing, Muhammad Aminu, Aparajith Kannapiran, John D. Hazle, L. Jeffrey Medeiros, Ignacio I. Wistuba, David Jaffray, Joseph D. Khoury, and Jia Wu
- Subjects
Cancer Research ,Oncology ,chronic lymphocytic leukemia (CLL) ,accelerated CLL ,Richter transformation (RT) ,large cell transformation ,disease progression ,cellular feature engineering ,unsupervised clustering ,feature fusion ,feature selection - Abstract
Identifying the progression of chronic lymphocytic leukemia (CLL) to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications as it prompts a major change in patient management. However, the differentiation between these disease phases may be challenging in routine practice. Unsupervised learning has gained increased attention because of its substantial potential in data intrinsic pattern discovery. Here, we demonstrate that cellular feature engineering, identifying cellular phenotypes via unsupervised clustering, provides the most robust analytic performance in analyzing digitized pathology slides (accuracy = 0.925, AUC = 0.978) when compared to alternative approaches, such as mixed features, supervised features, unsupervised/mixed/supervised feature fusion and selection, as well as patch-based convolutional neural network (CNN) feature extraction. We further validate the reproducibility and robustness of unsupervised feature extraction via stability and repeated splitting analysis, supporting its utility as a diagnostic aid in identifying CLL patients with histologic evidence of disease progression. The outcome of this study serves as proof of principle using an unsupervised machine learning scheme to enhance the diagnostic accuracy of the heterogeneous histology patterns that pathologists might not easily see.
- Published
- 2022
21. Imaging-Based Algorithm for the Local Grading of Glioma
- Author
-
Jeffrey S. Weinberg, Jonathan S. Lin, Dawid Schellingerhout, Evan Gates, Veerabhadran Baladandayuthapani, David Fuentes, Sujit S. Prabhu, Greg Fuller, John D. Hazle, and Jackson D. Hamilton
- Subjects
Adult ,Image-Guided Biopsy ,Male ,Stereotactic biopsy ,Brain tumor ,Neuroimaging ,030218 nuclear medicine & medical imaging ,Machine Learning ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,Text mining ,Glioma ,Image Interpretation, Computer-Assisted ,Biopsy ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Prospective Studies ,Grading (tumors) ,Aged ,medicine.diagnostic_test ,Brain Neoplasms ,business.industry ,Adult Brain ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Random forest ,Female ,Neurology (clinical) ,Neoplasm Grading ,business ,Algorithm ,030217 neurology & neurosurgery - Abstract
BACKGROUND AND PURPOSE: Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic criterion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models. MATERIALS AND METHODS: Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic biopsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen κ, precision, and recall. RESULTS: Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy sites. The random forest method was the best algorithm tested. Tumor grade was predicted at 96% accuracy (κ = 0.93) using 4 inputs (T2, ADC, CBV, and transfer constant from dynamic contrast-enhanced imaging). By means of the conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.79) and 43% of high-grade samples were misclassified as lower-grade disease. CONCLUSIONS: We found that local pathologic grade can be predicted with a high accuracy using clinical imaging data. Advanced imaging data improved this accuracy, adding value to conventional imaging. Confirmatory imaging trials are justified.
- Published
- 2020
22. Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia
- Author
-
Siba El Hussein, Pingjun Chen, L. Jeffrey Medeiros, John D. Hazle, Jia Wu, and Joseph D. Khoury
- Subjects
Cell Transformation, Neoplastic ,Artificial Intelligence ,Humans ,Lymphoma, Large B-Cell, Diffuse ,Leukemia, Lymphocytic, Chronic, B-Cell ,Article ,Pathology and Forensic Medicine ,Cell Proliferation - Abstract
Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL) is characterized morphologically by numerous small lymphocytes and pale nodules composed of prolymphocytes and paraimmunoblasts known as proliferation centers (PCs). Patients with CLL can undergo transformation to a more aggressive lymphoma, most often diffuse large B-cell lymphoma (DLBCL), known as Richter transformation (RT). An accelerated phase of CLL (aCLL) also may be observed which correlates with subsequent transformation to DLBCL, and may represent an early stage of transformation. Distinguishing PCs in CLL from aCLL or RT can be diagnostically challenging, particularly in small needle biopsy specimens. Available guidelines pertaining to distinguishing CLL from its' progressive forms are limited, subject to the morphologist's experience and are often not completely helpful in the assessment of scant biopsy specimens. To objectively assess the extent of PCs in aCLL and RT, and enhance diagnostic accuracy, we sought to design an artificial intelligence (AI)-based tool to identify and delineate PCs based on feature analysis of the combined individual nuclear size and intensity, designated here as the heat value. Using the mean heat value from the generated heat value image of all cases, we were able to reliably separate CLL, aCLL and RT with sensitive diagnostic predictive values.
- Published
- 2022
23. Deep Learning CT Signature Predicts Benefit from Immunotherapy in Metastatic NSCLC Independent of Standard Clinicopathological Markers
- Author
-
Maliazurina Binti Saad, Lingzhi Hong, Muhammad Aminu, Natalie I. Vokes, Pingjun Chen, Morteza Salehjahromi, Kang Qin, Sheeba J. Sujit, Carol C. Wu, Brett W. Carter, Steven H. Lin, Percy P. Lee, Saumil Gandhi, Joe Y. Chang, Ruijiang Li, Michael F. Gensheimer, Heather A. Wakelee, Joel W. Neal, Hyun-Sung Lee, Chao Cheng, Vamsi Velcheti, Milena Petranovic, Yanyan Lou, Waree Rinsurongkawong, Xiuning Le, Vadeerat Rinsurongkawong, Amy Spelman, Yasir Y. Elamin, Marcelo V. Negrao, Ferdinandos Skoulidis, Carl M. Gay, Tina Cascone, Mara B. Antonoff, Boris Sepesi, Jeff Lewis, John D. Hazle, Caroline Chung, David Jaffray, Don Gibbons, Ara Vaporciyan, J.Jack Lee, John Heymach, Jianjun Zhang, and Jia Wu
- Subjects
History ,Polymers and Plastics ,Business and International Management ,Industrial and Manufacturing Engineering - Published
- 2022
24. In-vivo Tissue Characterization of Brain by Synthetic MR Proton Relaxation and Statistical Chisquares Parameter Maps.
- Author
-
Kwan Hon Cheng, John D. Hazle, Edward Jackson, Roger Price, and K. Kian Ang
- Published
- 1995
- Full Text
- View/download PDF
25. A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer
- Author
-
Jorge E. Jimenez, Abeer Abdelhafez, Elizabeth A. Mittendorf, Nabil Elshafeey, Joshua P. Yung, Jennifer K. Litton, Beatriz E. Adrada, Rosalind P. Candelaria, Jason White, Alastair M. Thompson, Lei Huo, Peng Wei, Debu Tripathy, Vicente Valero, Clinton Yam, John D. Hazle, Stacy L. Moulder, Wei T. Yang, and Gaiane M. Rauch
- Subjects
Lymphocytes, Tumor-Infiltrating ,Humans ,Radiology, Nuclear Medicine and imaging ,Breast Neoplasms ,Female ,Triple Negative Breast Neoplasms ,General Medicine ,Magnetic Resonance Imaging ,Neoadjuvant Therapy ,Retrospective Studies - Abstract
We aimed to develop a predictive model based on pretreatment MRI radiomic features (MRIRF) and tumor-infiltrating lymphocyte (TIL) levels, an established prognostic marker, to improve the accuracy of predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) patients.This Institutional Review Board (IRB) approved retrospective study included a preliminary set of 80 women with biopsy-proven TNBC who underwent NAST, pretreatment dynamic contrast enhanced MRI, and biopsy-based pathologic assessment of TIL. A threshold of ≥ 20% was used to define high TIL. Patients were classified into pCR and non-pCR based on pathologic evaluation of post-NAST surgical specimens. pCR was defined as the absence of invasive carcinoma in the surgical specimen. Segmentation and MRIRF extraction were done using a Food and Drug Administration (FDA) approved software QuantX. The top five features were combined into a single MRIRF signature value.Of 145 extracted MRIRF, 38 were significantly correlated with pCR. Five nonredundant imaging features were identified: volume, uniformity, peak timepoint variance, homogeneity, and variance. The accuracy of the MRIRF model, P = .001, 72.7% positive predictive value (PPV), 72.0% negative predictive value (NPV), was similar to the TIL model (P = .038, 65.5% PPV, 72.6% NPV). When MRIRF and TIL models were combined, we observed improved prognostic accuracy (P .001, 90.9% PPV, 81.4% NPV). The models area under the receiver operating characteristic curve (AUC) was 0.632 (TIL), 0.712 (MRIRF) and 0.752 (TIL + MRIRF).A predictive model combining pretreatment MRI radiomic features with TIL level on pretreatment core biopsy improved accuracy in predicting pCR to NAST in TNBC patients.
- Published
- 2021
26. A deep-learning-based prediction model for the biodistribution of
- Author
-
Dimitris, Plachouris, Ioannis, Tzolas, Ilias, Gatos, Panagiotis, Papadimitroulas, Trifon, Spyridonidis, Dimitris, Apostolopoulos, Nikolaos, Papathanasiou, Dimitris, Visvikis, Kerasia-Maria, Plachouri, John D, Hazle, and George C, Kagadis
- Subjects
Tomography, Emission-Computed, Single-Photon ,Deep Learning ,Liver ,Positron Emission Tomography Computed Tomography ,Liver Neoplasms ,Humans ,Tissue Distribution ,Yttrium Radioisotopes ,Embolization, Therapeutic ,Technetium Tc 99m Aggregated Albumin ,Microspheres - Abstract
Radioembolization withThe purpose of the present study was to employ deep learning (DL) algorithms to differentiate patterns of pretreatment distribution ofData for 19 patients with liver cancer (10 with hepatocellular carcinoma, 5 with intrahepatic cholangiocarcinoma, 4 with liver metastases) who underwent radioembolization withThe comparison of the real and predicted PET/CT scans showed an average absorbed dose difference of 5.42% ± 19.31% and 0.44% ± 1.64% for the tumor and the liver area, respectively. The average absorbed dose differences were 7.98 ± 31.39 Gy and 0.03 ± 0.25 Gy for the tumor and the non-tumor liver parenchyma, respectively. Our model had a general tendency to underpredict the dosimetric results; the largest differences were noticed in one case, where the model underestimated the dose to the tumor area by 56.75% or 72.82 Gy.The proposed deep-learning-based pretreatment planning method for liver radioembolization accurately predicted
- Published
- 2021
27. Findings of the AAPM Ad Hoc committee on magnetic resonance imaging in radiation therapy: Unmet needs, opportunities, and recommendations
- Author
-
Matt A. Bernstein, Norbert J. Pelc, Edward F. Jackson, Sonja Dieterich, Olga L. Green, Minsong Cao, Geoffrey D. Hugo, James H. Goodwin, James M. Balter, Kiaran P. McGee, John E. Bayouth, Nathan Yanasak, Neelam Tyagi, B. Gino Fallone, David W. Jordan, Frank L. Goerner, Taeho Kim, Carri K Glide-Hurst, Kalpana M. Kanal, John D. Hazle, and Eric S. Paulson
- Subjects
safety ,medicine.medical_specialty ,Standardization ,medicine.medical_treatment ,Radiotherapy Planning ,Oncology and Carcinogenesis ,Biomedical Engineering ,Tertiary care ,radiation therapy ,Unmet needs ,magnetic resonance ,Computer-Assisted ,EMERGING IMAGING AND THERAPY MODALITIES ,Clinical Research ,medicine ,Humans ,Medical physics ,Adaptation (computer science) ,Task group ,medicine.diagnostic_test ,Radiotherapy ,business.industry ,Radiotherapy Planning, Computer-Assisted ,Magnetic resonance imaging ,Radiotherapy Dosage ,MRgRT ,General Medicine ,simulation ,Mr imaging ,Magnetic Resonance Imaging ,United States ,Radiation therapy ,Other Physical Sciences ,Nuclear Medicine & Medical Imaging ,Image-Guided ,Special Communications ,biomarker ,Biomedical Imaging ,Particle Accelerators ,business ,Radiotherapy, Image-Guided - Abstract
The past decade has seen the increasing integration of magnetic resonance (MR) imaging into radiation therapy (RT). This growth can be contributed to multiple factors, including hardware and software advances that have allowed the acquisition of high-resolution volumetric data of RT patients in their treatment position (also known as MR simulation) and the development of methods to image and quantify tissue function and response to therapy. More recently, the advent of MR-guided radiation therapy (MRgRT) - achieved through the integration of MR imaging systems and linear accelerators - has further accelerated this trend. As MR imaging in RT techniques and technologies, such as MRgRT, gain regulatory approval worldwide, these systems will begin to propagate beyond tertiary care academic medical centers and into more community-based health systems and hospitals, creating new opportunities to provide advanced treatment options to a broader patient population. Accompanying these opportunities are unique challenges related to their adaptation, adoption, and use including modification of hardware and software to meet the unique and distinct demands of MR imaging in RT, the need for standardization of imaging techniques and protocols, education of the broader RT community (particularly in regards to MR safety) as well as the need to continue and support research, and development in this space. In response to this, an ad hoc committee of the American Association of Physicists in Medicine (AAPM) was formed to identify the unmet needs, roadblocks, and opportunities within this space. The purpose of this document is to report on the major findings and recommendations identified. Importantly, the provided recommendations represent the consensus opinions of the committee's membership, which were submitted in the committee's report to the AAPM Board of Directors. In addition, AAPM ad hoc committee reports differ from AAPM task group reports in that ad hoc committee reports are neither reviewed nor ultimately approved by the committee's parent groups, including at the council and executive committee level. Thus, the recommendations given in this summary should not be construed as being endorsed by or official recommendations from the AAPM.
- Published
- 2021
28. Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment
- Author
-
John D. Hazle, Stavros Tsantis, Pavlos Zoumpoulis, Thanasis Loupas, Dimitris Karnabatidis, George C. Kagadis, I. Theotokas, Ilias Gatos, and Stavros Spiliopoulos
- Subjects
Liver Cirrhosis ,Time Factors ,Intraclass correlation ,Chronic liver disease ,Convolutional neural network ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Image Processing, Computer-Assisted ,medicine ,Humans ,Cluster analysis ,Mathematics ,Artificial neural network ,business.industry ,Deep learning ,Reproducibility of Results ,Wavelet transform ,Pattern recognition ,General Medicine ,medicine.disease ,Fibrosis ,Liver ,Case-Control Studies ,030220 oncology & carcinogenesis ,Chronic Disease ,Elasticity Imaging Techniques ,RGB color model ,Artificial intelligence ,business - Abstract
Purpose To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs). Materials and methods Two hundred SWE image sequences from 88 healthy individuals (F0 fibrosis stage) and 112 CLD patients (46 with mild fibrosis (F1), 16 with significant fibrosis (F2), 22 with severe fibrosis (F3), and 28 with cirrhosis (F4)) were analyzed to detect temporal stiffness stability between frames. An inverse Red, Green, Blue (RGB) colormap-to-stiffness process was performed for each image sequence, followed by a wavelet transform and fuzzy c-means clustering algorithm. This resulted in a binary mask depicting areas of high and low stiffness temporal stability. The mask was then applied to the first image of the SWE sequence, and the derived, masked SWE image was used to estimate its impact in standard clinical examination and CNN classification. Regarding the impact of the masked SWE image in clinical examination, one measurement by two radiologists was performed in each SWE image and two in the corresponding masked image measuring areas with high and low stiffness temporal stability. Then, stiffness stability parameters, interobserver variability evaluation and diagnostic performance by means of ROC analysis were assessed. The masked and unmasked sets of SWE images were fed into a CNN scheme for comparison. Results The clinical impact evaluation study showed that the masked SWE images decreased the interobserver variability of the radiologists' measurements in the high stiffness temporal stability areas (interclass correlation coefficient (ICC) = 0.92) compared to the corresponding unmasked ones (ICC = 0.76). In terms of diagnostic accuracy, measurements in the high-stability areas of the masked SWE images (area-under-the-curve (AUC) ranging from 0.800 to 0.851) performed similarly to those in the unmasked SWE images (AUC ranging from 0.805 to 0.893). Regarding the measurements in the low stiffness temporal stability areas of the masked SWE images, results for interobserver variability (ICC = 0.63) and diagnostic accuracy (AUC ranging from 0.622 to 0.791) were poor. Regarding the CNN classification, the masked SWE images showed improved accuracy (ranging from 82.5% to 95.5%) compared to the unmasked ones (ranging from 79.5% to 93.2%) for various CLD stage combinations. Conclusion Our detection algorithm excludes unreliable areas in SWE images, reduces interobserver variability, and augments CNN's accuracy scores for many combinations of fibrosis stages.
- Published
- 2019
29. Translating preclinical MRI methods to clinical oncology
- Author
-
Jared A. Weis, Junzhong Xu, Richard G. Abramson, Zaver M. Bhujwalla, Jennifer G. Whisenant, Anna G. Sorace, John D. Hazle, Ralph P. Mason, Pedro M. Enriquez-Navas, Robert J. Gillies, C. Chad Quarles, David A. Hormuth, John Virostko, and Thomas E. Yankeelov
- Subjects
Clinical Oncology ,medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Mechanism (biology) ,Cancer ,Magnetic resonance imaging ,Evidence-based medicine ,medicine.disease ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Medicine ,Radiology, Nuclear Medicine and imaging ,Medical physics ,Elastography ,Tissue stiffness ,Stage (cooking) ,business - Abstract
The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.
- Published
- 2019
30. Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with
- Author
-
Dimitris, Plachouris, Konstantinos A, Mountris, Panagiotis, Papadimitroulas, Trifon, Spyridonidis, Konstantinos, Katsanos, Dimitris, Apostolopoulos, Nikolaos, Papathanasiou, John D, Hazle, Dimitris, Visvikis, and George C, Kagadis
- Subjects
Lung Neoplasms ,Single Photon Emission Computed Tomography Computed Tomography ,Radiotherapy Planning, Computer-Assisted ,Liver Neoplasms ,Radiometric Dating ,Reproducibility of Results ,Dose-Response Relationship, Radiation ,Embolization, Therapeutic ,Microspheres ,Imaging, Three-Dimensional ,Dimensional Measurement Accuracy ,Positron Emission Tomography Computed Tomography ,Humans ,Yttrium Radioisotopes ,Radiopharmaceuticals ,Monte Carlo Method ,Algorithms - Published
- 2021
31. Estimating Local Cellular Density in Glioma Using MR Imaging Data
- Author
-
John D. Hazle, Jonathan S. Lin, Jackson D. Hamilton, Jeffrey S. Weinberg, Greg Fuller, David Fuentes, Evan Gates, Dawid Schellingerhout, Veerabhadran Baladandayuthapani, and Sujit S. Prabhu
- Subjects
Adult ,Male ,Fluid-attenuated inversion recovery ,Imaging data ,Machine Learning ,Glioma ,Fractional anisotropy ,Image Interpretation, Computer-Assisted ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Cellular density ,business.industry ,Brain Neoplasms ,Adult Brain ,Middle Aged ,medicine.disease ,Mr imaging ,Magnetic Resonance Imaging ,Random forest ,Female ,Neurology (clinical) ,CRITERION STANDARD ,business ,Biomedical engineering - Abstract
BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density. MATERIALS AND METHODS: We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard. RESULTS: The random forest methodology estimated cellular density with R2 = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R2 = 0.52). Outputs were also reported as graphic maps. CONCLUSIONS: Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.
- Published
- 2021
32. Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences
- Author
-
Ilias Gatos, I. Theotokas, Stavros Spiliopoulos, George C. Kagadis, Panagiotis Papadimitroulas, Pavlos Zoumpoulis, P. Drazinos, Stavros Tsantis, John D. Hazle, and Dimitris Karnabatidis
- Subjects
Male ,Cirrhosis ,Computer science ,Biopsy ,Stability (learning theory) ,Hepatic carcinoma ,Chronic liver disease ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,medicine ,Image Processing, Computer-Assisted ,Humans ,Radiology, Nuclear Medicine and imaging ,Shear wave elastography ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Deep learning ,Liver Diseases ,Ultrasound ,Pattern recognition ,Middle Aged ,medicine.disease ,Data set ,ROC Curve ,030220 oncology & carcinogenesis ,Liver biopsy ,Chronic Disease ,Elasticity Imaging Techniques ,Female ,Artificial intelligence ,business - Abstract
Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive technique among radiologists. Although many studies have been published validating the SWE technique either in a clinical setting, or by applying machine learning on SWE elastograms, minimal work has been done on comparing the performance of popular pre-trained deep learning networks on CLD assessment. Currently available literature reports suggest technical advancements on specific deep learning structures, with specific inputs and usually on a limited CLD fibrosis stage class group, with limited comparison on competitive deep learning schemes fed with different input types. The aim of the present study is to compare some popular deep learning pre-trained networks using temporally stable and full elastograms, with or without augmentation as well as propose suitable deep learning schemes for CLD diagnosis and progress assessment. 200 liver biopsy validated patients with CLD, underwent US SWE examination. Four images from the same liver area were saved to extract elastograms and processed to exclude areas that were temporally unstable. Then, full and temporally stable masked elastograms for each patient were separately fed into GoogLeNet, AlexNet, VGG16, ResNet50 and DenseNet201 with and without augmentation. The networks were tested for differentiation of CLD stages in seven classification schemes over 30 repetitions using liver biopsy as the reference. All networks achieved maximum mean accuracies ranging from 87.2%-97.4% and area under the receiver operating characteristic curves (AUCs) ranging from 0.979-0.990 while the radiologists had AUCs ranging from 0.800-0.870. ResNet50 and DenseNet201 had better average performance than the other networks. The use of the temporal stability mask led to improved performance on about 50% of inputs and network combinations while augmentation led to lower performance for all networks. These findings can provide potential networks with higher accuracy and better setting in the CLD diagnosis and progress assessment. A larger data set would help identify the best network and settings for CLD assessment in clinical practice.
- Published
- 2020
33. Advanced magnetic resonance imaging based algorithm for local grading of glioma
- Author
-
Dawid Schellingerhout, Jonathan S. Lin, Evan D. H. Gates, Jackson D. Hamilton, Jeffrey S. Weinberg, John D. Hazle, Sujit S. Prabhu, David Fuentes, Gregory N. Fuller, and Veera Baladandayuthapani
- Subjects
medicine.diagnostic_test ,Computer science ,business.industry ,Glioma ,medicine ,Magnetic resonance imaging ,medicine.disease ,Nuclear medicine ,business ,Grading (tumors) - Published
- 2020
34. Data from a terminated study on iron oxide nanoparticle magnetic resonance imaging for head and neck tumors
- Author
-
David I. Rosenthal, Stephen Y. Lai, Clifton D. Fuller, Steven J. Frank, Jihong Wang, Musaddiq J. Awan, G. Brandon Gunn, Abdallah S.R. Mohamed, Hesham Elhalawani, Lawrence E. Ginsberg, John D. Hazle, Yao Ding, Ahmed K. Elsayes, and Ibrahim Abu-Gheida
- Subjects
Statistics and Probability ,Data Descriptor ,medicine.medical_specialty ,Contrast Media ,Metal Nanoparticles ,Library and Information Sciences ,Ferric Compounds ,030218 nuclear medicine & medical imaging ,Education ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,Humans ,Medicine ,Prospective Studies ,lcsh:Science ,Head and neck cancer ,Lymph node ,medicine.diagnostic_test ,business.industry ,Melanoma ,Diagnostic markers ,Magnetic resonance imaging ,Institutional review board ,medicine.disease ,Magnetic Resonance Imaging ,Primary tumor ,Ferrosoferric Oxide ,3. Good health ,Computer Science Applications ,Ferumoxytol ,medicine.anatomical_structure ,Head and Neck Neoplasms ,030220 oncology & carcinogenesis ,Feasibility Studies ,lcsh:Q ,Radiology ,Molecular imaging ,Statistics, Probability and Uncertainty ,business ,Information Systems - Abstract
Node positive head and neck squamous cell carcinomas (HNSCCs) patients exhibit worse outcomes in terms of regional neck control, risk for distant metastases and overall survival. Smaller non-palpable lymph nodes may be inflammatory or may harbor clinically occult metastases, a characterization that can be challenging to make using routine imaging modalities. Ferumoxytol has been previously investigated as an intra-tumoral contrast agent for magnetic resonance imaging (MRI) for intracranial malignancies and lymph node agent in prostate cancer. Hence, our group was motivated to carry out a prospective feasibility study to assess the feasibility of ferumoxytol dynamic contrast enhanced (DCE)-weighted MRI relative to that of gadolinium-based DCE-MRI for nodal and primary tumor imaging in patients with biopsy-proven node-positive HNSCC or melanoma. Although this institutional review board (IRB)-approved study was prematurely terminated because of an FDA black box warning, the investigators sought to curate and publish this unique dataset of matched clinical, and anatomical and DCE MRI data for the enrolled five patients to be available for scientists interested in molecular imaging., Measurement(s)imaging assay • head and neck squamous cell carcinomaTechnology Type(s)magnetic resonance imagingFactor Type(s)contrast agentSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11409516
- Published
- 2020
- Full Text
- View/download PDF
35. Application value of biplane transrectal ultrasonography plus ultrasonic elastosonography and contrast-enhanced ultrasonography in preoperative T staging after neoadjuvant chemoradiotherapy for rectal cancer
- Author
-
John D. Hazle, Dongguo Wang, Jinming Wang, Dong Xu, Liping Wang, Ying Xiao, Chen Yang, and Haixing Ju
- Subjects
Male ,medicine.medical_specialty ,Colorectal cancer ,Contrast Media ,Sensitivity and Specificity ,Biplane ,03 medical and health sciences ,0302 clinical medicine ,Preoperative Care ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Neoplasm Staging ,Retrospective Studies ,Ultrasonography ,medicine.diagnostic_test ,Rectal Neoplasms ,business.industry ,Rectum ,Reproducibility of Results ,Chemoradiotherapy ,General Medicine ,Gold standard (test) ,Middle Aged ,medicine.disease ,Neoadjuvant Therapy ,030220 oncology & carcinogenesis ,Elasticity Imaging Techniques ,Transrectal ultrasonography ,T-stage ,Female ,030211 gastroenterology & hepatology ,Ultrasonic sensor ,Radiology ,business ,Neoadjuvant chemoradiotherapy - Abstract
To determine the accuracy of biplane transrectal ultrasonography (TRUS) plus ultrasonic elastosonography (UE) and contrast-enhanced ultrasonography (CEUS) in preoperative T staging after neoadjuvant chemoradiotherapy for rectal cancer.Fifty-three patients with advanced lower rectal cancer were examined before and after neoadjuvant chemoradiotherapy with use of TRUS plus UE and CEUS and were diagnosed as having T stage disease. We compared ultrasonic T stages before and after neoadjuvant chemoradiotherapy and analyzed any changes. Also, with postoperative pathological stages as the gold standard, we compared ultrasonic and pathological T stages and determined their consistency by the kappa statistic.For patients with rectal cancer, ultrasonic T stages were lower after neoadjuvant chemoradiotherapy than before, with a statistically significant difference (P 0.05). The posttreatment downstaging rate was 39.6% (21/53). A total of 84.9% received correct staging with use of biplane TRUS plus UE and CEUS in the evaluation of preoperative T staging after neoadjuvant chemoradiotherapy for rectal cancer, which was highly consistent with that of pathological staging (κ = 0.768, P 0.05). Its sensitivities were 80.0%, 50.0%, 75.0%, 96.3%, and 100% in the diagnoses of stages T0 to T4 rectal cancers, respectively; the specificities were 95.4%, 97.9%, 95.1%, 88.5%, and 100% at stages T0 to T4, respectively.Biplane TRUS plus UE and CEUS can be used to accurately perform preoperative T staging in rectal cancer after neoadjuvant chemoradiotherapy; in addition, this procedure well reflects changes in depth of rectal cancer invasion into the intestinal wall before and after neoadjuvant chemoradiotherapy. It is of great value in clinically evaluating the efficacy of neoadjuvant chemoradiotherapy, in selecting therapeutic regimens, and in avoiding overtreatment.
- Published
- 2018
36. Role of Wnt/β-catenin signaling in hepatocellular carcinoma, pathogenesis, and clinical significance
- Author
-
Ali Morshid, Mata R. Burke, John D. Hazle, David Fuentes, Ahmed Kaseb, Manal M. Hassan, Khaled M. Elsayes, and Ahmed M. Khalaf
- Subjects
0301 basic medicine ,Inflammation ,Review ,medicine.disease_cause ,Pathogenesis ,03 medical and health sciences ,0302 clinical medicine ,medicine ,neoplasms ,Wnt/β-catenin ,business.industry ,Wnt signaling pathway ,hepatocellular carcinoma ,HCCS ,medicine.disease ,digestive system diseases ,3. Good health ,030104 developmental biology ,030220 oncology & carcinogenesis ,Hepatocellular carcinoma ,Cancer research ,medicine.symptom ,Signal transduction ,gadoxetic acid-enhanced magnetic resonance imaging ,Carcinogenesis ,business ,Viral hepatitis ,molecular therapy - Abstract
Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies and one of the fastest-growing causes of cancer-related mortality in the United States. The molecular basis of HCC carcinogenesis has not been clearly identified. Among the molecular signaling pathways implicated in the pathogenesis of HCC, the Wnt/β-catenin signaling pathway is one of the most frequently activated. A great effort is under way to clearly understand the role of this pathway in the pathogenesis of HCC and its role in the transition from chronic liver diseases, including viral hepatitis, to hepatocellular adenomas (HCAs) and HCCs and its targetability in novel therapies. In this article, we review the role of the β-catenin pathway in hepatocarcinogenesis and progression from chronic inflammation to HCC, the novel potential treatments targeting the pathway and its prognostic role in HCC patients, as well as the imaging features of HCC and their association with aberrant activation of the pathway.
- Published
- 2018
37. Prospective analysis of in vivo landmark point-based MRI geometric distortion in head and neck cancer patients scanned in immobilized radiation treatment position: Results of a prospective quality assurance protocol
- Author
-
Chase C. Hansen, Stephen J. Frank, Joseph Weygand, Yao Ding, John D. Hazle, Jihong Wang, Ken-Pin Hwang, David I. Rosenthal, Clifton D. Fuller, and Abdallah S.R. Mohamed
- Subjects
medicine.medical_specialty ,Interclass correlation ,medicine.medical_treatment ,R895-920 ,Article ,030218 nuclear medicine & medical imaging ,Medical physics. Medical radiology. Nuclear medicine ,03 medical and health sciences ,0302 clinical medicine ,Distortion ,medicine ,Radiology, Nuclear Medicine and imaging ,Radiation Treatment ,RC254-282 ,business.industry ,Geometric Distortion ,Head and neck cancer ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Soft tissue ,Gold standard (test) ,Landmark point ,medicine.disease ,Head and Neck Cancer ,3. Good health ,Radiation therapy ,Oncology ,030220 oncology & carcinogenesis ,Radiology ,Quality Assurance ,business ,Quality assurance ,MRI ,CT - Abstract
Purpose: Uncertainties related to geometric distortion are a major obstacle for effectively utilizing MRI in radiation oncology. We aim to quantify the geometric distortion in patient images by comparing their in-treatment position MRIs with the corresponding planning CTs, using CT as the non-distorted gold standard. Methods: Twenty-one head and neck cancer patients were imaged with MRI as part of a prospective Institutional Review Board approved study. MR images were acquired with a T2 SE sequence (0.5 Ã 0.5 Ã 2.5 mm voxel size) in the same immobilization position as in the CTs. MRI to CT rigid registration was then done and geometric distortion comparison was assessed by measuring the corresponding anatomical landmarks on both the MRI and the CT images. Several landmark measurements were obtained including; skin to skin (STS), bone to bone, and soft tissue to soft tissue at specific levels in horizontal and vertical planes of both scans. Inter-observer variability was assessed and interclass correlation (ICC) was calculated. Results: A total of 430 landmark measurements were obtained. The median distortion for all landmarks in all scans was 1.06 mm (IQR 0.6â1.98). For each patient 48% of the measurements were done in the right-left direction and 52% were done in the anteroposterior direction. The measured geometric distortion was not statistically different in the right-left direction compared to the anteroposterior direction (1.5 ± 1.6 vs. 1.6 ± 1.7 mm, respectively, p = 0.4). The magnitude of distortion was higher in the STS peripheral landmarks compared to the more central landmarks (2.0 ± 1.9 vs. 1.2 ± 1.3 mm, pÂ
- Published
- 2017
38. Gaussian process classification of superparamagnetic relaxometry data: Phantom study
- Author
-
Kelsey B. Mathieu, David Fuentes, Sara L. Thrower, Javad Sovizi, Wolfgang Stefan, and John D. Hazle
- Subjects
Relaxometry ,Normal Distribution ,Contrast Media ,Medicine (miscellaneous) ,Image processing ,Iterative reconstruction ,Signal-To-Noise Ratio ,Imaging phantom ,030218 nuclear medicine & medical imaging ,Machine Learning ,Magnetics ,Mice ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Predictive Value of Tests ,Artificial Intelligence ,Image Processing, Computer-Assisted ,Animals ,Computer Simulation ,Magnetite Nanoparticles ,Gaussian process ,Early Detection of Cancer ,Phantoms, Imaging ,business.industry ,Reproducibility of Results ,Numerical Analysis, Computer-Assisted ,Pattern recognition ,Neoplasms, Experimental ,Inverse problem ,Magnetic Resonance Imaging ,Binary classification ,030220 oncology & carcinogenesis ,symbols ,Measurement uncertainty ,Artificial intelligence ,business ,Algorithms - Abstract
Motivation Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image. Objective Our goal is to examine the use of data-driven machine learning technique to detect a weak signal induced by a small cluster of SPIONs (surrogate tumor) in presence of background signal and measurement uncertainty. We aim to investigate the performance of both data-driven and image reconstruction models to characterize situations that one can replace the computationally-challenging reconstruction technique by the data-driven model. Methods We utilize Gaussian process (GP) classification model and a physics-based image reconstruction method, tailored to SPMR datasets that are obtained from (i) in silico simulations designed based on mouse cancer models and (ii) phantom experiments using MagSense system (Imagion Biosystems, Inc.). We investigate the performance of the GP classifier against the reconstruction technique, for different levels of measurement noise, different scenarios of SPIONs distribution, and different concentrations of SPIONs at the surrogate tumor. Results In our in silico source detection analysis, we were able to achieve high sensitivity results using GP model that outperformed the image reconstruction model for various choices of SPIONs concentration at the surrogate tumor and measurement noise levels. Moreover, in our phantom studies we were able to detect the surrogate tumor phantoms with 5% and 7.3% of the total used SPIONs, surrounded by 9 low-concentration phantoms with accuracies of 87.5% and 96.4%, respectively. Conclusions The GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data.
- Published
- 2017
39. A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography
- Author
-
I. Theotokas, Ilias Gatos, Dimitris Karnabatidis, Thanasis Loupas, Stavros Spiliopoulos, Pavlos Zoumpoulis, Stavros Tsantis, George C. Kagadis, and John D. Hazle
- Subjects
Male ,Adolescent ,Acoustics and Ultrasonics ,Computer science ,Biophysics ,Color ,Sensitivity and Specificity ,030218 nuclear medicine & medical imaging ,Machine Learning ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Diagnosis, Computer-Assisted ,Aged ,Radiological and Ultrasound Technology ,Receiver operating characteristic ,Liver Diseases ,Color analysis ,Middle Aged ,Confidence interval ,Support vector machine ,Data set ,Liver ,Feature (computer vision) ,Computer-aided diagnosis ,Chronic Disease ,Elasticity Imaging Techniques ,Female ,030211 gastroenterology & hepatology ,Algorithm ,Algorithms - Abstract
The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77–0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.
- Published
- 2017
40. Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images
- Author
-
Reza Madankan, R. Jason Stafford, David Fuentes, John D. Hazle, Samuel J. Fahrenholtz, and Shabbar F. Danish
- Subjects
Cancer Research ,medicine.medical_specialty ,Materials science ,Steady state (electronics) ,Physiology ,Mr thermometry ,medicine.medical_treatment ,Parameter space ,Article ,03 medical and health sciences ,0302 clinical medicine ,Physiology (medical) ,medicine ,Humans ,Laser ablation ,Brain ,Inverse problem ,Ablation ,Magnetic Resonance Imaging ,Outcome (probability) ,Surgery ,Treatment Outcome ,030220 oncology & carcinogenesis ,Calibration ,Lookup table ,Laser Therapy ,Algorithm ,030217 neurology & neurosurgery - Abstract
Purpose: Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent. Methods: A closed-form steady state model is trained on and then subsequently compared to N = 20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57 °C isotherms of the thermometry data and the model-predicted ablation regions; 57 °C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (ω) and optical parameter (μeff) values, throughout a parameter space totalling 11 440 value-pairs. This represents a lookup table of μeff–ω pairs with the corresponding DSC value for each patient dataset. The μeff–ω pair with the maximum DSC calibrates the model parameters, maximising predictive value for each patient. Finally, leave-one-out cross-validation with global optimisation information trains the model on the entire clinical dataset, and compares against the model naïvely using literature values for ω and μeff. Results: When using naïve literature values, the model’s mean DSC is 0.67 whereas the calibrated model produces 0.82 during cross-validation, an improvement of 0.15 in overlap with the patient data. The 95% confidence interval of the mean difference is 0.083–0.23 (p Conclusions: During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive.
- Published
- 2017
41. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization
- Author
-
Justin Yu, John D. Hazle, Ali Morshid, Ahmed M. Khalaf, Zhihui Wang, Manal M. Hassan, David Fuentes, Khaled M. Elsayes, Armeen Mahvash, Mohab M. Elmohr, and Ahmed Kaseb
- Subjects
medicine.medical_specialty ,Quantitative imaging ,Radiological and Ultrasound Technology ,business.industry ,medicine.disease ,Article ,Text mining ,Artificial Intelligence ,Hepatocellular carcinoma ,medicine ,Radiology, Nuclear Medicine and imaging ,Radiology ,business ,Transcatheter arterial chemoembolization - Abstract
PURPOSE: Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. MATERIALS AND METHODS: Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP
- Published
- 2019
42. Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A Pilot Study
- Author
-
Kareem Ahmed, Jonathan S. Lin, Manal M. Hassan, Ahmed Kaseb, David Fuentes, Khaled M. Elsayes, Aliya Qayyum, Ali Morshid, Janio Szklaruk, John D. Hazle, and Reham Abdel-Wahab
- Subjects
Sorafenib ,Male ,medicine.medical_specialty ,Carcinoma, Hepatocellular ,Imaging biomarker ,Pilot Projects ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Text mining ,Hounsfield scale ,Enhancing Lesion ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Retrospective Studies ,business.industry ,Proportional hazards model ,Liver Neoplasms ,Retrospective cohort study ,Cone-Beam Computed Tomography ,Middle Aged ,Random forest ,Treatment Outcome ,Regression Analysis ,Female ,Radiology ,business ,030217 neurology & neurosurgery ,medicine.drug - Abstract
PURPOSE This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib. METHODS In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest-based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods. RESULTS When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP. CONCLUSIONS Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations.
- Published
- 2019
43. Hepatocellular carcinoma response to transcatheter arterial chemoembolisation using automatically generated pre-therapeutic tumour volumes by a random forest-based segmentation protocol
- Author
-
A.O. Kaseb, John D. Hazle, K.M. Elsayes, M. Hassan, Ahmed M. Khalaf, David Fuentes, and A. Morshid
- Subjects
Male ,Treatment response ,medicine.medical_specialty ,Carcinoma, Hepatocellular ,Liver volume ,Urology ,Kaplan-Meier Estimate ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Clinical Protocols ,hemic and lymphatic diseases ,Carcinoma ,Medicine ,Humans ,heterocyclic compounds ,Radiology, Nuclear Medicine and imaging ,Chemoembolization, Therapeutic ,neoplasms ,Aged ,Proportional Hazards Models ,Retrospective Studies ,Aged, 80 and over ,business.industry ,Proportional hazards model ,Liver Neoplasms ,Retrospective cohort study ,General Medicine ,respiratory system ,Middle Aged ,medicine.disease ,Tumor Burden ,Low volume ,Treatment Outcome ,030220 oncology & carcinogenesis ,Hepatocellular carcinoma ,Female ,Outcome data ,business ,Tomography, X-Ray Computed ,therapeutics - Abstract
AIM To demonstrate the feasibility of correlating pre-therapeutic volumes and residual liver volume (RLV) with clinical outcomes: time to progression (TTP) and overall survival (OS) in hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolisation (TACE). MATERIALS AND METHODS TTP was calculated from a database of 105 patients, receiving first-line treatment with TACE. TTP cut-off for stratifying patients into responders and non-responders was 28 weeks. Pre-treatment tumour and liver volumes were correlated with the TTP and OS following treatment. Univariate cox-regression model was used to assess whether these volumes could predict TTP and/or OS. Kaplan–Meier analysis with log-rank test was used to compare the TTP between high and low volume groups for viable, necrotic, and total tumour. Kaplan–Meier analysis was performed comparing the OS of 10 patients with the longest TTP (mean=122 weeks) in the responder group and 10 patients with the shortest TTP (mean=7 weeks) in the non-responder group. RESULTS HCC in high tumour volume groups had a shorter TTP than lesions in low tumour volume groups (p=0.05, p=0.04, p=0.02, for enhancing, non-enhancing, total tumour groups, respectively). A negative (correlation coefficient [CC] 0.3) linear correlation between TTP and tumour volumes, and a positive linear correlation between TTP and residual liver volumes were also demonstrated (CC 0.3). Patients with the longest TTP had a higher OS than with the shortest TTP (p=0.03). CONCLUSION This demonstrates the feasibility of predicting treatment response of HCC to TACE using volumetric measurements of pre-treatment lesion and the feasibility of correlating RLV with TACE outcome data in HCC patients.
- Published
- 2019
44. Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT
- Author
-
Ali Morshid, Mouhammed Amir Habra, Khaled M. Elsayes, Priya Bhosale, Aliya Qayyum, John D. Hazle, Mohab M. Elmohr, Evan Gates, and David Fuentes
- Subjects
Adenoma ,Adult ,Male ,Adrenal Gland Neoplasms ,Contrast Media ,Computed tomography ,030218 nuclear medicine & medical imaging ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Adrenal masses ,Precontrast ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Patient group ,Aged ,Retrospective Studies ,medicine.diagnostic_test ,business.industry ,Carcinoma ,Mean age ,General Medicine ,Middle Aged ,030220 oncology & carcinogenesis ,Female ,Tomography ,Nuclear medicine ,business ,Tomography, X-Ray Computed - Abstract
To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.Quantitative CT texture analysis was used to evaluate 54 histopathologically proven adrenal masses (mean size=5.9 cm; range=4.1-10 cm) from 54 patients referred to Anderson Cancer Center from January 2002 through April 2014. The patient group included 32 women (mean age at mass evaluation=59 years) and 22 men (mean age at mass evaluation=61 years). Adrenal lesions seen on precontrast and venous-phase CT images were labelled by three different readers, and the labels were used to generate intensity- and geometry-based textural features. The textural features and the attenuation values were considered as input values for a random forest-based classifier. Similarly, the adrenal lesions were classified by two different radiologists based on morphological criteria. Prediction accuracy and interobserver agreement were compared.The textural predictive model achieved a mean accuracy of 82%, whereas the mean accuracy for the radiologists was 68.5% (p0.0001). The interobserver agreements between the predictive model and radiologists 1 and 2 were 0.44 (p0.0005; 95% confidence interval [CI]: 0.25-0.62) and 0.47 (p0.0005; 95% CI: 0.28-0.66), respectively. The Dice similarity coefficient between the readers' image labels was 0.875±0.04.CT texture analysis of large adrenal adenomas and carcinomas is likely to improve CT evaluation of adrenal cortical tumours.
- Published
- 2019
45. Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging
- Author
-
Dawid Schellingerhout, Gregory N. Fuller, Sujit S. Prabhu, Jackson D. Hamilton, John D. Hazle, Evan Gates, Jeffrey S. Weinberg, David Fuentes, Jonathan S. Lin, and Veera Baladandayuthapani
- Subjects
Adult ,Image-Guided Biopsy ,Male ,Cancer Research ,Neuroimaging ,Cross-validation ,Machine Learning ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Glioma ,Fractional anisotropy ,Biopsy ,Image Interpretation, Computer-Assisted ,Medical imaging ,Biomarkers, Tumor ,Medicine ,Humans ,Aged ,Aged, 80 and over ,medicine.diagnostic_test ,business.industry ,Brain Neoplasms ,Magnetic resonance imaging ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Ki-67 Antigen ,Oncology ,Cerebral blood flow ,Undersampling ,030220 oncology & carcinogenesis ,Female ,Neurology (clinical) ,business ,Nuclear medicine ,030217 neurology & neurosurgery - Abstract
BACKGROUND: Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard. METHODS: MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain. RESULTS: Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, K(trans)). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R(2) = 0.75). A less accurate predictive result (RMS error 5.4%, R(2) = 0.50) was found using conventional imaging only. CONCLUSION: Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.
- Published
- 2019
46. A flexible fast spin echo triple-echo Dixon technique
- Author
-
Jingfei Ma, Ersin Bayram, Jong Bum Son, Ken Pin Hwang, John E. Madewell, John D. Hazle, and Russell N. Low
- Subjects
medicine.diagnostic_test ,Computer science ,Echo (computing) ,Magnetic resonance imaging ,Pulse sequence ,Dead time ,Fast spin echo ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Nuclear magnetic resonance ,Data acquisition ,Region growing ,Consistency (statistics) ,medicine ,Radiology, Nuclear Medicine and imaging ,Algorithm ,030217 neurology & neurosurgery - Abstract
PURPOSE To develop a flexible fast spin echo (FSE) triple-echo Dixon (FTED) technique. METHODS An FSE pulse sequence was modified by replacing each readout gradient with three fast-switching bipolar readout gradients with minimal interecho dead time. The corresponding three echoes were used to generate three raw images with relative phase shifts of -θ, 0, and θ between water and fat signals. A region growing-based two-point Dixon phase correction algorithm was used to joint process two separate pairs of the three raw images, yielding a final set of water-only and fat-only images. The flexible FTED technique was implemented on 1.5T and 3.0T scanners and evaluated in five subjects for fat-suppressed T2-weighted imaging and in one subject for post-contrast fat-suppressed T1-weighted imaging. RESULTS The flexible FTED technique achieved a high data acquisition efficiency, comparable to that of FSE, and was flexible in scan protocols. The joint two-point Dixon phase correction algorithm helped to ensure consistency in the processing of the two separate pairs of raw images. Reliable and uniform separation of water and fat was achieved in all of the test cases. CONCLUSION The flexible FTED technique incorporates the benefits of both FSE and Dixon imaging and provided more flexibility than the original FTED in applications such as fat-suppressed T2-weighted and T1-weighted imaging. Magn Reson Med 77:1049-1057, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
- Published
- 2016
47. A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging
- Author
-
Stavros Tsantis, George C. Kagadis, Thanasis Loupas, Dimitris Karnabatidis, John D. Hazle, I. Theotokas, Stavros Spiliopoulos, Ilias Gatos, and Pavlos Zoumpoulis
- Subjects
Adult ,Male ,Pathology ,medicine.medical_specialty ,Adolescent ,Computer science ,Feature selection ,Chronic liver disease ,Young Adult ,03 medical and health sciences ,Elasticity Imaging Techniques ,0302 clinical medicine ,Fibrosis ,medicine ,Humans ,Diagnosis, Computer-Assisted ,Aged ,Mechanical Phenomena ,Shear wave elastography ,medicine.diagnostic_test ,Contextual image classification ,business.industry ,Liver Diseases ,Ultrasound ,Pattern recognition ,General Medicine ,Middle Aged ,medicine.disease ,Support vector machine ,Statistical classification ,Computer-aided diagnosis ,030220 oncology & carcinogenesis ,Chronic Disease ,Female ,030211 gastroenterology & hepatology ,Elastography ,Artificial intelligence ,Ultrasonography ,business - Abstract
Purpose: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. Methods: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. Results: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77–0.89] confidence interval. Conclusions: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.
- Published
- 2016
48. Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles
- Author
-
Wolfgang Stefan, Javad Sovizi, David Fuentes, Sara L. Thrower, Lynn Bi, John D. Hazle, and Kelsey Boitnott Mathieu
- Subjects
Physiologically based pharmacokinetic modelling ,Estimation theory ,Model selection ,Bayesian probability ,Bayes factor ,Likelihood function ,Bayesian inference ,Biological system ,Nested sampling algorithm ,Mathematics - Abstract
The growing use of superparamagnetic iron oxide nanoparticles (SPIONs) in early cancer detection technologies has created a demand for physiologically-based pharmacokinetic (PBPK) models that accurately model and predict the biodistribution of SPIONs in the mouse and human model. The objective of this work is to use a Bayesian approach built upon nested-sampling to select a model based on qualitative criteria of the fit of the model and the likelihood function landscape, as well as quantitative criteria of the evidence and maximum likelihood values. Four first-order PBPK compartmental models of ranging complexity are considered. Compartments included in the models comprise of a combination of the plasma, liver, spleen, tumor, and “other” (the remaining body tissue), with parameters including the volume, blood flow rate, and plasma:tissue distribution ratios. The model parameters for each model are evaluated using Bayesian inference, in addition to the respective evidence integrals, maximum log-likelihoods, and Bayes factors. The model containing all compartments and the model containing the plasma, liver, tumor and “other” had the highest log-likelihood and evidence values, indicating both a high goodness-of-fit and a high likelihood of the model given the data. This is similarly reflected in a faithful quality-of-fit and non-flat log-likelihood landscapes. Overall, these findings illustrate the strength of the Bayesian model selection framework in ranking different models to determine the best model that accurately represents the experimental data.
- Published
- 2018
49. Automated algorithms for improved pre-processing of magnetic relaxometry data
- Author
-
Wolfgang Stefan, C. Kaffes, Sara L. Thrower, Javad Sovizi, John D. Hazle, Kelsey Boitnott Mathieu, and David Fuentes
- Subjects
0301 basic medicine ,03 medical and health sciences ,Relaxometry ,030104 developmental biology ,0302 clinical medicine ,Materials science ,business.industry ,030220 oncology & carcinogenesis ,Pattern recognition ,Artificial intelligence ,business - Published
- 2018
50. Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry
- Author
-
Javad Sovizi, David Fuentes, Kelsey Boitnott Mathieu, Wolfgang Stefan, John D. Hazle, and Sara L. Thrower
- Subjects
Relaxometry ,Magnetic particle imaging ,business.industry ,Computer science ,Detector ,Reconstruction algorithm ,Pattern recognition ,Artificial intelligence ,Sensitivity (control systems) ,Inverse problem ,business ,Residual ,Imaging phantom - Abstract
Ovarian cancer survival rates could be greatly improved through effective early detection. However, several clinical studies have shown that proposed screening methodologies have no impact on overall survival. Our lab is participating in the development of a novel nanoparticle imaging device that can be incorporated as a third-line test to improve the specificity and sensitivity of the overall screening program. The device’s highly sensitive detectors can detect the residual magnetic field of only those nanoparticles that have become bound to cancer cells via specific antibody interactions. However, the reconstruction of the bound particle distribution from this residual field map is challenging due to the highly ill-posed nature of the inverse problem. Our lab has developed a sparse reconstruction algorithm to overcome this challenge. Here, we present the results of a blinded phantom study to simulate the pre-clinical scenario of detecting a tumor signal in the presence of a large signal from bound particles in the liver. Overall, our algorithm identified the correct location of bound particle sources with 84% accuracy. We were able to detect as little as 1.6ug of bound particles with 100% accuracy when the source was alone, and as little as 3.13ug when there was a stronger source present. We also show the effect of manual and automatic parameter selection on the performance of the algorithm. These results provide valuable information about the expected performance of the algorithm that we can use to optimize the design of future small animal studies as we work to bring this novel technology to the clinic.
- Published
- 2018
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.