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MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images. [ABSTRACT FROM AUTHOR]

Breast cancer, the earliest documented cancer in history, stands as a foremost cause of mortality, accounting for 684,996 deaths globally in 2020 (15.5% of all female cancer cases). Irrespective of socioeconomic factors, geographic locations, race, or ethnicity, breast cancer ranks as the most frequently diagnosed cancer in women. The standard grading for breast cancer utilizes the Nottingham Histopathology Grading (NHG) system, which considers three crucial features: mitotic counts, nuclear pleomorphism, and tubule formation. Comprehensive reviews on features, for example, mitotic count and nuclear pleomorphism have been available thus far. Nevertheless, a thorough investigation specifically focusing on tubule formation aligned with the NHG system is currently lacking. Motivated by this gap, the present study aims to unravel tubule formation in histopathology images via a comprehensive review of detection approaches involving tubule and tumor features. Without temporal constraints, a structured methodology is established in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in 12 articles for tubule detection and 67 included articles for tumor detection. Despite the primary focus on breast cancer, the structured search string extends beyond this domain to encompass any cancer type utilizing histopathology images as input, focusing on tubule and tumor detection. This broadened scope is essential. Insights from approaches in tubule and tumor detection for various cancers can be assimilated, integrated, and contributed to an enhanced understanding of tubule formation in breast histopathology images. This study compiles evidence-based analyses into a cohesive document, offering comprehensive information to a diverse audience, including newcomers, experienced researchers, and stakeholders interested in the subject matter. [ABSTRACT FROM AUTHOR]

Tumor detection in automated breast ultrasound (ABUS) images is critical for computer-aided diagnosis. A single deep learning based network is prone to make overconfident prediction. In this paper, a DML-YOLOX model based on deep mutual learning (DML) is proposed. To alleviate the overconfidence of a single network, the dual-model collaborative training strategy is designed based on YOLOX baseline. To implement the interaction between the dual models, the exploration loss is developed and combined with the consistency loss for supervision. The exploration loss aims at encouraging the models to explore and learn different feature representations in the feature extraction stage. The consistency loss aims at constraining the models to have consistent output representations for the same input. According to the ellipse-like characteristic of ABUS tumors, a rotation augmentation method is designed, which can decrease the overestimate of the bounding box. Besides, in order to reduce the regression error caused by large angle rotation, a rotation discriminant (RD) measurement is developed. The proposed method is verified on two ABUS datasets, one of which is the private ABUS dataset with 68 normal volumes and 68 tumor volumes, including 43,248 slices, and the other is the public ABUS dataset. On the private ABUS dataset, it achieves a promising detection result with a sensitivity of 0.90 and the false positives per slice (FPs/S) at 0.15. [ABSTRACT FROM AUTHOR]

Medical imaging, also known as radiology, is the field of medicine in which medical professionals recreate various images of parts of the body for diagnostic or treatment purposes. Medical imaging procedures include non-invasive tests that allow doctors to diagnose injuries and diseases without being intrusive TechTarget (n.d.). A number of tools and techniques are used to automate the analysis of medical images acquired with various image processing methods. The brain is one of the largest and most complex organs of the human body and anomaly detection from brain images (i.e., MRI, CT, PET, etc.) is one of the major research areas of medical image analysis. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning (ML) models, the recent deep learning (DL) models, and various hybrid models are used in brain image analysis. Brain tumors are one of the most common brain diseases with a high mortality rate, and it is difficult to analyze from brain images for the versatility of the shape, location, size, texture, and other characteristics. In this paper, a comprehensive review on brain tumor image analysis is presented with basic ideas of brain tumor, brain imaging, brain image analysis tasks, brain image analysis models, brain tumor image features, performance metrics used for evaluating the models, and some available datasets on brain tumor/medical images. Some challenges of brain tumor analysis are also discussed including suggestions for future research directions. The graphical abstract summarizes the contributions of this paper. [ABSTRACT FROM AUTHOR]

Objectives: The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces. Materials and methods: We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons. Results and conclusions: Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically. [ABSTRACT FROM AUTHOR]

Purpose: New generation of receptor tyrosine kinase inhibitors (RTKIs) have shown to improve survival in many solid tumors. However, an imaging biomarker is needed for patient selection and prediction of treatment response. This study evaluates the use of quantitative changes of HER3 on 68 Ga-NOTA-HER3P1 PET/MRI for prediction of early response to pan-RTKIs in gastric cancer (GCa).Procedures: GCa cell lines were evaluated for expression of RTKs, and downstream signaling pathways (AKT and MAPK). Cell viability was assessed following 24-72 h of treatment with 0.01-1 µmol/L of afatinib, a pan-RTKI. HER3-expressing afatinib-sensitive (NCI-N87) and resistant cells (SNU16) were selected for evaluation of changes in RTKs expression and downstream pathways, with 24-72 h of 0.1 µmol/L afatinib treatment. 68 Ga-NOTA-HER3P1 PET/MRI was performed in subcutaneous NCI-N87 and SNU16 xenografts (nu:nu, n = 12/group) at baseline and 4 days after afatinib treatment (10 mg/kg, PO, daily). Temporal changes in PET measures were correlated to HER3 expression in tumors, tumor growth rate, and treatment response.Results: With afatinib therapy, NCI-N87 cells showed increased total HER3 expression, and reduction of other RTKs and downstream nodes within 72 h, while SNU16 cells showed no significant change in total HER3 and downstream nodes. 68 Ga-HER3P1 PET/MRI showed increased uptake in NCI-N87 and no significant change in SNU16 tumors (day 4 vs. baseline SUVmean: 3.8 ± 0.7 vs. 1.6 ± 0.6, p < 0.05 in NCI-N87, and 1.5 ± 0.7 vs. 1.7 ± 0.7, p > 0.05 in SNU16). These findings were in concordance with HER3 expression in histopathological analyses and tumor growth over 3 weeks of treatment (mean tumor volume in treated vs. control: 11 ± 17 mm3 vs. 293 ± 79 mm3, p < 0.001 in NCI-N87, and 238 ± 91 mm3 vs. 282 ± 35 mm3, p > 0.05 in SNU16).Conclusions: Quantitative changes in HER3 PET could be used to predict response to pan-RTKI within few days after initiation of treatment and can help with personalizing GCa management. [ABSTRACT FROM AUTHOR]

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There is an emerging need for medical imaging data to provide patients with timely diagnosis. Magnetic resonance imaging (MRI) images based on brain tumor segmentation approaches possess greater importance in planning treatment. Though, mechanizing the process with different imaging conditions and accuracy is a major challenge due to variations in tumor structures. Hence, an efficient optimization-driven classifier, called BirCat optimization-based deep belief network (BirCat-based DBN) is developed to detect brain tumors. The introduced BirCat is devised by incorporating birdswarm algorithm (BSA) into cat swarm optimization (CSO) algorithm and is employed in tuning the DBN classifier. Here, the first step is pre-processing, where noises, as well as artifacts in input image, are eliminated by means of ROI extraction and filtering method. Then, for segmentation, region growing algorithm is used in which the distance is calculated by the modified Bhattacharya measure. Afterward, each segment is adapted for mining the segment-based features and pixel-based features used for classification. Then, the feature vector is formed and given to the DBN classifier, which is tuned with the help of the introduced BirCat for brain tumor detection. The introduced technique effectively determines the regions with the tumor in the input MRI image. Finally, the change detection is evaluated by analyzing the post-operative MRI image and the segmented image by means of pixel mapping strategy with respect to SURF features. The pixel mapping is utilized to evaluate the percentage change in tumor pixels. The proposed BirCat surpassed other prevailing approaches by producing maximal values of specificity, accuracy, sensitivity, F1-score, and Dice score at 0.92, 0.927, 0.938, 0.909, and 0.937, correspondingly, for dataset 2. [ABSTRACT FROM AUTHOR]

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature. [ABSTRACT FROM AUTHOR]

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Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods. [ABSTRACT FROM AUTHOR]

Segmentation of Magnetic Resonance Imaging(MRI) Brain images is a very important step in detection of brain tumor. This process is hand operated in labs which is rather an enervating and a long drawn out task and the resultant data so obtained has high degree of erroneity and inconsistency. Hence, automated segmentation systems have become the need of the hour. This paper presents a novel technique to automatically detect brain tumor. Also, segmentation of three main brain tissues is carried out namely white matter, gray matter and cerebrospinal fluid from real time Magnetic Resonance Imaging(MRI) images. The Intuitionistic Fuzzy Set theory is incorporated as it is more suitable for handling uncertainty as compared to fuzzy sets theory. The algorithm is based on the Co-Clustering approach as it offers the advantage of assigning membership functions to both object as well as features. The parameters in the IFCC algorithm are optimized using Particle Swarm Optimization(PSO). The performance of the algorithm is evaluated on the basis of quantitative measures such as match score, accuracy score, dice score and Jaccard's similarity coefficient. [ABSTRACT FROM AUTHOR]

Variation in mechanical properties is a useful marker for cancer in soft tissue and has been used in clinical diagnosis for centuries. However, to develop such methods as instrumented palpation, there remain challenges in using the mechanical response during palpation to quantify tumor load. This study proposes a computational framework of identification and quantification of cancerous nodules in soft tissue without a priori knowledge of its geometry, size, and depth. The methodology, using prostate tissue as an exemplar, is based on instrumented palpation performed at positions with various indentation depths over the surface of the relevant structure (in this case, the prostate gland). The profile of force feedback results is then compared with the benchmark in silico models to estimate the size and depth of the cancerous nodule. The methodology is first demonstrated using computational models and then validated using tissue-mimicking gelatin phantoms, where the depth and volume of the tumor nodule is estimated with good accuracy. The proposed framework is capable of quantifying a tumor nodule in soft tissue without a priori information about its geometry, thus presenting great promise in clinical palpation diagnosis for a wide variety of solid tumors including breast and prostate cancer. Graphical abstract This study proposes a computational framework of quantification of cancerous nodules in soft tissue. The methodology is based on instrumental palpation performed at positions with various indentation depths. The profile of force feedback results is then compared with the benchmark in silico models to estimate the size and depth of the cancerous nodule. [ABSTRACT FROM AUTHOR]

Manually finding and segmenting brain tumor is a tedious process in MR brain images due to the unpredictable appearance of tissues with a different pattern, contour, mass, and positions. The proposed work has three phases automatic tumor diagnosis system for tumorous slice detection, segmentation, and visualization from MRI human head volumes. The proposed method has an automatic classification followed by segmentation and is called as patch-based updated run length region growing technique (PR2G). In the first phase, classification is done through training and testing process using SVM classifier with 8 × 8 patches. Three optimal features are chosen using infinite feature selection (IFS) method. The purpose of the first phase is to automatically cluster the input MRI image into a normal or tumorous slice and localize the tumor. The second phase aims to segment the tumor in abnormal tumorous slices identified by the first phase using run length region growing technique. Finally, the third phase contains a post metric evaluation like 3D tumor volume construction and estimation from actual and segmented tumor volume using Carelieri's estimator. Classification accuracy is measured using sensitivity, specificity, accuracy, and error rates also calculated using false alarm (FA) and missed alarm (MA). Segmentation accuracy is calculated using Dice similarity, positive predictive value (PPV), sensitivity, and accuracy. Datasets used for this experiment are collected from whole brain atlas (WBA) and BraTS repositories. Experimental results show that the PR2G achieves 97% of classification accuracy and 80% of Dice segmentation accuracy. [ABSTRACT FROM AUTHOR]

The role of bronchoscopic brushing for tumor detection and molecular testing in central lung cancer is unclear. In this study, 50 consecutive subjects with suspected central lung cancer underwent bronchoscopic brushing (31 males, median age 70, 5 never smokers). Histological results were: NSCLC/SCLC/low-grade-NET/granulation tissue in 36/8/2/4 cases. Next generation sequencing (NGS) was feasible in 62% of tumor-positive brush smear samples. In 78% of these cases, NGS displayed identical results compared to histology samples, in 22% NGS from brush smears detected specific mutations, whereas DNA quality from forceps biopsy was insufficient for NGS analysis. Sensitivity, specificity, positive predictive value, negative predictive value of brush smear analysis were 66% (95% confidence interval 50–79), 100% (40–100), 100% (85–100), and 21% (7–46). For the combined analysis of brush smear, brush tip washing and sheath tube content sensitivity was slightly elevated at 69% (53–81). In central lung cancer, bronchoscopic brushing detects tumor cells in about two-third of cases and allows a decision for or against targeted therapy in the majority of tumor-positive cases on the basis of NGS analysis. [ABSTRACT FROM AUTHOR]

Purpose: To clarify clinicopathological features of false-negative clinically significant prostate cancer (csPC) at multiparametric prostate MRI (mpMRI).Methods: 95 patients with 139 csPC undergoing 3T mpMRI before radical prostatectomy were included. Two radiologists were independently evaluated mpMR images using PI-RADS v2. Clinicopathological findings were compared between (a) detectable and undetectable lesions using overall mpMRI criteria (o-mpMRI criteria) and (b) lesions with early enhancement effect (EEE) and lesions without EEE at DCE-MRI.Results: The detection rate of csPS using cutoff value of category 3 or more in PI-RADS v2 for positive lesion was 72.1% (98/136 lesions). In 38 false-negative lesions with less than PI-RADS v2 category 3, the DCE-MRI detected 14 lesions. 17 undetectable lesions on o-mpMR criteria had lower PSA and D'amico risk classification, and higher tumor apparent diffusion coefficient (ADC) than those of 118 detectable lesions (p ≤ 0.048). 89 lesions with EEE showed higher PSA, tumor size, prostatectomy GS grade, frequency of lesions with GS ≥ 4 + 3 and lower tumor ADC than those in 38 lesions without EEE (p ≤ 0.046).Conclusion: Tumor detectability of csPC with PI-RADS v2 was limited compared with o-mpMRI criteria in Japanese men. Moreover, false-negative lesions on o-mpMRI criteria were characterized as small in size, low risk and low cellularity. [ABSTRACT FROM AUTHOR]

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Conditionally replicating adenoviruses ( CRAds), or known as replication-selective adenoviruses, were discovered as oncolytic gene vectors several years ago. They have a strong ability of scavenging tumor and lesser toxicity to normal tissue. CRAds not only have a tumor-killing ability but also can combine with gene therapy, radiotherapy, and chemotherapy to induce tumor cell apoptosis. In this paper, we review the structure of CRAds and CRAd vectors and summarize the current application of CRAds in tumor detection as well as in radiotherapy and suicide gene-mediating chemotherapy. We also propose further research strategies that can improve the application value of CRAds, including enhancing tumor destruction effect, further reducing toxic effect, reducing immunogenicity, constructing CRAds that can target tumor stem cells, and trying to use mesenchymal stem cells ( MSCs) as the carriers for oncolytic adenoviruses. As their importance to cancer diagnosis, gene-radiation, and chemotherapy, CRAds may play a considerable role in clinical diagnosis and various cancer treatments in the future. [ABSTRACT FROM AUTHOR]

This paper estimates the short-run causal effect of tumor detection and treatment on psychosocial well-being, work and income. Tumor detection can be considered as a random event, so that we can compare individuals' average outcomes in the year of diagnosis with the year before. We argue for using panel data estimation techniques that enable us to control for observed and unobserved information intrinsic to the individual and time constants. We use data of a national representative panel in the Netherlands that includes health survey information and data on work, education, and income between 2007 and 2012. Our findings show differences in the psychosocial dysfunction of men and women in response to tumor detection and treatment. Women, not men, are decreasingly likely to participate in the labor force as a result of malignant tumor detection, while no significant effects are found on her personal or household income. We also demonstrate that fixed effects panel data models are superior to matching techniques. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a pipeline for the detection and extraction of 3D regions of bladder tumors via MR virtual cystoscopy. Methods: After the acquisition of volumetric bladder images with a high-resolution T2-weighted 3D sequence, the inner and outer surfaces of the bladder wall were segmented simultaneously by a coupled directional level-set method. Based on the Laplacian method, a potential field was built up between two surfaces so that the thickness of each voxel within the bladder wall was estimated. To detect bladder abnormalities, four volume-based morphological features, including bent rate, shape index, wall thickness, and a novel morphological feature, which reflects bent rate difference between the inner and outer surfaces, were extracted. The combination of these four features was used to detect seeds on the inner surface by using selected filtering criterion. Then all points on streamlines started from detected seeds formed 3D candidate regions. Finally the fuzzy c-means clustering with spatial information (sFCM) was used to extract tumors from surrounding bladder wall tissues in candidate regions. Results: The proposed pipeline was evaluated by a database of MR bladder images acquired from ten patients with bladder cancer. To find an optimal feature combination for tumor detection, the performance of different combinations of these features was evaluated with different filtering criteria. With the combination of all four features, the computer-aided detection pipeline shows a high performance of 100 % sensitivity with 2.3 FPs/case. Comparing with tumor regions delineated by radiological experts, the average overlap ratio of tumor regions extracted by sFCM is 86.3 %. Conclusions: The experimental result demonstrates the feasibility of the proposed pipeline on the detection and extraction of bladder tumors. It may provide an effective way to achieve the goal of evaluating the whole bladder for tumor detection and local staging. [ABSTRACT FROM AUTHOR]

Purpose: Laparoscopic surgery limits a surgeon's tactile sense. A tactile sensor could allow real-time tumor detection in laparoscopic surgery through lump inspection. This study was aimed at developing a simple and biocompatible tactile sensor for laparoscopic surgery. The proposed tactile sensor has a forceps-like shape, has no electrical elements in the tissue contact area, and can be sterilized and cleaned. Methods: We developed a tactile sensor using acoustic reflection. It is composed of a handle with a speaker and a microphone, an aluminum tube, and a sensor tip with a deformable elastic cavity. The acoustic wave in the tube is the superposition of the input wave and two waves reflected at the closed edge and the projection generated by deformation due to contact with an object. By measuring the acoustic wave in the tube, information of the deformation is derived. Results: The sensor is modeled, and the output is analyzed to determine design parameters of the sensor. Then, a prototype of the sensor is assembled. Fundamental experiments show that the sensor output increases with increasing normal deformation. Moreover, experiments using a phantom of the stomach wall with a 0-IIc type tumor (most common early stage gastric cancer) show that large sensor output is obtained for the lump when the sensor is moved across the back surface of the tumor. Conclusions: The theoretical and experimental results show that the sensor is sensitive to the deformation due to contact with an object and has the potential to detect a lump in laparoscopic surgery. [ABSTRACT FROM AUTHOR]

Purpose: To determine the utility of non-contrast-enhanced CT (NC-CT) for improved detection of hyperenhancement in hepatocellular carcinomas (HCC). Methods: We performed a retrospective analysis on CT studies of 104 consecutive patients with 124 HCCs between October 2006 and December 2012. The enhancement of HCC was evaluated on quadriphasic CT: non-contrast (NC), arterial phase (AP), portal-venous phase (PVP), and delayed phases (DP) were performed. For determination of enhancement, mean attenuation of the HCC and surrounding non-tumor bearing liver in the four phases were recorded. Hyperenhancement was defined by either HCC (AP-NC) representing absolute lesion enhancement or AP (HCC-liver), representing relative enhancement. An increase of ≥10 Hounsfield units (HU) was considered as enhancement. Two radiologists (R1 and R2) independently performed a conventional qualitative assessment for hyperenhancement of HCC. The accuracy of detection by proposed absolute lesion enhancement HCC (AP-NC) was compared to relative enhancement AP (HCC-liver) and qualitative assessment by readers with the McNemar test. Results: The proposed criteria of absolute lesion enhancement, HCC (AP-NC) identified more hypervascular nodules compared to qualitative analysis (92% vs. 62% for R1 and 69% for R2), equating to an improvement of 23%-31%. In contrast, relative enhancement AP (HCC-liver) was found to be inferior to conventional qualitative analysis (50% vs. 62%-69% for readers). Conclusion: NC-CT is useful to improve the detection of hyperenhancement in HCCs for imaging diagnosis. [ABSTRACT FROM AUTHOR]

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A GdSiO (GSO) tweezers-type coincidence detector was developed and tested for tumor detection in procedures such as F-fluorodeoxyglucose (FDG)-guided surgery. The detector consists of a pair of GSO scintillators, a pair of metal-packaged small-sized photomultiplier tubes (PMTs), and a coincidence circuit. Because the GSO scintillators are located on the tips of tweezers, a target organ such as a lymph node or the colon can be easily positioned between them. The size of a single GSO was 8 × 14 × 14 mm. The results show that the energy resolution was 30 % full-width at half-maximum (FWHM) and the timing resolution was 6 ns FWHM for 511-keV gamma photons. The point-spread function perpendicular to the detector was 4.5 mm FWHM, and the point-spread function parallel to the detector was 7.5 mm FWHM. The absolute sensitivity of the coincidence detector was 0.6 % at the center of the detector when the two GSOs were 5 mm apart. Background counts due to the accidental and scatter coincidence were 2 cps up to 48 MBq from the positron source contained in a 20-cm-diameter, 20-cm-high cylindrical phantom. From these results, we conclude that the proposed tweezers-type coincidence detector is useful for tumor detection by the use of FDG, such as that in radio-guided surgery. [ABSTRACT FROM AUTHOR]

This article presents a computer-aided detection system for capsule endoscopy (CE) images using contourlet-based color textural features to recognize tumors in the digestive tract. As tumor exhibits rich information in color texture, a novel color texture feature based on contourlet transform is proposed to describe characteristics of tumor in CE images. The proposed features are a hybrid of contourlet transform and uniform local binary pattern, yielding detailed and robust color texture features in multi-directions for CE images. Sequential floating forward search approach is further applied to refine the proposed features. With support vector machine for classification, comprehensive experiments on our present data reveal an encouraging accuracy of 93.6% for tumor detection in CE images using the proposed features. [ABSTRACT FROM AUTHOR]

Tumors are generally difficult to detect in Magnetic Resonance (MR) images as they can be of varying intensities and do not appear as clear structures on these images. This difficulty is more prominent in MR Cholangiopancreatography (MRCP), which is the MR technology using a special sequence of T2-weighted imaging to identify the biliary tract, pancreatic duct, and gallbladder in the liver region, as MRCP images are more noisy in nature and are acquired for a more focused area with too much flexibility in position orientation for convenient computer-aided diagnosis. Based on the principle that the tumor mass manifests itself as blockage of the biliary tree structure, this paper introduces a technique that uses a region growing algorithm to identify discontinuities in the biliary tree as a means to preliminary detection of a possible tumor, in a fashion similar to the visual observation used by most radiologists in making their preliminary diagnosis. Through the use of appropriate image normalization, watershed segmentation, thresholding, rule-based region growing, and region analysis, the proposed technique is shown in this paper to be successful in identifying MRCP images with liver carcinoma from those with normal liver. Acquisition standardization, interactive image selection, and optimum image orientation will further enhance the accuracy of this proposed scheme for use in aiding clinical diagnosis at medical institutions. [ABSTRACT FROM AUTHOR]

Purpose: This laboratory has previously published on phosphorodiamidate morpholino (MORF) pretargeting of tumor in which an anti-tumor antibody conjugated with MORF (a DNA analogue) is first administered, followed at a later time by the radiolabeled complementary MORF (cMORF) as the effector. In the present study, the pharmacokinetics of the antibody and effector were measured under different conditions in mice to establish their quantitative relationships with tumor accumulations by pretargeting. Methods: A cytosine-free 18 mer cMORF was conjugated with MAG3 for 99mTc labeling while the anti-CEA antibody MN14 was conjugated with DTPA for 111In labeling and with MORF to impart binding affinity for radiolabeled cMORF. Mice bearing LS174T thigh tumors were used to study: (1) the pharmacokinetics of MN14-MORF by administering 111In-MN14 at doses between 10 and 100 µg with sacrifice at 2 days and at 30 µg with sacrifice between 1 and 3 days; (2) the biodistribution of 99mTc-cMORF following one to four injections (containing 0.15 µg each and separated by 1 h) to animals having received 30 µg of antibody-MORF 2 days earlier and with sacrifice at 3 h after the final injection; and (3) the influence on the biodistribution of 99mTc-cMORF of a 2 to 4 day interval between the administration of 30 µg of antibody-MORF and 0.30 µg of 99mTc-cMORF. Results: (1) The biodistribution of antibody in percent accumulation (%ID or %ID/g) was largely independent of antibody dose but the absolute accumulation of antibody in tumor increased linearly with dose, showing no evdence of tumor saturation of CEA sites by MN14. Over 1-3 days post antibody administration, blood levels of radiolabeled antibody decreased as expected; however, tumor levels remained constant, thus showing an absence of antibody clearance in tumor over this period. (2) With fixed antibody-MORF dose and increasing number of injections of 99mTc-cMORF, cumulative percent blood levels steadily decreased in agreement with the values calculated based on the antibody-MORF in blood. In contrast, cumulative percent tumor levels stayed fairly constant over the first two injections. Thus the antibody-MORF in tumor became saturated with cMORF more slowly than that in blood owing to delivery differences. (3) As expected, percent blood levels decreased with increasing interval between injections of antibody-MORF and 99mTc-cMORF. The percent tumor accumulation, however, remained constant over the 3 day interval, thus demonstrating only slow loss of MORF expression in situ. The 99mTc-cMORF accumulation in tumor after saturation was mathematically determined based on the antibody-MORF concentration in tumor while the blood levels of 99mTc-cMORF were determined based on the concentration of antibody-MORF in blood. Conclusion: Contrary to conclusions arrived at in our earlier study, the results of this study show that tumor CEA sites were not saturated even at the highest antibody dose investigated, that accessibility of MORF sites in tumor by 99mTc-cMORF was unhindered and that the maximum percent tumor accumulation of 99mTc-cMORF depended only on the tumor delivery efficiency of 99mTc-cMORF. [ABSTRACT FROM AUTHOR]

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The purpose of this paper is to define and evaluate a classification category for contrast-enhanced (CE) MR imaging of the breast based on the BI-RADS mammographic categories of the American College of Radiology. Using five evaluation criteria for MR findings (initial signal increase, postinitial signal behavior, shape, border, and contrast material distribution within enhancing tumors) 522 patients (1031 breasts) were analyzed. Scores were given from 0 to 8 points and classified into five categories (group I: 0 points, negative; group II: 1–2 points, benign; group III: 3 points, probably benign; group IV: 4–5 points, suspicious abnormality; group V: 6–8 points, highly suspicious for malignancy) to 265 focal hypervascularized breast lesions in 244 breasts (patient group A). These findings were correlated with histology or follow-up. Additionally, this classification was correlated to the contrast medium uptake within the parenchyma of the remaining 787 breasts without any focal lesion (patient group B). Two hundred sixty-five hypervascularized lesions in 238 patients (244 breasts, patient group A) were classified into group I: 0%; group II: 27.3%; group III: 22.3%; group IV: 18.6%; and group V: 31.8%. Histology revealed 115 benign and 134 malignant tumors in these groups. Sixteen benign lesions were controlled by follow-up. Sensitivity for the detection of malignancy using the presented multifactorial MRM classification was 92%, and specificity was 92%. Excluding cases of ductal carcinoma in situ specificity increased to 95%. Seven hundred eighty-seven breasts without any focal hypervascularized lesion (patient group B) were classified into groups I or II. Follow-up (n=771) or histology (n=14) confirmed the diagnosis in 785 of these breasts. Histopathology revealed, however, malignant tumors in the remaining two cases. The classification of lesions based on a multifactorial analysis is very helpful in the interpretation of CE MRI of the breast. The evaluation of all diagnostic imaging modalities, however, is essential in determining the correct diagnosis and/or in deciding on the appropriate therapeutic procedure. [ABSTRACT FROM AUTHOR]

Frequency response analysis via pulse testing for engineering systems and near infrared (NIR) time resolved spectroscopy (TRS) for biological system characterization involve identical principles: the system of interest is disturbed by an input pulse and the output response is observed. Since a sharp pulse, such as the Dirac delta function, contains the information of multimodulation frequencies (theoretically from 0 to ∞ in frequency) a narrow pulse TRS input can produce a wide range frequency response for identifying any system of interest. Currently used NIR-TRS spectral analyses either fit the spectra with a known theoretical solution or use photon mean time-of-flight. Transforming the system time domain representation to the frequency domain generates five system parameters that can be valuable for process identification utility: steady state gain, time constant, system order, and magnitude ratio and phase shift over a wide frequency range. Optical contrast agents or fluorescent agents can be used to enhance the capability of optical instruments in detecting biological heterogeneities. In this article, magnitude ratio, phase shift, and other system parameters derived from the transfer function of systems with both a fluorescent absorber and a regular absorber are correlated with the position of the absorber. This technique is important in that ultimately it can be used to enhance optical medical imaging. © 1998 Biomedical Engineering Society. PAC98: 8764Je, 0757Ty [ABSTRACT FROM AUTHOR]

In 12 consecutive patients with suspected metastatic carcinoma of the liver, we evaluated the sensitivity of radiolabeled antibodies to tumor antigens, magnetic resonance imaging, and X-ray computed tomography imaging in the detection of hepatic malignancies. Studies were performed with I labeled antibodies to CEA and/or CSAp; polyclonal, monoclonal and F(ab′) antibodies were used. Nontarget radioactivity was diminished by administration of Tc reagents simulating nontumor distribution and use of a computer substraction method. In nine patients with confirmed liver neoplasms, radioimunodetection disclosed the foci of hepatic malignancies. In three patients with suspected liver neoplasms, the antibody studies were positive, but at this time have not been confirmed. X-ray computed tomography each disclosed mass lesions in five patients and magnetic resonance in three. These findings suggest that radioimmunodetection provides greater accuracy in the detection and localization of cancer than other diagnostic modalities currently used. [ABSTRACT FROM AUTHOR]

To develop18F-fluoroalkyl derivatives of methionine (MET) as a tumor detecting agent by mean of clinical PET, a pilot study assessing the potential of their parent compounds,11C-labeled ethionine (11C-ETH) and propionine (11C-PRO), was performed.11C-ETH and11C-PRO were prepared by the reaction of l-homocysteine thiolactone and corresponding11C-alkyl iodides. After i.v. injection of a mixture of3H-MET,14C-ETH and11C-PRO into mice bearing FM3A mammary carcinoma, the highest FM3A uptake was found in14C-ETH, followed by3H-MET and11C-PRO, while the FM3A-to-brain and FM3A-to-muscle ratios were nearly the same for all three compounds. The FM3A uptake of14C-ETH and11C-PRO were nearly equal or slightly higher than the liver uptake. In the pancreas, liver, FM3A and brain tissues, incorporation of14C-ETH into acid-precipitable materials was much lower than that of3H-MET, whereas no incorporation of11C-PRO was found. Brain uptake of all three compounds was significantly reduced by carrier MET-loading (5 min p.i.) or by cycloheximide treatment to inhibit protein synthesis (60 min p.i.), whereas the FM3A uptake was not affected. Incorporation of14C-ETH into acid-precipitable materials was inhibited by the cycloheximide. The results suggest that11C-labeled ETH has a similar potential for tumor detection by PET as11C-MET, and that11C-PRO has similar properties to those of other artificial amino acids. The development of18F-fluoroalkyl derivatives of MET is of interest as the next step. [ABSTRACT FROM AUTHOR]

Tc-MIBI (Sestamibi) was originally developed for myocardial perfusion studies. The agent also may be used for the depiction and characterization of tumors. Performing such examinations has shown uptake in skeletal structures in several patients suggesting bone engagement of the disease which later was excluded. Retrospective evaluation of 44 examinations withTc-MIBI performed in order to localize diseased parathyroid in patients with suspected hyperparathyroidism showed skeletal activity in 21 (48%) patients. Although these patients represent a selected group, the observation indicates a mechanism for skeletal accumulation of this radiopharmaceutical. Evaluation of another 13 normocalcemic patients undergoing whole-body registration for malignancy staging or to assess lower extremity ischemia withTc-MIBI showed skeletal activity in 6 (46%) patients. Complementary mouse experiments confirmed skeletal uptake ofTc-MIBI, where most of the activity is taken up by the red bone marrow. It is concluded that homogeneous, diffuse weak skeletal activity at examination withTc-MIBI is a normal finding and does not indicate malignancy. [ABSTRACT FROM AUTHOR]

From the early stage of pancreatic adenocarcinoma in hamsters and also of hepatocellular carcinoma in rats, induced by treatment with N-nitrosobis (2-oxopropyl)amine and 3′-methyl-dimethylaminoazobenzene, respectively, hepatic levels of metallothionein (MT) were found to be continuously elevated. In the hepatoma-induced rats, this elevation preceded that of serum γ-glutamyl transpeptidase activity, a marker enzyme for hepatocellular carcinoma. These results indicate that, in the course of chemical carcinogenesis, the elevation of hepatic MT level occurred and continued from the early stage of carcinogenesis. This type of elevation of hepatic MT level was also observed in lung metastasis-induced mice. On the other hand, in rats with pancreatitis caused by the administration of deoxycholate, the hepatic level of MT rose only transiently. [ABSTRACT FROM AUTHOR]