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Over the last decade, we have seen the emergence of tissue-based genomic prognostic markers that can be used for decision-making regarding the need for treatment. This review provides an up-to-date summary of the relevant literature surrounding these markers with a discussion of the relevant strength and limitations. We performed a literature search of tissue-based genomic prognostic markers and selected those that were currently available for clinical use. We selected the following markers for further review: Decipher (Decipher Bioscience), Polaris (Myriad), Genome Prostate Score (Oncotype Dx), and Promark. We selected the initial validation study for each marker along with other validation studies in independent cohorts. Furthermore, we selected available clinical utility studies or studies combining multi parametric MRI. In this article, we provide an in-depth review of four commercially available biomarkers and discuss the current literature surrounding these markers, including the benefits and limitations of their use. We found that each of these markers has evidence supporting their role as an independent predictor of relevant prostate cancer endpoints, which can be helpful for clinical decision-making. However, issues related to heterogeneity and a lack of prospective randomized studies supporting their utility are limitations. Evidence appears to suggest that MRI and genomic risk assessment maybe complementary. Although these markers can help in improved risk stratification of patients eligible for AS, more prospective studies with head to head comparison between markers are needed to elucidate the true potential of these markers in AS.

Conventional staging for bladder cancer involves CT and TURBT, and is thought to understage up to fifty-percent of T1 tumours. This report details the case of a 64-year-old male who whilst on cystoscopic surveillance for high grade bladder cancer, underwent a multi-parametric MRI Bladder due to clinical suspicion of occult muscle invasive disease. Despite minimal urothelial changes at cystoscopy, MRI demonstrated a well-defined T3 lesion. The patient proceeded to radical cystectomy and final pathology verified the MRI findings. The role of MRI in bladder cancer is yet to be defined but should be considered if clinical suspicion for understaging exists.

To investigate a variety of magnetic resonance imaging (MRI) quantitative metrics, which reflect different aspects of microstructural damage in deep gray matter (dGM) regions and white matter T2 lesions in patients with relapsing-remitting multiple sclerosis (RRMS), and to determine the level of pathological interconnection between these two entities as well as their association with clinical disability. We recruited thirty RRMS patients along with thirty age-matched healthy controls (HCs). Both groups were scanned at 3 T MRI using 3D high-resolution T1-, T2-, and T2*-weighted, magnetization transfer (MT)–prepared gradient echo for MT ratio (MTR) mapping, and eight repeats of T1-weighted images acquired at different inversion times to create T1 maps. dGM structures were segmented from T1-weighted images using FreeSurfer, WM-T2 lesions were extracted from T2-weighted images, and iron maps were calculated from the phase part of the T2*-weighted sequence. Extracted dGM MRI indices were compared between both groups. In the RRMS group, dGM MRI indices were correlated with those of WM-T2 lesions, expanded disability status scale, and disease duration. dGM volumetric metrics of RRMS patients were significantly (p < 0.01) smaller than those of HCs and showed a significant moderate association with lesions’ load (p < 0.05) and lesions’ iron concentration (p < 0.01). dGM MTRs of RRMS patients were significantly (p < 0.01) smaller than those of HCs and showed a significant (p < 0.01) moderate correlation with lesion T1 times. While T1 changes in some dGM regions of RRMS patients associated weakly with those of T2 lesions, dGM iron concentration did not show any association with any of lesions’ metrics. Furthermore, lesions’ MTR changes did not show any association with any dGM metrics. Most dGM metrics did not show any correlation with disease severity. Contrarily, most lesions’ metrics showed weak association with disease severity. dGM changes occur in a non-uniform pattern and, almost, do not link directly to MS disease severity. Contrarily, most WM-T2 lesions’ metrics tend to correlate with MS disease severity better than those of dGM. • Deep gray matter (dGM) structures are very much involved in the MS disease process and quite substantial neurodegeneration is undergone during the relapsing-remitting phase of the MS disease. • Deep gray matter (dGM) quantitative changes occur in a non-uniform and non-linked pattern and, except for CN’s iron deposition, do not directly associate with the MS disease severity. • Most white matter T2 lesions’ metrics tend to correlate with MS disease severity better than those of dGM structures.

Background Acute necrotizing encephalopathy of children (ANEC) is a rare fulminant type of acute encephalopathy that mainly occurs in children with a characteristic clinico-radiological pattern. It is commonly preceded by viral infections. The condition carries a poor prognosis with high morbidity and mortality rates. We highlight the relationship between diagnostic multi-parametric magnetic resonance imaging (MRI) findings and correlate them with the clinical outcome of children with ANEC by enrollment of MRI scoring. Results The thalami were involved in all 30 patients, brain stem in 80%, basal ganglia (13.3%), cerebral white matter (WM) in 73%, and cerebellar WM in 33%. Hemorrhage was present in 86.7% patients, edema 80%, and necrosis in 13.3%. We found that the patients having the highest MRI scores were in the poor outcome category; whereas the patients with lower MR score (1 or 2) had better outcomes. Statistically positive correlation (r= 0.1198) was found between the multi-parametric MR score and the outcome category. Conclusion An extended multi-parametric MRI should be performed in ANEC, allowing early detection and scoring of the disease for better prognosis. There is a positive correlation between the clinical outcome and the MR scoring.

BACKGROUND: To compare the diagnostic performance of (68)Ga-prostate-specific membrane antigen positron emission tomography/computed tomography ((68)Ga-PSMA PET/CT) with multi-parametric magnetic resonance imaging (mpMRI) on extracapsular extension (ECE) and seminal vesicle invasion (SVI) in primary prostate cancer and its impact on therapeutic decisions. METHODS: We retrospectively enrolled 54 patients with both PET/CT and mpMRI before radical prostatectomy. Diagnostic performance of mpMRI, PET/CT and their combination (com-MRI/PET) on ECE and SVI on a patient basis were analyzed. The impact of additional PET/CT scanning on therapeutic decisions were presented. RESULTS: Among the 54 patients, 17 had tumor limited in the prostate gland, 25 only had ECE and 12 patients had both SVI and ECE on pathology. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ECE were 54%, 94%, 95%, 48% on mpMRI, 78%, 94%, 97%, 67% on PET/CT and 83%, 88%, 94%, 71% on com-MRI/PET. Both PET/CT and com-MRI/PET had a higher sensitivity than mpMRI on ECE diagnosis (78% vs. 54%, P

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas., This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.

Purpose To evaluate the utility of multi-parametric magnetic resonance imaging (MP-MRI), including dynamic contrast-enhanced MRI and diffusion-weighted MRI, for monitoring tumor tissue changes after volumetric-modulated arc radiotherapy in localized prostate cancer (PCa), and to compare the radiotherapy induced tumor tissue changes between conventional, moderate and extreme hypofractionated groups. Furthermore, we aimed to evaluate if follow-up by MRI has an incremental value compared to the standard care by prostate-specific antigen (PSA) serum level measurement. Materials and methods Fifty-five men (mean age: 70 ± 5 [SD] years; range: 60–79 years) with biopsy-proven PCa underwent MRI examination before radiotherapy, and at 3 and 12 months after radiotherapy. Pharmacokinetic analysis post-processing platform with dedicated software (Tissue 4D) was used to generate colorized parametric maps of enhancing tumors. The volume transfer constant (Ktrans), reflux constant (Kep), and initial area under curve (iAUC) were calculated from the tumors. Tumor apparent diffusion coefficient (ADC) value was measured on the ADC map. The patients were allocated into three radiotherapy groups: 17 conventional (39 × 2 Gy), 16 moderate (20 × 3 Gy) and 22 extreme hypofractionated (5 × 7.25 Gy) regimen. Results Sixty lesions were detected in the prostates of the 55 patients. Follow-up MRI showed decreases in tumor size and degree of enhancement. Ktrans, Kep, and iAUC all decreased at 3 months (P Conclusion MP-MRI is a reliable tool for lesion detection and follow-up, providing both qualitative and quantitative data.

INTRODUCTION AND OBJECTIVE:Multi parametric MRI (mpMRI) has emerged as the most common tool to evaluate the need for biopsy in men referred for suspicion of prostate cancer, however PIRADS 3 remain...

INTRODUCTION AND OBJECTIVE:European and American urological guidelines of 2020 recommend to perform multiparametric (mp) MRI in men with suspicion of prostate cancer (PCa), before the execution of ...

BACKGROUND: Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MRI) to detect possible clinically significant lesions using the Prostate Imaging Reporting and Data System (PI-RADS) protocol. The assessment of imaging, however, relies on the experience and judgement of radiologists creating opportunity for inter-reader variability. Quantitative metrics, such as z-score and signal to clutter ratio (SCR), are therefore needed. METHODS: Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes for patients undergoing radical prostatectomy. Multi-parametric signatures that characterize prostate tumors were inserted into z-score and SCR. The multispectral covariance matrix was computed for the outlined normal prostate. The z-score from each MRI image was computed and summed. To reduce noise in the covariance matrix, following matrix decomposition, the noisy eigenvectors were removed. Also, regularization and modified regularization was applied to the covariance matrix by minimizing the discrimination score. The filtered and regularized covariance matrices were inserted into the SCR calculation. The z-score and SCR were quantitatively compared to Gleason scores from clinical pathology assessment of the histology of sectioned wholemount prostates. RESULTS: Twenty-six consecutive patients were enrolled in this retrospective study. Median patient age was 60 years (range, 49 to 75 years), median prostate-specific antigen (PSA) was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL), and median Gleason score was 7 (range, 6 to 9). A linear fit of the summed z-score against Gleason score found a correlation of R=0.48 and a P value of 0.015. A linear fit of the SCR from regularizing covariance matrix against Gleason score found a correlation of R=0.39 and a P value of 0.058. The SCR employing the modified regularizing covariance matrix against Gleason score found a correlation of R=0.52 and a P value of 0.007. A linear fit of the SCR from filtering out 3 and 4 eigenvectors from the covariance matrix against Gleason score found correlations of R=0.50 and 0.44, respectively, and P values of 0.011 and 0.027, respectively. CONCLUSIONS: Z-score and SCR using filtered and regularized covariance matrices derived from spatially registered multi-parametric MRI correlates with Gleason score with highly significant P values.

ObjectivesTo investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors.MethodsData of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively.ResultsThe efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures.ConclusionsThe radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

According to the data of World Cancer Research Fund International prostate cancer is the second most common after lung cancer and the fifth most common cause of cancer death amongst men. Prostate cancer is also the fourth most frequent tumor between both genders worldwide. Biopsy is the only way to detect prostate cancer so far. Statistics show that it is able to detect only 70-80% of clinically significant cancer cases. Multi parametric magnetic resonance imaging technique comes to play to help in determining the location to perform biopsy on. The first step to automating the detection of the location is applying prostate segmentation on magnetic resonance images. The fact that there is lack of standardization of signal intensity to acquire those images burdens the problem of automated prostate segmentation. Authors review the results of Prostate MR Image Segmentation (PROMISE12) challenge, designed to evaluate and compare different prostate segmentation algorithms, and provide insights on automated prostate segmentation by applying best open source algorithm under different circumstances. Authors applied selected algorithm on two different datasets and showed how segmentation results can be improved by applying even the most primitive image stretching techniques. Authors also showed that algorithm is promising in segmenting unseen dataset.

To explore radiological changes of the lacrimal gland (LG) in Graves' ophthalmopathy (GO) based on multi-parametric quantitative MRI and its clinical utility in LG diagnosis and activity in GO.We enrolled 99 consecutive patients with GO (198 eyes) and 12 Graves' Disease (GD) patients (24 eyes) from July 2018 to June 2020. Clinical, laboratory, and MRI data were collected at the first visit. Based on clinical activity scores, eyes with GO were subdivided into active and inactive groups. T2-relaxation time (T2) and the absolute reduction in T1-relaxation time (ΔT1) were determined. After MRI and processing, we performed descriptive data analysis and group comparisons. Novel logistic regression predictive models were developed for diagnosing and staging GO. Diagnostic performance of MRI parameters and models was assessed by receiver operating characteristic curve analysis.LG in GO group had significantly higher T2 and ΔT1 values than the GD group [106.25(95.30,120.21) vs. 83.35(78.15,91.45), P0.001, and 662.62(539.33,810.95) vs. 547.35(458.62,585.57), P = 0.002, respectively]. The GO group had higher T2 of LG indicating higher disease activity [110.93(102.54,127.67) vs. 93.29(87.06,101.96), P 0.001]. Combining T2 and ΔT1 values of LG, Model I had higher diagnostic value for distinguishing GO from GD (AUC=0.94, 95 %CI: 0.89,0.99, P0.001). Meanwhile, T2 of LG had higher diagnostic value for grading GO activity (AUC = 0.84, 95 %CI: 0.76,0.92, P0.001).Multi-parametric quantitative MRI parameters of the LG in GO were significantly altered. Novel models combining LG T2 and ΔT1 values showed excellent predictive performances in diagnosing GO. Furthermore, T2 of LG showed practical utility for staging GO.

Item does not contain fulltext BACKGROUND: PSMA-PET is a novel imaging modality for the staging of prostate cancer (PCa). While there are several PSMA ligands available, F-18-PSMA-1007 is particularly of interest as it is not renally excreted and therefore does not impair the imaging of the pelvic area. Hence, this study aimed to investigate the F-18-PSMA-1007-PET for the primary staging of PCa and compared it to multi-parametric (mp) MRI and histopathology. METHODS: A retrospective study was performed of men with intermediate and high-risk PCa patients that underwent a F-18-PSMA-1007-PET after mpMRI with subsequent MR-guided target biopsy (MRGB). Suspicious mpMRI lesions and F-18-PSMA-1007-PET were simultaneously reviewed on both a per patient and per-lesion basis. Results were subsequently evaluated with histopathological outcome of MRGB, and if performed, the radical prostatectomy specimen. RESULTS: A total of 66 suspicious mpMRI lesions were identified in 53 patients and underwent MRGB. Two lesions had a maximum standardized uptake value (SUV(max)) less than the mean SUV(max) of healthy prostate tissue and were considered as non-PSMA-expressing. All PSMA avid tumors had higher SUV(max) than the mean SUV(mean) of the bladder/urine, therefore all lesions were clearly distinguishable in the pelvic area. Twenty-three patients received a radical prostatectomy of which the histopathology specimens were evaluated. F-18-PSMA-1007-PET/CT correctly staged seminal vesicle invasion (i.e. pT3b) more often than mpMRI (90 vs. 76%), whereas mpMRI more accurately detected extracapsular extension (i.e. pT3a) compared to F-18-PSMA-1007-PET (90% vs 57%). CONCLUSIONS: The present study of a selected cohort suggest that dual imaging with mpMRI and F-18-PSMA-1007-PET may improve staging of primary PCa. F-18-PSMA-1007-PET/CT had low renal clearance, which could assist the evaluation of tumors in proximity of the bladder.

Highlight • Quantitative mpMRI parameters predict failure of salvage HIFU in radiorecurrent prostate cancer • Tumour microenvironment might produce heat-sinks which counter the effect of HIFU • Ve value measured in the DCE sequence of the mpMRI is an independent predictor of treatment failure, Objectives Focal salvage HIFU is a feasible therapeutic option in some men who have recurrence after primary radiotherapy for prostate cancer. We aimed to determine if multi-parametric quantitative parameters, in addition to clinical factors, might have a role in independently predicting focal salvage HIFU outcomes. Methods A retrospective registry analysis included 150 consecutive men who underwent focal salvage HIFU (Sonablate500) (2006-2015); 89 had mpMRI available. Metastatic disease was excluded by nodal assessment on pelvic MRI, a radioisotope bone-scan and/or choline or FDG PET/CT scan. All men had mpMRI and either transperineal template prostate mapping biopsy or targeted and systematic TRUS-biopsy. mpMRI included T2‐weighted, diffusion‐weighted and dynamic contrast‐enhancement. Pre-HIFU quantitative mpMRI data was obtained using Horos DICOM Viewer v3.3.5 for general MRI parameters and IB DCE v2.0 plug-in. Progression-free survival (PFS) was defined by biochemical failure and/or positive localized or distant imaging results and/or positive biopsy and/or systemic therapy and/or metastases/prostate cancer‐specific death. Potential predictors of PFS were analyzed by univariable and multivariable Cox-regression. Results Median age at focal salvage HIFU was 71 years (interquartile range [IQR] 65–74.5) and median PSA pre-focal salvage treatment was 5.8ng/ml (3.8-8). Median follow-up was 35 months (23-47) and median time to failure was 15 months (7.8–24.3). D-Amico low, intermediate and high-risk disease was present in 1% (1/89), 40% (36/89) and 43% (38/89) prior to focal salvage HIFU (16% missing data). 56% (50/89) failed by the composite outcome. A total of 22 factors were evaluated on univariable and 8 factors on multivariable analysis. The following quantitative parameters were included: Ktrans, Kep, Ve, Vp, IS, rTTP and TTP. On univariable analysis, PSA, prostate volume at time of radiotherapy failure and Ve (median) value were predictors for failure. Ve represents extracellular fraction of the whole tissue volume. On multivariable analysis, only Ve (median) value remained as an independent predictor. Conclusions One pharmacokinetic quantitative parameter based on DCE sequences seems to independently predict failure following focal salvage HIFU for radio-recurrent prostate cancer. This likely relates to the tumor microenvironment producing heat-sinks which counter the heating effect of HIFU. Further validation in larger datasets and evaluating mechanisms to reduce heat-sinks are required.

Objectives: To compare transperineal biopsies (TPBx) with transrectal ultrasound-guided biopsy (TRUSBx) in order to provide evidence, making clinicians able to select the appropriate biopsy approach under different conditions. Methods: A comparative prospective study, conducted in King Khalid University Hospital (KKUH) and King Faisal Specialist Hospital and Research Centre (KFSH&RC), Riyadh, Kingdom of Saudi Arabia, between March 2019 and February 2020. All patients with raised prostate-specific antigen or atypical digital rectal examination findings were subjected to multi-parametric magnetic resonance imaging (MRI). Those with positive findings were referred to targeted fusion- guided biopsy either TPBx or TRUSBx, randomly. Complication rate, cancer detection rate, and procedure time were recorded. Results: Transperineal biopsies and TRUSBx had an equivalent complication rate. However, both case detection rate and clinically significant cancer detection rate were significantly higher in TPBx versus TRUSBx (45.1% versus 29.1%, p=0.003; and 71.8% versus 43.7%, p=0.002; respectively). Transperineal biopsies was a longer procedure than TRUSBx (41.2±0.7 min versus 13±2.3 min, p=0.0001). Conclusion: No difference in complication rate was detected between the 2 procedures; however, TPBx was more effective for cancer detection in general and clinically significant cancer detection in particular.

The use of flow cytometry allows simultaneous measurement and multiparametric analysis of single cells in a heterogenous solution. The purpose of flow cytometry can vary depending on the use of antibodies and dyes targeted for specific cell molecules. The method of immune-phenotyping with fluorescently conjugated antibodies to label cell proteins or DNA works in tandem with fluidic, optic, and electrical systems present in the flow cytometer. Some flow cytometers can detect numerous fluorescent molecules on a single cell, allowing the measurement of more than 30 parameters. This ability to detect, measure, and quantitate multiple fluorescent markers on a single cell makes the flow cytometer a useful tool for analyzing various aspects of cell phenotype and function. Here we describe a standardized protocol for surface and intracellular immune-phenotyping of murine lungs, beginning with the building of an optimal antibody panel and ending with data analysis and representation, including sample gating strategies for innate and adaptive immune responses.

International audience; This article presents an open source software able to convert, display and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large database. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows to display up to 4 images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are here exposed: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.

Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

Multiparametric magnetic resonance (mpMR) images are increasingly being used for diagnosis and monitoring of prostate cancer. Detection of malignancy from prostate mpMR images requires expertise, is time consuming and prone to human error. The recent developments of U-net have demonstrated promising detection results in many medical applications. Straightforward use of U-net tends to result in over-detection in mpMR images. The recently developed attention mechanism can help retain only features relevant for malignancy detection, thus improving the detection accuracy. In this work, we propose a U-net architecture that is enhanced by the attention mechanism to detect malignancy in prostate mpMR images. This approach resulted in improved performance in terms of higher Dice score and reduced over-detection when compared to U-net in detecting malignancy.

Ultrasound tissue characterization (UTC) provides ultrasound elastography and quantitative metrics extracted from RF data for evaluating tissue mechanics and microstructure. Our goal is to learn multi-parametric models on the same patient population for regression of Magnetic Resonance Imaging Proton-Density Fat-Fraction (MRI-PDFF). The results show that non-linear multi-parameteric UTC models (e.g., multi-layer perceptron, random forests, Gaussian processes) are superior to multi-linear regression approaches and outperform qualitative radiologist assessment. Integration of radiologist feedback with UTC further improves PDFF regression.

This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network–based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.

Purpose To identify intra-lesion imaging heterogeneity biomarkers in multi-parametric Magnetic Resonance Imaging (mpMRI) for breast lesion diagnosis. Methods Dynamic Contrast Enhanced (DCE) and Diffusion Weighted Imaging (DWI) of 73 female patients, with 85 histologically verified breast lesions were acquired. Non-rigid multi-resolution registration was utilized to spatially align sequences. Four (4) DCE (2nd post-contrast frame, Initial-Enhancement, Post-Initial-Enhancement and Signal-Enhancement-Ratio) and one (1) DWI (Apparent-Diffusion-Coefficient) representations were analyzed, considering a representative lesion slice. 11 1st-order-statistics and 16 texture features (Gray-Level-Co-occurrence-Matrix (GLCM) and Gray-Level-Run-Length-Matrix (GLRLM) based) were derived from lesion segments, provided by Fuzzy C-Means segmentation, across the 5 representations, resulting in 135 features. Least-Absolute-Shrinkage and Selection-Operator (LASSO) regression was utilized to select optimal feature subsets, subsequently fed into 3 classification schemes: Logistic-Regression (LR), Random-Forest (RF), Support-Vector-Machine-Sequential-Minimal-Optimization (SVM-SMO), assessed with Receiver-Operating-Characteristic (ROC) analysis. Results LASSO regression resulted in 7, 6 and 7 features subsets from DCE, DWI and mpMRI, respectively. Best classification performance was obtained by the RF multi-parametric scheme (Area-Under-ROC-Curve, (AUC) ± Standard-Error (SE), AUC ± SE = 0.984 ± 0.025), as compared to DCE (AUC ± SE = 0.961 ± 0.030) and DWI (AUC ± SE = 0.938 ± 0.032) and statistically significantly higher as compared to DWI. The selected mpMRI feature subset highlights the significance of entropy (1st-order-statistics and 2nd-order-statistics (GLCM)) and percentile features extracted from 2nd post-contrast frame, PIE, SER maps and ADC map. Conclusion Capturing breast intra-lesion heterogeneity, across mpMRI lesion segments with 1st-order-statistics and texture features (GLCM and GLRLM based), offers a valuable diagnostic tool for breast cancer.

Background: Multi-parametric MRI used for preoperative assessment of orbital lesions does not routinely include DCE-MRI, since its accuracy in differential diagnosis of orbital mass is still under debate. Aim of this study is to characterize orbital lesions by multi-parametric MRI, analysing the incremental predictive value of DCE-MRI in differential diagnosis of orbital lesions. Methods: In this prospective triple-blind study, 43 consecutive patients with unilateral orbital lesion underwent conventional multimodal MRI and DCE-MRI before biopsy in a tertiary referral centre. Pre-operative MRI examination including conventional unenhanced MRI protocol, DWI with ADC maps, static CE 3D-T1 w and dynamic CE T1 w sequences, was performed within 1 week from surgery (anterior/lateral orbitotomy depending on location of the lesion, to carry out incisional/excisional biopsy). Results: Comparison between conventional T1 w/T2 w, DWI, CE 3D-T1 w and DCE-MRI groups showed a statistically significant difference in scores distribution (p

Background Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour necrosis rate can currently be evaluated only in resected specimens after NACT. A preoperatively accurate, noninvasive, and reproducible method of response assessment to NACT is required. In this study, the value of multi-parametric magnetic resonance imaging (MRI) combined with machine learning for assessment of tumour necrosis after NACT for osteosarcoma was investigated. Methods Twelve patients with primary osteosarcoma of limbs underwent NACT and received MRI examination before surgery. Postoperative tumour specimens were made corresponding to the transverse image of MRI. One hundred and two tissue samples were obtained and pathologically divided into tumour survival areas (non-cartilaginous and cartilaginous tumour viable areas) and tumour-nonviable areas (non-cartilaginous tumour necrosis areas, post-necrotic tumour collagen areas, and tumour necrotic cystic/haemorrhagic and secondary aneurismal bone cyst areas). The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation. Results For distinguishing non-cartilaginous tumour survival from tumour nonviable, the classifier constructed with ADC achieved an AUC of 0.93, while the classifier with multi-parametric MRI improved to 0.97 (P = 0.0933). For distinguishing tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.83, while the classifier with multi-parametric MRI improved to 0.90 (P P Conclusions The combination of multi-parametric MRI and machine learning significantly improved the discriminating ability of viable cartilaginous tumour components. Our study suggests that this method may provide an objective and accurate basis for NACT response evaluation in osteosarcoma.

INTRODUCTION AND OBJECTIVE:We aimed to evaluate the prognostic value of seminal vesicle invasion (SVI) on preoperative multiparametric MRI (mpMRI) in pathological T3b prostate cancer (PCa).METHODS:...

Multi-parametric magnetic resonance imaging (mpMRI) has been shown to be a powerful tool in the detection, treatment, and prognosis of prostate cancer. The pre-processing of MR images prior to any type of quantitative analysis is crucial to the quality, reproducibility, and reliability of the results. Co-registration of mpMRI data, in particular, plays an important role in combining the imaging data from each MR modality (i.e., T2, diffusion- weighted imaging (DWI), dynamic contrast enhanced (DCE)) for use in machine learning tools and quantitative analyses. The aim of intensity-based deformable registration is to determine a transformation that relates one image to another. The ideal transformation is both biologically and mathematically sound (smooth and regular transformation) and provides the highest degree of alignment that can be achieved with the least amount of inference or loss of information. Despite the necessity for accurate co-registration of prostate mpMRI, there is no standardization for an optimal approach. Furthermore, once a method has been chosen, an accurate assessment of registration quality can be difficult to establish using many of the standard techniques. These methods are typically only surrogate measures of accuracy that may produce misleading results. Thus, the determination of a strategy for consistent alignment of prostate imaging data, as well as a more strategic choice of registration quality evaluation, could prove to be particularly useful in the emerging context of radiomics analysis for lesion characterization. We aim to accomplish this by investigating the quality of state-of-the-art co-registration obtainable by commonly used open source toolkits.

Objective The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Materials and methods Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. Results A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. Conclusion In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort.

INTRODUCTION: Nerve-sparing prostatectomy is recommended in cases of organ-confined prostate cancer but is generally contraindicated in patients with suspected extra-prostatic extension (EPE). PSMA ligand imaging has been shown to be valuable in predicting EPE when performed on a hybrid PET/MRI scanner; however, the majority of PSMA PET imaging is performed using PSMA-PET. To our knowledge, there are no established PET/CT criteria for assessing EPE. In this study, we aim to provide a reproducible method for evaluating EPE on PSMA-PET/CT imaging and assess its utility compared with MRI. METHODS: Imaging findings and histopathology were reviewed for 100 consecutive patients who underwent a radical prostatectomy after imaging with MRI and 18F-DCFPyL PSMA-PET/CT. RESULTS: A broad tumour-capsule interface measured using a standardised technique on fused PSMA-PET/CT imaging is associated with a higher risk for established EPE (P

Prostate cancer (PCa) imaging is a rapidly evolving field. Dramatic improvements in prostate MRI during the last decade will probably change the accuracy of diagnosis. This chapter reviews recent current evidence about MRI diagnostic performance and impact on PCa management. The International Consultation on Urological Diseases nominated a committee to review the literature on prostate MRI. A search of the PubMed database was conducted to identify articles focussed on MP-MRI detection and staging protocols, reporting and scoring systems, the role of MP-MRI in diagnosing PCa prior to biopsy, in active surveillance, in focal therapy and in detecting local recurrence after treatment. Differences in opinion were reported in the use of the strength of magnets [1.5 Tesla (T) vs. 3T] and coils. More agreement was found regarding the choice of pulse sequences; diffusion-weighted MRI (DW-MRI), dynamic contrast-enhanced MRI (DCE MRI), and/or MR spectroscopy imaging (MRSI) are recommended in addition to conventional T2-weighted anatomical sequences. In 2015, the Prostate Imaging Reporting and Data System (PI-RADS version 2) was described to standardize image acquisition and interpretation. MP-MRI improves detection of clinically significant PCa (csPCa) in the repeat biopsy setting or before the confirmatory biopsy in patients considering active surveillance. It is useful to guide focal treatment and to detect local recurrences after treatment. Its role in biopsy-naive patients or during the course of active surveillance remains debated. MP-MRI is increasingly used to improve detection of csPCa and for the selection of a suitable therapeutic approach.

OBJECTIVE: To compare the proportion of clinically significant prostate cancers (PCa) found in lesions detected by multiparametric MRI (mpMRI) with that found in lesions detected by multiparametric ultrasound (mpUSS), in men at risk. PATIENTS AND METHODS: CADMUS (Cancer Detection by Multiparametric Ultrasound of the prostate) is a prospective, multi-centre paired cohort diagnostic utility study with built-in randomisation of order of biopsies. The trial is registered ISRCTN38541912. All patients will undergo the index test under evaluation (mpUSS±biopsies), as well as the standard test (mpMRI±biopsies). Eligible men will be those at risk of harbouring prostate cancer usually recommended for prostate biopsy, either for the first time or as a repeat, who have not had any prior treatment for prostate cancer. Men in need of repeat biopsy will include those with prior negative results but ongoing suspicion, and those with an existing prostate cancer diagnosis but a need for accurate risk stratification. Both scans will be reported blind to the results of the other and the order in which the targeted biopsies derived from the two different imaging modalities are taken will be randomised. Comparison will be drawn between biopsy results of lesions detected by mpUSS with those lesions detected by mpMRI. Agreement over position between the two imaging modalities will be studied. DISCUSSION: CADMUS will provide level one evidence on the performance of mpUSS derived targeted biopsies in the identification of clinically significant prostate cancer in comparison to mpMRI targeted biopsies. Recruitment is underway and expected to complete in 2018.

Background Atherosclerosis is prevalent in cardiovascular disease, but present imaging modalities have limited capabilities for characterizing lesion stage, progression and response to intervention. This study tests whether intravascular magnetic resonance imaging (IVMRI) measures of relaxation times (T1, T2) and proton density (PD) in a clinical 3 Tesla scanner could characterize vessel disease, and evaluates a practical strategy for accelerated quantification. Methods IVMRI was performed in fresh human artery segments and swine vessels in vivo, using fast multi-parametric sequences, 1–2 mm diameter loopless antennae and 200–300 μm resolution. T1, T2 and PD data were used to train a machine learning classifier (support vector machine, SVM) to automatically classify normal vessel, and early or advanced disease, using histology for validation. Disease identification using the SVM was tested with receiver operating characteristic curves. To expedite acquisition of T1, T2 and PD data for vessel characterization, the linear algebraic method (‘SLAM’) was modified to accommodate the antenna’s highly-nonuniform sensitivity, and used to provide average T1, T2 and PD measurements from compartments of normal and pathological tissue segmented from high-resolution images at acceleration factors of R ≤ 18-fold. The results were validated using compartment-average measures derived from the high-resolution scans. Results The SVM accurately classified ~80% of samples into the three disease classes. The ‘area-under-the-curve’ was 0.96 for detecting disease in 248 samples, with T1 providing the best discrimination. SLAM T1, T2 and PD measures for R ≤ 10 were indistinguishable from the true means of segmented tissue compartments. Conclusion High-resolution IVMRI measures of T1, T2 and PD with a trained SVM can automatically classify normal, early and advanced atherosclerosis with high sensitivity and specificity. Replacing relaxometric MRI with SLAM yields good estimates of T1, T2 and PD an order-of-magnitude faster to facilitate IVMRI-based characterization of vessel disease. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0399-6) contains supplementary material, which is available to authorized users.

Summary Flow cytometry, incorporating either point- or imaging-based detection schemes, is recognized to be the gold-standard tool for high-throughput manipulation and analysis of single cells in flow but is typically limited in either the number of cells that can be interrogated per unit of time or the resolution with which individual cells can be imaged. To address these limitations, we present a sheathless, microfluidic imaging flow cytometer incorporating stroboscopic illumination for blur-free cellular analysis at throughputs exceeding 50,000 cells/s. By combining inertial focusing of cells in parallel microchannels and stroboscopic illumination, the chip-based cytometer is able to extract multi-color fluorescence, bright-field, and dark-field images and perform accurate sizing of individual cells and analysis of heterogeneous cell suspensions while maintaining operational simplicity. To showcase the efficacy of the approach, we apply the method to the rapid enumeration of apoptotic cells and the high-throughput discrimination of cell-cycle phases.

// Bin Zhang 1, 2, * , Fusheng Ouyang 3, * , Dongsheng Gu 4, * , Yuhao Dong 5 , Lu Zhang 5 , Xiaokai Mo 5 , Wenhui Huang 5 and Shuixing Zhang 1, 2 1 Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China 2 Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China 3 Department of Radiology, The First People’s Hospital of Shunde, Foshan, P.R. China 4 Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, P.R. China 5 Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China * Bin Zhang, Fusheng Ouyang, and Shengdong Gu contributed equally to this work Correspondence to: Shuixing Zhang, email: shui7515@126.com Keywords: imaging, biomarkers, nasopharyngeal carcinoma, progression, radiomics Received: May 07, 2017 Accepted: June 28, 2017 Published: August 02, 2017 ABSTRACT We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression. The selected features were used to construct radiomics-based models and the predictive performance of which were assessed with respect to the area under the curve (AUC). As a result, eight features significantly associated with the progression of advanced NPC were identified. In the training cohort, a radiomic model based on combined CET1-w and T2-w images (AUC: 0.886, 95%CI: 0.815-0.956) demonstrated better prognostic performance than models based on CET1-w (AUC: 0.793, 95%CI: 0.698-0.889) or T2-w images alone (AUC: 0.813, 95%CI: 0.721-0.904). These results were confirmed in the validation cohort. Accordingly, MRI-based radiomic biomarkers present high accuracy in the pre-treatment prediction of progression in advanced NPC.

Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to 'see' the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our multimodal CNNs but have not been carefully studied previously. (1) Given limited training data, how can these be augmented in sufficient numbers and variety for fine-tuning deep CNN networks for PCa diagnosis? (2) How can multimodal MP-MRI information be effectively combined in CNNs? (3) What is the impact of different CNN architectures on the accuracy of PCa diagnosis? Experimental results on extensive clinical data from 364 patients with a total of 463 PCa lesions and 450 identified noncancerous image patches demonstrate that our system can achieve a sensitivity of 89.85% and a specificity of 95.83% for distinguishing cancer from noncancerous tissues and a sensitivity of 100% and a specificity of 76.92% for distinguishing indolent PCa from CS PCa. This result is significantly superior to the state-of-the-art method relying on handcrafted features.

The ability to image the prostate accurately and better characterize cancerous lesions makes multiparametric magnetic resonance imaging (mpMRI) an invaluable tool to improve management of localized prostate cancer (PCa). Improved risk stratification is warranted given the evidence of significant overtreatment of indolent PCa. mpMRI can more accurately rule out clinically significant PCa in men deciding between surveillance and definitive treatment to reduce overtreatment. mpMRI improves detection of clinically significant PCa, which helps minimize sampling error, a major limitation of the traditional diagnostic paradigm. Aside from helping determine candidacy for initial surveillance vs. treatment, mpMRI is a useful tool for following men on active surveillance (AS) with the potential to reduce the need for serial biopsies. When definitive treatment is warranted, mpMRI can be used to determine the local extent of disease. This provides information that is useful in the treatment decision, counseling about outcomes, and surgical planning. While mpMRI is a significant step forward in PCa management, it is necessary to understand its limitations. mpMRI and MRI-guided fusion biopsy techniques still do not detect all clinically significant tumors. The utility of current mpMRI techniques is limited by the multifocal nature of PCa with poor detection of non-index lesions, inaccurate estimation of tumor size and geometry, and the need for interpretation by specialized radiologists. The role of mpMRI will continue to expand as improvements in technology and experience help overcome these limitations.

INTRODUCTION AND OBJECTIVE:It is well known that mp-MRI of the prostate is associated with a high negative predictive value (NPV) for clinically significant prostate cancer (csPCa). However, not in...