scholarly journals Textured-Based Deep Learning in Prostate Cancer Classification with 3T Multiparametric MRI: Comparison with PI-RADS-Based Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1785
Author(s):  
Yongkai Liu ◽  
Haoxin Zheng ◽  
Zhengrong Liang ◽  
Qi Miao ◽  
Wayne G. Brisbane ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Peripheral Zone ◽  
Significant Degree ◽  
Classification Performance ◽  
Multiparametric Mri ◽  
Superior Performance ◽  
Study Cohort

The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar’s test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment.

scholarly journals Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neda Gholizadeh ◽  
Peter B. Greer ◽  
John Simpson ◽  
Jonathan Goodwin ◽  
Caixia Fu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Transition Zone ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Spectroscopic Imaging ◽  
Multiparametric Mri ◽  
Low Risk ◽  
Intermediate Risk

Abstract Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

scholarly journals Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 563
Author(s):  
Chen Shenhar ◽  
Hadassa Degani ◽  
Yaara Ber ◽  
Jack Baniel ◽  
Shlomit Tamir ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Predictive Value ◽  
Breast Imaging ◽  
Normal Tissue ◽  
Diffusion Tensor ◽  
Peripheral Zone ◽  
Predictive Values ◽  
Directional Diffusion ◽  
Central Gland

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

scholarly journals Can the predictive value of multiparametric MRI for prostate cancer be improved by a liquid biopsy with SelectMDx ?

2021 ◽  
Author(s):  
Mohammad Sajjad Rahnama'i ◽  
Christian Bach ◽  
Maximilian Schulze‐Hagen ◽  
Christiane K Kuhl ◽  
Thomas Alexander Vögeli
Keyword(s):  
Prostate Cancer ◽  
Predictive Value ◽  
Liquid Biopsy ◽  
Multiparametric Mri

scholarly journals Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

2021 ◽  
Author(s):  
Nikita Sushentsev ◽  
Leonardo Rundo ◽  
Oleg Blyuss ◽  
Tatiana Nazarenko ◽  
Aleksandr Suvorov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Cancer Progression ◽  
Predictive Value ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Control Group ◽  
P Value ◽  
Lasso Regression

Abstract Objectives To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). Methods The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5–16 years’ experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong’s test. Results The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34–0.77). Conclusions PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. Key Points • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Diagnostic accuracy of Multiparametric MRI in detection of prostate cancer compared with histopathology obtained by MRI directed TRUS guided cognitive fusion biopsy are T2WI, DWI and DCE enough for Indian scenario

2021 ◽  
Vol 20 (1) ◽  
pp. 10-12
Author(s):  
Mallinath Biradar ◽  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Prostatic Cancer ◽  
Tertiary Care ◽  
Multiparametric Mri ◽  
Care Hospital ◽  
Cognitive Fusion ◽  
Fusion Biopsy ◽  
Indian Scenario

Background: The incidence of prostatic carcinoma is increasing worldwide. With its high resolution, ability to provide excellent tissue characterization and multiplanar imaging capabilities, multi-parametric magnetic resonance imaging (mpMRI) plays a crucial role in detection, local staging and follow-up of carcinoma prostate. It also helps guide targeted biopsies in initial biopsy negative patient. Objectives: Study diagnostic accuracy of mp-MRI and primarily that of the three MR sequences T2, DWI and DCE in detection of prostatic cancer by correlating them with histopathology and thus whether it is feasible for a short MRI of 3 sequences to be used on a large scale in Indian scenario. Materials and Methods: A prospective study was done at a tertiary care hospital between April 2017 to November 2018 in which 50 patients who presented with suspicion of prostate cancer were referred to radiology department for evaluation using MRI. MRIexamination was done using 3T Siemens Magnetom Verio. Followed by this MRI directed TRUS guided cognitive fusion biopsy was done from the prostate. Samples were sent for histopathology. Results: Out of 50 cases studied, 24 cases (48%) were found to be malignant and 26 cases (52 %) were benign on histopathology. In our study, combined T2 + DWI + DCE gave sensitivity of 95.83 %, specificity of 57.69%, positive predictive value of 68.21 % and negative predictive value of 93.75%. Conclusion: Multiparametric MRI using T2, DWI and DCE has a high diagnostic accuracy for evaluation of prostatic cancer.

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

scholarly journals Diagnostic accuracy of the Novel 29 MHz micro-ultrasound “ExactVuTM” for the detection of clinically significant prostate cancer: A prospective single institutional study. A step forward in the diagnosis of prostate cancer

2021 ◽  
Vol 93 (2) ◽  
pp. 132-138
Author(s):  
Francesco Chessa ◽  
Riccardo Schiavina ◽  
Amelio Ercolino ◽  
Caterina Gaudiano ◽  
Davide Giusti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Predictive Value ◽  
Transrectal Ultrasound ◽  
Peripheral Zone ◽  
Risk Identification ◽  
The Novel ◽  
Fusion Biopsy ◽  
Ultrasound System ◽  
Clinically Significant ◽  
Sensitivity Specificity

Introduction and Objective: ExactVuTM is a real-time micro-ultrasound system which provides, according to the Prostate Risk Identification Using Micro-Ultrasound protocol (PRI-MUS), a 300% higher resolution compared to conventional transrectal ultrasound. To evaluate the performance of ExactVuTM in the detection of Clinically significant Prostate Cancer (CsPCa). Materials and methods: Patients with Prostate Cancer diagnosed at fusion biopsy were imaged with ExactVuTM. CsPCa was defined as any Gleason Score ≥ 3+4. ExactVuTM examination was considered as positive when PRI-MUS score was ≥ 3. PRI-MUS scoring system was considered as correct when the fusion biopsy was positive for CsPCa. A transrectal fusion biopsy- proven CsPCa was considered as a gold standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operator characteristic (ROC) curve (AUC) were calculated. Results: 57 patients out of 68 (84%) had a csPCa. PRI-MUS score was correctly assessed in 68% of cases. Regarding the detection of CsPCa, ExactVuTM ’s sensitivity, specificity, PPV, and NPV was 68%, 73%, 93%, and 31%, respectively and the AUC was 0.7 (95% CI 0.5-0-8). For detecting CsPCa in the transition/ anterior zone the sensitivity, specificity, PPV, and NPV was 45%, 66%, 83% and 25% respectively ant the AUC was 0.5 (95% CI 0.2-0.9). Accounting only the CsPCa located in the peripheral zone, sensitivity, specificity, PPV, and NPV raised up to 74%, 75%, 94%, 33%, respectively with AUC 0.75 (95% CI 0.5-0-9). Conclusions: ExactVuTM provides high resolution of the prostatic peripheral zone and could represent a step forward in the detection of CsPCa as a triage tool. Further studies are needed to confirm these promising results.

scholarly journals The utility of prostate MRI within active surveillance: description of the evidence

2021 ◽  
Author(s):  
Georgina Dominique ◽  
Wayne G. Brisbane ◽  
Robert E. Reiter
Keyword(s):  
Prostate Cancer ◽  
Negative Predictive Value ◽  
Active Surveillance ◽  
Predictive Value ◽  
Multiparametric Mri ◽  
Cochrane Library ◽  
Resonance Imaging ◽  
Active Monitoring ◽  
Initial Biopsy ◽  
Clinically Significant

Abstract Purpose We present an overview of the literature regarding the use of MRI in active surveillance of prostate cancer. Methods Both MEDLINE® and Cochrane Library were queried up to May 2020 for studies of men on active surveillance with MRI and later confirmatory biopsy. The terms studied were ‘prostate cancer’ as the anchor followed by two of the following: active surveillance, surveillance, active monitoring, MRI, NMR, magnetic resonance imaging,  MRI, and multiparametric MRI. Studies were excluded if pathologic reclassification (GG1 →  ≥ GG2) and PI-RADS or equivalent was not reported. Results Within active surveillance, baseline MRI is effective for identifying clinically significant prostate cancer and thus associated with fewer reclassification events. A positive initial MRI (≥ PI-RADS 3) with GG1 identified at biopsy has a positive predictive value (PPV) of 35–40% for reclassification by 3 years. MRI possessed a stronger negative predictive value, with a negative MRI (≤ PI-RADS 2) yielding a negative predictive value of up to 85% at 3 years. Surveillance MRI, obtained after initial biopsy, yielded a PPV of 11–65% and NPV of 85–95% for reclassification. Conclusion MRI is useful for initial risk stratification of prostate cancer in men on active surveillance, especially if MRI is negative when imaging is obtained during surveillance. While useful, MRI cannot replace biopsy and further research is necessary to fully integrate MRI into active surveillance.

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