A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging.

Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out. T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n = 1043 in-house and n = 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n = 1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n = 150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results. Median age was 66 years (IQR = 60-71), PSA 6.7 ng/ml (IQR = 4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%). Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.

Mehralivand S ，Yang D ，Harmon SA ，Xu D ，Xu Z ，Roth H ，Masoudi S ，Sanford TH ，Kesani D ，Lay NS ，Merino MJ ，Wood BJ ，Pinto PA ，Choyke PL ，Turkbey B ... -  《-》

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Lin Y ，Yilmaz EC ，Belue MJ ，Harmon SA ，Tetreault J ，Phelps TE ，Merriman KM ，Hazen L ，Garcia C ，Yang D ，Xu Z ，Lay NS ，Toubaji A ，Merino MJ ，Xu D ，Law YM ，Gurram S ，Wood BJ ，Choyke PL ，Pinto PA ，Turkbey B ... -  《-》

Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI.

To present fully automated DL-based prostate cancer detection system for prostate MRI. MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics. 525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0-6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0-5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0-7), 1.40 (range 0-6) in UNet, AHNet models, respectively. We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.

Mehralivand S ，Yang D ，Harmon SA ，Xu D ，Xu Z ，Roth H ，Masoudi S ，Kesani D ，Lay N ，Merino MJ ，Wood BJ ，Pinto PA ，Choyke PL ，Turkbey B ... -  《-》

Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.

Schelb P ，Kohl S ，Radtke JP ，Wiesenfarth M ，Kickingereder P ，Bickelhaupt S ，Kuder TA ，Stenzinger A ，Hohenfellner M ，Schlemmer HP ，Maier-Hein KH ，Bonekamp D ... -  《-》

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Yilmaz EC ，Harmon SA ，Law YM ，Huang EP ，Belue MJ ，Lin Y ，Gelikman DG ，Ozyoruk KB ，Yang D ，Xu Z ，Tetreault J ，Xu D ，Hazen LA ，Garcia C ，Lay NS ，Eclarinal P ，Toubaji A ，Merino MJ ，Wood BJ ，Gurram S ，Choyke PL ，Pinto PA ，Turkbey B ... -  《-》