Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network

Abstract

PurposeTo develop a cascaded deep learning model trained with apparent diffusion coefficient (ADC) and T2-weighted imaging (T2WI) for fully automated detection and localization of clinically significant prostate cancer (csPCa).MethodsThis retrospective study included 347 consecutive patients (235 csPCa, 112 non-csPCa) with high-quality prostate MRI data, which were randomly selected for training, validation, and testing. The ground truth was obtained using manual csPCa lesion segmentation, according to pathological results. The proposed cascaded model based on Res-UNet takes prostate MR images (T2WI+ADC or only ADC) as inputs and automatically segments the whole prostate gland, the anatomic zones, and the csPCa region step by step. The performance of the models was evaluated and compared with PI-RADS (version 2.1) assessment using sensitivity, specificity, accuracy, and Dice similarity coefficient (DSC) in the held-out test set.ResultsIn the test set, the per-lesion sensitivity of the biparametric (ADC + T2WI) model, ADC model, and PI-RADS assessment were 95.5% (84/88), 94.3% (83/88), and 94.3% (83/88) respectively (all p > 0.05). Additionally, the mean DSC based on the csPCa lesions were 0.64 ± 0.24 and 0.66 ± 0.23 for the biparametric model and ADC model, respectively. The sensitivity, specificity, and accuracy of the biparametric model were 95.6% (108/113), 91.5% (665/727), and 92.0% (773/840) based on sextant, and were 98.6% (68/69), 64.8% (46/71), and 81.4% (114/140) based on patients. The biparametric model had a similar performance to PI-RADS assessment (p > 0.05) and had higher specificity than the ADC model (86.8% [631/727], p< 0.001) based on sextant.ConclusionThe cascaded deep learning model trained with ADC and T2WI achieves good performance for automated csPCa detection and localization.

Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study

“…The commercially available software provided at https://fuse-ai.de/prostatecarcinoma-ai has a comparable level of reliability to that of radiologists in detecting carcinomas, with a patient-level sensitivity of 0.86, which, in contrast, is inferior to our model’s sensitivity value of 0.94. Several researchers ( 18 , 26 , 29 ) have sought to evaluate the efficacy of the U-Net network for PCa detection at various levels, including the lesion, sextant, and patient levels. However, these studies did not examine the effect of different cancer foci on the diagnostic efficiency of the models.…”

“…Subsequently, their proposed co-trained CNNs were fed with pairs of aligned ADC and T2WI squares. Expanding on this work, Wang et al ( 35 ) and Zhu et al ( 29 ) adapted the workflow to create an end-to-end trainable deep neural network comprising two sub-networks. The first sub-network was responsible for prostate detection and ADC-T2WI registration, while the second sub-network was a dual-path multimodal CNN that generated a classification score for csPCa and non-csPCa.…”

AI-predicted mpMRI image features for the prediction of clinically significant prostate cancer

“…Using artificial intelligence (AI) methods, such as deep learning (DL), can improve the detection and classification of PCa on mpMRI images [ 4 ]. Previous studies have shown that AI can improve the accuracy and efficiency of PCa detection on mpMRI images by automatically detecting and segmenting suspicious areas for further evaluation by a radiologist [ 5 ]. Several recent studies have indicated the potential of AI in predicting tumor invasiveness of biopsy pathology [ 6 – 8 ].…”