AUTHOR=Zhu Lina , Gao Ge , Zhu Yi , Han Chao , Liu Xiang , Li Derun , Liu Weipeng , Wang Xiangpeng , Zhang Jingyuan , Zhang Xiaodong , Wang Xiaoying 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.958065

DOI=10.3389/fonc.2022.958065

ISSN=2234-943X

ABSTRACT=Purpose   To 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).
Methods   This 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.
Results   In the test set, the per-lesion sensitivity of biparametric (ADC and 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 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 similar performance to PI-RADS assessment (P > 0.05) and had higher specificity than ADC model (86.8% [631/727], P < 0.001) based on sextant.
Conclusion   The cascaded deep learning model trained with ADC and T2WI achieves good performance for automated csPCa detection and localization.