AUTHOR=Geng Yujun , Zhang Xinlei , Zhang Ming , Li Jingwen , Yang Meng , Tian Junzhang , Ma Xiaofen 

TITLE=Significance of multi-task deep learning neural networks for diagnosing clinically significant prostate cancer in plain abdominal CT

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1543230

DOI=10.3389/fonc.2025.1543230

ISSN=2234-943X

ABSTRACT=ObjectiveEarly detection and timely surgical intervention are crucial in reducing mortality rates associated with clinically significant prostate cancer (csPCa). Currently, clinical diagnostics primarily depend on magnetic resonance imaging (MRI) and nuclear medicine, with the potential diagnostic value of abdominal computed tomography (CT) remaining underexplored. This study aims to evaluate the effectiveness of multi-task deep learning neural networks in identifying early-stage prostate cancer using CT scans.MethodsIn this study, we enrolled 539 patients from the Department of Radiology (N=461) and Nuclear Medicine (N=78). We utilized a multi-task deep learning network model (MTDL), based on the 3DUnet architecture, to segment and analyze the collected abdominal plain CT images. The predictive performance of this model was compared with a radiomics model and a single-task deep learning model using ResNet18. A diagnostic nomogram was then developed using the multi-task deep learning approach, incorporating prediction results and PSAD, age. The diagnostic performance of the different models was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC).ResultsThe 461 patients from the Department of Radiology were divided into training and test sets at a ratio of 6:4, while the patients from the Department of Nuclear Medicine formed the validation set. Our MTDL nomogram demonstrated AUCs of 0.941 (95% confidence interval [CI]: 0.905valceedi 0.912 (95% CI: 0.904valceedi and 0.932 (95% CI: 0.883valceed in the training, test, and validation cohorts, respectively. This study indicates that combining abdominal CT with a multi-task neural network model effectively diagnoses csPCa, offering superior diagnostic performance compared to clinical models. Additionally, the multi-task neural network model outperformed both the single-task neural network model and the radiomics model in diagnostic accuracy.ConclusionOur study demonstrated that the MTDL nomogram can accurately predict the presence of prostate cancer using abdominal CT scans, offering significant value for the early diagnosis of prostate cancer.