DL Model to Detect Clinically Significant Prostate Cancer at MRI – Advanced Prostate Cancer Consensus Conference

Advanced Prostate Cancer Consensus Conference posted on X about recent paper by Jason C. Cai et al., titled “Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI” published on Radiology RSNA.

Authors: Jason C. Cai, Hirotsugu Nakai, Shiba Kuanar, Adam T. Froemming, Candice W. Bolan, Akira Kawashima, Hiroaki Takahashi, Lance A. Mynderse, Chandler D. Dora, Mitchell R. Humphreys, Panagiotis Korfiatis, Pouria Rouzrokh, Alexander K. Bratt, Gian Marco Conte, Bradley J. Erickson, Naoki Takahashi.

“Fully Automated DL Model to Detect Clinically Significant Prostate Cancer at MRI out on Radiology RSNA

A deep learning (DL) model was developed to predict clinically significant Prostate Cancer (csPCa) using multiparametric MRI data from 5735 examinations in 5215 patients, without needing specific tumor location information

The DL model, trained on various MRI sequences, demonstrated similar performance to radiologists, with areas under the receiver operating characteristic curves (AUCs) of 0.89 for both on the internal test set and 0.86 and 0.84 on the external test set, respectively.

Combining the DL classifier with radiologists improved the AUC to 0.89. Gradient-weighted class activation maps (Grad-CAMs) effectively highlighted csPCa lesions, supporting the model’s accuracy.

Thus, the DL model performed comparably to radiologists in detecting csPCa, offering a reliable and consistent diagnostic tool.”

Source: Advanced Prostate Cancer Consensus Conference/X