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Abstract #2285

The Value of a Clinical-Radiomics-Deep Learning-Pathomics Fused Models for Biochemical Recurrence Prediction After Radical Prostatectomy

Chenhan Hu1, Xiaomeng Qiao1, Ximing Wang1, Jie Bao1, Yang Song2, Jing Zhang3, and Zeyu Zhao1
1The First Affiliated Hospital of Soochow University, Suzhou, China, 2MR Scientific Marketing, Siemens Healthineers Ltd. Shanghai, China., Shanghai, China, 3MR Research Collaboration Team, Siemens Healthineers Ltd. Shanghai, China, Shanghai, China

Synopsis

Keywords: Urogenital, Prostate, Machine learning

Motivation: There are currently a few studies that utilize artificial intelligence technology to analyze both for precise prediction of prostate cancer prognosis.

Goal(s): To explore the value of a multi-modality fused model integrating clinical feature, radiomics model, MRI DL model and pathomics model in enhancing predictive efficacy of post-RP BCR.

Approach: The CRDH fused model was established by combining clinical model, radiomics model, MRI DL model and pathomics model with COX regression.

Results: In the testing set, the CRDH model achieved a C-index of 0.87, significantly higher than pathological T stage, radiomics model, MRI DL model, pathomics model and clinical score (P<0.05).

Impact: The multi-modality fused model incorporating clinical variable, radiomics model, MRI DL model, pathomics model was better than all single-modality models. Our model could assess the prognosis of patients with PCa after surgery, providing strong support for formulating subsequent treatment plans.

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Keywords