Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [

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Tác giả: Lixuan Chen, Heng Lin, Qi Lin, Zhifang Pan, Hongyan Wang, Jian Xu, Yingnan Xue, Yunjun Yang, Fei Yao, Xinwen Yi, Yaping Yuan, Yuandi Zhuang

Ngôn ngữ: eng

Ký hiệu phân loại: 627.12 Rivers and streams

Thông tin xuất bản: Germany : European journal of nuclear medicine and molecular imaging , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 214393

PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [ METHODS: 341 PCa patients who underwent radical prostatectomy (RP) with mpMRI and PET/CT scans were retrospectively analyzed. We generated deep learning signature from mpMRI and PET/CT with a multimodal deep learning model (MPC) based on convolutional neural networks and transformer, which was subsequently incorporated with clinical characteristics to construct an integrated model (MPCC). These models were compared with clinical models and single mpMRI or PET/CT models. RESULTS: The MPCC model showed the best performance in predicting AP (AUC, 0.955 [95% CI: 0.932-0.975]), which is higher than MPC model (AUC, 0.930 [95% CI: 0.901-0.955]). The performance of the MPC model is better than that of single PET/CT (AUC, 0.813 [95% CI: 0.780-0.845]) or mpMRI (AUC, 0.865 [95% CI: 0.829-0.901]). Additionally, MPCC model is also effective in predicting single adverse pathological features. CONCLUSION: The deep learning model that integrates mpMRI and [
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