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June 11, 2024Frontiers in OncologyOpen Access

A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients

Song Xinyang Shen Tianci Xiangyu Hu Shuang Zhang Yangyang Wang Du Mengying Xu Tonghui Jingran Zhou Feng Yang

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Abstract

To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI)

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Cite This Study

Xinyang et al. (2024) studied this question.

synapsesocial.com/papers/68e6542bb6db6435875e3102 https://doi.org/10.3389/fonc.2024.1298516

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI)2024 · 2 citations
2Biparametric MRI classification model for prostate cancer detection using a combination of prediction maps2024
3Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.2025 · 9 citations
4Developing a multi-channel deep-learning model for automatically quantifying malignant bone disease from Multiparametric Whole-Body MRI2025
5Radiograph-based Deep Learning Algorithm for Assisting Bone Tumor Risk Assessment: A Multicenter StudyCohort Study · 2026

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