■为了开发一种集成影像组学的半自动模型,深度学习,使用双参数MRI(bpMRI)图像预测前列腺癌(PCa)患者骨转移(BM)的临床特征。
■一项回顾性研究包括414名PCa患者(BM,n=136;NO-BM,2016年1月至2022年12月期间,来自两个机构(中心1,n=318;中心2,n=96)的n=278)。MRI扫描通过PET-CT或ECT预处理证实BM状态。使用自动描绘肿瘤模型将bpMRI图像上的肿瘤区域描绘为肿瘤感兴趣区域(ROI),用骰子相似系数(DSC)评估。样本是自动绘制的,精致,并用于训练ResNetBM预测模型。临床,影像组学,深度学习数据被合成到ResNet-C模型中,使用接收器工作特性(ROC)进行评估。
■自动分割模型的DSC为0.607。临床BM预测的内部验证的准确性(ACC)为0.650,曲线下面积(AUC)为0.713;外部队列的ACC为0.668,AUC为0.757。深度学习模型的内部ACC为0.875,AUC为0.907,外部队列的ACC为0.833,AUC为0.862。Radiomics模型在内部注册的ACC为0.819,AUC为0.852,外部的ACC为0.885,AUC为0.903。ResNet-C显示出内部的最高ACC为0.902,AUC为0.934,外部队列的ACC为0.885,AUC为0.903。
■ResNet-C模型,利用bpMRI扫描策略,准确评估新诊断前列腺癌(PCa)患者的骨转移(BM)状态,促进精确的治疗计划和改善患者预后。
UNASSIGNED: 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) images.
UNASSIGNED: A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor\'s region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the
ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the
ResNet-C model, evaluated using receiver operating characteristic (ROC).
UNASSIGNED: The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction\'s internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally.
ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.
UNASSIGNED: The
ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.