Table 1_Prediction of bone metastasis of prostate cancer based on intratumoral and peritumoral radiomics of MRI T2WI combined with ADC images.docx

ObjectiveTo investigate the value of intratumoral and peritumoral MRI radiomic models in predicting bone metastasis of prostate cancer patients using T2WI combined with ADC images.
Materials and methodA total of 144 patients with prostate cancer who underwent preoperative MRI (T2WI and DWI) were retrospectively included. All patients were categorized into two groups based on the presence of bone metastasis. The radiomics features were calculatd for the entire tumor and 3mm-peritumoral components on pre-processed T2WI combined with ADC images. The radiomics models based on intratumoral features, peritumoral features as well as their merged features were respectively constructed. The independent risk factors of bone metastasis of prostate cancer were used to constructed clinical prediction model. The performance of the clincal model, radiomics models and clinic-imaging combined models was evaluated by the receiver operating characteristic curve and compared with the bootstrap methods. T-test was used to compare the evaluation indicators of different prediction models.
ResultsThe clinic-imaging combined model had the best predictive efficacy among all models. The area under the curve (AUC) of the clinic-imaging combined model for predicting bone metastasis of prostate cancer in the training dataset and test dataset were 0.937 and 0.893, respectively. The accuracy, sensitivity and specificity of this model in predicting bone metastasis of prostate cancer in the training dataset were 84.2%, 91.2% and 80.6%, respectively; the accuracy, sensitivity and specificity of the testing dataset were 76.7%, 73.3% and 78.6%, respectively.
ConclusionsT2WI and ADC intratumoral and peritumoral radiomic models can be used to noninvasively predict the primary diagnosis of PCa BM, and peritumoral radiomic model can add independent predictive value. And the clinic-imaging combined model has the better predictive value.

研究目的：本研究旨在探讨基于T2加权成像（T2WI）联合表观扩散系数（ADC）图像的肿瘤内及肿瘤周围MRI影像组学模型，在预测前列腺癌患者骨转移中的应用价值。
材料与方法：本研究回顾性纳入144例术前接受MRI（T2加权成像T2WI及扩散加权成像DWI）检查的前列腺癌患者。根据是否发生骨转移将所有患者分为两组。在预处理后的T2WI联合ADC图像上，提取整个肿瘤区域及3mm肿瘤周围区域的影像组学特征。分别构建基于肿瘤内特征、肿瘤周围特征及其融合特征的影像组学模型。基于前列腺癌骨转移的独立危险因素构建临床预测模型。采用受试者工作特征（ROC）曲线评估临床模型、影像组学模型及临床影像联合模型的预测性能，并通过Bootstrap法进行组间比较；采用t检验对比不同预测模型的评估指标。
结果：本研究中，临床影像联合模型的预测效能在所有模型中最优。该模型在训练数据集与测试数据集中预测前列腺癌骨转移的曲线下面积（AUC）分别为0.937与0.893。在训练数据集中，该模型的预测准确率、灵敏度与特异度分别为84.2%、91.2%与80.6%；在测试数据集中，其准确率、灵敏度与特异度分别为76.7%、73.3%与78.6%。
结论：基于T2WI与ADC的肿瘤内及肿瘤周围影像组学模型可无创预测前列腺癌骨转移，其中肿瘤周围影像组学模型可提供独立的预测价值；而临床影像联合模型具备更优的预测价值。