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Abstract：
We investigated whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone, using pathologically confirmed bone metastasis as the reference standard, in patients with gastric cancer. In this retrospective study, 96 patients (mean age, 58.4 ± 13.3 years; range, 28-85 years) with pathologically confirmed bone metastasis in iliac bones were included. The dataset was categorized into three feature sets: (1) mean and standard deviation values of attenuation in the region of interest (ROI), (2) radiomic features extracted from the same ROI, and (3) combined features of (1) and (2). Five machine learning models were developed and evaluated using these feature sets, and their predictive performance was assessed. The predictive performance of the best-performing model in the test set (based on the area under the curve [AUC] value) was validated in the external validation group. A Random Forest classifier applied to the combined radiomics and attenuation dataset achieved the highest performance in predicting bone marrow metastasis in patients with gastric cancer (AUC, 0.96), outperforming models using only radiomics or attenuation datasets. Even in the pathology-positive CT-negative group, the model demonstrated the best performance (AUC, 0.93). The model\'s performance was validated both internally and with an external validation cohort, consistently demonstrating excellent predictive accuracy. Radiomic features derived from CT images can serve as effective imaging biomarkers for predicting bone marrow metastasis in patients with gastric cancer. These findings indicate promising potential for their clinical utility in diagnosing and predicting bone marrow metastasis through routine evaluation of abdominopelvic CT images during follow-up.
摘要：
我们研究了计算机断层扫描（CT）图像数据的影像组学是否能够区分CT上不可见的骨转移瘤与未受影响的骨骼，使用病理证实的骨转移作为参考标准，胃癌患者。在这项回顾性研究中,96名患者(平均年龄，58.4±13.3年；范围，包括28-85岁），经病理证实的骨骨转移。数据集分为三个特征集：（1）感兴趣区域（ROI）中衰减的平均值和标准偏差值，(2)从相同的ROI中提取放射学特征,(3)(1)和(2)的组合特征。使用这些特征集开发和评估了五种机器学习模型，并对其预测性能进行了评估。在外部验证组中验证了测试集中表现最好的模型的预测性能（基于曲线下面积[AUC]值）。应用于联合影像组学和衰减数据集的随机森林分类器在预测胃癌患者的骨髓转移方面取得了最高的性能（AUC，0.96),仅使用影像组学或衰减数据集的性能优于模型。即使在病理阳性CT阴性组中，该模型表现出最佳性能(AUC，0.93)。模型的性能已在内部和外部验证队列中进行了验证，始终如一地证明了出色的预测准确性。来自CT图像的放射学特征可以作为预测胃癌患者骨髓转移的有效成像生物标志物。这些发现表明，通过在随访期间常规评估腹骨盆CT图像，它们在诊断和预测骨髓转移方面的临床应用潜力巨大。
