INTRODUCTION Adverse histopathological status (AHS) decreases outcomes of gastric cancer (GC). With the lack of a single factor with great reliability to preoperatively predict AHS, we developed a computational approach by integrating large-scale imaging factors, especially radiomic features at contrast-enhanced computed tomography, to predict AHS and clinical outcomes of patients with GC. METHODS Five hundred fifty-four patients with GC (370 training and 184 test) undergoing gastrectomy were retrospectively included. Six radiomic scores (R-scores) related to pT stage, pN stage, Lauren & Borrmann (L&B) classification, World Health Organization grade, lymphatic vascular infiltration, and an overall histopathologic score (H-score) were, respectively, built from 7,000+ radiomic features. R-scores and radiographic factors were then integrated into prediction models to assess AHS. The developed AHS-based Cox model was compared with the American Joint Committee on Cancer (AJCC) eighth stage model for predicting survival outcomes. RESULTS Radiomics related to tumor gray-level intensity, size, and inhomogeneity were top-ranked features for AHS. R-scores constructed from those features reflected significant difference between AHS-absent and AHS-present groups (P < 0.001). Regression analysis identified 5 independent predictors for pT and pN stages, 2 predictors for Lauren & Borrmann classification, World Health Organization grade, and lymphatic vascular infiltration, and 3 predictors for H-score, respectively. Area under the curve of models using those predictors was training/test 0.93/0.94, 0.85/0.83, 0.63/0.59, 0.66/0.63, 0.71/0.69, and 0.84/0.77, respectively. The AHS-based Cox model produced higher area under the curve than the eighth AJCC staging model for predicting survival outcomes. Furthermore, adding AHS-based scores to the eighth AJCC staging model enabled better net benefits for disease outcome stratification. DISCUSSION The developed computational approach demonstrates good performance for successfully decoding AHS of GC and preoperatively predicting disease clinical outcomes.

中文翻译：

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从大规模放射影像学图像衍生出的基于机器学习的计算模型可以帮助预测胃癌的不良组织病理学状态。

引言不良的组织病理学状态（AHS）会降低胃癌（GC）的预后。由于缺乏可靠可靠的术前预测AHS的单一因素，我们开发了一种计算方法，通过整合大规模成像因素（尤其是对比增强计算机断层扫描的放射学特征）来预测AHS和GC患者的临床结局。方法回顾性分析了54例接受了胃切除术的GC（370个训练和184个测试）患者。从7,000分别建立了与pT阶段，pN阶段，Lauren＆Borrmann（L＆B）分类，世界卫生组织等级，淋巴管浸润和组织病理学总得分（H得分）相关的六个放射学得分（R得分）。 +放射功能。然后将R得分和放射线照相因素整合到预测模型中以评估AHS。将已开发的基于AHS的Cox模型与美国癌症联合委员会（AJCC）第八阶段模型进行比较，以预测生存结果。结果与肿瘤灰阶强度，大小和不均匀性相关的放射线学是AHS的首要特征。由这些特征构成的R评分反映了AHS缺失组和AHS存在组之间的显着差异（P <0.001）。回归分析确定了pT和pN分期的5个独立预测因子，Lauren＆Borrmann分类，世界卫生组织等级和淋巴管浸润的2个预测因子以及H评分的3个预测因子。使用这些预测变量的模型曲线下的面积为训练/测试0.93 / 0.94、0.85 / 0.83、0.63 / 0.59、0.66 / 0.63、0。71 / 0.69和0.84 / 0.77。与第八个AJCC分期模型相比，基于AHS的Cox模型在曲线下面积更大，可预测生存结果。此外，将基于AHS的评分添加到第八个AJCC分期模型可以为疾病结果分层带来更好的净收益。讨论发达的计算方法展示了成功解码GC的AHS并在术前预测疾病临床结果的良好性能。