国际肺癌研究协会 (IASLC) 病理学委员会于 2020 年推出了侵袭性肺腺癌 (LUAD) 的组织学分级系统。IASLC 分级系统以评估主要和高级组织学模式为基础，已被证明可以对侵入性 LUAD 具有实用性和预后意义。然而，评估IA期LUAD的预后仍存在局限性。放射组学可能是一个有价值的补充。建立一个整合IASLC分级和放射组学的模型，旨在预测IA期LUAD的预后。我们对2015年1月至2015年1月至2015年1月期间诊断为IA期LUAD的628例接受手术切除的患者进行了回顾性分析。 2018 年 12 月在我们机构。将患者按 7:3 的比例随机分为训练组（n = 439）和测试组（n = 189）。以总生存期（OS）和无病生存期（DFS）作为终点。放射组学特征由 PyRadiomics 获得。使用最小绝对收缩和选择算子（LASSO）进行特征选择。 OS 和 DFS 的预测模型是使用多元 Cox 回归分析开发的，并且模型通过列线图可视化。使用曲线下面积 (AUC)、一致性指数 (C-index)、校准曲线和生存决策曲线分析 (DCA) 评估模型的性能。 OS 预测模型总共选择了 9 个放射组学特征，15 个放射组学特征被选择为 OS 预测模型。 DFS 预测模型选择放射组学特征。放射组学评分高的患者预后较差 (p < 0.001)。我们单独使用放射组学或 IASLC 构建了单独的预测模型，以及组合预测模型。在 OS 预测中，我们观察到组合模型（C 指数：0.812 ± 0.024，3 年 AUC：0.692，5 年 AUC：0.792）比放射组学（C 指数：0.743 ± 0.038，3年 AUC：0.633，5 年 AUC：0.768）和 IASLC 分级（C 指数：0.765 ± 0.042，3 年 AUC：0.658，5 年 AUC：0.743）单独模型。在 DFS 模型中也得到了类似的结果。放射组学和 IASLC 病理分级的结合被证明是预测 IA 期 LUAD 预后的有效方法。这对于指导早期 LUAD 的治疗决策具有重要的临床意义。© 2024 美国医学物理学家协会。

The International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high-grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement.To establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD.We conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n = 439) and testing set (n = 189) at a ratio of 7:3. Overall survival (OS) and disease-free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C-index), calibration curves, and survival decision curve analysis (DCA).In total, nine radiomics features were selected for the OS prediction model, and 15 radiomics features were selected for the DFS prediction model. Patients with high radiomics scores were associated with a worse prognosis (p < 0.001). We built separate prediction models using radiomics or IASLC alone, as well as a combined prediction model. In the prediction of OS, we observed that the combined model (C-index: 0.812 ± 0.024, 3 years AUC: 0.692, 5 years AUC: 0.792) achieved superior predictive performance than the radiomics (C-index: 0.743 ± 0.038, 3 years AUC: 0.633, 5 years AUC: 0.768) and IASLC grading (C-index: 0.765 ± 0.042, 3 years AUC: 0.658, 5 years AUC: 0.743) models alone. Similar results were obtained in the models for DFS.The combination of radiomics and IASLC pathological grading proves to be an effective approach for predicting the prognosis of stage IA LUAD. This has substantial clinical relevance in guiding treatment decisions for early-stage LUAD.© 2024 American Association of Physicists in Medicine.