Aim:An Early-Stage Non-Small Cell Lung Cancer (ES-NSCLC) patient candidate for stereotactic body radiotherapy (SBRT) may start their treatment without a histopathological assessment, due to relevant comorbidities. The aim of this study is twofold: (i) build prognostic models to test the association between CT-derived radiomic features (RFs) and the outcomes of interest (overall survival (OS), progression-free survival (PFS) and loco-regional progression-free survival (LRPFS)); (ii) quantify whether the combination of clinical and radiomic descriptors yields better prediction than clinical descriptors alone in prognostic modeling for ES-NSCLC patients treated with SBRT.Methods:Simulation CT scans of ES-NSCLC patients treated with curative-intent SBRT at the European Institute of Oncology (IEO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy between 2013 and 2023 were retrospectively retrieved. PyRadiomics v3.0.1 was used for image preprocessing and subsequent RFs extraction and selection. A radiomic score was calculated for each patient, and three prognostic models (clinical model, radiomic model, clinical-radiomic model) for each survival endpoint were built. Relative performances were compared using the C-index. All analyses were considered statistically significant ifp< 0.05. The statistical analyses were performed using R Software version 4.1.Results:A total of 100 patients met the inclusion criteria. Median age at diagnosis was 76 (IQR: 70–82) years, with a median Charlson Comorbidity Index (CCI) of 7 (IQR: 6–8). At the last available follow-up, 76 patients were free of disease, 17 were alive with disease, and 7 were deceased. Considering relapses, progression of any kind was diagnosed in 31 cases. Regarding model performances, the radiomic score allowed for excellent prognostic discrimination for all the considered endpoints. Of note, the use of RFs alone proved to be more informative than clinical characteristics alone for the prediction of both OS and LRPFS, but not for PFS, for which the individual predictive performances slightly favored the clinical model.Conclusion:The use of RFs for outcome prediction in this clinical setting is promising, and results seem to be rather consistent across studies, despite some methodological differences that should be acknowledged. Further studies are being planned in our group to externally validate these findings, and to better determine the potential of RFs as non-invasive and reproducible biomarkers in ES-NSCLC.
目的：因存在相关合并症，早期非小细胞肺癌（ES-NSCLC）患者在接受立体定向放射治疗（SBRT）前可能未进行组织病理学评估。本研究目的有二：（一）建立预后模型以检验CT影像组学特征（RFs）与关键结局指标（总生存期（OS）、无进展生存期（PFS）及局部区域无进展生存期（LRPFS））的关联性；（二）量化在ES-NSCLC患者SBRT预后建模中，联合临床与影像组学特征是否较单纯临床特征具有更优的预测效能。 方法：回顾性收集2013年至2023年间在意大利米兰欧洲肿瘤研究所（IEO）接受根治性SBRT治疗的ES-NSCLC患者的模拟定位CT影像。采用PyRadiomics v3.0.1进行图像预处理及后续RFs提取与筛选。计算每位患者的影像组学评分，并针对各生存终点构建三种预后模型（临床模型、影像组学模型、临床-影像组学联合模型）。使用C指数比较模型相对性能，以p<0.05为统计学显著性阈值。所有统计分析均通过R软件4.1版本完成。 结果：共100例患者符合纳入标准。诊断时中位年龄76岁（四分位距：70-82），查尔森合并症指数（CCI）中位数为7（四分位距：6-8）。末次随访时，76例患者无病生存，17例带病生存，7例死亡。复发方面，共31例出现任何类型的疾病进展。模型性能评估显示，影像组学评分对所有终点均具有优异的预后区分能力。值得注意的是，在OS和LRPFS预测中，单独使用RFs较单纯临床特征更具信息价值；但在PFS预测中，临床模型的个体预测性能略优于影像组学模型。 结论：在该临床场景中应用RFs进行结局预测具有良好前景，尽管存在需关注的方法学差异，但各研究结果呈现较高一致性。本团队正计划开展进一步研究以外部验证这些发现，并深入探索RFs作为ES-NSCLC无创性、可重复生物标志物的潜力。
肿瘤(癌症)患者之家