The standard treatment for patients with locally advanced non-small cell lung cancer (NSCLC) is concurrent chemoradiation. However, clinical responses are heterogeneous and generally not known until after the completion of therapy. Multiple studies have investigated imaging predictors (radiomics) for different cancer histologies, but little exists for NSCLC. The objective of this study was to develop a multivariate CT-based radiomics model to a priori predict responses to definitive chemoradiation in patients with lung adenocarcinoma. Methods: Patients diagnosed with locally advanced unresectable lung adenocarcinoma who had undergone chemoradiotherapy followed by at least one dose of maintenance durvalumab were included. The PyRadiomics Python library was used to determine statistical, morphological, and textural features from normalized patient pre-treatment CT images and their wavelet-filtered versions. A nested leave-one-out cross-validation was used for model building and evaluation. Results: Fifty-seven patients formed the study cohort. The clinical stage was IIIA-C in 98% of patients. All but one received 6000–6600 cGy of radiation in 30–33 fractions. All received concurrent platinum-based chemotherapy. Based on RECIST 1.1, 20 (35%) patients were classified as responders (R) to chemoradiation and 37 (65%) patients as non-responders (NR). A three-feature model based on a KNNk= 1 machine learning classifier was found to have the best performance, achieving a recall, specificity, accuracy, balanced accuracy, precision, negative predictive value, F1-score, and area under the curve of 84%, 70%, 80%, 77%, 84%, 70%, 84%, and 0.77, respectively. Conclusions: Our results suggest that a CT-based radiomics model may be able to predict chemoradiation response for lung adenocarcinoma patients with estimated accuracies of 77–84%.
局部晚期非小细胞肺癌（NSCLC）的标准治疗方案是同步放化疗。然而，临床反应存在异质性，通常在治疗结束后才能明确。多项研究已针对不同癌症组织学类型探索了影像学预测因子（影像组学），但针对NSCLC的研究尚不充分。本研究旨在构建基于CT的多变量影像组学模型，以预先预测肺腺癌患者对根治性放化疗的反应。方法：研究纳入诊断为局部晚期不可切除肺腺癌、接受放化疗后至少接受一剂度伐利尤单抗维持治疗的患者。使用PyRadiomics Python库从标准化预处理CT图像及其小波滤波版本中提取统计、形态和纹理特征。采用嵌套留一交叉验证进行模型构建与评估。结果：研究队列共纳入57例患者，其中98%临床分期为IIIA-C期。除1例外，所有患者均接受30-33次分割、总剂量6000-6600 cGy的放疗，并同步接受铂类化疗。根据RECIST 1.1标准，20例（35%）患者被归类为放化疗应答者（R），37例（65%）为非应答者（NR）。基于KNNk=1机器学习分类器的三特征模型表现最佳，其召回率、特异性、准确率、平衡准确率、精确率、阴性预测值、F1分数和曲线下面积分别为84%、70%、80%、77%、84%、70%、84%和0.77。结论：研究结果表明，基于CT的影像组学模型可能预测肺腺癌患者的放化疗反应，预估准确率可达77-84%。
肿瘤(癌症)患者之家