No predictive biomarkers can robustly identify patients with non-small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3 DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22-2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.

中文翻译：

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与淋巴细胞分布相关的 CT 放射学特征的变化可预测非小细胞肺癌的总体生存率和对免疫治疗的反应。

没有预测性生物标志物可以强有力地识别非小细胞肺癌 (NSCLC) 患者将从免疫检查点抑制剂 (ICI) 治疗中受益。在这里，在机器学习环境中，我们比较了 ICI 治疗两到三个周期之前和之后肿瘤结节内部和外部 CT 模式的放射组学纹理 (DelRADx) 的变化 (“delta”)。我们发现 DelRADx 模式可以预测 NSCLC 患者对 ICI 治疗的反应和总生存期 (OS)。我们回顾性分析了从两个机构的 139 名 NSCLC 患者获得的数据，这些患者被分为一个发现集（D1 = 50）和两个独立验证集（D2 = 62，D3 = 27）。提取结节内和结节周围纹理描述符，并计算相对差异。使用 8 个 DelRADx 特征训练线性判别分析 (LDA) 分类器来预测 RECIST 衍生的响应。确定了 delta 放射学风险评分 (DRS) 与 OS 的关联。还评估了 DelRADx 特征与诊断活检 (n = 36) 上肿瘤浸润淋巴细胞 (TIL) 密度的关联。LDA 分类器在区分 D1 中的应答者和无应答者时产生的 AUC 为 0.88 ± 0.08，在 D2 和 D3 中 DRS 与 OS 相关的 AUC 为 0.85 和 0.81 [HR：1.64；95%置信区间（CI），1.22-2.21；P = 0.0011；C 指数 = 0.72）。瘤周 Gabor 特征与诊断活检样本上的 TIL 密度相关。我们的结果表明 DelRADx 可用于识别 NSCLC 患者的早期功能反应。