Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China.
Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
J Transl Med. 2024 Jan 22;22(1):87. doi: 10.1186/s12967-024-04904-6.
Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression.
We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed.
Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552).
The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.
在治疗前识别非小细胞肺癌（NSCLC）免疫治疗反应的精确生物标志物具有挑战性。本研究旨在构建并研究亚区域放射组学模型（SRRM）作为一种新的肿瘤生物标志物，用于预测接受免疫检查点抑制剂治疗的 NSCLC 患者的反应，并检验其预测性能是否优于传统放射组学、肿瘤突变负担（TMB）评分和程序性死亡配体-1（PD-L1）表达。
我们将来自两个中心回顾性数据库的 264 名患者分为训练集（n=159）和验证集（n=105）。使用 K-means 方法从肿瘤感兴趣区域的三个亚区域提取放射组学特征。我们从每个亚区域提取了 1896 个特征，每个样本共提取了 5688 个特征。使用最小绝对值收缩和选择算子回归方法选择亚区域放射组学特征。使用支持向量机算法构建和验证 SRRM。我们使用下一代测序将来自两个队列的患者分为高 TMB（≥10 muts/Mb）和低 TMB（<10 muts/Mb）组；使用福尔马林固定、石蜡包埋的肿瘤切片进行 PD-L1 表达的免疫组织化学评估，高表达定义为≥50%的肿瘤细胞阳性。评估了 SRRM 与无进展生存期（PFS）和变异基因之间的相关性。
采用 11 个亚区域放射组学特征来建立 SRRM。所提出的 SRRM 在训练集和验证集中的受试者工作特征曲线下面积（AUC）分别为 0.90（95%置信区间[CI] 0.84-0.96）和 0.86（95%CI 0.76-0.95）。SRRM（低 vs. 高；截断值=0.936）与训练集（风险比[HR] = 0.35[0.24-0.50]，P<0.001）和验证集（HR=0.42[0.26-0.67]，P=0.001）的 PFS 显著相关。还观察到 SRRM 与三个变异基因（H3C4、PAX5 和 EGFR）之间存在显著相关性。在验证队列中，SRRM 的 AUC（0.86，P<0.001）高于 PD-L1 表达（0.66，P=0.034）和 TMB 评分（0.54，P=0.552）。
SRRM 具有更好的预测性能，优于传统放射组学、PD-L1 表达和 TMB 评分。SRRM 可有效分层接受免疫治疗的 NSCLC 患者的无进展生存期（PFS）风险。
