Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
Acad Radiol. 2024 Sep;31(9):3801-3810. doi: 10.1016/j.acra.2024.01.033. Epub 2024 Feb 23.
To determine the additional value of peritumoral radiomics in predicting overall survival (OS) in surgically resected non-small cell lung cancer (NSCLC) and its correlation with pathological findings.
A total of 526 patients with surgically resected NSCLC were included (191 training, 160 internal validation, and 175 external validation cohorts). CT images were used to segment the gross tumor volume (GTV) and peritumoral volume (PTV) within distances of 3, 6, 9 mm from the tumor boundary (PTV3, PTV6, and PTV9), and radiomic features were extracted. Four prognostic models for OS (GTV, GTV + PTV3, GTV + PTV6, and GTV + PTV9) were constructed using the training cohort. The prognostic ability and feature importance were evaluated using the validation cohorts. Pathological findings were compared between the two patient groups (n = 30 for each) having the top 30 and bottom 30 values of the most important peritumoral feature.
The GTV+ PTV3 models exhibited the highest predictive ability, which was higher than that of the GTV model in the internal validation cohort (C-index: 0.666 vs. 0.616, P = 0.027) and external validation cohort (C-index: 0.705 vs. 0.656, P = 0.048). The most important feature was GLDM_Dependence_Entropy, extracted from PTV3. High peritumoral GLDM_Dependence_Entropy was associated with a high proportion of invasive histological types, tumor spread through air spaces, and tumor-infiltrating lymphocytes (all P < 0.05).
The GTV and PTV3 combination demonstrated a higher prognostic ability, compared to GTV alone. Peritumoral radiomic features may be associated with various pathological prognostic factors.
旨在确定肿瘤周围放射组学在预测手术切除的非小细胞肺癌（NSCLC）患者总生存期（OS）方面的额外价值，及其与病理发现的相关性。
共纳入 526 例手术切除的 NSCLC 患者（191 例训练队列，160 例内部验证队列，175 例外部验证队列）。使用 CT 图像对肿瘤边界 3、6、9 mm 范围内的大体肿瘤体积（GTV）和肿瘤周围体积（PTV）进行分割，并提取放射组学特征。利用训练队列构建 4 个用于 OS 的预测模型（GTV、GTV+PTV3、GTV+PTV6 和 GTV+PTV9）。使用验证队列评估预后能力和特征重要性。比较两组患者（每组 30 例）的病理发现，两组患者的肿瘤周围最重要特征值处于前 30 位和后 30 位。
GTV+PTV3 模型的预测能力最高，在内部验证队列（C 指数：0.666 比 0.616，P=0.027）和外部验证队列（C 指数：0.705 比 0.656，P=0.048）中均高于 GTV 模型。最重要的特征是从 PTV3 中提取的 GLDM_Dependence_Entropy。肿瘤周围 GLDM_Dependence_Entropy 较高与侵袭性组织学类型、空气传播肿瘤扩散和肿瘤浸润淋巴细胞比例较高有关（均 P<0.05）。
与单独 GTV 相比，GTV 和 PTV3 联合具有更高的预后预测能力。肿瘤周围放射组学特征可能与各种病理预后因素相关。
