Hong Jingjing, Yang Liyang, Huo Jiekun, Huang Guoci, Shan Bowen, Cai Tingting, Zhang Lianlian, Huang Weikang, Wen Ge
Nanfang Hospital, Southern Medical University, Guangzhou, China.
Front Med (Lausanne). 2025 May 21;12:1541682. doi: 10.3389/fmed.2025.1541682. eCollection 2025.
To evaluate the predictive value of CT radiomics features within and surrounding tumors in determining the invasiveness of primary solitary nodular pulmonary adenocarcinoma.
This retrospective study analyzed 107 patients with pathologically confirmed nodular pulmonary adenocarcinoma who underwent conventional non-enhanced CT Scans in our hospital from 2019 to 2023. Patients were categorized as non-invasive or invasive based on pathology findings. Clinical and imaging data from both groups were collected and compared, and logistic regression was used to independent factors associated with invasiveness. Radiologists manually outlined 3-dimensional regions of intratumoral and peritumoral areas to extract radiomics features, creating separate intratumor, peritumor, and combined intra-peritumor radiomics models. Radiomics models were trained using LASSO with 10-fold cross-validation in training dataset. Additionally, integrated models combining radiomics with clinical data were developed: intratumor-clinical, peritumor-clinical, and an intra-peri-clinical models.
Of the 107 patients, 73 were in the non-invasive group (mean age 49.73 ± 13.92, 22 males) and 34 were in the invasive group (mean age 57.53 ± 12, 14 males). The clinical model identified average nodule diameter and vascular type as independent risk factors for invasiveness (both < 0.025). The combined intra-peri-clinical model demonstrated superior predictive performance compared to other models, with an AUC of 0.93, sensitivity of 0.91, and specificity of 0.86.
The combined model incorporating intratumor and peritumor radiomics features with clinical data showed significant value in predicting the invasiveness of nodular pulmonary adenocarcinoma, aiding in the precise selection of surgical methods.
评估肿瘤内部及周围的CT影像组学特征在判定原发性孤立性肺结节腺癌侵袭性方面的预测价值。
本回顾性研究分析了2019年至2023年在我院接受常规非增强CT扫描且病理确诊为结节性肺腺癌的107例患者。根据病理结果将患者分为非侵袭性或侵袭性。收集并比较两组的临床和影像数据，采用逻辑回归分析与侵袭性相关的独立因素。放射科医生手动勾勒肿瘤内和肿瘤周围区域的三维区域以提取影像组学特征，创建单独的肿瘤内、肿瘤周围以及肿瘤内-肿瘤周围联合影像组学模型。在训练数据集中使用LASSO结合10倍交叉验证对影像组学模型进行训练。此外，还开发了将影像组学与临床数据相结合的综合模型：肿瘤内-临床、肿瘤周围-临床以及肿瘤内-肿瘤周围-临床模型。
107例患者中，非侵袭性组73例（平均年龄49.73±13.92岁，男性22例），侵袭性组34例（平均年龄57.53±12岁，男性14例）。临床模型确定平均结节直径和血管类型为侵袭性的独立危险因素（均<0.025）。与其他模型相比，肿瘤内-肿瘤周围-临床联合模型表现出更好的预测性能，曲线下面积（AUC）为0.93，灵敏度为0.91，特异度为0.86。
结合肿瘤内和肿瘤周围影像组学特征与临床数据的联合模型在预测结节性肺腺癌侵袭性方面具有显著价值，有助于精确选择手术方法。
