CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer

医学 淋巴结 肺癌 列线图 逻辑回归 支持向量机 淋巴 无线电技术 转移 淋巴结转移 队列 肿瘤科 放射科 人工智能 癌症 病理 内科学 计算机科学
作者
Tianyu Lu,Jianbing Ma,Jiajun Zou,Chenxu Jiang,Yangyang Li,Jun Han
出处
期刊:Journal of X-ray Science and Technology [IOS Press]
卷期号:32 (3): 597-609
标识
DOI:10.3233/xst-230326
摘要

BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE: This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS: We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS: Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776–0.907) and 0.955 (95% CI: 0.926–0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677–0.948) and 0.893 (95% CI: 0.795–0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS: Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.
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