Prediction of high-grade pathological components in early invasive lung adenocarcinoma based on CT radiomics

doi:10.16098/j.issn.0529-1356.2025.05.009

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Acta Anatomica Sinica ›› 2025, Vol. 56 ›› Issue (5) : 576-584. DOI: 10.16098/j.issn.0529-1356.2025.05.009

Cancer Biology

Prediction of high-grade pathological components in early invasive lung adenocarcinoma based on CT radiomics

LOU Jin-jin¹ WANG He-ping² HUANG Yan-yan¹ LI Chun-yan¹ XU Li-yun^1*

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Abstract

Objective To construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC) based on radiomics. Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019, including gender, age, pathological findings, tumor markers and preoperative chest CT images. The micropapillary and solid components in postoperative pathology were defined as “high-grade pathological components”, while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group. And patients were randomly divided into the training set(343 cases) and the validation set(152 cases) with a ratio of 7∶3 using the simple randomization grouping method. The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted. And then those features were filtrated by Ftest, Pearson correlation coefficient, and L1 based feature selection. A model was built by using Logistic regression machine learning classifier, named mod 2, and radscore was also obtained. Differences between general information and CT features were analyzed. Binary Logistic regression analysis was used to construct a model for statistically significant variables, named mod 1. At the same time, Radscore was added to build the mod and named comb mod. The area under the curve(AUC), sensitivity and specificity of the three models were calculated. A nomogram was also drawn. Results A total of 495 patients were divided into the training set (n=343) and the validation set (n=152). Gender, carcinoma embryonic antigen(CEA), nodule, and maximum diameter were screened out in clinical features and involved in constructing the mod 1. Twelve features were selected from the radiomics features to build mod 2. Comb mod performed best, training set AUC:0.887, validation set AUC:0.875, and had good clinical practicability. Conclusion The model composed of general feature, CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC, and provide references for clinicians to choose surgical method for patients.

Key words

Invasive lung adenocarcinoma

/ Radiomics / Pulmonary module / Computed tomography / Human

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LOU Jin-jin WANG He-ping HUANG Yan-yan LI Chun-yan XU Li-yun. Prediction of high-grade pathological components in early invasive lung adenocarcinoma based on CT radiomics[J]. Acta Anatomica Sinica. 2025, 56(5): 576-584 https://doi.org/10.16098/j.issn.0529-1356.2025.05.009

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