An explainable machine learning model combining intratumoral and peritumoral computed tomography radiomics with maximum standard uptake value for predicting International Association for the Study of Lung Cancer grading and prognosis in stage I pure-solid lung adenocarcinoma
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system provides important prognostic information for lung adenocarcinoma; however, preoperative noninvasive prediction of this grading remains challenging. This study aimed to evaluate the predictive value of a combined intratumoral and peritumoral radiomics model based on computed tomography (CT) and maximum standard uptake value (SUV max ) for preoperative prediction of the novel IASLC grading and prognosis in stage I pure-solid invasive lung adenocarcinoma (LADC). Methods: Patients with stage I pure-solid LADC from The First Affiliated Hospital of Wenzhou Medical University were retrospectively included and randomly assigned to a training set (n=207) and a testing set (n=89) in a 7:3 ratio. There were 851 radiomics features extracted from the gross tumor volume (GTV) and the GTV incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A combined model based on an individual radiomics signature, SUV max , and clinical independent predictors was constructed. Recurrence-free survival (RFS) was analyzed using Kaplan-Meier survival method, while independent prognostic factors were identified through Cox proportional hazards models. Results: The GPTV3 model demonstrated a higher area under the curve (AUC) compared to other individual radiomics models, with AUCs of 0.804 and 0.715 in the training and testing sets. Tumor size was identified as an independent predictor of high-grade IASLC grading in the clinical model. The combined model showed AUCs of 0.837 and 0.767 for the training and testing sets, respectively, which were higher than those of the individual GPTV3 and SUV max models. The high-risk group was associated with poorer RFS (P<0.05). Pathological and predictive IASLC grade, smoking history, and tumor size were independent prognostic factors. Conclusions: The interpretable machine learning model can predict the novel IASLC grading and prognosis in stage I pure-solid LADC.
Source: Journal of Thoracic Disease
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7224125541