biostudies-literatureUnknown21(2)Qin S<h4>Objectives</h4>The International Association for the Study of Lung Cancer (IASLC) grading system is key to the prognosis and treatment of Invasive Pulmonary Adenocarcinoma (IPA). However, current radiomics and other radiological approaches poorly capture tumor heterogeneity, limiting predictive power. This study aimed to develop an interpretable CT-based model that predicts the histological grading of IPA by decoding its intratumoral spatial heterogeneity.<h4>Materials and methods</h4>This multi‑center retrospective study enrolled 355 IPA patients, split into training/validation (7:3) and an independent test cohort. Tumors were graded as low‑grade (Ⅰ/Ⅱ) or high‑grade (Ⅲ) per IASLC criteria. Intratumoral subregions were generated via unsupervised clustering of CT images, and their spatiPloS onee0341163https://www.ebi.ac.uk/biostudies/studies/S-EPMC12863497biostudies-literatureIntratumoral spatial heterogeneity at non-contrast CT predicts histological grading of invasive pulmonary adenocarcinoma: a multicenter retrospective study.PMC12863497Jia QLiu YSu DLi YZhou SZhang XShen GQin SfalseIntratumoral spatial heterogeneity at non-contrast CT predicts histological grading of invasive pulmonary adenocarcinoma: a multicenter retrospective study.<h4>Objectives</h4>The International Association for the Study of Lung Cancer (IASLC) grading system is key to the prognosis and treatment of Invasive Pulmonary Adenocarcinoma (IPA). However, current radiomics and other radiological approaches poorly capture tumor heterogeneity, limiting predictive power. This study aimed to develop an interpretable CT-based model that predicts the histological grading of IPA by decoding its intratumoral spatial heterogeneity.<h4>Materials and methods</h4>This multi‑center retrospective study enrolled 355 IPA patients, split into training/validation (7:3) and an independent test cohort. Tumors were graded as low‑grade (Ⅰ/Ⅱ) or high‑grade (Ⅲ) per IASLC criteria. Intratumoral subregions were generated via unsupervised clustering of CT images, and their spati2026-01-01T00:00:00Z20262026-07-15T00:40:50.808Z2026-06-27T03:06:05.298ZS-EPMC128634974162815410.1371/journal.pone.0341163