# Intratumoral spatial heterogeneity at non-contrast CT predicts histological grading of invasive pulmonary adenocarcinoma: a multicenter retrospective study

## TL;DR

This study shows that analyzing spatial patterns in CT scans can predict the aggressiveness of lung cancer, improving pre-surgery risk assessment.

## Contribution

A novel CT-based model using spatial interaction heterogeneity improves histological grading prediction of invasive pulmonary adenocarcinoma.

## Key findings

- The MSI model achieved an AUC of 0.806 in predicting high-grade IPA, outperforming other models.
- Subregion 1 was more prevalent in high-grade tumors, while Subregion 2 was less so.
- MSI_border_proportion_2_3 was identified as the most influential feature for grading prediction.

## Abstract

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.

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 spatial interaction heterogeneity was quantified using a Multi-regional Spatial Interaction (MSI) matrix. Five models (clinical‑radiological, radiomics, MSI, radiomics‑combined, MSI‑combined) were built using four preprocessors and five classifiers. The optimal model was selected based on the Receiver Operating Characteristic (ROC) curve in the validation cohort, with generalizability assessed in the test cohort. Performance was compared via the DeLong test, and SHapley Additive exPlanations (SHAP) analysis interpreted feature contributions.

Three subregions were generated. The high-grade group exhibited a larger proportion of Subregion 1, while showing a smaller proportion of Subregion 2. The MSI model based on 10 MSI features achieved an AUC of 0.806 in the test cohort, outperforming clinical‑radiological, radiomics, and radiomics‑combined models (p = 0.002, 0.010, 0.022). Adding clinical‑radiological features did not improve the MSI model (p = 0.083). SHAP identified MSI_border_proportion_2_3 (relative border proportion between Subregions 2 and 3) as the most influential feature, with lower values indicating high‑grade IPA.

The CT-based MSI model can predict the histological grade of IPA by decoding the spatial interaction heterogeneity of different subregions in the tumor, thereby providing reliable imaging evidence for preoperative individualized risk assessment.