# Multi‐omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation

## TL;DR

This study uses multi-omics data to predict how small cell lung cancer patients will respond to whole brain radiotherapy, improving treatment personalization.

## Contribution

The novel contribution is the integration of dosiomics, radiomics, and clinical factors to predict radiotherapy response in small cell lung cancer patients.

## Key findings

- A multi-omics model combining clinical, dosiomic, and radiomic features achieved a mean AUC of 0.792 in predicting WBRT response.
- Concurrent chemoradiotherapy, conformal boost radiotherapy, and dosiomic/radiomic features were identified as independent predictors of treatment response.
- The developed nomogram charts showed good clinical value for individualized radiation therapy response prediction.

## Abstract

Dosiomics and radiomics elaborate the low‐and high‐order features extracted from images to predict clinical outcomes. Whole‐brain radiotherapy (WBRT) has been widely used in patients with diffuse brain metastases of small cell lung cancer (SCLC). The objective of this study is to ascertain the predictors of treatment response in patients with SCLC treated with WBRT. Furthermore, the study seeks to develop accurate machine learning models to predict the radiotherapy response of WBRT.

This study retrospectively enrolled BM patients who received whole brain irradiation in Yunnan Cancer Hospital from January 2020 to June 2024. Radiomics features and dosiomics features were extracted from pre‐treatment CT images and dose images of TPS using 3D slicer software, features were screened by Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Logistic Regression (LR) models assessed the association of the features with WBRT reaction. Patients who showed complete response (CR) or partial response (PR) were classified as the Radiation Response Group, while those with stable disease (SD) or progressive disease (PD) were categorized as the Radiation Non‐Response Group. A total of seven classification models were constructed, clinic factors (CFM)), radiomics features (RFM), dosiomics features (DFM), clinical factors combined with radiomics features (FM + RFM), clinical factors combined with dosiomics features (CFM + DFM), radiomics combined with dosiomics features (RFM + DFM), and the hybrid features combining clinical factors, radiomics, and dosiomics features (HFM). The HFM was our focus, evaluated the prediction performance of the model, used nomograms to visualize individualized Radiation Therapy (RT) response prediction, and prospectively collected a subset of patients for external validation set.

Based on univariate analysis combined with LASSO regression, three dosiomics features and four radiomics features related to the therapeutic effect were respectively selected from 851 dosiomics and radiomic features. Multivariate analysis indicated that concurrent chemoradiotherapy (CCRT), conformal boost radiotherapy (CBRT), radiomics, and dosiomics were independent predictors of the radiotherapy response of WBRT. The multicomponent model based on dosiomics, radiomics and clinical factors showed optimal predictive power in the patient cohort, with a mean AUC = 0.792 (95% CI 0.708–0.852), AUC of external validation set = 0.711 (95%CI 0.487–0.934) and the constructed nomogram charts have good clinical value.

The integration of clinical parameters with dosiomics and radiomic features in a multi‐omics framework demonstrates enhanced predictive accuracy for assessing whole‐brain radiation therapy outcomes in small‐cell lung carcinoma. This comprehensive approach may facilitate clinical decision‐making by enabling more precise treatment customization and individualized therapeutic strategies.