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Radiomics-clinical integration guides prophylactic cranial irradiation decisions in limited-stage small cell lung
Yuntao Zhou1, Li Xiao2, Siyi Yang1
1Department of Radiotherapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin, China.
Background:
Limited-stage small-cell lung cancer (LS-SCLC) is highly aggressive and prone to brain metastasis (BM). Early identification of BM risk is crucial for devising personalized prophylactic cranial irradiation (PCI) strategies. This study aimed to develop a multimodal model integrating radiomic and clinical features to stratify BM risk in LS-SCLC patients and guide personalized PCI strategies.
Methods:
This study analyzed 141 LS-SCLC patients (2013-2021) using computed tomography (CT) images and clinical records. Patients were randomly divided into training (n=98), internal validation (n=43), and external validation cohorts (n=24). Radiomic features were extracted and optimized using the minimum redundancy maximum relevance (mRMR) algorithm to form a radiomic score (RadScore). Clinical predictors were identified via univariate logistic regression (LR). Four machine learning models-LR, support vector machine, random forest, and eXtreme Gradient Boosting-were used to develop predictive models. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC).
Results:
A total of 141 patients (mean age, 59.03 years; 109 men and 32 women) were evaluated. A total of 1,037 radiomic features were extracted from the simulated positioning CT images, with 10 optimal features selected to form the RadScore. By incorporating dynamic changes in platelet count, hemoglobin levels, and leukocyte indices before and after radiotherapy, along with the baseline lymphocyte-to-monocyte ratio (LMR), the LR combined model demonstrated superior predictive capability. The LR combined model showed superior performance with AUCs of 0.831 (training), 0.831 (internal validation), and 0.863 (external validation). Risk stratification indicated that PCI reduced BM risk in high-risk patients [hazard ratio (HR) =0.270, P<0.001] but not in low-risk patients (HR =0.225, P=0.13).
Conclusions:
The LR combined radiomic-clinical model demonstrated superior predictive performance. PCI significantly reduced the risk of BM in high-risk patients, whereas no statistically significant benefit was observed in low-risk patients.
