Construction of Brain Metastasis Prediction Model and Optimization of Prophylactic Cranial Irradiation Selection for Limited-Stage Small-Cell Lung Cancer

Abstract

Prophylactic cranial irradiation (PCI), as an essential part of the treatment of limited-stage small-cell lung cancer (LS-SCLC), inevitably leads to neurotoxicity. This study aimed to construct a brain metastasis prediction model and identify low-risk patients to avoid PCI; 236 patients with LS-SCLC were retrospectively analyzed and divided into PCI (63 cases) and non-PCI groups (173 cases). The nomogram was developed based on variables determined by univariate and multivariate analyses in the non-PCI group. According to the cutoff nomogram score, all patients were divided into high- and low-risk cohorts. A log-rank test was used to compare the incidence of brain metastasis between patients with and without PCI in the low-risk and high-risk groups, respectively. The nomogram included five variables: chemotherapy cycles (ChT cycles), time to radiotherapy (RT), lactate dehydrogenase (LDH), pro-gastrin-releasing peptide precursor (ProGRP), and lymphocytes–monocytes ratio (LMR). The area under the receiver operating characteristics (AUC) of the nomogram was 0.763 and 0.782 at 1 year, and 0.759 and 0.732 at 2 years in the training and validation cohorts, respectively. Based on the nomogram, patients were divided into high- and low-risk groups with a cutoff value of 165. In the high-risk cohort, the incidence of brain metastasis in the non-PCI group was significantly higher than in the PCI group (p < 0.001), but there was no difference in the low-risk cohort (p = 0.160). Propensity score-matching (PSM) analysis showed similar results; the proposed nomogram showed reliable performance in assessing the individualized brain metastasis risk and has the potential to become a clinical tool to individualize PCI treatment for LS-SCLC.

Construction of brain metastasis prediction model in limited stage small cell lung cancer patients without prophylactic cranial irradiation

“…In this study, our findings support the notion that patients with TNM stage III, fewer than four chemotherapy cycles and elevated pretreatment LDH (≥200 IU/L) face an increased risk of developing BM. These results echo the recent study conducted by Hou et al, 16 where they similarly observed significant associations between TNM stage (III vs. I to II), LDH levels (high versus low) and chemotherapy cycles (≥4 vs. <4) with the occurrence of BM, further reinforcing the consistency and robustness of these factors in predicting BM risk.…”

“…At present, there are many risk factors for BM of SCLC reported worldwide, such as LDH, 14 CEA, 15 gastrin releasing peptide precursor (ProGRP) 16 and PLT, 17 , 18 HGB 18 , neutrophil to lymphocyte ratio (NLR), 19 , 20 , 21 monocyte–lymphocyte ratio (MLR), 21 prognostic‐nutrition index (PNI), 21 TNM stages, 15 , 16 , 18 , 19 , 22 , 23 , 24 , 25 , 26 , 27 lymphovascular invasion or not, 23 radical surgery or not, 22 concurrent chemotherapy (CCRT) or not, 21 response to chemotherapy, 28 weight loss, 28 time to thoracic radiotherapy, 16 , 19 chemotherapy cycles, 16 , 19 dose of thoracic radiotherapy, 29 size of primary tumour, 25 segmentation method of thoracic radiotherapy, 26 age, 27 gender, 18 , 27 and so forth. However, only three of these studies established a predictive model for BM in SCLC; it is expected that prediction models for these factors of high risk of BM can be subsequently established and verified both internally and externally.…”

Risk prediction model for postoperative brain metastasis in IIB-IIIB non-small cell lung cancer: based on radiomics and clinicopathology

“…Prophylactic whole-brain irradiation has been proven effective at reducing the incidence of BM (Gore et al 2011;Witlox et al 2018), but it may also lead to certain adverse reactions (Chalubinska et al 2021). The challenge for clinicians lies in accurately weighing the bene ts and drawbacks of radiotherapy while identifying high-risk patients who would bene t from early intervention(Chalubinska et al 2021; Hou et al 2022). Therefore, precise prediction and strati cation of BM can assist clinicians in implementing targeted preventive measures.…”