Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy
BMC Cancer
volume 25, Article number: 834 (2025)
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Background
The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machine learning (ML) approaches to enhance prediction accuracy by focusing on patients with locally advanced gastric cancer (LAGC).
Methods
We retrospectively enrolled 277 patients with LAGC and randomly divided them into training (n = 193) and validation (n = 84) sets at a 7:3 ratio. In total, 1,130 radiomics features were extracted from pre-treatment portal venous phase computed tomography scans. These features were linearly combined to develop a radiomics score (rad score) through feature engineering. Then, using the rad score and clinical biomarkers as input features, we applied simple statistical strategies (relying on a single ML model) and integrated statistical strategies (including classification model integration techniques, such as hard voting, soft voting, and stacking) to predict LN+ post-NAC. The diagnostic performance of the model was assessed using receiver operating characteristic curves with corresponding areas under the curve (AUC).
Results
Of all ML models, the stacking classifier, an integrated statistical strategy, exhibited the best performance, achieving an AUC of 0.859 for predicting LN+ in patients with LAGC. This predictive model was transformed into a publicly available online risk calculator.
Conclusions
We developed a stacking classifier that integrates radiomics and clinical biomarkers to predict LN+ in patients with LAGC undergoing surgical resection, providing personalized treatment insights.
Ling, T., Zuo, Z., Huang, M. et al. Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.
BMC Cancer 25, 834 (2025). https://doi.org/10.1186/s12885-025-14259-w
https://doi.org/10.1186/s12885-025-14259-w bu içeriği en az 2500 kelime olacak şekilde ve alt başlıklar ve madde içermiyecek şekilde ünlü bir science magazine için İngilizce olarak yeniden yaz. Teknik açıklamalar içersin ve viral olacak şekilde İngilizce yaz. Haber dışında başka bir şey içermesin. Haber içerisinde en az 14 paragraf ve her bir paragrafta da en az 50 kelime olsun. Cevapta sadece haber olsun. Ayrıca haberi yazdıktan sonra içerikten yararlanarak aşağıdaki başlıkların bilgisi var ise haberin altında doldur. Eğer bilgi yoksa ilgili kısmı yazma.:
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Ling, T., Zuo, Z., Huang, M. et al. Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.
BMC Cancer 25, 834 (2025). https://doi.org/10.1186/s12885-025-14259-w
Image Credits: Scienmag.com
DOI: https://doi.org/10.1186/s12885-025-14259-w
Keywords
Tags: clinical biomarkers for gastric cancerCT radiomics in cancer predictionfusion of clinical data and imagingindividualized treatment strategiesintegrated machine learning modelslocally advanced gastric cancer researchlymph node metastasis predictionmachine learning in healthcareneoadjuvant chemotherapy outcomesprediction accuracy in oncologyretrospective study on cancer patientsstacking classifiers in machine learning