N Oli, P Ms Pradhan, R A Sagtani, A Shrestha, L M Jaacks, B M Karmacharya, G Danaei, A Vaidya
Research Capacity for Prevention and Control of Non-communicable Diseases and their Risk Factors in Nepal: Findings of a Needs Assessment Study
Kathmandu University medical journal (KUMJ)
study seeks construct machine learning model merges clinical characteristics ultrasound radiomic analysisencompassing intratumoral peritumoralto predict status axillary lymph nodes patients earlystage breast cancer study employed retrospective methods collecting clinical information ultrasound data postoperative pathological results 321 breast cancer patients including 224 training group 97 validation group correlation analysis univariate analysis lasso regression analysis independent risk factors related axillary lymph node metastasis breast cancer identified conventional ultrasound immunohistochemical indicators clinical feature model constructed additionally features extracted ultrasound images intratumoral 15 mm peritumoral establish radiomics feature formula furthermore combining clinical features ultrasound radiomics features six machine learning models logistic regression decision tree support vector machine extreme gradient boosting random forest knearest neighbors compared diagnostic efficacy constructing joint prediction model based optimal ml algorithm use shapley additive explanations shap enhanced visualization interpretability model diagnostic process among 321 breast cancer patients 121 axillary lymph node metastasis 200 clinical feature model auc 0779 0777 training validation groups respectively radiomics model analysis showed model including intratumor 3 mm peritumor area best diagnostic performance aucs 0847 0844 training validation groups respectively joint prediction model based xgboost algorithm reached aucs 0917 0905 training validation groups respectively shap analysis indicated rad score highest weight prediction model playing significant role predicting axillary lymph node metastasis breast cancer predictive model integrates clinical features radiomic characteristics using xgboost algorithm demonstrates significant diagnostic value axillary lymph node metastasis breast cancer model provide significant references preoperative surgical strategy selection prognosis evaluation breast cancer patients helping reduce postoperative complications improve longterm survival rates additionally utilization shap enhancing global local interpretability model