Value of predicting axillary lymph node metastasis of breast cancer based on intra-tumoral and peri-tumoral digital breast tomosynthesis imaging Nomogram
Objective To evaluate the worth of intra-tumoral and peri-tumoral radiomics Nomogram in the pre-operative prognostication of axillary lymph node(ALN)metastasis based on digital breast tomosynthesis(DBT)for breast cancer.Methods A total of 210 breast cancer patients performed breast DBT examinations were retrospectively collected at the First Affiliated Hospital of Bengbu Medical University from January 2019 to December 2023,all patients were stochastically allocated to a training set(n=147)and a verification set(n=63)in a 7:3 ratio.Select the largest dimension of the tumor in the DBT image to manually delineate the ROI of the intra-tumoral region of interest,and the peri-tumoral ROI was obtained by expanding outward by 3 mm.Radiomics features were extracted and screened.Support vector machine was used to construct the models of intra-tumoral,peritumoral and intra-tumoral+peri-tumoral and calculate predictions.The predicted value of the radiomics model with the highest predictive efficiency was selected,and a Nomogram model was created by combining the clinical features.The forecast power of the model was analyzed using the ROC curve,calibration and decision curves.Results The"intra-tumoral+peri-tumoral"model constructed from the 15 best radiomics features performed better than the"intra-tumoral"and"peri-tumoral"models.ALN palpation,DBT_ALN and"intra-tumoral+peri-tumoral"model's forecast value are separate risk elements(P<0.05),and the best predictive efficacy was achieved by the constructed Nomogram model,with sensitivity of 82.7%,specificity of 94.7%,accuracy of 86.4%,AUC of 0.942 in the training set,and 0.932,90.5%,83.3%and 87.3%in the verification set.Conclusion The Nomogram incorporating intra-tumoural and peri-tumoural DBT radiomics characteristics and clinical elements are effective in predicting ALN metastasis before the operation of breast cancer,regarding as a noninvasive predictive approach to assist clinical policy development.
breast canceraxillary nodeperi-tumoraldigital breast tomosynthesisradiomicsNomogram
万方数据
维普
