A study on the prediction of prognosis of ductal carcinoma in situ at different pathological stages based on deep learning mammography combined with natural language processing
10.3760/cma.j.cn112149-20220321-00258
- VernacularTitle:基于深度学习乳腺X线摄影联合自然语言处理预测不同病理进展期乳腺导管原位癌预后研究
- Author:
Lin LI
1
;
Rushan OUYANG
;
Xiaohui LIN
;
Meng LI
;
Xiaohui LAI
;
Zengyan LI
;
Guanxun CHENG
;
Jie MA
Author Information
1. 暨南大学第二临床医学院，深圳 518020
- Keywords:
Carcinoma, ductal, breast;
Mammography;
Deep learning;
Prognosis
- From:
Chinese Journal of Radiology
2022;56(11):1215-1222
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish the predictive models for the prognosis of ductal carcinoma in situ (DCIS) at different pathological stages, and to evaluate the predictive performance of the models.Methods:Complete data of 273 patients with confirmed DCIS at different pathological stages who underwent mammography examination in Shenzhen People′s Hospital, Peking University Shenzhen Hospital and Shenzhen Luohu People′s Hospital from November 2014 to December 2020 were retrospectively collected, including 110 cases in the DCIS+ductal carcinoma in situ with microinvasion (DCIS-MI) group and 163 cases in the invasive ductal carcinoma (IDC)-DCIS group. The clinical, imaging and pathological features were analyzed. Mammary Mammo AI fusion model and deep learning-based natural language processing (NLP) structured diagnostic report model were used for image feature extraction. Patients in each group were randomly divided into training set and validation set with a ratio of 6∶4, and the predictors were screened by univariate and multivariate logistic regression analysis. The lowest Akaike information criterion value of each group was selected to construct the final predictive model. The receiver operating characteristic (ROC) curve was drawn to evaluate the performance of each model.Results:Taking estrogen receptor (-) or human epidermal growth factor receptor 2 (3+) as the poor prognostic reference, there were 62 cases considered with poor prognosis and 48 cases with good prognosis in DCIS+DCIS-MI group; while in the IDC-DCIS group, taking the Nottingham prognostic index as the reference, 33 cases were considered with poor prognosis, 73 cases with moderate prognosis, and 57 cases with good prognosis. Four predictive factors were screened to construct the DCIS+DCIS-MI-group predictive model, including DCIS nuclear grade, calcification with suspicious morphology in mammography, DCIS pathologic subtype and DCIS with microinvasion. Five predictive factors were screened to construct the IDC-DCIS-group predictive model, including neural or vascular invasion, Ki67 level, DCIS subtype, DCIS component proportion and associated features in mammography. The area under curve (AUC) for predicting poor prognosis of DCIS+DCIS-MI was 0.92 (95%CI 0.84-1.00) in the training set and 0.90 (95%CI 0.82-0.99) in the validation set; while the AUC for predicting poor prognosis of IDC-DCIS was 0.84 (95%CI 0.76-0.93) in the training set and 0.78 (95%CI 0.64-0.91) in the validation set.Conclusion:The developed models based on deep learning combined with NLP can effectively predict the prognosis of DCIS at different pathological stages, which are beneficial to the risk stratification of patients with DCIS, providing a reference for clinical decision.
