Background: Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. Methods: A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. Results: This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607–0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model’s predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. Conclusion: In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.

The prognostic value of infiltrating lymphocytes distribution and their densities in breast cancer is unknown. This study aimed to develop a tumor immune microenvironment-based model to predict prognosis in early breast cancer patients. Densities of CD3⁺ and CD8⁺ lymphocytes in the core of tumor (CT) and invasive margin (IM) regions were quantified. A combined score of CD3⁺ and CD8⁺ infiltrating lymphocytes in both CT and IM regions was performed to construct the prognostic immunoindex (PII) as follows, low-PII (score 0), intermediate-PII (score 1 to 2), and high-PII (score 3 to 4). The PII was developed in the training cohort and verified in the internal and external validation cohorts. A total of 1194 patients were included. In the training cohort, a higher 5-year DFS was observed in the high- or intermediate-PII groups compared with the low-PII group (95.1 %, 89.1 %, 83.7 %, respectively, P = 0.008). Patients with a high PII also had the best 5-year OS of 97.5 %, compared to 96.4 % and 92.7 % in the intermediate- and low-PII groups (P = 0.016), which was also found in the internal (P = 0.011 for DFS, P = 0.013 for OS) and external validation cohort (P = 0.010 for DFS, P = 0.143 for OS). PII was shown to be an independent favorable prognostic factor for DFS (P < 0.001) and OS (P = 0.001), especially in HER2-positive (P = 0.009) and triple-negative (P = 0.007) breast cancer patients. The PII model can predict survival in breast cancer patients, which might provide a novel risk classification to guide further individualized treatment.