Log odds of positive lymph nodes (LODDS)-based novel nomogram for survival estimation in patients with invasive micropapillary carcinoma of the breast.

BACKGROUND: Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study introduces a log odds of positive lymph nodes (LODDS) method to address this issue and develops a novel LODDS-based nomogram to provide accurate prognostic information. METHODS: The study analyzed data from 1,901 patients with breast IMPC from the Surveillance, Epidemiology, and End Results database. It assessed the relationships between LODDS and the number of excised LN (eLN), positive LN (pLN), and the pLN ratio (pLNR), identifying an optimal threshold value using a restricted cubic spline method. Predictive factors were identified by the Cox least absolute shrinkage and selection operator (Cox-LASSO) regression and validated through multivariate Cox regression to construct a nomogram. The model's accuracy, discrimination, and utility were assessed. The study also explored the consequences of excluding LODDS from the nomogram and compared its effectiveness with the tumor-node-metastasis (TNM) staging system. RESULTS: LODDS improved N status classification by identifying heterogeneity in patients with pLN ratios of 0% (pLN =0) or 100% (pLN =eLN) and setting -1.08 as the ideal cutoff. Five independent prognostic factors for breast cancer-specific survival (BCSS) were identified: tumor size, N status, LODDS, progesterone receptor status, and histological grade. The LODDS-based nomogram achieved a strong concordance index of 0.802 (95% CI: 0.741-0.863), surpassing both the version without LODDS and the conventional TNM staging in all tests. CONCLUSIONS: For breast IMPC, LODDS served as an independent prognostic factor, its effectiveness unaffected by the anatomical LN count, enhancing the accuracy of N staging. The LODDS-based nomogram showed promise in offering more personalized prognostic information.

Nomogram-Based Risk Stratification to Identify Patients with T3N0M0 Breast Cancer with Survival Benefit from Postmastectomy Radiotherapy.

BACKGROUND: The survival benefit of postmastectomy radiotherapy (PMRT) for patients with T3N0M0 breast cancer remains controversial. This study aimed to identify patients with a survival benefit from PMRT by developing a novel risk stratification model. PATIENTS AND METHODS: The study recruited 2062 patients with pT3N0M0 breast cancer from the Surveillance, Epidemiology, and End Results (SEER) database who underwent mastectomy between 2010 and 2019. Overall survival (OS) and breast-cancer-specific survival (BCSS) prognostic nomograms based on multivariate Cox regression were constructed to quantify the survival risk and classify patients into low- and high-risk groups. Subgroup analyses were undertaken to assess the role of PMRT according to age and risk stratification. RESULTS: In the overall cohort, PMRT was beneficial in improving OS in patients with pT3N0 breast cancer (5-year OS, non-PMRT versus PMRT: 76.6% vs. 84.2%, P < 0.001), while the benefit on BCSS was not significant (P = 0.084). On the basis of the risk stratification nomogram, in the high-risk group, PMRT improved OS in young patients by 10.1%, OS in elderly patients by 12.4%, and BCSS by 10.2% (P < 0.05), but the use of PMRT in the low-risk group did not improve OS and BCSS in all patients (P > 0.05). CONCLUSIONS: We presented a new method for quantifying risk using the nomogram to identify patients with high risk of pT3N0M0 breast cancer. This study found that older patients in the newly constructed high-risk group benefited from OS and BCSS benefits from PMRT, while for younger high-risk patients, there was only a benefit in terms of OS.

Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation.

Background: Locally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time. Methods: LABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan-Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model. Results: CS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1-6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients. Conclusions: The real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.