Nomograms to predict the long-term prognosis for non-metastatic invasive lobular breast carcinoma: a population-based study.

ABSTRACT

Invasive lobular breast carcinoma (ILC) is one potential subset that "clinicopathologic features" can conflict with "long-term outcome" and the optimal management strategy is unknown in such discordant situations. The present study aims to predict the long-term, overall survival (OS) and cancer-specific survival (CSS) of ILC. The clinical information of patients with non-metastatic ILC was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2020. A total of 31451 patients were enrolled and divided into the training cohort (n=22,017) and validation cohort (n=9434). The last follow-up was December, 31, 2020 and the median follow-up period was 99 months (1-203). Age, marriage, estrogen (ER) status, progesterone (PR) status, grade, tumor size, lymph node ratio (LNR) and combined summary (CS) stage were prognostic factors for both OS and CSS of ILC, whereas chemotherapy and radiation were independent protect factors for OS. The nomograms exhibited satisfactory discriminative ability. For the training and validation cohorts, the C-index of the OS nomogram was 0.765 (95% CI 0.762-0.768) and 0.757 (95% CI 0.747-0.767), and the C-index of the CSS nomogram were 0.812 (95% CI 0.804-0.820) and 0.813 (95% CI 0.799-0.827), respectively. Additionally, decision curve analysis (DCA) demonstrated that the nomograms had superior predictive performance than traditional American Joint Committee on Cancer (AJCC) TNM stage. The novel nomograms to predict long-term prognosis based on LNR are reliable tools to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments for patients with ILC. Our findings should aid public health prevention strategies to reduce cancer burden. We provide two R/Shiny apps ( https//ilc-survival2024.shinyapps.io/osnomogram/ ; https//ilc-survival2024.shinyapps.io/cssnomogram/ ) to visualize findings.