ArticleBreast cancer research and treatment2025
The DCIS-IBC guide board on predicting postoperative upgrading in breast ductal carcinoma in situ: clinical insights from a multicenter study.
Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Development of a Nomogram-Based Clinical Estimation Model for Pathological Upstaging in Patients With Preoperative Ductal Carcinoma In Situ.The Kaohsiung journal of medical sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundDuctal carcinoma in situ (DCIS) carries a significant risk of postoperative upgrading to invasive breast cancer (IBC), yet existing prediction models lack validation in Asian populations. This study aimed to develop and validate a population-specific nomogram to preoperatively predict DCIS-to-IBC upgrading in Asian patients.
methodsA multicenter retrospective cohort of 465 Asian women diagnosed with DCIS by core needle biopsy (2015-2021) was analyzed. Patients were randomly divided into training (n = 257), internal validation (n = 110), and external validation cohorts (n = 98). Predictors were selected via LASSO regression and multivariable logistic regression. Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA). An interactive online nomogram was developed for clinical application.
resultsPostoperative upgrading occurred in 49.46% (230/465) of patients. Four independent predictors were identified: palpable mass (OR = 2.55, p = 0.096), lesion palpability (OR = 2.58, p = 0.043), low nuclear grade (OR = 0.55, p = 0.098), and suspected invasion (OR = 6.59, p < 0.001). The nomogram demonstrated robust discrimination in the training cohort (AUC = 0.802, 95% CI 0.748-0.856), with maintained performance in internal validation (AUC = 0.753) and acceptable generalizability in external validation (AUC = 0.680). DCA confirmed clinical utility across risk thresholds. The dynamic nomogram ( https://duancl777.shinyapps.io/dynnomapp/ ) enabled real-time risk stratification.
conclusionsThe DCIS-IBC Guide Board is the first Asian-specific model integrating clinicopathological predictors to identify high-risk DCIS patients. It facilitates personalized decisions, such as omitting sentinel lymph node biopsy while reducing overtreatment. Although external validation showed moderate performance, this tool addresses critical population heterogeneity and enhances preoperative risk assessment. Prospective multicenter studies are warranted to optimize generalizability and explore multimodal predictors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.