Outcome without any adjuvant systemic treatment in stage I ER+/HER2- breast cancer patients included in the MINDACT trial.

BACKGROUND: Adjuvant systemic treatments (AST) reduce mortality, but have associated short- and long-term toxicities. Careful selection of patients likely to benefit from AST is needed. We evaluated outcome of low-risk breast cancer patients of the EORTC 10041/BIG 3-04 MINDACT trial who received no AST. PATIENTS AND METHODS: Patients with estrogen receptor-positive, HER2-negative, lymph node-negative tumors ≤2 cm who received no AST were matched 1 : 1 to patients with similar tumor characteristics treated with adjuvant endocrine therapy (ET), using propensity score matching and exact matching on age, genomic risk (70-gene signature) and grade. In a post hoc analysis, distant metastasis-free interval (DMFI) and overall survival (OS) were assessed by Kaplan-Meier analysis and hazard ratios (HR) by Cox regression. Cumulative incidences of locoregional recurrence (LRR) and contralateral breast cancer (CBC) were assessed with competing risk analyses. RESULTS: At 8 years, DMFI rates were 94.8% [95% confidence interval (CI) 92.7% to 96.9%] in 509 patients receiving no AST, and 97.3% (95% CI 95.8% to 98.8%) in 509 matched patients who received only ET [absolute difference: 2.5%, HR 0.56 (95% CI 0.30-1.03)]. No statistically significant difference was seen in 8-year OS rates, 95.4% (95% CI 93.5% to 97.4%) in patients receiving no AST and 95.6% (95% CI 93.8% to 97.5%) in patients receiving only ET [absolute difference: 0.2%, HR 0.86 (95% CI 0.53-1.41)]. Cumulative incidence rates of LRR and CBC were 4.7% (95% CI 3.0% to 7.0%) and 4.6% (95% CI 2.9% to 6.9%) in patients receiving no AST versus 1.4% (95% CI 0.6% to 2.9%) and 1.5% (95% CI 0.6% to 3.1%) in patients receiving only ET. CONCLUSIONS: In patients with stage I low-risk breast cancer, the effect of ET on DMFI was limited, but overall significantly fewer breast cancer events were observed in patients who received ET, after the relatively short follow-up of 8 years. These benefits and side-effects of ET should be discussed with all patients, even those at a very low risk of distant metastasis.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.

BACKGROUND: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. METHODS: We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. RESULTS: Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. CONCLUSION: Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.