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Review
. 2020 Mar 25:6:11.
doi: 10.1038/s41523-020-0152-4. eCollection 2020.

Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice

Rashmi K Murthy  1 , Juhee Song  2 , Akshara S Raghavendra  1 , Yisheng Li  2 , Limin Hsu  1 , Kenneth R Hess  2 , Carlos H Barcenas  1 , Vicente Valero  1 , Robert W Carlson  3   4 , Debu Tripathy  1 , Gabriel N Hortobagyi  1
Affiliations
Review

Incorporation of clinical and biological factors improves prognostication and reflects contemporary clinical practice

Rashmi K Murthy et al. NPJ Breast Cancer. .

Abstract

We developed prognostic models for breast cancer-specific survival (BCSS) that consider anatomic stage and other important determinants of prognosis and survival in breast cancer, such as age, grade, and receptor-based subtypes with the intention to demonstrate that these factors, conditional on stage, improve prediction of BCSS. A total of 20,928 patients with stage I-III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1990 and 2016, who received surgery as an initial treatment were identified to generate prognostic models by Fine-Gray competing risk regression model. Model predictive accuracy was assessed using Harrell's C-index. The Aalen-Johansen estimator and a selected Fine-Gray model were used to estimate the 5-year and 10-year BCSS probabilities. The performance of the selected model was evaluated by assessing discrimination and prediction calibration in an external validation dataset of 29,727 patients from the National Comprehensive Cancer Network (NCCN). The inclusion of age, grade, and receptor-based subtype in addition to stage significantly improved the model predictive accuracy (C-index: 0.774 (95% CI 0.755-0.794) vs. 0.692 for stage alone, p < 0.0001). Young age (<40), higher grade, and TNBC subtype were significantly associated with worse BCSS. The selected model showed good discriminative ability but poor calibration when applied to the validation data. After recalibration, the predictions showed good calibration in the training and validation data. More refined BCSS prediction is possible through a model that has been externally validated and includes clinical and biological factors.

Keywords: Breast cancer.

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Conflict of interest statement

Competing interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1. BCSS estimation.
Estimated BCSS by combining age group, receptor subtype, grade within each stage group (1, 2A, 2B) based on Fine–Gray model 7 using average baseline survival (N = 14,781).
Fig. 2
Fig. 2. BCSS probability by risk group.
a Training data and b validation data.
Fig. 3
Fig. 3. Calibration plots.
a Training data, 5 years, b training data, 10 years, c validation data, 5 years and d validation data, 10 years.
Fig. 4
Fig. 4. New calibration plots after recalibration.
a Training data, 5 years, b training data, 10 years, c validation data, 5 years, and d validation data, 10 years.
See this image and copyright information in PMC

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