ArticleJCO global oncology2023
Combination of Anatomical and Biological Factors to Predict Disease-Free Survival in Breast Cancer.
Article in JCO global oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.Scientific reports · 2025Article
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Abstract
purposeThe combination of anatomical and biological factors of breast cancer in a new staging system has a prognostic role. This study investigates the prognostic value of the Bioscore among patients with breast cancer with respect to disease-free survival (DFS). MATERIAL AND
methodsThis study included 317 patients with breast cancer who were identified between January 2015 and December 2018 at Clinical Oncology Department of Assiut University Hospital. Their cancer baseline characteristics were recorded: pathologic stage (PS), T stage (T), nodal stage (N), grade (G), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2) status. Univariate and two multivariate analyses were performed to identify which of these variables are associated with DFS. Model performance was quantified using Harrell's concordance index (C-index), and the Akaike information criterion (AIC) was used to compare model fits.
resultsThe significant factors in the univariate analysis were PS3, T2, T3, T4, N3, G2, G3, ER-negative, PR-negative, and HER2-negative. In the first multivariate analysis, PS3, G3, and ER-negative were the significant factors, and in the second multivariate analysis, T2, T4, N3, G3, and ER-negative were the significant factors. Two sets of models were built to determine the utility of combining variables. Models incorporating G and ER status had the highest C-index (0.72) for T + N + G + ER in comparison with (0.69) PS + G + ER and the lowest AIC (953.01) for T + N + G + ER and (966.9) for PS + G + ER.
conclusionUsing the Bioscore in breast cancer staging helps to identify patients at increased risk of recurrence. It provides more optimistic prognostic stratification than the anatomical staging alone for DFS.
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