Proposal of an automated tumor‐stromal ratio assessment algorithm and a nomogram for prognosis in early‐stage invasive breast cancer

BACKGROUND: The tumor‐stromal ratio (TSR) has been verified to be a prognostic factor in many solid tumors. In most studies, it was manually assessed on routinely stained H&E slides. This study aimed to assess the TSR using image analysis algorithms developed by the Qupath software, and integrate the TSR into a nomogram for prediction of the survival in invasive breast cancer (BC) patients. METHODS: A modified TSR assessment algorithm based on the recognition of tumor and stroma tissues was developed using the Qupath software. The TSR of 234 invasive BC specimens in H&E‐stained tissue microarrays (TMAs) were assessed with the algorithm and categorized as stroma‐low or stroma‐high. The consistency of TSR estimation between Qupath prediction and pathologist annotation was analyzed. Univariable and multivariable analyses were applied to select potential risk factors and a nomogram for predicting survival in invasive BC patients was constructed and validated. An extra TMA containing 110 specimens was obtained to validate the conclusion as an independent cohort. RESULTS: In the discovery cohort, stroma‐low and stroma‐high were identified in 43.6% and 56.4% cases, respectively. Good concordance was observed between the pathologist annotated and Qupath predicted TSR. The Kaplan–Meier curve showed that stroma‐high patients were associated with worse 5‐DFS compared to stroma‐low patients (p = 0.007). Multivariable analysis identified age, T stage, N status, histological grade, ER status, HER‐2 gene, and TSR as potential risk predictors, which were included in the nomogram. The nomogram was well calibrated and showed a favorable predictive value for the recurrence of BC. Kaplan–Meier curves showed that the nomogram had a better risk stratification capability than the TNM staging system. In the external validation of the nomogram, the results were further validated. CONCLUSIONS: Based on H&E‐stained TMAs, this study successfully developed image analysis algorithms for TSR assessment and constructed a nomogram for predicting survival in invasive BC.