The majority of published findings on chemotherapy-induced febrile neutropenia (FN) are restricted to three ethnic groups: Asians, Caucasians, and African Americans. In this two-part study, we examined FN incidence and risk factors in Chinese, Malay, and Indian chemotherapy-treated breast cancer (BC) patients. Hospital records or ICD codes were used to identify patients with FN. In both the Singapore Breast Cancer Cohort (SGBCC) and the Joint Breast Cancer Registry (JBCR), the time of the first FN from the start of chemotherapy was estimated using Cox regression. Multinomial regression was used to evaluate differences in various characteristics across ethnicities. FN was observed in 170 of 1014 patients in SGBCC. The Cox model showed that non-Chinese were at higher risk of developing FN (HR_Malay [95% CI]:2.04 [1.44–2.88], p < 0.001; HR_Indian:1.88 [1.11–3.18], p = 0.018). In JBCR, FN was observed in 965 of 7449 patients. Univariable Cox models identified ethnicity, a lower baseline absolute neutrophil count, non-luminal A proxy subtypes, and anthracycline-containing regimens as risk factors. Disparities across ethnicities’ risk (HR_Malay:1.29 [1.07–1.54], p = 0.006; HR_Indian:1.50 [1.19–1.88], p < 0.001) remained significant even after further adjustments. Finally, an age-adjusted multinomial model showed that Malays (p = 0.006) and Indians (p = 0.009) were significantly more likely to develop multiple episodes of FN during treatment. Ethnic differences in chemotherapy-induced FN among BC patients exist. Further studies can focus on investigating pharmacogenetic differences across ethnicities.

Background: Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or treatment decisions. Their relationship to tumor characteristics and patient outcomes remains incompletely characterized. Methods: We conducted a retrospective cohort study of 7603 patients with Stage I–III invasive breast cancer (diagnosed 1991–2022, age < 80 years) from the Joint Breast Cancer Registry in Singapore. Natural language processing (NLP) was applied to 9754 free-text pathology reports to extract co-existing background breast features, with accuracy validated by dual-reviewer assessment of 200 reports. Because background features are most reliably assessed on excision specimens, the primary analytic cohort comprised 3988 patients with available excision pathology reports. Unsupervised hierarchical clustering grouped extracted features into three categories. Associations with tumor characteristics were assessed with multinomial logistic regression and ten-year overall survival by Cox proportional hazards models (median follow-up 9.6 years; 620 deaths). Results: Here, we show that NLP-based extraction of background breast features from routine pathology reports achieves an accuracy of over 90% across features. Lobular neoplasia and benign proliferative changes are associated with less aggressive tumor characteristics, whereas early neoplastic and papillary lesions are more prevalent in HER2-enriched and luminal B tumor subtypes. Benign proliferative changes are associated with better survival in age- and year-adjusted models (hazard ratio 0.91, 95% CI 0.86–0.97), but this association is attenuated after adjustment for stage and subtype. Conclusions: NLP-enabled extraction of background breast features from pathology text is feasible at scale. These features reflect tumor biology but do not independently add prognostic information beyond established clinical variables.