Correlation of Neutrophil-Lymphocyte and Albumin-Globulin Ratios With Outcomes in Patients With Breast Cancer Undergoing Neoadjuvant Chemotherapy or Upfront Surgery.

PURPOSE: Higher neutrophil-lymphocyte ratio (NLRs) indicate a pro-inflammatory state and are associated with poor survival. Conversely, higher albumin-globulin ratio (AGRs) may be associated with improved prognosis. We aimed to investigate the association between NLR and AGR and prognosis and survival in patients with breast cancer. METHODS: This retrospective study included all patients with stage I-III breast cancer between 2011 and 2017 in Singapore General Hospital and National Cancer Center Singapore. Multivariate logistic regression analysis of NLR, AGR, age, stage, grade, and subtype was performed. Survival data between groups were compared using Cox regression analysis and log-rank tests. RESULTS: A total of 1,188 patients were included, of whom 323 received neoadjuvant chemotherapy (NACT) and 865 underwent upfront surgery. In patients who underwent NACT, a higher AGR was significantly associated with a higher pCR rate (cut-off > 1.28; odds ratio [OR], 2.03; 95% confidence interval [CI], 1.13-3.74; p = 0.020), better DFS (cut off > 1.55; hazard ratio [HR], 0.37; 95% CI, 0.16-0.85; p = 0.019), and better CSS (cut off > 1.46; HR, 0.39; 95% CI, 0.17-0.92; p = 0.031). Higher NLR was significantly associated with worse DFS (cut off > 4.09; HR, 1.77; 95% CI, 1.07-2.91; p = 0.026) and worse CSS (cut off > 4.09; HR, 1.98; 95% CI, 1.11-3.53; p = 0.021). In patients who underwent upfront surgery, higher AGR correlated with significantly better OS (cut off > 1.17; HR, 0.54; 95% CI, 0.36-0.82; p = 0.004) and higher NLR correlated with worse OS (cut off > 2.38; HR, 1.63; 95% CI, 1.09-2.44; p = 0.018). CONCLUSION: NLR and AGR are useful in predicting the response to NACT as well as prognosis of patients with breast cancer. Further studies are needed to explore their value in clinical decision making.

Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer.

BACKGROUND: Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors' response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. METHODS: The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. RESULTS: The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). CONCLUSIONS: Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.

Screening Uptake Differences Are Not Implicated in Poorer Breast Cancer Outcomes among Singaporean Malay Women.

PURPOSE: This study was undertaken to examine the impact of screening and race on breast cancer outcomes in Singapore. METHODS: An institutional database was reviewed, and invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) data were analyzed separately. Overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS) were assessed. RESULTS: The study included 6,180 IDC and 1,031 DCIS patients. The median follow-up time was 4.1 years. Among IDC patients, Malay women were the youngest when first diagnosed, and were more likely to present with advanced stage disease. Malay women also had the highest proportion of T3 and T4 tumors at 14.2%, compared with Chinese women at 8.7% and Indian women at 9.6% (p<0.001). Malay women had a higher incidence of node-positive disease at 58.3% compared with Chinese women at 46.4% and Indian women at 54.9% (p<0.001). Malay subjects also had higher-grade tumors; 61.8% had grade 3 tumors compared with 45.8% of Chinese women and 52% of Indian women (p<0.001). Furthermore, tumors in Malay subjects were less endocrine-sensitive and more human epidermal growth factor receptor 2 enriched. Malay women had the lowest 5- and 10-year OS, DFS, and CSS rates (p<0.001). After separating clinically and screen-detected tumors, multivariate analysis showed that race was still significant for outcomes. For screen-detected tumors, the OS hazard ratio (HR) for Malay women compared to Chinese women was 5.78 (95% confidence interval [CI], 2.64-12.64), the DFS HR was 2.18 (95% CI, 1.19-3.99), and the CSS HR was 5.93 (95% CI, 2.15-16.39). For DCIS, there were no statistically significant differences in the tumor size, grade, histology subtypes, or hormone sensitivity. CONCLUSION: Malay race is a poor prognostic factor in both clinically and screen-detected IDC. Special attention should be given to the detection and follow-up of breast cancer in this group.