Cha CD, et al. J Breast Cancer. 2024 Oct;27:e32. https://doi.org/10.4048/jbc.2024.0213

Dexamethasone is widely used in cancer patients despite the concern that perioperative glucocorticoids may potentially cause immunosuppression. However, studies on the influence of dexamethasone on cancer recurrence after curative surgery have produced conflicting results. The goal of our study was to compare postoperative recurrence-free survival and overall survival between patients with breast cancer who received perioperative dexamethasone and those who did not. The medical records of 2729 patients who underwent breast cancer surgery between November 2005 and December 2010 were reviewed. These patients were followed up until December 2015. The patients were categorised according whether they received a single dose of intravenous dexamethasone perioperatively or not. Cox regression analyses were conducted to evaluate any associations between dexamethasone usage with postoperative recurrence and mortality. Additionally, we performed a sensitivity test with propensity score matching to adjust for selection bias. Among the 2628 patients, 236 (8.5%) received perioperative dexamethasone. No increasing risk for recurrence (hazard ratio [HR], 1.442; 95% confidence interval [CI], 0.969–2.145; P = 0.071) or mortality (HR, 1.256; 95% CI, 0.770–2.047; P = 0.361) after breast cancer surgery were identified in patients who received dexamethasone. Similarly, propensity score matching did not show significant associations in postoperative recurrence (HR, 1.389; 95% CI, 0.904–2.132; P = 0.133) or mortality (HR, 1.506; 95% CI, 0.886–2.561; P = 0.130) in patients who received dexamethasone. We found that a perioperative single dose of dexamethasone is not associated with increased recurrence or mortality after curative surgery in breast cancer patients.

Since triple-negative breast cancers (TNBCs) have varying prognoses, it is important to identify subgroups with particularly poor prognosis. The aim of this study was to assess whether changes in the neutrophil-to-lymphocyte ratio (NLR) during the treatment process were associated with poor prognosis in TNBC patients. This study included 600 TNBC patients who underwent surgery from January 2005 to December 2016. The associations of the NLR and clinicopathologic factors with breast cancer recurrence and survival in patients who underwent both definitive local treatment (total mastectomy or breast-conserving surgery with radiotherapy) and systemic chemotherapy were analyzed. The NLRs at four time points (before surgery, before chemotherapy, before radiotherapy, and 1 year after surgery) were assessed. The univariate analysis showed that changes in the NLR before the start of radiotherapy (odds ratio: 1.115, confidence interval: 1.011–1.229) and 1 year after surgery (odds ratio: 1.196, confidence interval: 1.057–1.354) significantly increased the risk of recurrence or death. In multivariate analysis, T stage, N stage, and changes in the NLR were significant factors. A time-sequenced NLR may reflect the prognosis of TNBC patients. A poor prognosis is expected in patients whose NLR increases during treatment compared to the preoperative NLR, and additional treatment is needed.

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2− breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2− breast cancer.

Kim JH, et al. Ann Robot Innov Surg. 2024 May;5(1):1-9. https://doi.org/10.37007/aris.2024.5.1.1

Kim JH, et al. Ann Robot Innov Surg. 2021 Nov;2(2):23-30. https://doi.org/10.37007/aris.2021.2.2.23