The dynamic change of neutrophil to lymphocyte ratio is predictive of pathological complete response after neoadjuvant chemotherapy in breast cancer patients

Breast Cancer ◽  
2020 ◽  
Vol 27 (5) ◽  
pp. 982-988
Author(s):  
Jiaqiang Dan ◽  
Jinya Tan ◽  
Junhua Huang ◽  
Xiaoli Zhang ◽  
Yao Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Pathological Complete Response ◽  
Dynamic Change ◽  
Complete Response ◽  
Neutrophil To Lymphocyte Ratio ◽  
Breast Cancer Patients ◽  
Lymphocyte Ratio

scholarly journals Systemic Immune-Inflammation Index Is Superior to Neutrophil to Lymphocyte Ratio in Prognostic Assessment of Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Cong Jiang ◽  
Yubo Lu ◽  
Shiyuan Zhang ◽  
Yuanxi Huang
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Regression Models ◽  
Roc Analysis ◽  
Neutrophil To Lymphocyte Ratio ◽  
Breast Cancer Patients ◽  
Lymphocyte Ratio ◽  
Immune Inflammation ◽  
The Impact

Background and Methods. As a parameter integrating neutrophil (N), lymphocyte (L), and platelet (P) levels, altered systemic immune-inflammation index (SII) has been investigated in a number of malignant tumor types. Here, we explore the impact of SII in a cohort of 249 breast cancer patients receiving neoadjuvant chemotherapy (NAC), investigating the prognostic value of SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). All patients had complete follow-up data and pathological confirmation of breast cancer by a core needle biopsy prior to NAC treatment and surgery. All blood samples were obtained within one week prior to NAC. Receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value for patient classification by SII, NLR, and PLR. Associations between clinicopathological variables by SII, NLR, and PLR were determined by a chi-squared test or Fisher’s exact test. Overall survival (OS) analysis was performed using Kaplan-Meier plots, log-rank tests, and Cox proportional hazards regression models. The Z test is used to compare the prognostic ability of SII, NLR, and PLR. Results. SII, NLR, and PLR did not define patient groups with distinct clinicopathological characteristics. SII, NLR, and PLR cut-off values were 547, 2.13, and 88.23, as determined by ROC analysis; the corresponding areas under the curve (AUCs) were 0.625, 0.555, and 0.571, respectively. Cox regression models identified SII as independently associated with OS. Patients with low SII had prolonged OS (65 vs. 41 months, P = 0.017 , HR: 3.24, 95% CI: 1.23-8.55). In the Z test, the difference in AUC between SII and NLR was statistically significant ( Z = 2.721 , 95% CI: 0.0194-0.119, P = 0.0065 ). Conclusion. Our study suggests that the pretreatment SII value is significantly correlated with OS in breast cancer patients undergoing NAC and that the prognostic utility of SII is superior to that of NLR and PLR.

MRI in diagnosis of pathological complete response in breast cancer patients after neoadjuvant chemotherapy

2015 ◽  
Vol 84 (2) ◽  
pp. 242-249 ◽  
Author(s):  
Yan-Ling Li ◽  
Xiao-Peng Zhang ◽  
Jie Li ◽  
Kun Cao ◽  
Yong Cui ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Breast Cancer Patients

scholarly journals Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fengling Li ◽  
Yongquan Yang ◽  
Yani Wei ◽  
Ping He ◽  
Jie Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Pathological Complete Response ◽  
Predictive Biomarkers ◽  
Predictive Performance ◽  
Complete Response ◽  
Breast Cancer Patients ◽  
Histological Images

Abstract Background Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. Methods In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. Results The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P  =  0.001). Conclusion The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC.

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