Dataset Information

Predicting Pathological Complete Response in Breast Cancer After Two Cycles of Neoadjuvant Chemotherapy by Tumor Reduction Rate: A Retrospective Case-Control Study.

ABSTRACT:

Purpose

We aimed to identify effectiveness-associated indicators and evaluate the optimal tumor reduction rate (TRR) after two cycles of neoadjuvant chemotherapy (NAC) in patients with invasive breast cancer.

Methods

This retrospective case-control study included patients who underwent at least four cycles of NAC at the Department of Breast Surgery between February 2013 and February 2020. A regression nomogram model for predicting pathological responses was constructed based on potential indicators.

Results

A total of 784 patients were included, of whom 170 (21.68%) reported pathological complete response (pCR) after NAC and 614 (78.32%) had residual invasive tumors. The clinical T stage, clinical N stage, molecular subtype, and TRR were identified as independent predictors of pCR. Patients with a TRR > 35% were more likely to achieve pCR (odds ratio, 5.396; 95% confidence interval [CI], 3.299-8.825). The receiver operating characteristic (ROC) curve was plotted using the probability value, and the area under the ROC curve was 0.892 (95% CI, 0.863-0.922).

Conclusion

TRR > 35% is predictive of pCR after two cycles of NAC, and an early evaluation model using a nomogram based on five indicators, age, clinical T stage, clinical N stage, molecular subtype, and TRR, is applicable in patients with invasive breast cancer.

SUBMITTER: Yao L

PROVIDER: S-EPMC10139844 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Predicting Pathological Complete Response in Breast Cancer After Two Cycles of Neoadjuvant Chemotherapy by Tumor Reduction Rate: A Retrospective Case-Control Study.

Yao Litong L Liu Xiaoyan X Wang Mozhi M Yu Keda K Xu Shouping S Qiu Pengfei P Lv Zhidong Z Zhang Xinwen X Xu Yingying Y

Journal of breast cancer 20230316 2

<h4>Purpose</h4>We aimed to identify effectiveness-associated indicators and evaluate the optimal tumor reduction rate (TRR) after two cycles of neoadjuvant chemotherapy (NAC) in patients with invasive breast cancer.<h4>Methods</h4>This retrospective case-control study included patients who underwent at least four cycles of NAC at the Department of Breast Surgery between February 2013 and February 2020. A regression nomogram model for predicting pathological responses was constructed based on po ...[more]

PMID: 37051647

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Project description:PurposeThis study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.MethodsThis retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People's Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People's Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.ResultsIn the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.ConclusionThe developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.

Project description:BackgroundPatients with locally advanced rectal cancer (LARC) have an improved prognosis if achieved a pathological complete response (pCR) on account of neoadjuvant chemoradiation therapy (nCRT). However, the proportion of patients achieving pCR is only 8-24%. The purpose of this study was to explore whether the addition of consolidation chemotherapy to nCRT could improve pCR rate in patients with LARC.Materials and methodsThe subjects were 144 individuals with clinical stage II (T3-4, N0) or III (any T, N1-2) LARC who had received neoadjuvant CRT followed by total mesorectal excision (TME). Eighty-three patients in the consolidation chemotherapy group received two cycles XELOX between CRT and TME, while 61 patients in the standard treatment group without consolidation chemotherapy. The pCR (ypT0N0), tumor downstaging (ypT0-2N0) after TME and adverse events (AEs) during and post treatment were compared between the treatment groups using multivariable logistic regression analysis. To adjust the unbalanced variables for the primary endpoint, logistic regression analysis and stratified analysis were performed.ResultsThe consolidation chemotherapy group improved pCR rate (19.3% vs 4.9%, p = 0.01) and tumor downstaging rate (45.8% vs 24.6%, p = 0.009) compared to the standard treatment group. After adjustment for clinical tumor stage, clinical nodal stage and time interval to surgery, patients with consolidation chemotherapy were more likely to reach pCR (adjusted odds ratio 4.91, 95% CI [1.01-23.79], p = 0.048). AEs during and post treatment in the two groups were 54.1% vs 49.3% (p = 0.57), respectively. In addition, the incidence of any grade 1-2 AEs in the two groups was 93.4% vs 95.1% (p = 0.93), while the incidence of grade 3 AEs was 1.6% versus 2.4% (p = 0.74), respectively. No grade 4 AEs occurred in two groups.ConclusionsThe addition of neoadjuvant consolidation chemotherapy after CRT significantly increased the pCR rate and did not increase the AEs during and post treatment and in patients with LARC.

Project description:BackgroundInvasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy.MethodsIn this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs).ResultsWe developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts.ConclusionThe proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

Dataset Information

Predicting Pathological Complete Response in Breast Cancer After Two Cycles of Neoadjuvant Chemotherapy by Tumor Reduction Rate: A Retrospective Case-Control Study.

Purpose

Methods

Results

Conclusion

Publications

Predicting Pathological Complete Response in Breast Cancer After Two Cycles of Neoadjuvant Chemotherapy by Tumor Reduction Rate: A Retrospective Case-Control Study.

Similar Datasets

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