Pathological responses and long-term outcome analysis after neoadjuvant chemotheraphy in breast cancer patients from Kuwait over a period of 15 years

Krishnan, Yamini; Alawadhi, Shafika A.; Sreedharan, P S; Gopal, Murali; Thuruthel, Sanjay

doi:10.5144/0256-4947.2013.443

Cited by 20 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, this study uses a variety of potential factors combined with the radiomics signature to build the prediction model, which also improved the accuracy of this model. In this study, we found that ER and PR expression were independent predictors of patients with pCR, which is similar to other studies (37,38). The diagnostic efficacy of the nomogram was higher than the signature after adding pathological indicators, which further demonstrates the important role of hormone receptors in the prediction of pCR status.…”

Section: Discussionsupporting

confidence: 90%

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Chen

Shu

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, Chen et al. Radiomics Predicted Efficancy of NACT specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.

show abstract

Section: Discussionsupporting

confidence: 90%

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Chen

Shu

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Therefore, a total of 25 studies were included in the analysis (Figure S1). Among these included studies, there were 4 RCTs (12, 32-34), 2 single-arm clinical trials (35,36), 18 cohort studies (4,(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53), and 1 pooled analysis (8) (Table S1). The pooled analysis (Cortazar et al 2014) included 12 neoadjuvant chemotherapy RCTs and patients across different BC subtypes.We only included the results specific to TNBC in the pooled analysis, which were based on 1,157 patients with early-stage TNBC from 4 clinical trials (GeparTrio, GeparQuattro, PREPARE, and EORTC 10994).…”

Section: Literature Search and Description Of Included Studiesmentioning

confidence: 99%

Association of Pathologic Complete Response with Long-Term Survival Outcomes in Triple-Negative Breast Cancer: A Meta-Analysis

et al. 2020

View full text Add to dashboard Cite

is an employee of Analysis Group, Inc., which received consultancy fees from Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA to conduct this study. Jipan Xie is an employee of Analysis Group, Inc., which received consultancy fees from Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA to conduct this study. Muhan Yuan is an employee of Analysis Group, Inc., which received consultancy fees from Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA to conduct this study. John Cook received grants from Merck & Co., Inc. during the conduct of this study.

show abstract

“…8 Previous research has indicated that patients who attain pathological complete response (pCR) to NAC have significantly longer overall survival (OS) and disease-free survival (DFS), particularly for triple negative and HER2+ breast cancer. [9][10][11] It is therefore important to identify those patients who are most likely to benefit from NAC treatment and to understand the advantages of NAC with respect to long-term outcomes. The purpose of this review is to describe the evaluations of NAC efficacy in breast cancer.…”

Section: Introductionmentioning

confidence: 99%

<p>Evaluation of the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer</p>

Wang¹,

Mao²

2020

DDDT

134

View full text Add to dashboard Cite

Neoadjuvant chemotherapy is increasingly used in breast cancer, especially for downstaging the primary tumor in the breast and the metastatic axillary lymph node. Accurate evaluations of the response to neoadjuvant chemotherapy provide important information on the impact of systemic therapies on breast cancer biology, prognosis, and guidance for further therapy. Moreover, pathologic complete response is a validated and valuable surrogate prognostic factor of survival after therapy. Evaluations of neoadjuvant chemotherapy response are very important in clinical work and basic research. In this review, we will elaborate on evaluations of the efficacy of neoadjuvant chemotherapy in breast cancer and provide a clinical evaluation procedure for neoadjuvant chemotherapy.

show abstract

Pathological responses and long-term outcome analysis after neoadjuvant chemotheraphy in breast cancer patients from Kuwait over a period of 15 years

Cited by 20 publications

References 16 publications

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Association of Pathologic Complete Response with Long-Term Survival Outcomes in Triple-Negative Breast Cancer: A Meta-Analysis

<p>Evaluation of the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer</p>

Contact Info

Product

Resources

About