&lt;p&gt;Evaluation of the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer&lt;/p&gt;

Wang, Huan; Mao, Xiaoyun

doi:10.2147/dddt.s253961

Cited by 131 publications

(108 citation statements)

References 87 publications

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“…3 Platinum cannot enter the nucleus to play a role after being combined with GSH. 4 Drugs that directly or indirectly damage DNA can induce breast cancer cells to produce DDR. 5 Trastuzumab can induce nuclear HER4 up-regulation and nuclear HER2 translocation.…”

Section: Exosomesmentioning

confidence: 99%

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New Advances in the Research of Resistance to Neoadjuvant Chemotherapy in Breast Cancer

Peng

Tang

et al. 2021

IJMS

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Breast cancer has an extremely high incidence in women, and its morbidity and mortality rank first among female tumors. With the increasing development of medicine today, the clinical application of neoadjuvant chemotherapy has brought new hope to the treatment of breast cancer. Although the efficacy of neoadjuvant chemotherapy has been confirmed, drug resistance is one of the main reasons for its treatment failure, contributing to the difficulty in the treatment of breast cancer. This article focuses on multiple mechanisms of action and expounds a series of recent research advances that mediate drug resistance in breast cancer cells. Drug metabolizing enzymes can mediate a catalytic reaction to inactivate chemotherapeutic drugs and develop drug resistance. The drug efflux system can reduce the drug concentration in breast cancer cells. The combination of glutathione detoxification system and platinum drugs can cause breast cancer cells to be insensitive to drugs. Changes in drug targets have led to poorer efficacy of HER2 receptor inhibitors. Moreover, autophagy, epithelial–mesenchymal transition, and tumor microenvironment can all contribute to the development of resistance in breast cancer cells. Based on the relevant research on the existing drug resistance mechanism, the current treatment plan for reversing the resistance of breast cancer to neoadjuvant chemotherapy is explored, and the potential drug targets are analyzed, aiming to provide a new idea and strategy to reverse the resistance of neoadjuvant chemotherapy drugs in breast cancer.

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Section: Exosomesmentioning

confidence: 99%

“…CYP can inactivate drugs 3. Platinum cannot enter the nucleus to play a role after being combined with GSH 4. Drugs that directly or indirectly damage DNA can induce breast cancer cells to produce DDR 5.…”

mentioning

confidence: 99%

New Advances in the Research of Resistance to Neoadjuvant Chemotherapy in Breast Cancer

Peng

Tang

et al. 2021

IJMS

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“…NAC is a cost-efficient approach to downstage primary tumor and metastatic axillary lymph node (6), and has especially achieved notable pathologic complete response (pCR) rates in the most aggressive tumors (7), and conversely, also provides obvious benefits to breastconservation therapy (8,9). NAC takes advantage of and contributes to the impact of systemic therapies on breast cancer biology (10). However, the therapeutic effect of NAC relies on the patient's biological phenotypes including HER2(+) positive cancers (11) or triple-negative cancers (12), which characterize the many patients who experience higher local recurrence rates (13).…”

Section: Introductionmentioning

confidence: 99%

A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy

et al. 2021

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Neoadjuvant chemotherapy (NAC) may increase the resection rate of breast cancer and shows promising effects on patient prognosis. It has become a necessary treatment choice and is widely used in the clinical setting. Benefitting from the clinical information obtained during NAC treatment, computational methods can improve decision-making by evaluating and predicting treatment responses using a multidisciplinary approach, as there are no uniformly accepted protocols for all institutions for adopting different treatment regiments. In this study, 166 Chinese breast cancer cases were collected from patients who received NAC treatment at the First Bethune Hospital of Jilin University. The Miller–Payne grading system was used to evaluate the treatment response. Four machine learning multiple classifiers were constructed to predict the treatment response against the 26 features extracted from the patients’ clinical data, including Random Forest (RF) model, Convolution Neural Network (CNN) model, Support Vector Machine (SVM) model, and Logistic Regression (LR) model, where the RF model achieved the best performance using our data. To allow a more general application, the models were reconstructed using only six selected features, and the RF model achieved the highest performance with 54.26% accuracy. This work can efficiently guide optimal treatment planning for breast cancer patients.

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“…NAC provides a greater chance to determine the extent of the disease’s biology, prognosis, and surgical guidance. It can also provide more information on the systemic effects of therapy on breast cancer [ 2 ]. Furthermore, patients who undergo NAC have a better pathological complete response (pCR) and have been proven to have a better outcome [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Accuracy of MRI in Evaluating Response After Neoadjuvant Systemic Therapy in Operable Breast Cancer

Khazindar¹,

Hashem²,

Abusanad³

et al. 2021

Cureus

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Background Neoadjuvant chemotherapy (NAC) is an important step in the treatment of various types of breast cancer by downsizing the tumor to make it operable. Determining disease extent after NAC is essential for accurate surgical planning. MRI has been the gold standard for detecting tumors that are usually difficult to detect on ultrasound or mammography. However, the use of MRI after NAC is controversial. Therefore, we aimed to evaluate the diagnostic accuracy of post-NAC MRI in the detection of residual disease preoperatively and to investigate the factors associated with pathological complete response (pCR). Methodology This retrospective review study was approved by the institutional review board with waiving of the informed consent. A total of 90 charts between January 2016 and January 2019 were reviewed. Baseline lesion size was measured as the maximal diameter in a single dimension by pretreatment MRI. To assess the diagnostic accuracy of MRI in detecting residual disease, we used two different definitions of pCR in the breast. The first is the resolution of both invasive disease and ductal carcinoma in situ. The second is the resolution of the invasive disease only. As a secondary objective of the study, we assessed the association between different patients’ characteristics and both MRI and pathologic response using univariate and multivariate analysis. Results A total of 52 women (mean age: 47.4 years; range: 28-74) with 56 breast masses were eligible for the study. Complete MRI response was noted in 22 (39%) masses. pCR was achieved in 14 (25%) and 25 (44.6%) masses using the first and second pCR definitions, respectively. The negative predictive value (NPV) and overall accuracy of MRI for detecting residual disease were 50% and 75%, respectively, using the first pCR definition. With the second pCR definition, NPV and accuracy were 77.3% and 76.8%, respectively. Positive axillary lymph nodes were the only significant factor associated with incomplete MRI and pathological responses. Conclusions MRI NPV for residual disease was higher with the second pCR definition; however, overall accuracy was not different. MRI accuracy in detecting residual disease after NAC is not adequate to replace pathological assessment.

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

Cited by 131 publications

References 87 publications

New Advances in the Research of Resistance to Neoadjuvant Chemotherapy in Breast Cancer

New Advances in the Research of Resistance to Neoadjuvant Chemotherapy in Breast Cancer

A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy

Diagnostic Accuracy of MRI in Evaluating Response After Neoadjuvant Systemic Therapy in Operable Breast Cancer

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