Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy

Pfob, André; Sidey‐Gibbons, Chris; Lee, Han‐Byoel; Tasoulis, Marios Konstantinos; Koelbel, Vivian; Golatta, Michael; Rauch, Gaiane M.; Smith, Benjamin D.; Valero, Vicente; Han, Wonshik; MacNeill, Fiona; Weber, Walter; Rauch, Geraldine; Kuerer, Henry Mark; Heil, Joerg

doi:10.1016/j.ejca.2020.11.006

Cited by 49 publications

(58 citation statements)

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“…Machine learning algorithms identify complex patterns in data to make accurate outcome predictions of future events at an individual level [ [8] , [9] , [10] ]. Such algorithms have shown great performance in other areas of breast cancer treatment like identifying exceptional responders to neoadjuvant treatment or patients at risk of experiencing financial toxicity related to their cancer treatment [ 11 , 12 ]. As post-surgical satisfaction with breasts is a recommended key outcome for women undergoing cancer-related mastectomy and breast reconstruction [ 13 ], we hypothesized that machine learning algorithms may allow accurate, individualized predictions of long-term satisfaction with reconstructed breasts prior to the initiation of the breast reconstruction process to better inform the decision-making process for these women.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

et al. 2021

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Background: Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer. Methods: We trained, tested, and validated three machine learning algorithms (logistic regression (LR) with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) to predict clinically important differences in satisfaction with breasts at 2-year follow-up using the validated BREAST-Q. We used data from 1553 women undergoing cancer-related mastectomy and reconstruction who were followed-up for two years at eleven study sites in North America from 2011 to 2016. 10-fold cross-validation was used to train and test the algorithms on data from 10 of the 11 sites which were further validated using the additional site's data. Area-under-the-receiver-operating-characteristicscurve (AUC) was the primary outcome measure. Results: Of 1553 women, 702 (45.2%) experienced an improved satisfaction with breasts and 422 (27.2%) a decreased satisfaction. In the validation set (n ¼ 221), the algorithms showed equally high performance to predict improved or decreased satisfaction with breasts (all P > 0.05): For improved satisfaction AUCs were 0.86e0.87 and for decreased satisfaction AUCs were 0.84e0.85. Conclusion: Long-term, individual patient-reported outcomes for women undergoing mastectomy and breast reconstruction can be accurately predicted using machine learning algorithms. Our algorithms may be used to better inform clinical treatment decisions for these patients by providing accurate estimates of expected quality of life.

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Section: Introductionmentioning

confidence: 99%

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

et al. 2021

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“…Previous research on machine learning to improve diagnostic accuracy has shown promising results. [32][33][34] When omission of breast cancer surgery is considered, oncologic safety is of utmost importance. The fear of leaving residual disease behind is evident.…”

Section: Discussionmentioning

confidence: 99%

Vacuum-Assisted Breast Biopsy After Neoadjuvant Systemic Treatment for Reliable Exclusion of Residual Cancer in Breast Cancer Patients

et al. 2021

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Background About 40 % of women with breast cancer achieve a pathologic complete response in the breast after neoadjuvant systemic treatment (NST). To identify these women, vacuum-assisted biopsy (VAB) was evaluated to facilitate risk-adaptive surgery. In confirmatory trials, the rates of missed residual cancer [false-negative rates (FNRs)] were unacceptably high (> 10%). This analysis aimed to improve the ability of VAB to exclude residual cancer in the breast reliably by identifying key characteristics of false-negative cases. Methods Uni- and multivariable logistic regressions were performed using data of a prospective multicenter trial (n = 398) to identify patient and VAB characteristics associated with false-negative cases (no residual cancer in the VAB but in the surgical specimen). Based on these findings FNR was exploratively re-calculated. Results In the multivariable analysis, a false-negative VAB result was significantly associated with accompanying ductal carcinoma in situ (DCIS) in the initial diagnostic biopsy [odds ratio (OR), 3.94; p < 0.001], multicentric disease on imaging before NST (OR, 2.74; p = 0.066), and age (OR, 1.03; p = 0.034). Exclusion of women with DCIS or multicentric disease (n = 114) and classication of VABs that did not remove the clip marker as uncertain representative VABs decreased the FNR to 2.9% (3/104). Conclusion For patients without accompanying DCIS or multicentric disease, performing a distinct representative VAB (i.e., removing a well-placed clip marker) after NST suggests that VAB might reliably exclude residual cancer in the breast without surgery. This evidence will inform the design of future trials evaluating risk-adaptive surgery for exceptional responders to NST.

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“…Regimen selection has several parameters, including type, grade, subtype, ER, PR, Her2, Ki-67, and gene expression [ 25 ]. Many types of software can help in the selection of chemotherapy regimens and predict responses, including MD Anderson chemotherapy calculators, the SVM (Support Vector Machine) Chemotherapy Response Support Calculator, and the Breast Cancer Treatment Outcome Calculator [ [67] , [68] , [69] ].…”

Section: Prevention Of Chemoresistancementioning

confidence: 99%

Breast cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?

Prihantono

2021

Annals of Medicine and Surgery

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Chemotherapy is an essential treatment for breast cancer, inducing cancer cell death. However, chemoresistance is a problem that limits the effectiveness of chemotherapy. Many factors influence chemoresistance, including drug inactivation, changes in drug targets, overexpression of ABC transporters, epithelial-to-mesenchymal transitions, apoptotic dysregulation, and cancer stem cells. The effectiveness of chemotherapy can be assessed clinically and pathologically. Clinical response evaluation is based on physical examination or imaging (mammography, ultrasonography, computed tomography scan, or magnetic resonance imaging) and includes tumor size changes after chemotherapy. Pathological response evaluation is a method based on tumor residues in histopathological preparations. We should be suspicious of chemoresistance if there are no significant changes clinically according to the Response Evaluation Criteria in Solid Tumors and World Health Organization criteria or pathological changes according to the Miller and Payne criteria, especially after 2–3 cycles of chemotherapy treatments. Chemoresistance is mostly detected after the administration of chemotherapy drugs. No reliable parameters or biomarkers can predict chemotherapy responses appropriately and effectively. Well-known parameters such as cancer type, grade, subtype, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, and MDR-1/P-gP have been used for selecting chemotherapy regimens. Some new methods for predicting chemoresistance include chemosensitivity and chemoresistance assays, multigene expressions, and positron emission tomography assays. The latest approaches are based on evaluation of molecular processes and the metabolic activity of cancer cells. Some methods for preventing chemoresistance include using the right regimen, using some combination of chemotherapy methods, conducting adequate monitoring, and using drugs that could prevent the emergence of multidrug resistance.

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Identification of breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment by an intelligent vacuum-assisted biopsy

Cited by 49 publications

References 46 publications

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

Vacuum-Assisted Breast Biopsy After Neoadjuvant Systemic Treatment for Reliable Exclusion of Residual Cancer in Breast Cancer Patients

Breast cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?

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