Intelligent Vacuum-Assisted Biopsy to Identify Breast Cancer Patients With Pathologic Complete Response (ypT0 and ypN0) After Neoadjuvant Systemic Treatment for Omission of Breast and Axillary Surgery.

André Pfob, Chris Sidey-Gibbons, Geraldine Rauch, Bettina Thomas, Benedikt Schaefgen, Sherko Kuemmel, Toralf Reimer, Markus Hahn, Marc Thill, Jens-Uwe Blohmer, John Hackmann, Wolfram Malter, Inga Bekes, Kay Friedrichs, Sebastian Wojcinski, Sylvie Joos, Stefan Paepke, Tom Degenhardt, Joachim Rom, Achim Rody, Marion van Mackelenbergh, Maggie Banys-Paluchowski, Regina Große, Mattea Reinisch, Maria Karsten, Michael Golatta, Joerg Heil

Journal: Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2022;40(17):1903-1915

PMID: 35108029

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Abstract

PURPOSE

Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in 40%-70% of women with breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, nonsurgical approaches, including imaging or vacuum-assisted biopsy (VAB), were not able to accurately identify patients without residual cancer in the breast or axilla. We evaluated the feasibility of a machine learning algorithm (intelligent VAB) to identify exceptional responders to NST.

METHODS

We trained, tested, and validated a machine learning algorithm using patient, imaging, tumor, and VAB variables to detect residual cancer after NST (ypT+ or in situ or ypN+) before surgery. We used data from 318 women with cT1-3, cN0 or +, human epidermal growth factor receptor 2-positive, triple-negative, or high-proliferative Luminal B-like breast cancer who underwent VAB before surgery (ClinicalTrials.gov identifier: NCT02948764, RESPONDER trial). We used 10-fold cross-validation to train and test the algorithm, which was then externally validated using data of an independent trial (ClinicalTrials.gov identifier: NCT02575612). We compared findings with the histopathologic evaluation of the surgical specimen. We considered false-negative rate (FNR) and specificity to be the main outcomes.

RESULTS

In the development set (n = 318) and external validation set (n = 45), the intelligent VAB showed an FNR of 0.0%-5.2%, a specificity of 37.5%-40.0%, and an area under the receiver operating characteristic curve of 0.91-0.92 to detect residual cancer (ypT+ or in situ or ypN+) after NST. Spiegelhalter's Z confirmed a well-calibrated model ( score -0.746, = .228). FNR of the intelligent VAB was lower compared with imaging after NST, VAB alone, or combinations of both.

CONCLUSION

An intelligent VAB algorithm can reliably exclude residual cancer after NST. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials.

Address: University Breast Unit, Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany.; MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX.; MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX.; Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX.; Institute of Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.; Coordination Centre for Clinical Trials (KKS), University Heidelberg, Heidelberg, Germany.; University Breast Unit, Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany.; Breast Unit, Kliniken Essen-Mitte, Essen, Germany.; Department of Gynecology/Breast Unit, University Hospital Rostock, Rostock, Germany.; Department of Gynecology/Breast Unit, University Hospital Tuebingen, Tuebingen, Germany.; Department of Gynecology and Gynecological Oncology/Breast Unit, Agaplesion Markus Hospital Frankfurt, Frankfurt, Germany.; Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Gynecology with Breast Center, Berlin, Germany.; Department of Gynecology/Breast Unit, Marienhospital, Witten, Germany.; Department of Gynecology and Obstetrics, Breast Cancer Center, Medical Faculty, University of Cologne, Cologne, Germany.; Department of Gynecology/Breast Unit, University Hospital Ulm, Ulm, Germany.; Department of Gynecology/Breast Unit, Jerusalem Hospital Hamburg, Hamburg, Germany.; Department of Gynecology and Obstetrics, Breast Cancer Center, Klinikum Bielefeld Mitte GmbH, Bielefeld, Germany.; Radiologische Allianz Hamburg, Hamburg, Germany.; Department of Gynecology/Breast Unit, Hospital rechts der Isar, Munich, Germany.; Department of Gynecology/Breast Unit, University Hospital Munich, Munich, Germany.; Department of Gynecology/Breast Unit, Klinikum Frankfurt-Höchst, Frankfurt, Germany.; Department of Gynecology/Breast Unit, University Hospital Schleswig-Holstein, Luebeck, Germany.; Department of Gynecology/Breast Unit, University Hospital Schleswig-Holstein, Luebeck, Germany.; Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.; Department of Gynecology/Breast Unit, University Hospital Halle, Halle, Germany.

DOI: 10.1200/JCO.21.02439

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MeSH Terms: Axilla, Breast Neoplasms, Female, Humans, Image-Guided Biopsy, Neoadjuvant Therapy, Neoplasm, Residual