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

Author:

Pfob André12ORCID,Sidey-Gibbons Chris23ORCID,Rauch Geraldine4,Thomas Bettina5,Schaefgen Benedikt1ORCID,Kuemmel Sherko6ORCID,Reimer Toralf7ORCID,Hahn Markus8,Thill Marc9ORCID,Blohmer Jens-Uwe10ORCID,Hackmann John11,Malter Wolfram12ORCID,Bekes Inga13,Friedrichs Kay14ORCID,Wojcinski Sebastian15ORCID,Joos Sylvie16,Paepke Stefan17,Degenhardt Tom18,Rom Joachim19ORCID,Rody Achim20,van Mackelenbergh Marion20ORCID,Banys-Paluchowski Maggie2021,Große Regina22,Reinisch Mattea6ORCID,Karsten Maria10,Golatta Michael1ORCID,Heil Joerg1ORCID

Affiliation:

1. University Breast Unit, Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany

2. MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX

3. Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX

4. Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany

5. Coordination Centre for Clinical Trials (KKS), University Heidelberg, Heidelberg, Germany

6. Breast Unit, Kliniken Essen-Mitte, Essen, Germany

7. Department of Gynecology/Breast Unit, University Hospital Rostock, Rostock, Germany

8. Department of Gynecology/Breast Unit, University Hospital Tuebingen, Tuebingen, Germany

9. Department of Gynecology and Gynecological Oncology/Breast Unit, Agaplesion Markus Hospital Frankfurt, Frankfurt, Germany

10. Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Gynecology with Breast Center, Berlin, Germany

11. Department of Gynecology/Breast Unit, Marienhospital, Witten, Germany

12. Department of Gynecology and Obstetrics, Breast Cancer Center, Medical Faculty, University of Cologne, Cologne, Germany

13. Department of Gynecology/Breast Unit, University Hospital Ulm, Ulm, Germany

14. Department of Gynecology/Breast Unit, Jerusalem Hospital Hamburg, Hamburg, Germany

15. Department of Gynecology and Obstetrics, Breast Cancer Center, Klinikum Bielefeld Mitte GmbH, Bielefeld, Germany

16. Radiologische Allianz Hamburg, Hamburg, Germany

17. Department of Gynecology/Breast Unit, Hospital rechts der Isar, Munich, Germany

18. Department of Gynecology/Breast Unit, University Hospital Munich, Munich, Germany

19. Department of Gynecology/Breast Unit, Klinikum Frankfurt-Höchst, Frankfurt, Germany

20. Department of Gynecology/Breast Unit, University Hospital Schleswig-Holstein, Luebeck, Germany

21. Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

22. Department of Gynecology/Breast Unit, University Hospital Halle, Halle, Germany

Abstract

PURPOSENeoadjuvant 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.METHODSWe 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.RESULTSIn 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 ( z score –0.746, P = .228). FNR of the intelligent VAB was lower compared with imaging after NST, VAB alone, or combinations of both.CONCLUSIONAn 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.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

Cancer Research,Oncology

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