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

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

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  • Media type: E-Article
  • Title: 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
  • Contributor: Pfob, André; Sidey-Gibbons, Chris; Rauch, Geraldine; Thomas, Bettina; Schaefgen, Benedikt; Kuemmel, Sherko; Reimer, Toralf; Hahn, Markus; Thill, Marc; Blohmer, Jens-Uwe; Hackmann, John; Malter, Wolfram; Bekes, Inga; Friedrichs, Kay; Wojcinski, Sebastian; Joos, Sylvie; Paepke, Stefan; Degenhardt, Tom; Rom, Joachim; Rody, Achim; van Mackelenbergh, Marion; Banys-Paluchowski, Maggie; Große, Regina; Reinisch, Mattea; [...]
  • imprint: American Society of Clinical Oncology (ASCO), 2022
  • Published in: Journal of Clinical Oncology
  • Language: English
  • DOI: 10.1200/jco.21.02439
  • ISSN: 0732-183X; 1527-7755
  • Origination:
  • Footnote:
  • Description: <jats:sec><jats:title>PURPOSE</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>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 ( z score –0.746, P = .228). FNR of the intelligent VAB was lower compared with imaging after NST, VAB alone, or combinations of both.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSION</jats:title><jats:p>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.</jats:p></jats:sec>
  • Access State: Open Access