Universität zu Köln

Pfob, Andre, Sidey-Gibbons, Chris, Rauch, Geraldine, Thomas, Bettina, Schaefgen, Benedikt, Kuemmel, Sherko ORCID: 0000-0001-9355-494X, Reimer, Toralf ORCID: 0000-0003-3014-370X, Hahn, Markus, Thill, Marc, Blohmer, Jens-Uwe ORCID: 0000-0002-7969-250X, 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, Karsten, Maria, Golatta, Michael and Heil, Joerg (2022). 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. J. Clin. Oncol., 40 (17). S. 1903 - 1917. PHILADELPHIA: LIPPINCOTT WILLIAMS & WILKINS. ISSN 1527-7755

Full text not available from this repository.

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: , 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: ). 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 (z score -0.746, P = .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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Pfob, Andre UNSPECIFIED UNSPECIFIED UNSPECIFIED Sidey-Gibbons, Chris UNSPECIFIED UNSPECIFIED UNSPECIFIED Rauch, Geraldine UNSPECIFIED UNSPECIFIED UNSPECIFIED Thomas, Bettina UNSPECIFIED UNSPECIFIED UNSPECIFIED Schaefgen, Benedikt UNSPECIFIED UNSPECIFIED UNSPECIFIED Kuemmel, Sherko UNSPECIFIED orcid.org/0000-0001-9355-494X UNSPECIFIED Reimer, Toralf UNSPECIFIED orcid.org/0000-0003-3014-370X UNSPECIFIED Hahn, Markus UNSPECIFIED UNSPECIFIED UNSPECIFIED Thill, Marc UNSPECIFIED UNSPECIFIED UNSPECIFIED Blohmer, Jens-Uwe UNSPECIFIED orcid.org/0000-0002-7969-250X UNSPECIFIED Hackmann, John UNSPECIFIED UNSPECIFIED UNSPECIFIED Malter, Wolfram UNSPECIFIED UNSPECIFIED UNSPECIFIED Bekes, Inga UNSPECIFIED UNSPECIFIED UNSPECIFIED Friedrichs, Kay UNSPECIFIED UNSPECIFIED UNSPECIFIED Wojcinski, Sebastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Joos, Sylvie UNSPECIFIED UNSPECIFIED UNSPECIFIED Paepke, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Degenhardt, Tom UNSPECIFIED UNSPECIFIED UNSPECIFIED Rom, Joachim UNSPECIFIED UNSPECIFIED UNSPECIFIED Rody, Achim UNSPECIFIED UNSPECIFIED UNSPECIFIED van Mackelenbergh, Marion UNSPECIFIED UNSPECIFIED UNSPECIFIED Banys-Paluchowski, Maggie UNSPECIFIED UNSPECIFIED UNSPECIFIED Grosse, Regina UNSPECIFIED UNSPECIFIED UNSPECIFIED Reinisch, Mattea UNSPECIFIED UNSPECIFIED UNSPECIFIED Karsten, Maria UNSPECIFIED UNSPECIFIED UNSPECIFIED Golatta, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Heil, Joerg UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-696459
DOI:	10.1200/JCO.21.02439
Journal or Publication Title:	J. Clin. Oncol.
Volume:	40
Number:	17
Page Range:	S. 1903 - 1917
Date:	2022
Publisher:	LIPPINCOTT WILLIAMS & WILKINS
Place of Publication:	PHILADELPHIA
ISSN:	1527-7755
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PREDICTION; CHEMOTHERAPY Multiple languages Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/69645