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

被引:40
|
作者
Pfob, Andre [1 ,2 ]
Sidey-Gibbons, Chris [2 ,3 ]
Rauch, Geraldine [4 ,5 ,6 ]
Thomas, Bettina [7 ]
Schaefgen, Benedikt [1 ]
Kuemmel, Sherko [8 ]
Reimer, Toralf [9 ]
Hahn, Markus [10 ]
Thill, Marc [11 ]
Blohmer, Jens-Uwe [5 ,12 ,13 ]
Hackmann, John [14 ]
Malter, Wolfram [15 ]
Bekes, Inga [16 ]
Friedrichs, Kay [17 ]
Wojcinski, Sebastian [18 ]
Joos, Sylvie [19 ]
Paepke, Stefan [20 ]
Degenhardt, Tom [21 ]
Rom, Joachim [22 ]
Rody, Achim [23 ]
van Mackelenbergh, Marion [23 ]
Banys-Paluchowski, Maggie [23 ,24 ]
Grosse, Regina [25 ]
Reinisch, Mattea [8 ]
Karsten, Maria [5 ,12 ,13 ]
Golatta, Michael [1 ]
Heil, Joerg [1 ]
机构
[1] Heidelberg Univ Hosp, Univ Breast Unit, Dept Obstet & Gynecol, Heidelberg, Germany
[2] Univ Texas MD Anderson Canc Ctr, MD Anderson Ctr INSPiRED Canc Care Integrated Sys, Houston, TX 77030 USA
[3] Univ Texas MD Anderson Canc Ctr, Dept Symptom Res, Houston, TX 77030 USA
[4] Charite Univ Med Berlin, Inst Biometry & Clin Epidemiol, Berlin, Germany
[5] Free Univ Berlin, Berlin, Germany
[6] Humboldt Univ, Berlin, Germany
[7] Heidelberg Univ, Coordinat Ctr Clin Trials KKS, Heidelberg, Germany
[8] Kliniken Essen Mitte, Breast Unit, Essen, Germany
[9] Univ Hosp Rostock, Dept Gynecol, Breast Unit, Rostock, Germany
[10] Univ Hosp Tuebingen, Dept Gynecol, Breast Unit, Tubingen, Germany
[11] Agaplesion Markus Hosp Frankfurt, Dept Gynecol & Gynecol Oncol, Breast Unit, Frankfurt, Germany
[12] Charite Univ Med Berlin, Berlin, Germany
[13] Humboldt Univ, Dept Gynecol, Breast Ctr, Berlin, Germany
[14] Marien Hosp, Dept Gynecol, Breast Unit, Witten, Germany
[15] Univ Cologne, Med Fac, Breast Canc Ctr, Dept Gynecol & Obstet, Cologne, Germany
[16] Univ Hosp Ulm, Dept Gynecol, Breast Unit, Ulm, Germany
[17] Jerusalem Hosp Hamburg, Dept Gynecol, Breast Unit, Hamburg, Germany
[18] Klinikum Bielefeld Mitte GmbH, Dept Gynecol & Obstet, Breast Canc Ctr, Bielefeld, Germany
[19] Radiol Allianz Hamburg, Hamburg, Germany
[20] Hosp Rechts Isar, Dept Gynecol, Breast Unit, Munich, Germany
[21] Univ Hosp Munich, Dept Gynecol, Breast Unit, Munich, Germany
[22] Klinikum Frankfurt Hochst, Dept Gynecol, Breast Unit, Frankfurt, Germany
[23] Univ Hosp Schleswig Holstein, Dept Gynecol, Breast Unit, Lubeck, Germany
[24] Heinrich Heine Univ Dusseldorf, Med Fac, Dusseldorf, Germany
[25] Univ Hosp Halle, Dept Gynecol, Breast Unit, Halle, Germany
关键词
PREDICTION; THERAPY; CHEMOTHERAPY; MANAGEMENT;
D O I
10.1200/JCO.21.02439
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
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收藏
页码:1903 / +
页数:14
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