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Kapetas P, Aggarwal R, Altuwayjiri B, Pinker K, Clauser P, Helbich TH, Baltzer PAT. A model combining BI-RADS® descriptors from pre-treatment B-mode breast ultrasound with clinicopathological tumor features shows promise in the prediction of residual disease after neoadjuvant chemotherapy. Eur J Radiol 2024; 178:111649. [PMID: 39094464 DOI: 10.1016/j.ejrad.2024.111649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE To create a simple model using standard BI-RADS® descriptors from pre-treatment B-mode ultrasound (US) combined with clinicopathological tumor features, and to assess the potential of the model to predict the presence of residual tumor after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. METHOD 245 female BC patients receiving NAC between January 2017 and December 2019 were included in this retrospective study. Two breast imaging fellows independently evaluated representative B-mode tumor images from baseline US. Additional clinicopathological tumor features were retrieved. The dataset was split into 170 training and 83 validation cases. Logistic regression was used in the training set to identify independent predictors of residual disease post NAC and to create a model, whose performance was evaluated by ROC curve analysis in the validation set. The reference standard was postoperative histology to determine the absence (pathological complete response, pCR) or presence (non-pCR) of residual invasive tumor in the breast or axillary lymph nodes. RESULTS 100 patients (40.8%) achieved pCR. Logistic regression demonstrated that tumor size, microlobulated margin, spiculated margin, the presence of calcifications, the presence of edema, HER2-positive molecular subtype, and triple-negative molecular subtype were independent predictors of residual disease. A model using these parameters demonstrated an area under the ROC curve of 0.873 in the training and 0.720 in the validation set for the prediction of residual tumor post NAC. CONCLUSIONS A simple model combining standard BI-RADS® descriptors from pre-treatment B-mode breast US with clinicopathological tumor features predicts the presence of residual disease after NAC.
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Affiliation(s)
- Panagiotis Kapetas
- Department of Biomedical Imaging and Image-guided treatment, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria; Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY 10065, USA.
| | - Reena Aggarwal
- University Hospitals of Leicester, NHS Trust, LE1 5WW Leicester, Leicestershire, United Kingdom.
| | | | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY 10065, USA.
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided treatment, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided treatment, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided treatment, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
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Pfob A, Cai L, Schneeweiss A, Rauch G, Thomas B, Schaefgen B, Kuemmel S, Reimer T, Hahn M, Thill M, Blohmer JU, Hackmann J, Malter W, Bekes I, Friedrichs K, Wojcinski S, Joos S, Paepke S, Degenhardt T, Rom J, Rody A, van Mackelenbergh M, Banys-Paluchowski M, Große R, Reinisch M, Karsten MM, Sidey-Gibbons C, Wallwiener M, Golatta M, Heil J. Minimally Invasive Breast Biopsy After Neoadjuvant Systemic Treatment to Identify Breast Cancer Patients with Residual Disease for Extended Neoadjuvant Treatment: A New Concept. Ann Surg Oncol 2024; 31:957-965. [PMID: 37947974 PMCID: PMC10761434 DOI: 10.1245/s10434-023-14551-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Breast cancer patients with residual disease after neoadjuvant systemic treatment (NAST) have a worse prognosis compared with those achieving a pathologic complete response (pCR). Earlier identification of these patients might allow timely, extended neoadjuvant treatment strategies. We explored the feasibility of a vacuum-assisted biopsy (VAB) after NAST to identify patients with residual disease (ypT+ or ypN+) prior to surgery. METHODS We used data from a multicenter trial, collected at 21 study sites (NCT02948764). The trial included women with cT1-3, cN0/+ breast cancer undergoing routine post-neoadjuvant imaging (ultrasound, MRI, mammography) and VAB prior to surgery. We compared the findings of VAB and routine imaging with the histopathologic evaluation of the surgical specimen. RESULTS Of 398 patients, 34 patients with missing ypN status and 127 patients with luminal tumors were excluded. Among the remaining 237 patients, tumor cells in the VAB indicated a surgical non-pCR in all patients (73/73, positive predictive value [PPV] 100%), whereas PPV of routine imaging after NAST was 56.0% (75/134). Sensitivity of the VAB was 72.3% (73/101), and 74.3% for sensitivity of imaging (75/101). CONCLUSION Residual cancer found in a VAB specimen after NAST always corresponds to non-pCR. Residual cancer assumed on routine imaging after NAST corresponds to actual residual cancer in about half of patients. Response assessment by VAB is not safe for the exclusion of residual cancer. Response assessment by biopsies after NAST may allow studying the new concept of extended neoadjuvant treatment for patients with residual disease in future trials.
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Affiliation(s)
- André Pfob
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg University, 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, USA.
- National Center for Tumor Diseases, Heidelberg University Hospital and German Cancer Research Center, Heidelberg, Germany.
| | - Lie Cai
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Andreas Schneeweiss
- National Center for Tumor Diseases, Heidelberg University Hospital and German Cancer Research Center, Heidelberg, Germany
| | - Geraldine Rauch
- Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bettina Thomas
- Coordination Centre for Clinical Trials (KKS), University Heidelberg, Heidelberg, Germany
| | - Benedikt Schaefgen
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Sherko Kuemmel
- Breast Unit, Kliniken Essen-Mitte, Essen, Germany
- Department of Gynecology with Breast Center, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Toralf Reimer
- Department of Gynecology/Breast Unit, University Hospital Rostock, Rostock, Germany
| | - Markus Hahn
- Department of Gynecology/Breast Unit, University Hospital Tuebingen, Tübingen, Germany
| | - Marc Thill
- Department of Gynecology and Gynecological Oncology/Breast Unit, Agaplesion Markus Hospital Frankfurt, Frankfurt, Germany
| | - Jens-Uwe Blohmer
- Department of Gynecology with Breast Center, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - John Hackmann
- Department of Gynecology/Breast Unit, Marienhospital, Witten, Germany
| | - Wolfram Malter
- Department of Gynecology and Obstetrics, Medical Faculty, Breast Cancer Center, University of Cologne, Cologne, Germany
| | - Inga Bekes
- Department of Gynecology/Breast Unit, University Hospital Ulm, Ulm, Germany
| | - Kay Friedrichs
- Department of Gynecology/Breast Unit, Jerusalem Hospital Hamburg, Hamburg, Germany
| | - Sebastian Wojcinski
- Department of Gynecology and Obstetrics, Breast Cancer Center, Klinikum Bielefeld Mitte GmbH, Bielefeld, Germany
| | - Sylvie Joos
- Radiologische Allianz Hamburg, Hamburg, Germany
| | - Stefan Paepke
- Frauenklinik, Interdisziplinäres Brustzentrum des Klinikums rechts der Isar der Technischen Universität München, Munich, Germany
| | - Tom Degenhardt
- Department of Gynecology/Breast Unit, University Hospital Munich, Munich, Germany
| | - Joachim Rom
- Department of Gynecology/Breast Unit, Klinikum Frankfurt-Höchst, Frankfurt, Germany
| | - Achim Rody
- Department of Gynecology/Breast Unit, University Hospital Schleswig-Holstein, Lübeck, Germany
| | | | | | - Regina Große
- Department of Gynecology/Breast Unit, University Hospital Halle, Halle, Germany
| | | | - Maria Margarete Karsten
- Department of Gynecology with Breast Center, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Chris Sidey-Gibbons
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Markus Wallwiener
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Michael Golatta
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Breast Unit, Klinikum Sankt Elisabeth, Heidelberg, Germany
| | - Joerg Heil
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Breast Unit, Klinikum Sankt Elisabeth, Heidelberg, Germany
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Pfob A, Heil J. Artificial intelligence to de-escalate loco-regional breast cancer treatment. Breast 2023; 68:201-204. [PMID: 36842193 PMCID: PMC9988657 DOI: 10.1016/j.breast.2023.02.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/14/2023] [Accepted: 02/18/2023] [Indexed: 02/22/2023] Open
Abstract
In this review, we evaluate the potential and recent advancements in using artificial intelligence techniques to de-escalate loco-regional breast cancer therapy, with a special focus on surgical treatment after neoadjuvant systemic treatment (NAST). The increasing use and efficacy of NAST make the optimal loco-regional management of patients with pathologic complete response (pCR) a clinically relevant knowledge gap. It is hypothesized that patients with pCR do not benefit from therapeutic surgery because all tumor has already been eradicated by NAST. It is unclear, however, how residual cancer after NAST can be reliably excluded prior to surgery to identify patients eligible for omitting breast cancer surgery. Evidence from clinical trials evaluating the potential of imaging and minimally-invasive biopsies to exclude residual cancer suggests that there is a high risk of missing residual cancer. More recently, AI-based algorithms have shown promising results to reliably exclude residual cancer after NAST. This example illustrates the great potential of AI-based algorithms to further de-escalate and individualize loco-regional breast cancer treatment.
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Affiliation(s)
- André Pfob
- Department of Obstetrics & Gynecology, Heidelberg University Hospital, Germany; National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Joerg Heil
- Department of Obstetrics & Gynecology, Heidelberg University Hospital, Germany; Breast Centre Heidelberg, Klinik St. Elisabeth, Heidelberg, Germany
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Pfob A, Dubsky P. The underused potential of breast conserving therapy after neoadjuvant system treatment - Causes and solutions. Breast 2023; 67:110-115. [PMID: 36669994 PMCID: PMC9982288 DOI: 10.1016/j.breast.2023.01.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/08/2023] [Accepted: 01/15/2023] [Indexed: 01/19/2023] Open
Abstract
Breast conserving therapy (BCT), consisting of breast conserving surgery and subsequent radiotherapy, is an equivalent option to mastectomy for women with early breast cancer. Although BCT after neoadjuvant systemic treatment (NAST) has been routinely recommend by international guidelines since many years, the rate of BCT worldwide varies largely and its potential is still underused. While the rate of BCT in western countries has increased over the past decades to currently about 70%, the rate of BCT is as low as 10% in other countries. In this review, we will evaluate the underused potential of breast conservation after NAST, identify causes, and discuss possible solutions. We identified clinical and non-clinical causes for the underuse of BCT after NAST including uncertainties within the community regarding oncologic outcomes, the correct tumor localization after NAST, the management of multifocal and multicentric tumors, margin assessment, disparities of socio-economic aspects on a patient and national level, and psychological biases affecting the shared decision-making process between patients and clinicians. Possible solutions to mitigate the underuse of BCT after NAST include interdisciplinary teams that keep the whole patient pathway in mind, optimized treatment counseling and shared decision-making, and targeted financial support to alleviate disparities.
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Affiliation(s)
- André Pfob
- Department of Obstetrics & Gynecology, Heidelberg University Hospital, Germany; National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Peter Dubsky
- Breast Centre, Hirslanden Klinik St. Anna, Luzern, Switzerland,Department of Surgery and Comprehensive Cancer Center, Medical University of Vienna, Austria
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Pfob A, Sidey-Gibbons C, Rauch G, Thomas B, Schaefgen B, Kuemmel S, Reimer T, Hahn M, Thill M, Blohmer JU, Hackmann J, Malter W, Bekes I, Friedrichs K, Wojcinski S, Joos S, Paepke S, Degenhardt T, Rom J, Rody A, van Mackelenbergh M, Banys-Paluchowski M, Große R, Reinisch M, Karsten M, Golatta M, Heil J. 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 2022; 40:1903-1915. [PMID: 35108029 DOI: 10.1200/jco.21.02439] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/24/2021] [Accepted: 01/05/2022] [Indexed: 12/23/2022] Open
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 (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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Affiliation(s)
- André Pfob
- 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
| | - Chris Sidey-Gibbons
- 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
| | - Geraldine Rauch
- Institute of Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bettina Thomas
- Coordination Centre for Clinical Trials (KKS), University Heidelberg, Heidelberg, Germany
| | - Benedikt Schaefgen
- University Breast Unit, Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Toralf Reimer
- Department of Gynecology/Breast Unit, University Hospital Rostock, Rostock, Germany
| | - Markus Hahn
- Department of Gynecology/Breast Unit, University Hospital Tuebingen, Tuebingen, Germany
| | - Marc Thill
- Department of Gynecology and Gynecological Oncology/Breast Unit, Agaplesion Markus Hospital Frankfurt, Frankfurt, Germany
| | - Jens-Uwe Blohmer
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Gynecology with Breast Center, Berlin, Germany
| | - John Hackmann
- Department of Gynecology/Breast Unit, Marienhospital, Witten, Germany
| | - Wolfram Malter
- Department of Gynecology and Obstetrics, Breast Cancer Center, Medical Faculty, University of Cologne, Cologne, Germany
| | - Inga Bekes
- Department of Gynecology/Breast Unit, University Hospital Ulm, Ulm, Germany
| | - Kay Friedrichs
- Department of Gynecology/Breast Unit, Jerusalem Hospital Hamburg, Hamburg, Germany
| | - Sebastian Wojcinski
- Department of Gynecology and Obstetrics, Breast Cancer Center, Klinikum Bielefeld Mitte GmbH, Bielefeld, Germany
| | - Sylvie Joos
- Radiologische Allianz Hamburg, Hamburg, Germany
| | - Stefan Paepke
- Department of Gynecology/Breast Unit, Hospital rechts der Isar, Munich, Germany
| | - Tom Degenhardt
- Department of Gynecology/Breast Unit, University Hospital Munich, Munich, Germany
| | - Joachim Rom
- Department of Gynecology/Breast Unit, Klinikum Frankfurt-Höchst, Frankfurt, Germany
| | - Achim Rody
- Department of Gynecology/Breast Unit, University Hospital Schleswig-Holstein, Luebeck, Germany
| | | | - Maggie Banys-Paluchowski
- Department of Gynecology/Breast Unit, University Hospital Schleswig-Holstein, Luebeck, Germany
- Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Regina Große
- Department of Gynecology/Breast Unit, University Hospital Halle, Halle, Germany
| | | | - Maria Karsten
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Gynecology with Breast Center, Berlin, Germany
| | - Michael Golatta
- University Breast Unit, Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Joerg Heil
- University Breast Unit, Department of Obstetrics & Gynecology, Heidelberg University Hospital, Heidelberg, Germany
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The use of breast ultrasound for prediction of pathologic complete response in different subtypes of early breast cancer within the WSG-ADAPT subtrials. Breast 2021; 59:58-66. [PMID: 34166854 PMCID: PMC8239457 DOI: 10.1016/j.breast.2021.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 06/03/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE We assessed the value of breast ultrasound (US) performed at week 3 and 6 and at the end (EOT) of neoadjuvant therapy (NAT) for prediction of pathologic complete response (pCR, ypT0/is ypN0) in patients with HR+/HER2+, HR-/HER2-or HR-/HER2+ early breast cancer enrolled in the WSG-ADAPT subtrials. METHODS US was performed at week 3 and 6 of NAT and at EOT in 401, 517, and 553 patients, respectively. Tumors with complete or partial response by US (RECIST 1.1) were classified as responders and those with stable or progressive disease as non-responders. RESULTS pCR rate was higher in US responders than in non-responders. US tended to yield the highest positive predictive value in HR-/HER2+ (69%) and HR-/HER2-tumors (65%) at week 3, and the highest negative predictive value in HR+/HER2+ tumors at week 6 and at EOT (88.9% and 86.9%, respectively) and in HR-/HER2-tumors at EOT (87.9%). Multivariable analysis of patients with US at week 3 and 6 identified tumor subtype (HR-/HER2+ vs HR+/HER2+; odds ratio (OR) 2.77, 95%CI 1.45-5.29, and OR 4.17, 95%CI 2.26-7.68, respectively) and each 10% change in lesion dimension on US from baseline (OR 1.15, 95%CI 1.08-1.24, and OR 1.25, 95%CI 1.16-1.35, respectively) as parameters associated with pCR. CONCLUSIONS Our data support the use of week 3 and EOT US for prediction of pCR in response-guided NAT and in planning of breast-conserving surgery. Change in tumor diameter on US as a continuous variable could be a valuable alternative to categorical RECIST 1.1 criteria.
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