Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
Background: The aim of this study was to assess whether multiparametric <sup>18</sup>F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of...
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2022-03-01
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author | Lale Umutlu Julian Kirchner Nils-Martin Bruckmann Janna Morawitz Gerald Antoch Saskia Ting Ann-Kathrin Bittner Oliver Hoffmann Lena Häberle Eugen Ruckhäberle Onofrio Antonio Catalano Michal Chodyla Johannes Grueneisen Harald H. Quick Wolfgang P. Fendler Christoph Rischpler Ken Herrmann Peter Gibbs Katja Pinker |
author_facet | Lale Umutlu Julian Kirchner Nils-Martin Bruckmann Janna Morawitz Gerald Antoch Saskia Ting Ann-Kathrin Bittner Oliver Hoffmann Lena Häberle Eugen Ruckhäberle Onofrio Antonio Catalano Michal Chodyla Johannes Grueneisen Harald H. Quick Wolfgang P. Fendler Christoph Rischpler Ken Herrmann Peter Gibbs Katja Pinker |
author_sort | Lale Umutlu |
collection | DOAJ |
description | Background: The aim of this study was to assess whether multiparametric <sup>18</sup>F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of 73 female patients (mean age 49 years; range 27–77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous <sup>18</sup>F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model. Results: The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2− group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets. Conclusion: <sup>18</sup>F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2− receptor status. |
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spelling | doaj.art-1f92b4840d364cf7a627c7b5778517022023-11-30T23:01:13ZengMDPI AGCancers2072-66942022-03-01147172710.3390/cancers14071727Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast CancerLale Umutlu0Julian Kirchner1Nils-Martin Bruckmann2Janna Morawitz3Gerald Antoch4Saskia Ting5Ann-Kathrin Bittner6Oliver Hoffmann7Lena Häberle8Eugen Ruckhäberle9Onofrio Antonio Catalano10Michal Chodyla11Johannes Grueneisen12Harald H. Quick13Wolfgang P. Fendler14Christoph Rischpler15Ken Herrmann16Peter Gibbs17Katja Pinker18Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, GermanyDepartment of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, GermanyDepartment of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, GermanyDepartment of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, GermanyInstitute of Pathology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen and the German Cancer Consortium (DKTK), 45147 Essen, GermanyDepartment Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, GermanyDepartment Gynecology and Obstetrics, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, GermanyInstitute of Pathology, Medical Faculty, Heinrich-Heine-University and University Hospital Duesseldorf, 40225 Duesseldorf, GermanyDepartment of Gynecology, University Dusseldorf, Medical Faculty, 40225 Dusseldorf, GermanyDivision of Abdominal Radiology, Massachusetts General Hospital, Boston, MA 02129, USADepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, GermanyDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, GermanyErwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, 45141 Essen, GermanyDepartment of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, 45147 Essen, GermanyDepartment of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, 45147 Essen, GermanyDepartment of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, 45147 Essen, GermanyDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USADepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USABackground: The aim of this study was to assess whether multiparametric <sup>18</sup>F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of 73 female patients (mean age 49 years; range 27–77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous <sup>18</sup>F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model. Results: The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2− group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets. Conclusion: <sup>18</sup>F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2− receptor status.https://www.mdpi.com/2072-6694/14/7/1727multiparametric <sup>18</sup>F-FDG PET/MRIradiomicsbreast cancerradiomics-based prediction of pathologic complete response |
spellingShingle | Lale Umutlu Julian Kirchner Nils-Martin Bruckmann Janna Morawitz Gerald Antoch Saskia Ting Ann-Kathrin Bittner Oliver Hoffmann Lena Häberle Eugen Ruckhäberle Onofrio Antonio Catalano Michal Chodyla Johannes Grueneisen Harald H. Quick Wolfgang P. Fendler Christoph Rischpler Ken Herrmann Peter Gibbs Katja Pinker Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Cancers multiparametric <sup>18</sup>F-FDG PET/MRI radiomics breast cancer radiomics-based prediction of pathologic complete response |
title | Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer |
title_full | Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer |
title_fullStr | Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer |
title_full_unstemmed | Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer |
title_short | Multiparametric <sup>18</sup>F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer |
title_sort | multiparametric sup 18 sup f fdg pet mri based radiomics for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer |
topic | multiparametric <sup>18</sup>F-FDG PET/MRI radiomics breast cancer radiomics-based prediction of pathologic complete response |
url | https://www.mdpi.com/2072-6694/14/7/1727 |
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