Multiparametric (18)F-FDG PET/MRI-based radiomics for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer Journal Article

   

Authors:	Umutlu, L.; Kirchner, J.; Bruckmann, N. M.; Morawitz, J.; Antoch, G.; Ting, S.; Bittner, A. K.; Hoffmann, O.; Häberle, L.; Ruckhäberle, E.; Catalano, O. A.; Chodyla, M.; Grueneisen, J.; Quick, H. H.; Fendler, W. P.; Rischpler, C.; Herrmann, K.; Gibbs, P.; Pinker, K.
Article Title:	Multiparametric (18)F-FDG PET/MRI-based radiomics for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer
Abstract:	Background: The aim of this study was to assess whether multiparametric18F-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 simultaneous18F-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:18F-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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords:	immunohistochemistry; adult; controlled study; human tissue; treatment response; aged; major clinical study; clinical feature; histopathology; positron emission tomography; cancer diagnosis; cancer grading; diagnostic accuracy; sensitivity and specificity; quality control; breast cancer; cohort analysis; retrospective study; automation; cancer therapy; prediction; histology; age; patient care; fluorodeoxyglucose f 18; data analysis; neoadjuvant chemotherapy; predictive value; breast biopsy; breast lesion; triple negative breast cancer; process development; diagnostic test accuracy study; elastic tissue; human epidermal growth factor receptor 2 positive breast cancer; multiparametric magnetic resonance imaging; human; female; article; radiomics; human epidermal growth factor receptor 2 negative breast cancer; social stratification; multiparametric18f-fdg pet/mri; radiomics-based prediction of pathologic complete response
Journal Title:	Cancers
Volume:	14
Issue:	7
ISSN:	2072-6694
Publisher:	MDPI
Date Published:	2022-04-01
Start Page:	1727
Language:	English
DOI:	10.3390/cancers14071727
PROVIDER:	scopus
PMCID:	PMC8996836
PUBMED:	35406499
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127412684&doi=10.3390%2fcancers14071727&partnerID=40&md5=1426664bf2f8bebcbf8d31497f3e028b
Notes:	Article -- Export Date: 2 May 2022 -- Source: Scopus

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