Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment

In the recent years, several deep learning methods for medical image segmentation have been developed for different purposes such as diagnosis, radiotherapy planning or to correlate images findings with other clinical data. However, few studies focus on longitudinal images and response assessment. To the best of our knowledge, this is the first study to date evaluating the use of automatic segmentation to obtain imaging biomarkers that can be used to assess treatment response in patients with metastatic breast cancer. Moreover, the statistical analysis of the different biomarkers shows that automatic segmentation can be successfully used for their computation, reaching similar performances compared to manual segmentation. Analysis also demonstrated the potential of the different biomarkers including novel/original ones to determine treatment response.

Abstract

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICURE

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study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images. From the automatic segmentations, four imaging biomarkers were computed and evaluated: SUL

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, Total Lesion Glycolysis (TLG), PET Bone Index (PBI) and PET Liver Index (PLI). The first network obtained a mean Dice score of 0.66 on baseline acquisitions. The second network obtained a mean Dice score of 0.58 on follow-up acquisitions. SUL

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, with a 32% decrease between baseline and follow-up, was the biomarker best able to assess patients’ response (sensitivity 87%, specificity 87%), followed by TLG (43% decrease, sensitivity 73%, specificity 81%) and PBI (8% decrease, sensitivity 69%, specificity 69%). Our networks constitute promising tools for the automatic segmentation of lesions in patients with metastatic breast cancer allowing treatment response assessment with several biomarkers. View Full-Text

Keywords: deep learning; automatic segmentation; metastatic breast cancer; imaging biomarkers; disease monitoring

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MDPI and ACS Style

Moreau, N.; Rousseau, C.; Fourcade, C.; Santini, G.; Brennan, A.; Ferrer, L.; Lacombe, M.; Guillerminet, C.; Colombié, M.; Jézéquel, P.; Campone, M.; Normand, N.; Rubeaux, M. Automatic Segmentation of Metastatic Breast Cancer Lesions on ¹⁸F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment. Cancers 2022, 14, 101. https://doi.org/10.3390/cancers14010101

AMA Style

Moreau N, Rousseau C, Fourcade C, Santini G, Brennan A, Ferrer L, Lacombe M, Guillerminet C, Colombié M, Jézéquel P, Campone M, Normand N, Rubeaux M. Automatic Segmentation of Metastatic Breast Cancer Lesions on ¹⁸F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment. Cancers. 2022; 14(1):101. https://doi.org/10.3390/cancers14010101

Chicago/Turabian Style

Moreau, Noémie, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Aislinn Brennan, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Mathilde Colombié, Pascal Jézéquel, Mario Campone, Nicolas Normand, and Mathieu Rubeaux. 2022. "Automatic Segmentation of Metastatic Breast Cancer Lesions on ¹⁸F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment" Cancers 14, no. 1: 101. https://doi.org/10.3390/cancers14010101

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Automatic Segmentation of Metastatic Breast Cancer Lesions on ¹⁸F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment

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