PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-26

AUTHORS

Lidija Antunovic, Rita De Sanctis, Luca Cozzi, Margarita Kirienko, Andrea Sagona, Rosalba Torrisi, Corrado Tinterri, Armando Santoro, Arturo Chiti, Renata Zelic, Martina Sollini

ABSTRACT

PURPOSE: To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. METHODS: Seventy-nine patients who had undergone pretreatment staging 18F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models. RESULTS: Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint. CONCLUSIONS: Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients. More... »

PAGES

1-10

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00259-019-04313-8

DOI

http://dx.doi.org/10.1007/s00259-019-04313-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113006083

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30915523


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