Lidija Antunovic1, Rita De Sanctis2, Luca Cozzi3, Margarita Kirienko3, Andrea Sagona4, Rosalba Torrisi2, Corrado Tinterri4, Armando Santoro2, Arturo Chiti5,3, Renata Zelic6, Martina Sollini3. 1. Department of Nuclear Medicine, Humanitas Clinical and Research Center- IRCCS, via Manzoni 56, 20089, Rozzano, Milan, Italy. lidija.antunovic@humanitas.it. 2. Department of Medical Oncology and Hematology, Humanitas Clinical and Research Center- IRCCS, Rozzano, Milan, Italy. 3. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy. 4. Breast Surgery Department, Humanitas Clinical and Research Center- IRCCS, Rozzano, Milan, Italy. 5. Department of Nuclear Medicine, Humanitas Clinical and Research Center- IRCCS, via Manzoni 56, 20089, Rozzano, Milan, Italy. 6. Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
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.
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 cancerpatients.
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