Jane Wang1, Tiffany Ting-Fang Shih, Ruoh-Fang Yen. 1. From the *Department of Radiology, Taipei Veterans General Hospital, Taipei; †Department of Radiology, National Taiwan University College of Medicine, Taipei; ‡Department of Midwifery and Women Health Care, National Taipei University of Nursing and Health Sciences, Taipei; §Department of Medical Imaging, Taipei City Hospital, Taipei; and Departments of ∥Medical Imaging, and ¶Nuclear Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Abstract
PURPOSE: The aim of this study was to investigate whether integrated PET/MR system can predict the treatment response to neoadjuvant chemotherapy (NAC) early in the course of breast cancer treatment. METHODS: Fourteen women with newly diagnosed invasive breast cancer (median age, 54.5 years) were recruited. Each participant underwent 2 PET/MR studies. Study 1 was pre-NAC; study 2 was early in NAC treatment (after the first or second cycle). PET parameters included SUVmax and total lesion glycolysis (TLG). MRI parameters included choline signal-to-noise ratio (ChoSNR), peak enhancement ratio (PER), and the minimum apparent diffusion coefficient (ADCmin). The pathologic response was categorized as a pathologic complete response or residual cellularity of less than 10% (group 1) and residual cellularity of 10% or greater (group 2). The accuracy of the NAC response prediction was obtained by receiver operating characteristic analysis. RESULTS: Group 1 showed a greater reduction of SUVmax (percentage change, [INCREMENT]% SUVmax, P = 0.013; area under the receiver operating characteristic curve [AUC], 0.898), TLG ([INCREMENT]%TLG, P = 0.018; AUC = 0.878), and PER ([INCREMENT]% PER, P = 0.035; AUC = 0.837) than did group 2. The ChoSNR, ADCmin, [INCREMENT]%ChoSNR, and [INCREMENT]%ADCmin did not differ significantly between the 2 groups. The hybrid markers, [INCREMENT]%SUVmax/[INCREMENT]%ADCmin (AUC = 0.976) and [INCREMENT]%TLG/[INCREMENT]%ADCmin (AUC = 0.905), showed greater accuracy in predicting NAC response than the individual PET/MR parameters. CONCLUSIONS: The PET/MR parameters can predict the NAC response early in the course of breast cancer treatment. The hybrid markers more accurately predicted treatment response than the individual PET/MR parameters.
PURPOSE: The aim of this study was to investigate whether integrated PET/MR system can predict the treatment response to neoadjuvant chemotherapy (NAC) early in the course of breast cancer treatment. METHODS: Fourteen women with newly diagnosed invasive breast cancer (median age, 54.5 years) were recruited. Each participant underwent 2 PET/MR studies. Study 1 was pre-NAC; study 2 was early in NAC treatment (after the first or second cycle). PET parameters included SUVmax and total lesion glycolysis (TLG). MRI parameters included choline signal-to-noise ratio (ChoSNR), peak enhancement ratio (PER), and the minimum apparent diffusion coefficient (ADCmin). The pathologic response was categorized as a pathologic complete response or residual cellularity of less than 10% (group 1) and residual cellularity of 10% or greater (group 2). The accuracy of the NAC response prediction was obtained by receiver operating characteristic analysis. RESULTS: Group 1 showed a greater reduction of SUVmax (percentage change, [INCREMENT]% SUVmax, P = 0.013; area under the receiver operating characteristic curve [AUC], 0.898), TLG ([INCREMENT]%TLG, P = 0.018; AUC = 0.878), and PER ([INCREMENT]% PER, P = 0.035; AUC = 0.837) than did group 2. The ChoSNR, ADCmin, [INCREMENT]%ChoSNR, and [INCREMENT]%ADCmin did not differ significantly between the 2 groups. The hybrid markers, [INCREMENT]%SUVmax/[INCREMENT]%ADCmin (AUC = 0.976) and [INCREMENT]%TLG/[INCREMENT]%ADCmin (AUC = 0.905), showed greater accuracy in predicting NAC response than the individual PET/MR parameters. CONCLUSIONS: The PET/MR parameters can predict the NAC response early in the course of breast cancer treatment. The hybrid markers more accurately predicted treatment response than the individual PET/MR parameters.
Authors: Amy M Fowler; Manoj Kumar; Leah Henze Bancroft; Kelley Salem; Jacob M Johnson; Jillian Karow; Scott B Perlman; Tyler J Bradshaw; Samuel A Hurley; Alan B McMillan; Roberta M Strigel Journal: Radiol Imaging Cancer Date: 2021-01-15