Siyao Du1, Si Gao1, Ruimeng Zhao1, Hongbo Liu2, Yan Wang3, Xixun Qi1, Shu Li1, Jibin Cao1, Lina Zhang4. 1. Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China. 2. Department of Epidemiology and Medical Statistics, School of Public Health, China Medical University, No. 77, Puhe Road, Shenyang North Xin Area, Shenyang City, 110122, Liaoning Province, China. 3. Department of Pathology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China. 4. Department of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China. zhanglnda@163.com.
Abstract
OBJECTIVES: To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. METHODS: From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. RESULTS: In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. CONCLUSIONS: Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. KEY POINTS: • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.
OBJECTIVES: To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. METHODS: From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. RESULTS: In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. CONCLUSIONS: Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. KEY POINTS: • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.
Authors: Giorgio Conte; Lorenzo Preda; Emilia Cocorocchio; Sara Raimondi; Caterina Giannitto; Marta Minotti; Francesca De Piano; Giuseppe Petralia; Pier Francesco Ferrucci; Massimo Bellomi Journal: Eur Radiol Date: 2017-03-29 Impact factor: 5.315
Authors: Patricia Cortazar; Lijun Zhang; Michael Untch; Keyur Mehta; Joseph P Costantino; Norman Wolmark; Hervé Bonnefoi; David Cameron; Luca Gianni; Pinuccia Valagussa; Sandra M Swain; Tatiana Prowell; Sibylle Loibl; D Lawrence Wickerham; Jan Bogaerts; Jose Baselga; Charles Perou; Gideon Blumenthal; Jens Blohmer; Eleftherios P Mamounas; Jonas Bergh; Vladimir Semiglazov; Robert Justice; Holger Eidtmann; Soonmyung Paik; Martine Piccart; Rajeshwari Sridhara; Peter A Fasching; Leen Slaets; Shenghui Tang; Bernd Gerber; Charles E Geyer; Richard Pazdur; Nina Ditsch; Priya Rastogi; Wolfgang Eiermann; Gunter von Minckwitz Journal: Lancet Date: 2014-02-14 Impact factor: 79.321
Authors: E A Eisenhauer; P Therasse; J Bogaerts; L H Schwartz; D Sargent; R Ford; J Dancey; S Arbuck; S Gwyther; M Mooney; L Rubinstein; L Shankar; L Dodd; R Kaplan; D Lacombe; J Verweij Journal: Eur J Cancer Date: 2009-01 Impact factor: 9.162
Authors: Anwar R Padhani; Carmel Hayes; Laura Assersohn; Trevor Powles; Andreas Makris; John Suckling; Martin O Leach; Janet E Husband Journal: Radiology Date: 2006-03-16 Impact factor: 11.105
Authors: Xia Li; Lori R Arlinghaus; Gregory D Ayers; A Bapsi Chakravarthy; Richard G Abramson; Vandana G Abramson; Nkiruka Atuegwu; Jaime Farley; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie Means-Powell; Ana M Grau; Melinda Sanders; Sandeep R Bhave; Thomas E Yankeelov Journal: Magn Reson Med Date: 2013-05-09 Impact factor: 4.668
Authors: Waqar Haque; Vivek Verma; Sandra Hatch; V Suzanne Klimberg; E Brian Butler; Bin S Teh Journal: Breast Cancer Res Treat Date: 2018-04-24 Impact factor: 4.872