Ying-Shi Sun1, Ying-Jian He2, Jie Li3, Yan-Ling Li3, Xiao-Ting Li3, Ai-Ping Lu4, Zhao-Qing Fan2, Kun Cao3, Tao Ouyang5. 1. From Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, 100142, China. Electronic address: sys27@163.com. 2. From Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Breast Center, Peking University Cancer Hospital & Institute, Beijing, 100142, China. 3. From Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, 100142, China. 4. From Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, 100142, China. 5. From Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Breast Center, Peking University Cancer Hospital & Institute, Beijing, 100142, China. Electronic address: ouyanghongtao@263.net.
Abstract
OBJECTIVE: This study proposed to establish a predictive model using dynamic enhanced MRI multi-parameters for early predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS: In this prospective cohort study, 170 breast cancer patients treated with NAC were enrolled and were randomly grouped into training sample (136 patients) and validation sample (34 patients). DCE-MRI parameters achieved at the end of the first cycle of NAC were screened to establish the predictive model by using multivariate logistic regression model according to pCR status. Receiver operating characteristic curves were conducted to assess the predictive capability. The association between MRI-predicted pCR and actual pCR in survival outcomes was estimated by using the Kaplan-Meier method with log-rank test. RESULTS: Multivariate analysis showed ΔAreamax and ΔSlopemax were independent predictors for pCR, odds ratio were 0.939 (95%CI, 0.915 to 0.964), and 0.966 (95%CI, 0.947 to 0.986), respectively. A predictive model was established using training sample as "Y = -0.063*ΔAreamax - 0.034*ΔSlopemax", a cut-off point of 3.0 was determined. The AUC for training and validation sample were 0.931 (95%CI, 0.890-0.971) and 0.971 (95%CI, 0.923-1.000), respectively. MRI-predicted pCR patients showed similar RFS (p = 0.347), DDFS (p = 0.25) and OS (p = 0.423) with pCR patients. CONCLUSION: The multi-parameter MRI model can be potentially used for early prediction of pCR status at the end of the first NAC cycle, which might allow timely regimen refinement before definitive surgical treatment.
OBJECTIVE: This study proposed to establish a predictive model using dynamic enhanced MRI multi-parameters for early predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS: In this prospective cohort study, 170 breast cancerpatients treated with NAC were enrolled and were randomly grouped into training sample (136 patients) and validation sample (34 patients). DCE-MRI parameters achieved at the end of the first cycle of NAC were screened to establish the predictive model by using multivariate logistic regression model according to pCR status. Receiver operating characteristic curves were conducted to assess the predictive capability. The association between MRI-predicted pCR and actual pCR in survival outcomes was estimated by using the Kaplan-Meier method with log-rank test. RESULTS: Multivariate analysis showed ΔAreamax and ΔSlopemax were independent predictors for pCR, odds ratio were 0.939 (95%CI, 0.915 to 0.964), and 0.966 (95%CI, 0.947 to 0.986), respectively. A predictive model was established using training sample as "Y = -0.063*ΔAreamax - 0.034*ΔSlopemax", a cut-off point of 3.0 was determined. The AUC for training and validation sample were 0.931 (95%CI, 0.890-0.971) and 0.971 (95%CI, 0.923-1.000), respectively. MRI-predicted pCR patients showed similar RFS (p = 0.347), DDFS (p = 0.25) and OS (p = 0.423) with pCR patients. CONCLUSION: The multi-parameter MRI model can be potentially used for early prediction of pCR status at the end of the first NAC cycle, which might allow timely regimen refinement before definitive surgical treatment.
Authors: Karen Drukker; Alexandra Edwards; Christopher Doyle; John Papaioannou; Kirti Kulkarni; Maryellen L Giger Journal: J Med Imaging (Bellingham) Date: 2019-09-30
Authors: Michael J Flister; Shirng-Wern Tsaih; Alexander Stoddard; Cody Plasterer; Jaidip Jagtap; Abdul K Parchur; Gayatri Sharma; Anthony R Prisco; Angela Lemke; Dana Murphy; Mona Al-Gizawiy; Michael Straza; Sophia Ran; Aron M Geurts; Melinda R Dwinell; Andrew S Greene; Carmen Bergom; Peter S LaViolette; Amit Joshi Journal: Breast Cancer Res Treat Date: 2017-05-31 Impact factor: 4.872