Rajat Thawani1, Lina Gao2, Ajay Mohinani3, Alina Tudorica4, Xin Li5, Zahi Mitri6, Wei Huang5. 1. Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US. thawani@ohsu.edu. 2. Biostatistics Shared Resource, Knight Cancer Institute, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US. 3. Department of Internal Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US. 4. Department of Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US. 5. Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, 97239, Portland, OR, US. 6. Division of Hematology and Oncology, Knight Cancer Institute, Oregon Health & Science University, Sam Jackson Park Road, OCH14110, 97239, Portland, OR, US.
Abstract
INTRODUCTION: Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. METHODS: Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. RESULTS: Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. CONCLUSION: Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.
INTRODUCTION: Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. METHODS: Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. RESULTS: Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. CONCLUSION: Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.
Authors: E Montagna; V Bagnardi; N Rotmensz; G Viale; G Renne; G Cancello; A Balduzzi; E Scarano; P Veronesi; A Luini; S Zurrida; S Monti; M G Mastropasqua; L Bottiglieri; A Goldhirsch; M Colleoni Journal: Ann Oncol Date: 2011-04-27 Impact factor: 32.976
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: M O Leach; B Morgan; P S Tofts; D L Buckley; W Huang; M A Horsfield; T L Chenevert; D J Collins; A Jackson; D Lomas; B Whitcher; L Clarke; R Plummer; I Judson; R Jones; R Alonzi; T Brunner; D M Koh; P Murphy; J C Waterton; G Parker; M J Graves; T W J Scheenen; T W Redpath; M Orton; G Karczmar; H Huisman; J Barentsz; A Padhani Journal: Eur Radiol Date: 2012-05-07 Impact factor: 5.315
Authors: Jacqueline S Jeruss; Elizabeth A Mittendorf; Susan L Tucker; Ana M Gonzalez-Angulo; Thomas A Buchholz; Aysegul A Sahin; Janice N Cormier; Aman U Buzdar; Gabriel N Hortobagyi; Kelly K Hunt Journal: J Clin Oncol Date: 2007-12-03 Impact factor: 44.544