Faranak Aghaei1, Maxine Tan1, Alan B Hollingsworth2, Bin Zheng3. 1. School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA. 2. Mercy Women's Center, Mercy Health Center, Oklahoma City, Oklahoma, USA. 3. School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA. Bin.Zheng-1@ou.edu.
Abstract
PURPOSE: To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. MATERIALS AND METHODS: A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. RESULTS: From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05). CONCLUSION: This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.
PURPOSE: To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. MATERIALS AND METHODS: A dataset involving breast MR images acquired from 151 cancerpatients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. RESULTS: From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05). CONCLUSION: This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.
Keywords:
assessment of breast cancer prognosis; bilateral asymmetry of parenchyma breast MR enhancement; dynamic contrast-enhanced breast magnetic resonance imaging; quantitative image feature analysis; tumor response to neoadjuvant chemotherapy
Authors: Valencia King; Jennifer D Brooks; Jonine L Bernstein; Anne S Reiner; Malcolm C Pike; Elizabeth A Morris Journal: Radiology Date: 2011-04-14 Impact factor: 11.105
Authors: Michael L Marinovich; Nehmat Houssami; Petra Macaskill; Francesco Sardanelli; Les Irwig; Eleftherios P Mamounas; Gunter von Minckwitz; Meagan E Brennan; Stefano Ciatto Journal: J Natl Cancer Inst Date: 2013-01-07 Impact factor: 13.506
Authors: J A van der Hage; C J van de Velde; J P Julien; M Tubiana-Hulin; C Vandervelden; L Duchateau Journal: J Clin Oncol Date: 2001-11-15 Impact factor: 44.544
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: B Fisher; J Bryant; N Wolmark; E Mamounas; A Brown; E R Fisher; D L Wickerham; M Begovic; A DeCillis; A Robidoux; R G Margolese; A B Cruz; J L Hoehn; A W Lees; N V Dimitrov; H D Bear Journal: J Clin Oncol Date: 1998-08 Impact factor: 44.544
Authors: Lindsey J Graham; Matthew P Shupe; Erika J Schneble; Frederick L Flynt; Michael N Clemenshaw; Aaron D Kirkpatrick; Chris Gallagher; Aviram Nissan; Leonard Henry; Alexander Stojadinovic; George E Peoples; Nathan M Shumway Journal: J Cancer Date: 2014-01-05 Impact factor: 4.207
Authors: Morteza Heidari; Seyedehnafiseh Mirniaharikandehei; Wei Liu; Alan B Hollingsworth; Hong Liu; Bin Zheng Journal: IEEE Trans Med Imaging Date: 2019-10-09 Impact factor: 10.048
Authors: Gopichandh Danala; Theresa Thai; Camille C Gunderson; Katherine M Moxley; Kathleen Moore; Robert S Mannel; Hong Liu; Bin Zheng; Yuchen Qiu Journal: Acad Radiol Date: 2017-05-26 Impact factor: 3.173
Authors: Abolfazl Zargari; Yue Du; Morteza Heidari; Theresa C Thai; Camille C Gunderson; Kathleen Moore; Robert S Mannel; Hong Liu; Bin Zheng; Yuchen Qiu Journal: Phys Med Biol Date: 2018-08-06 Impact factor: 3.609