BACKGROUND: Functional diffusion mapping (fDM) is a cancer imaging technique that uses voxel-wise changes in apparent diffusion coefficients (ADC) to evaluate response to treatment. Despite promising initial results, uncertainty in image registration remains the largest barrier to widespread clinical application. The current study introduces a probabilistic approach to fDM quantification to overcome some of these limitations. METHODS: A total of 143 patients with newly diagnosed glioblastoma who were undergoing standard radiochemotherapy were enrolled in this retrospective study. Traditional and probabilistic fDMs were calculated using ADC maps acquired before and after therapy. Probabilistic fDMs were calculated by applying random, finite translational, and rotational perturbations to both pre-and posttherapy ADC maps, then repeating calculation of fDMs reflecting changes after treatment, resulting in probabilistic fDMs showing the voxel-wise probability of fDM classification. Probabilistic fDMs were then compared with traditional fDMs in their ability to predict progression-free survival (PFS) and overall survival (OS). RESULTS: Probabilistic fDMs applied to patients with newly diagnosed glioblastoma treated with radiochemotherapy demonstrated shortened PFS and OS among patients with a large volume of tumor with decreasing ADC evaluated at the posttreatment time with respect to the baseline scans. Alternatively, patients with a large volume of tumor with increasing ADC evaluated at the posttreatment time with respect to baseline scans were more likely to progress later and live longer. Probabilistic fDMs performed better than traditional fDMs at predicting 12-month PFS and 24-month OS with use of receiver-operator characteristic analysis. Univariate log-rank analysis on Kaplan-Meier data also revealed that probabilistic fDMs could better separate patients on the basis of PFS and OS, compared with traditional fDMs. CONCLUSIONS: Results suggest that probabilistic fDMs are a more predictive biomarker in terms of 12-month PFS and 24-month OS in newly diagnosed glioblastoma, compared with traditional fDM analysis.
BACKGROUND: Functional diffusion mapping (fDM) is a cancer imaging technique that uses voxel-wise changes in apparent diffusion coefficients (ADC) to evaluate response to treatment. Despite promising initial results, uncertainty in image registration remains the largest barrier to widespread clinical application. The current study introduces a probabilistic approach to fDM quantification to overcome some of these limitations. METHODS: A total of 143 patients with newly diagnosed glioblastoma who were undergoing standard radiochemotherapy were enrolled in this retrospective study. Traditional and probabilistic fDMs were calculated using ADC maps acquired before and after therapy. Probabilistic fDMs were calculated by applying random, finite translational, and rotational perturbations to both pre-and posttherapy ADC maps, then repeating calculation of fDMs reflecting changes after treatment, resulting in probabilistic fDMs showing the voxel-wise probability of fDM classification. Probabilistic fDMs were then compared with traditional fDMs in their ability to predict progression-free survival (PFS) and overall survival (OS). RESULTS: Probabilistic fDMs applied to patients with newly diagnosed glioblastoma treated with radiochemotherapy demonstrated shortened PFS and OS among patients with a large volume of tumor with decreasing ADC evaluated at the posttreatment time with respect to the baseline scans. Alternatively, patients with a large volume of tumor with increasing ADC evaluated at the posttreatment time with respect to baseline scans were more likely to progress later and live longer. Probabilistic fDMs performed better than traditional fDMs at predicting 12-month PFS and 24-month OS with use of receiver-operator characteristic analysis. Univariate log-rank analysis on Kaplan-Meier data also revealed that probabilistic fDMs could better separate patients on the basis of PFS and OS, compared with traditional fDMs. CONCLUSIONS: Results suggest that probabilistic fDMs are a more predictive biomarker in terms of 12-month PFS and 24-month OS in newly diagnosed glioblastoma, compared with traditional fDM analysis.
Authors: Benjamin M Ellingson; Timothy F Cloughesy; Taryar Zaw; Albert Lai; Phioanh L Nghiemphu; Robert Harris; Shadi Lalezari; Naveed Wagle; Kourosh M Naeini; Jose Carrillo; Linda M Liau; Whitney B Pope Journal: Neuro Oncol Date: 2012-01-22 Impact factor: 12.300
Authors: F Earnest; P J Kelly; B W Scheithauer; B A Kall; T L Cascino; R L Ehman; G S Forbes; P L Axley Journal: Radiology Date: 1988-03 Impact factor: 11.105
Authors: M Brant-Zawadzki; D Norman; T H Newton; W M Kelly; B Kjos; C M Mills; W Dillon; D Sobel; L E Crooks Journal: Radiology Date: 1984-07 Impact factor: 11.105
Authors: T L Chenevert; L D Stegman; J M Taylor; P L Robertson; H S Greenberg; A Rehemtulla; B D Ross Journal: J Natl Cancer Inst Date: 2000-12-20 Impact factor: 13.506
Authors: Alex A T Bui; William Hsu; Corey Arnold; Suzie El-Saden; Denise R Aberle; Ricky K Taira Journal: J Am Med Inform Assoc Date: 2013-06-17 Impact factor: 4.497
Authors: Ararat Chakhoyan; Davis C Woodworth; Robert J Harris; Albert Lai; Phioanh L Nghiemphu; Linda M Liau; Whitney B Pope; Timothy F Cloughesy; Benjamin M Ellingson Journal: J Neurooncol Date: 2018-05-31 Impact factor: 4.130
Authors: Michelle M Kim; Yilun Sun; Madhava P Aryal; Hemant A Parmar; Morand Piert; Benjamin Rosen; Charles S Mayo; James M Balter; Matthew Schipper; Nicolette Gabel; Emily M Briceño; Daekeun You; Jason Heth; Wajd Al-Holou; Yoshie Umemura; Denise Leung; Larry Junck; Daniel R Wahl; Theodore S Lawrence; Yue Cao Journal: Int J Radiat Oncol Biol Phys Date: 2021-01-29 Impact factor: 8.013