Minsu Lee1, Kyunghwa Han2, Sung Soo Ahn3, Sohi Bae2, Yoon Seong Choi2, Je Beom Hong4, Jong Hee Chang5, Se Hoon Kim6, Seung-Koo Lee2. 1. Department of Radiology, Aerospace Medical Center, Republic of Korea Air Force, Chungcheongbuk-do, Cheongju-si, Republic of Korea. 2. Departments of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea. 3. Departments of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea. sungsoo@yuhs.ac. 4. Department of Neurosurgery, CHA Bundang Medical Center, School of Medicine, CHA University, Seongnam, Republic of Korea. 5. Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea. 6. Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Abstract
PURPOSE: To determine whether radiological phenotype can improve the predictive performance of the risk model based on molecular subtype and clinical risk factors in anaplastic glioma patients. METHODS: This retrospective study was approved by our institutional review board with waiver of informed consent. MR images of 86 patients with pathologically diagnosed anaplastic glioma (WHO grade III) between January 2007 and February 2016 were analyzed according to the Visually Accessible Rembrandt Images (VASARI) features set. Significant imaging findings were selected to generate a radiological risk score (RRS) for overall survival (OS) and progression-free survival (PFS) using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The prognostic value of RRS was evaluated with multivariate Cox regression including molecular subtype and clinical risk factors. The C-indices of multivariate models with and without RRS were compared by bootstrapping. RESULTS: Eight VASARI features contributed to RRS for OS and six contributed to PFS. Multifocality or multicentricity was the most influential feature, followed by restricted diffusion. RRS was significantly associated with OS and PFS (P < .001), as well as age and molecular subtype. The multivariate model with RRS demonstrated a significantly higher predictive performance than the model without (C-index difference: 0.074, 95% confidence interval [CI]: 0.031, 0.148 for OS; C-index difference: 0.054, 95% CI: 0.014, 0.123 for PFS). CONCLUSION: RRS derived from VASARI features was an independent predictor of survival in patients with anaplastic gliomas. The addition of RRS significantly improved the predictive performance of the molecular feature based model.
PURPOSE: To determine whether radiological phenotype can improve the predictive performance of the risk model based on molecular subtype and clinical risk factors in anaplastic gliomapatients. METHODS: This retrospective study was approved by our institutional review board with waiver of informed consent. MR images of 86 patients with pathologically diagnosed anaplastic glioma (WHO grade III) between January 2007 and February 2016 were analyzed according to the Visually Accessible Rembrandt Images (VASARI) features set. Significant imaging findings were selected to generate a radiological risk score (RRS) for overall survival (OS) and progression-free survival (PFS) using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The prognostic value of RRS was evaluated with multivariate Cox regression including molecular subtype and clinical risk factors. The C-indices of multivariate models with and without RRS were compared by bootstrapping. RESULTS: Eight VASARI features contributed to RRS for OS and six contributed to PFS. Multifocality or multicentricity was the most influential feature, followed by restricted diffusion. RRS was significantly associated with OS and PFS (P < .001), as well as age and molecular subtype. The multivariate model with RRS demonstrated a significantly higher predictive performance than the model without (C-index difference: 0.074, 95% confidence interval [CI]: 0.031, 0.148 for OS; C-index difference: 0.054, 95% CI: 0.014, 0.123 for PFS). CONCLUSION: RRS derived from VASARI features was an independent predictor of survival in patients with anaplastic gliomas. The addition of RRS significantly improved the predictive performance of the molecular feature based model.
Authors: Eun Kyoung Hong; Seung Hong Choi; Dong Jae Shin; Sang Won Jo; Roh Eul Yoo; Koung Mi Kang; Tae Jin Yun; Ji Hoon Kim; Chul Ho Sohn; Sung Hye Park; Jae Kyoung Won; Tae Min Kim; Chul Kee Park; Il Han Kim; Soon Tae Lee Journal: Korean J Radiol Date: 2020-09-10 Impact factor: 3.500