Zongwei Xu1, Chao Ke2, Jie Liu1, Shijie Xu2, Lujun Han3, Yadi Yang3, Long Qian4, Xin Liu5, Hairong Zheng5, Xiaofei Lv6, Yin Wu7. 1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China. 2. Department of Neurosurgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China. 3. Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China. 4. MR Research, GE Healthcare, Beijing, China. 5. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China. 6. Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China. Electronic address: lvxf@sysucc.org.cn. 7. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China. Electronic address: yin.wu@siat.ac.cn.
Abstract
PURPOSE: Accurate glioma grading and IDH mutation status prediction are critically essential for individualized preoperative treatment decisions. This study aims to compare the diagnostic performance of magnetic resonance (MR) amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction. METHOD: Fifty-one glioma patients without treatment were retrospectively included. APT-weighted (APTw) effect and DKI indices, including mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK), and kurtosis FA (KFA) were obtained from APT and diffusion-weighted images, respectively. DKI indices in tumors were normalized to that in contralateral normal appearing white matter (CNAWM) and the APTw difference (ΔAPTw) between the two regions was calculated. Student's t-test, one-way ANOVA and ROC analyses were conducted. RESULTS: Among the enrolled 51 patients, 13 had glioma-II, 17 had glioma-III and 21 had glioma-IV. 25 patients were diagnosed as IDH-mutant, and 26 as IDH-wild type. MD and MK differed significantly between glioma-IV and glioma II/III (P < 0.05), but not between glioma-II and glioma-III. FA and KFA showed no significant difference among the three groups (P > 0.05). IDH-mutant group exhibited significantly higher MD and lower FA, MK and ΔAPTw than IDH-wild type (P < 0.05), whereas the two groups showed comparable KFA values. In contrast, ΔAPTw differed significantly across tumor grades and IDH mutation status (P < 0.05), with consistently better discriminatory performance than DKI indices in glioma grading and IDH mutation status prediction. CONCLUSIONS: APT imaging was superior to DKI in glioma grading and IDH mutation status prediction, benefiting accurate diagnoses and treatment decisions.
PURPOSE: Accurate glioma grading and IDH mutation status prediction are critically essential for individualized preoperative treatment decisions. This study aims to compare the diagnostic performance of magnetic resonance (MR) amide proton transfer (APT) and diffusion kurtosis imaging (DKI) in glioma grading and IDH mutation status prediction. METHOD: Fifty-one gliomapatients without treatment were retrospectively included. APT-weighted (APTw) effect and DKI indices, including mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK), and kurtosis FA (KFA) were obtained from APT and diffusion-weighted images, respectively. DKI indices in tumors were normalized to that in contralateral normal appearing white matter (CNAWM) and the APTw difference (ΔAPTw) between the two regions was calculated. Student's t-test, one-way ANOVA and ROC analyses were conducted. RESULTS: Among the enrolled 51 patients, 13 had glioma-II, 17 had glioma-III and 21 had glioma-IV. 25 patients were diagnosed as IDH-mutant, and 26 as IDH-wild type. MD and MK differed significantly between glioma-IV and glioma II/III (P < 0.05), but not between glioma-II and glioma-III. FA and KFA showed no significant difference among the three groups (P > 0.05). IDH-mutant group exhibited significantly higher MD and lower FA, MK and ΔAPTw than IDH-wild type (P < 0.05), whereas the two groups showed comparable KFA values. In contrast, ΔAPTw differed significantly across tumor grades and IDH mutation status (P < 0.05), with consistently better discriminatory performance than DKI indices in glioma grading and IDH mutation status prediction. CONCLUSIONS: APT imaging was superior to DKI in glioma grading and IDH mutation status prediction, benefiting accurate diagnoses and treatment decisions.
Authors: Elisabeth Springer; Pedro Lima Cardoso; Bernhard Strasser; Wolfgang Bogner; Matthias Preusser; Georg Widhalm; Mathias Nittka; Gregor Koerzdoerfer; Pavol Szomolanyi; Gilbert Hangel; Johannes A Hainfellner; Wolfgang Marik; Siegfried Trattnig Journal: Cancers (Basel) Date: 2022-01-30 Impact factor: 6.575
Authors: Jinyuan Zhou; Moritz Zaiss; Linda Knutsson; Phillip Zhe Sun; Sung Soo Ahn; Silvio Aime; Peter Bachert; Jaishri O Blakeley; Kejia Cai; Michael A Chappell; Min Chen; Daniel F Gochberg; Steffen Goerke; Hye-Young Heo; Shanshan Jiang; Tao Jin; Seong-Gi Kim; John Laterra; Daniel Paech; Mark D Pagel; Ji Eun Park; Ravinder Reddy; Akihiko Sakata; Sabine Sartoretti-Schefer; A Dean Sherry; Seth A Smith; Greg J Stanisz; Pia C Sundgren; Osamu Togao; Moriel Vandsburger; Zhibo Wen; Yin Wu; Yi Zhang; Wenzhen Zhu; Zhongliang Zu; Peter C M van Zijl Journal: Magn Reson Med Date: 2022-04-22 Impact factor: 3.737