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Frosina G. Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated. Cancers (Basel) 2024; 16:1566. [PMID: 38672647 PMCID: PMC11048778 DOI: 10.3390/cancers16081566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.
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Affiliation(s)
- Guido Frosina
- Mutagenesis & Cancer Prevention Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
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Yasuda S, Yano H, Ikegame Y, Ikuta S, Maruyama T, Kumagai M, Muragaki Y, Iwama T, Shinoda J, Izumo T. Predicting Isocitrate Dehydrogenase Status in Non-Contrast-Enhanced Adult-Type Astrocytic Tumors Using Diffusion Tensor Imaging and 11C-Methionine, 11C-Choline, and 18F-Fluorodeoxyglucose PET. Cancers (Basel) 2024; 16:1543. [PMID: 38672625 PMCID: PMC11048577 DOI: 10.3390/cancers16081543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
We aimed to differentiate the isocitrate dehydrogenase (IDH) status among non-enhanced astrocytic tumors using preoperative MRI and PET. We analyzed 82 patients with non-contrast-enhanced, diffuse, supratentorial astrocytic tumors (IDH mutant [IDH-mut], 55 patients; IDH-wildtype [IDH-wt], 27 patients) who underwent MRI and PET between May 2012 and December 2022. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) values using diffusion tensor imaging. We evaluated the tumor/normal brain uptake (T/N) ratios using 11C-methionine, 11C-choline, and 18F-fluorodeoxyglucose PET; extracted the parameters with significant differences in distinguishing the IDH status; and verified their diagnostic accuracy. Patients with astrocytomas were significantly younger than those with glioblastomas. The following MRI findings were significant predictors of IDH-wt instead of IDH-mut: thalamus invasion, contralateral cerebral hemisphere invasion, location adjacent to the ventricular walls, higher FA value, and lower MD value. The T/N ratio for all tracers was significantly higher for IDH-wt than for IDH-mut. In a composite diagnosis based on nine parameters, including age, 84.4% of cases with 0-4 points were of IDH-mut; conversely, 100% of cases with 6-9 points were of IDH-wt. Composite diagnosis using all parameters, including MRI and PET findings with significant differences, may help guide treatment decisions for early-stage gliomas.
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Affiliation(s)
- Shoji Yasuda
- Department of Neurosurgery, Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Minokamo 505-0034, Japan; (H.Y.); (Y.I.); (M.K.); (J.S.)
- Department of Neurosurgery, Chubu Neurorehabilitation Hospital, Minokamo 505-0034, Japan
- Department of Neurosurgery, Gifu University Graduate School of Medicine, Gifu 501-1194, Japan;
| | - Hirohito Yano
- Department of Neurosurgery, Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Minokamo 505-0034, Japan; (H.Y.); (Y.I.); (M.K.); (J.S.)
- Department of Neurosurgery, Chubu Neurorehabilitation Hospital, Minokamo 505-0034, Japan
- Department of Clinical Brain Sciences, Gifu University Graduate School of Medicine, Gifu 501-1194, Japan
| | - Yuka Ikegame
- Department of Neurosurgery, Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Minokamo 505-0034, Japan; (H.Y.); (Y.I.); (M.K.); (J.S.)
- Department of Neurosurgery, Chubu Neurorehabilitation Hospital, Minokamo 505-0034, Japan
| | - Soko Ikuta
- Department of Neurosurgery, Tokyo Women’s Medical University, Tokyo 162-8666, Japan; (S.I.); (T.M.); (Y.M.)
| | - Takashi Maruyama
- Department of Neurosurgery, Tokyo Women’s Medical University, Tokyo 162-8666, Japan; (S.I.); (T.M.); (Y.M.)
| | - Morio Kumagai
- Department of Neurosurgery, Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Minokamo 505-0034, Japan; (H.Y.); (Y.I.); (M.K.); (J.S.)
- Department of Neurosurgery, Chubu Neurorehabilitation Hospital, Minokamo 505-0034, Japan
| | - Yoshihiro Muragaki
- Department of Neurosurgery, Tokyo Women’s Medical University, Tokyo 162-8666, Japan; (S.I.); (T.M.); (Y.M.)
| | - Toru Iwama
- Department of Neurosurgery, Gifu Municipal Hospital, Gifu 500-8513, Japan;
| | - Jun Shinoda
- Department of Neurosurgery, Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Minokamo 505-0034, Japan; (H.Y.); (Y.I.); (M.K.); (J.S.)
- Department of Neurosurgery, Chubu Neurorehabilitation Hospital, Minokamo 505-0034, Japan
- Department of Clinical Brain Sciences, Gifu University Graduate School of Medicine, Gifu 501-1194, Japan
| | - Tsuyoshi Izumo
- Department of Neurosurgery, Gifu University Graduate School of Medicine, Gifu 501-1194, Japan;
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Ma H, Zeng S, Xie D, Zeng W, Huang Y, Mazu L, Zhu N, Yang Z, Chu J, Zhao J. Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience. Radiol Oncol 2024; 58:23-32. [PMID: 38378035 PMCID: PMC10878771 DOI: 10.2478/raon-2024-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/01/2023] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI). PATIENTS AND METHODS We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded. RESULTS There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively). CONCLUSIONS Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.
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Affiliation(s)
- Hui Ma
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Shanmei Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Dingxiang Xie
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Wenting Zeng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Yingqian Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Liwei Mazu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Nengjin Zhu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Jianping Chu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, China
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Fontana G, Riva G, Orlandi E. Editorial for "Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm". J Magn Reson Imaging 2023; 58:1243-1244. [PMID: 36732944 DOI: 10.1002/jmri.28627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 01/14/2023] [Indexed: 02/04/2023] Open
Affiliation(s)
- Giulia Fontana
- Clinical Department, CNAO National Center for Oncological Hadrontherapy, Pavia, Italy
| | - Giulia Riva
- Clinical Department, CNAO National Center for Oncological Hadrontherapy, Pavia, Italy
| | - Ester Orlandi
- Clinical Department, CNAO National Center for Oncological Hadrontherapy, Pavia, Italy
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Song Z, Zhao Z, Zhu S, Jin Q, Zhang S, Wang Z, Shen B, Wang Z, Zhao Z. Arylsulfatase D is a prognostic biomarker that promotes glioma cells progression through JAK2/STAT3 pathway and M2 macrophage infiltration. Front Oncol 2023; 13:1228426. [PMID: 37766864 PMCID: PMC10521731 DOI: 10.3389/fonc.2023.1228426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/21/2023] [Indexed: 09/29/2023] Open
Abstract
Background Arylsulfatase D (ARSD) belongs to the sulfatase family and plays a crucial role in maintaining the proper structure of bone and cartilage matrix. Although several researches have revealed the functions of ARSD in tumor progression, the prognostic value of ARSD in glioma and the related mechanisms have not been fully investigated. Methods We performed a pan-cancer analysis of ARSD, and investigated the relationship between expression of ARSD and overall survival (OS) in multiple glioma datasets. ROC curves and nomograms were created to investigate the predictive capacity of ARSD. Immune and analysis were conducted to investigate the mechanisms underlying the roles of ARSD in glioma. Glioma tissue samples were collected to verify the expression of ARSD in glioma, while the functions of ARSD were explored using cell experiment. M2 macrophage infiltration assay was used to determine the relation between ARSD and tumor immune microenvironment. Results Survival analysis indicated that individuals with high ARSD expression in glioma had a shorter survival time. Cox analysis showed that ARSD had a good ability for predicting prognosis in glioma. Immune analysis suggested that ARSD could regulate immune cell infiltration and affect the Cancer-Immunity Cycle to create an immunosuppressive environment. Combined with cell experiment and bioinformatic analysis, we found that ARSD can promote glioma progression through regulation of JAK2/STAT3 pathway and M2 macrophage infiltration. Conclusion Our study found that ARSD can promote glioma development by regulating immune microenvironment and JAK2/STAT3 signaling pathway, which provided a potential therapy target for glioma treatment.
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Affiliation(s)
- Zihan Song
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zijun Zhao
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Siyu Zhu
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Qianxu Jin
- Department of Neurosurgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Shiyang Zhang
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zairan Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Bowei Shen
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zijian Wang
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zongmao Zhao
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- Department of Neurosurgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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He A, Wang P, Zhu A, Liu Y, Chen J, Liu L. Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:diagnostics12122995. [PMID: 36553002 PMCID: PMC9776893 DOI: 10.3390/diagnostics12122995] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
The IDH somatic mutation status is an important basis for the diagnosis and classification of gliomas. We proposed a "6-Step" general radiomics model to noninvasively predict the IDH mutation status by simultaneously tuning combined multi-sequence MRI and optimizing the full radiomics processing pipeline. Radiomic features (n = 3776) were extracted from multi-sequence MRI (T1, T2, FLAIR, and T1Gd) in low-grade gliomas (LGGs), and a total of 45,360 radiomics pipeline were investigated according to different settings. The predictive ability of the general radiomics model was evaluated with regards to accuracy, stability, and efficiency. Based on numerous experiments, we finally reached an optimal pipeline for classifying IDH mutation status, namely the T2+FLAIR combined multi-sequence with the wavelet image filter, mean data normalization, PCC dimension reduction, RFE feature selection, and SVM classifier. The mean and standard deviation of AUC, accuracy, sensitivity, and specificity were 0.873 ± 0.05, 0.876 ± 0.09, 0.875 ± 0.11, and 0.877 ± 0.15, respectively. Furthermore, 14 radiomic features that best distinguished the IDH mutation status of the T2+FLAIR multi-sequence were analyzed, and the gray level co-occurrence matrix (GLCM) features were shown to be of high importance. Apart from the promising prediction of the molecular subtypes, this study also provided a general tool for radiomics investigation.
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Affiliation(s)
- Ailing He
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Peng Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Aihua Zhu
- Department of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Yankui Liu
- Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
| | - Jianhuan Chen
- Laboratory of Genomic and Precision Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
- Correspondence: (J.C.); (L.L.)
| | - Li Liu
- Big Data Center, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
- Correspondence: (J.C.); (L.L.)
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