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Yao Y, Tan X, Yin W, Kou Y, Wang X, Jiang X, Chen S, Liu Y, Dang J, Yin J, Cheng Z. Performance of 18 F-FAPI PET/CT in assessing glioblastoma before radiotherapy: a pilot study. BMC Med Imaging 2022; 22:226. [PMID: 36566187 PMCID: PMC9789562 DOI: 10.1186/s12880-022-00952-w] [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: 09/05/2022] [Accepted: 12/14/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND We aimed to determine the performance of 18 F-FAPI PET/CT used for preprocedural assessment of glioblastoma before radiotherapy. METHODS Twelve glioblastoma patients having undergone incomplete surgical resection or biopsy were examined with 18 F-FAPI PET/CT and MRI scanning before radiotherapy. All patients had confirmed tumor residues according to findings of histopathological and/or long-term clinical and radiological follow-ups. Lesion characterization data, including SUVmax and tumor-to-background ratio (TBR) on PET/CT were attained. PET/CT and MRI findings were compared in terms of number of lesions. The correlation between immunohistochemistry, molecular expression, and PET/CT parameters was also evaluated. RESULTS 18 F-FAPI PET/CT detected 16 FAPI-avid out of 23 lesions in 12 patients described on MRI. MRI was statistically different from 18 F-FAPI PET/CT for lesion detection according to the exact McNemar statistical test (P = 0.0156). The SUVmax and TBR of the glioblastomas was 7.08 ± 3.55 and 19.95 ± 13.22, respectively. The sensitivity and positive predictive value (PPV) of 18 F-FAPI PET were 69.6% and 100%, respectively. Neither the Ki-67 index nor the molecular expression was correlated with the FAPI-PET/CT parameters. CONCLUSION 18 F-FAPI PET/CT detects glioblastomas at a lower rate than MRI. However, the 100% PPV of the examination may make it useful for differentiating controversial lesions detected on MRI. The 18 F-FAPI-avid lesions are displayed more clearly probably due to a higher TBR. 18 F-FAPI PET/CT imaging might find application in glioblastoma biopsy and radiotherapy planning.
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Affiliation(s)
- Yutang Yao
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Xiaofei Tan
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Wenya Yin
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, 610041 Chengdu, China
| | - Ying Kou
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Xiaoxiong Wang
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Xiao Jiang
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China ,grid.410655.30000 0001 0157 8259Institute of Isotope, China Institute of Atomic Energy, 102413 Beijing, China
| | - Shirong Chen
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Yongli Liu
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Jun Dang
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
| | - Jun Yin
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, 610041 Chengdu, China
| | - Zhuzhong Cheng
- grid.54549.390000 0004 0369 4060Department of Nuclear Medicine, Sichuan Cancer Hospital&Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, No.55, Section 4, South People’s Road, Sichuan 610041 Chengdu, China
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Wiltgen T, Fleischmann DF, Kaiser L, Holzgreve A, Corradini S, Landry G, Ingrisch M, Popp I, Grosu AL, Unterrainer M, Bartenstein P, Parodi K, Belka C, Albert N, Niyazi M, Riboldi M. 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy. Radiat Oncol 2022; 17:198. [DOI: 10.1186/s13014-022-02164-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 10/07/2022] [Indexed: 12/04/2022] Open
Abstract
Abstract
Background
Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy.
Methods
A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results.
Results
First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression.
Conclusions
18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.
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Liu D, Chen J, Ge H, Yan Z, Luo B, Hu X, Yang K, Liu Y, Liu H, Zhang W. Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma. Eur Radiol 2022; 33:209-220. [PMID: 35881182 DOI: 10.1007/s00330-022-09012-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVES The tumor microenvironment and immune cell infiltration (ICI) associated with glioblastoma (GBM) play a vital role in cancer development, progression, and prognosis. This study aimed to establish an ICI-related prognostic biomarker and explore the associations between ICI signatures and radiomic features in patients with GBM. METHODS The gene expression and survival data of patients with GBM were obtained from three databases. Based on the ICI pattern, an individualized ICI score for each GBM patient was developed in the discovery set (n = 400) and independently verified in the validation set (n = 374). A total of 5915 radiomic features were extracted from the intratumoral and peritumoral regions. Recursive feature elimination and support vector machine methods were performed to select the key features and generate a model predictive of low- or high- ICI scores. The prognostic value of the identified radio genomic model was examined in an independent dataset (n = 149) using imaging and survival data. RESULTS We found that higher ICI scores often indicated worse patient prognosis (multivariable hazard ratio: 0.48 and 0.63 in discovery and validation set, respectively) and higher expression levels of immune checkpoint-related genes. A model that combined 11 radiomic features could well distinguish tumors with different ICI scores (AUC = 0.96, accuracy = 94%). This model was proven to be helpful for noninvasive prognostic stratification in an independent validation cohort. CONCLUSIONS ICI scores may serve as an effective prognostic biomarker to characterize potential biological processes in patients with GBM. This ICI signature can be evaluated noninvasively through radiogenomic analysis. KEY POINTS • Immune cell infiltration (ICI) scores can serve as an effective prognostic biomarker in patients with glioblastoma. • The ICI signature can be evaluated noninvasively through radiomic features derived from the intratumoral and peritumoral regions. • The prognostic value of the radiogenomic model can be verified by independent survival and MRI data.
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Affiliation(s)
- Dongming Liu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Honglin Ge
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Zhen Yan
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Bei Luo
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Xinhua Hu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Kun Yang
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Yong Liu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Hongyi Liu
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China. .,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
| | - Wenbin Zhang
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China. .,Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
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