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Critical Reviews in Oncogenesis 2024Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are...
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.
Topics: Humans; Glioma; Prognosis; Brain Neoplasms; Neural Networks, Computer; Deep Learning; Magnetic Resonance Imaging; Image Processing, Computer-Assisted
PubMed: 38683153
DOI: 10.1615/CritRevOncog.2023050852 -
Quantitative Imaging in Medicine and... Aug 2023Positron emission tomography (PET) imaging is a promising molecular neuroimaging technique and has been proposed as one of the criteria for glioma management. However,...
BACKGROUND
Positron emission tomography (PET) imaging is a promising molecular neuroimaging technique and has been proposed as one of the criteria for glioma management. However, there is some controversy concerning the diagnostic accuracy of PET using different radiotracers to differentiate between glioma pseudoprogression (PsP) and true progression (TPR). The purpose of this meta-analysis was to systematically evaluate the methodological quality and clinical value of original studies for distinguishing PsP from TPR in glioma.
METHODS
The Medline, Web of Science, Embase, Cochrane Library, and ClinicalTrials.gov were searched from inception until September 1, 2022. Retrieved clinical studies only investigated the PsP cases but did not include the cases of radiation necrosis or other treatment-related changes. Eligible studies were screened for data extraction and evaluated by 2 independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A random effects model was used to describe summary receiver operating characteristics. Meta-regression and subgroup analyses were applied to identify any sources of heterogeneity.
RESULTS
The meta-analysis included 20 studies, comprising 317 (30.9%) patients with PsP and 708 (69.1%) with TPR. The summary sensitivity and specificity of general PET for identifying PsP were 0.86 [95% confidence interval (CI): 0.77-0.91] and 0.84 (95% CI: 0.79-0.88), respectively. The statistical heterogeneity was explained by sample size, study design, World Health Organization (WHO) grade, gold standard, and radiotracer type. The summary sensitivity and specificity of O-(2-F-fluoroethyl)-L-tyrosine (F-FET PET) were 0.80 (95% CI: 0.68-0.88) and 0.81 (95% CI: 0.75-0.85), respectively. The maximum tumor-to-brain ratio (TBRmax) and the mean tumor-to-brain ratio (TBRmean) both showed excellent diagnostic performance in F-FET studies, the summary sensitivity was 0.83 (95% CI: 0.72-0.91) and 0.79 (95% CI: 0.65-0.98), respectively, and the specificity was 0.76 (95% CI: 0.68-0.84) and 0.78 (95% CI: 0.64-0.88), respectively.
CONCLUSIONS
PET imaging is generally accurate in identifying glioma PsP. Considering the credibility of meta-evidence and the practicability of using radiotracer, F-FET PET holds the highest clinical value, while TBRmax and TBRmean should be regarded as reliable parameters. PET used with the radiotracers and multiple-parameter combinations of PET with magnetic resonance imaging (MRI) and radiomics analysis have broad research and application prospects, whose diagnostic values for identifying glioma PsP warrant further investigation.
PubMed: 37581048
DOI: 10.21037/qims-22-1340 -
Journal of Neuroimaging : Official... Mar 2023Postradiation treatment necrosis is one of the most serious late sequelae and appears within 6 months. The magnetic resonance spectroscopy imaging (MRSI) has been used... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND AND PURPOSE
Postradiation treatment necrosis is one of the most serious late sequelae and appears within 6 months. The magnetic resonance spectroscopy imaging (MRSI) has been used for the detection of brain tumors. The study aimed to determine the radiological accuracy and efficacy in distinguishing recurrent brain tumor from radiation-induced necrosis by identifying pseudoprogression.
METHODS
The research was performed in accordance with the preferred reporting items for systematic review and meta-analysis guidelines. International electronic databases including 15 English sources were investigated. A total of 4281 papers with 2159 citations from 15 databases from 2011 to 2021 met the search strategies of magnetic resonance (MR) spectroscopy in recurrent brain tumors and postradiation necrosis.
RESULTS
Nine studies were enrolled in the meta-analysis with a total of 354 patients (203 male and 151 female) whose average age ranged from 4 to 74 years. Anbarloui et al., Elias et al., Nemattalla et al., Smith et al., Zeng et al., and Weybright et al. showed strong evidence of heterogeneity regarding choline/N-acetylaspartate (Cho/NAA) ratio in the evaluation of the nine studies. Elias et al., Nemattalla et al., Bobek-Billewicz et al., and Smith et al. showed a high heterogeneity in Cho/creatine (Cr) ratio. Elias et al., Nemattalla et al., Smith et al., and Weybright et al. revealed high heterogeneity in NAA/Cr ratio estimates.
CONCLUSION
MR spectroscopy is effective in distinguishing recurrent brain tumors from necrosis. Our meta-analysis revealed that Cho/NAA, Cho/Cr, and NAA/Cr ratios were significantly better predictor of detected recurrent tumor. Therefore, the MRSI is an informative tool in the distinction of tumor recurrence versus necrosis.
Topics: Humans; Male; Female; Child, Preschool; Child; Adolescent; Young Adult; Adult; Middle Aged; Aged; Neoplasm Recurrence, Local; Diagnosis, Differential; Aspartic Acid; Brain Neoplasms; Magnetic Resonance Spectroscopy; Creatine; Radiation Injuries; Choline; Necrosis; Brain
PubMed: 36631883
DOI: 10.1111/jon.13080 -
Radiotherapy and Oncology : Journal of... Dec 2022Treatment response assessment in patients with brain metastasis uses contrast enhanced T1-weighted MRI. Advanced MRI techniques have been studied, but the diagnostic... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Treatment response assessment in patients with brain metastasis uses contrast enhanced T1-weighted MRI. Advanced MRI techniques have been studied, but the diagnostic accuracy is not well known. Therefore, we performed a metaanalysis to assess the diagnostic accuracy of the currently available MRI techniques for treatment response.
METHODS
A systematic literature search was done. Study selection and data extraction were done by two authors independently. Meta-analysis was performed using a bivariate random effects model. An independent cohort was used for DSC perfusion external validation of diagnostic accuracy.
RESULTS
Anatomical MRI (16 studies, 726 lesions) showed a pooled sensitivity of 79% and a specificity of 76%. DCE perfusion (4 studies, 114 lesions) showed a pooled sensitivity of 74% and a specificity of 92%. DSC perfusion (12 studies, 418 lesions) showed a pooled sensitivity was 83% with a specificity of 78%. Diffusion weighted imaging (7 studies, 288 lesions) showed a pooled sensitivity of 67% and a specificity of 79%. MRS (4 studies, 54 lesions) showed a pooled sensitivity of 80% and a specificity of 78%. Combined techniques (6 studies, 375 lesions) showed a pooled sensitivity of 84% and a specificity of 88%. External validation of DSC showed a lower sensitivity and a higher specificity for the reported cut-off values included in this metaanalysis.
CONCLUSION
A combination of techniques shows the highest diagnostic accuracy differentiating tumor progression from treatment induced abnormalities. External validation of imaging results is important to better define the reliability of imaging results with the different techniques.
Topics: Humans; Reproducibility of Results; Sensitivity and Specificity; Brain Neoplasms; Magnetic Resonance Imaging; Diffusion Magnetic Resonance Imaging
PubMed: 36377093
DOI: 10.1016/j.radonc.2022.10.026 -
Quantitative Imaging in Medicine and... Oct 2022Tumor recurrence and pseudoprogression (PsP) have similar imaging manifestations in conventional magnetic resonance imaging (MRI), although the subsequent treatments are...
BACKGROUND
Tumor recurrence and pseudoprogression (PsP) have similar imaging manifestations in conventional magnetic resonance imaging (MRI), although the subsequent treatments are completely different. This study aimed to evaluate the value of perfusion-weighted imaging (PWI) in differentiating PsP from glioma recurrence.
METHODS
A comprehensive literature search was performed to evaluate clinical studies focused on differentiating recurrent glioma from PsP using PWI, including dynamic susceptibility contrast MRI (DSC-MRI), dynamic contrast enhanced MRI (DCE-MRI), and arterial spin labeling (ASL). Study selection and data extraction were independently completed by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was applied to evaluate the quality of the included studies. The software Stata 16.0 and Meta-Disc 1.4 were used for the meta-analysis. Meta-regression and subgroup analyses were applied to identify the sources of heterogeneity in the studies. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) prior to initiation (CRD42022304404).
RESULTS
A total of 40 studies were included, including 27 English studies and 13 Chinese studies. There were 1,341 patients with glioma recurrence and 876 patients with PsP. The pooled sensitivity and specificity of DSC-MRI for differentiating glioma recurrence from PsP were 0.82 [95% confidence interval (CI): 0.78 to 0.86] and 0.87 (95% CI: 0.80 to 0.92), respectively. The pooled sensitivity and specificity of DCE-MRI were 0.83 (95% CI: 0.76 to 0.89) and 0.83 (95% CI: 0.78 to 0.87), respectively. The pooled sensitivity and specificity of ASL were 0.80 (95% CI: 0.73 to 0.86) and 0.86 (95% CI: 0.76 to 0.92), respectively.
DISCUSSION
The DSC-MRI, DCE-MRI, and ASL perfusion techniques displayed high accuracy in distinguishing glioma recurrence from PsP, and DSC-MRI had a higher diagnostic performance than the other two techniques. However, due to the diversity of the parameters and threshold differences, further investigation and standardization are needed.
PubMed: 36185045
DOI: 10.21037/qims-22-32 -
Frontiers in Oncology 2022Amide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients,...
BACKGROUND
Amide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients, but the initial results from recent studies are different.
AIM
The aim of this study is to systematically review and quantify the diagnostic performance of APT in assessing treatment response in patients with post-treatment gliomas.
METHODS
A systematic search in PubMed, EMBASE, and the Web of Science was performed to retrieve related original studies. For the single and added value of APT imaging in distinguishing TR from TEs, we calculated pooled sensitivity and specificity by using Bayesian bivariate meta-analyses.
RESULTS
Six studies were included, five of which reported on single APT imaging parameters and four of which reported on multiparametric MRI combined with APT imaging parameters. For single APT imaging parameters, the pooled sensitivity and specificity were 0.85 (95% CI: 0.75-0.92) and 0.88 (95% CI: 0.74-0.97). For multiparametric MRI including APT, the pooled sensitivity and specificity were 0.92 (95% CI: 0.85-0.97) and 0.83 (95% CI: 0.55-0.97), respectively. In addition, in the three studies reported on both single and added value of APT imaging parameters, the combined imaging parameters further improved diagnostic performance, yielding pooled sensitivity and specificity of 0.91 (95% CI: 0.80-0.97) and 0.92 (95% CI: 0.79-0.98), respectively, but the pooled sensitivity was 0.81 (95% CI: 0.65-0.93) and specificity was 0.82 (95% CI: 0.61-0.94) for single APT imaging parameters.
CONCLUSION
APT imaging showed high diagnostic performance in assessing treatment response in patients with post-treatment gliomas, and the addition of APT imaging to other advanced MRI techniques can improve the diagnostic accuracy for distinguishing TR from TE.
PubMed: 35978813
DOI: 10.3389/fonc.2022.852076 -
Scientific Reports Aug 2022High-grade gliomas remain the most common primary brain tumour with limited treatments options and early recurrence rates following adjuvant treatments. However,... (Meta-Analysis)
Meta-Analysis
High-grade gliomas remain the most common primary brain tumour with limited treatments options and early recurrence rates following adjuvant treatments. However, differentiating true tumour progression (TTP) from treatment-related effects or pseudoprogression (PsP), may critically influence subsequent management options. Structural MRI is routinely employed to evaluate treatment responses, but misdiagnosis of TTP or PsP may lead to continuation of ineffective or premature cessation of effective treatments, respectively. A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses method. Embase, MEDLINE, Web of Science and Google Scholar were searched for methods applied to differentiate PsP and TTP, and studies were selected using pre-specified eligibility criteria. The sensitivity and specificity of included studies were summarised. Three of the identified methods were compared in a separate subgroup meta-analysis. Thirty studies assessing seven distinct neuroimaging methods in 1372 patients were included in the systematic review. The highest performing methods in the subgroup analysis were DWI (AUC = 0.93 [0.91-0.95]) and DSC-MRI (AUC = 0.93 [0.90-0.95]), compared to DCE-MRI (AUC = 0.90 [0.87-0.93]). 18F-fluoroethyltyrosine PET (18F-FET PET) and amide proton transfer-weighted MRI (APTw-MRI) also showed high diagnostic accuracy, but results were based on few low-powered studies. Both DWI and DSC-MRI performed with high sensitivity and specificity for differentiating PsP from TTP. Considering the technical parameters and feasibility of each identified method, the authors suggested that, at present, DSC-MRI technique holds the most clinical potential.
Topics: Brain Neoplasms; Glioma; Humans; Magnetic Resonance Imaging; Sensitivity and Specificity; Treatment Outcome
PubMed: 35918373
DOI: 10.1038/s41598-022-16726-x -
Cancers May 2022Pediatric brain tumors are the most common solid tumor in children. Traditionally, tumor diagnosis and molecular analysis were carried out on tumor tissue harvested... (Review)
Review
BACKGROUND
Pediatric brain tumors are the most common solid tumor in children. Traditionally, tumor diagnosis and molecular analysis were carried out on tumor tissue harvested either via biopsy or resection. However, liquid biopsy allows analysis of circulating tumor DNA in corporeal fluids such as cerebrospinal fluid or blood.
METHODS
We performed a systematic review in Pubmed and Embase regarding the role of liquid biopsy in pediatric brain tumors.
RESULTS
Nine studies with a total of 570 patients were included. The preferred corporeal fluid for analysis with a relatively high yield of ct-DNA was cerebrospinal fluid (CSF). For high-grade glioma, liquid biopsy can successfully characterize H3K27mutations and predict tumor progression before it is radiographically detected. Moreover, liquid biopsy has the potential to distinguish between pseudo-progression and actual progression. In medulloblastoma, ct-DNA in the CSF can be used as a surrogate marker of measurable residual disease and correlates with response to therapy and progression of the tumor up to three months before radiographic detection.
CONCLUSION
Liquid biopsy is primarily useful in high-grade pediatric brain tumors such as diffuse midline glioma or medulloblastoma. Disease detection and monitoring is feasible for both tumor entities. More trials to standardize its use for pediatric brain tumors are necessary.
PubMed: 35681663
DOI: 10.3390/cancers14112683 -
Journal of Medical Imaging and... Sep 2022Chemotherapy and radiotherapy can produce treatment-related effects, which may mimic tumour progression. Advances in Artificial Intelligence (AI) offer the potential to... (Meta-Analysis)
Meta-Analysis Review
Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis.
INTRODUCTION
Chemotherapy and radiotherapy can produce treatment-related effects, which may mimic tumour progression. Advances in Artificial Intelligence (AI) offer the potential to provide a more consistent approach of diagnosis with improved accuracy. The aim of this study was to determine the efficacy of machine learning models to differentiate treatment-related effects (TRE), consisting of pseudoprogression (PsP) and radiation necrosis (RN), and true tumour progression (TTP).
METHODS
The systematic review was conducted in accordance with PRISMA-DTA guidelines. Searches were performed on PubMed, Scopus, Embase, Medline (Ovid) and ProQuest databases. Quality was assessed according to the PROBAST and CLAIM criteria. There were 25 original full-text journal articles eligible for inclusion.
RESULTS
For gliomas: PsP versus TTP (16 studies, highest AUC = 0.98), RN versus TTP (4 studies, highest AUC = 0.9988) and TRE versus TTP (3 studies, highest AUC = 0.94). For metastasis: RN vs. TTP (2 studies, highest AUC = 0.81). A meta-analysis was performed on 9 studies in the gliomas PsP versus TTP group using STATA. The meta-analysis reported a high sensitivity of 95.2% (95%CI: 86.6-98.4%) and specificity of 82.4% (95%CI: 67.0-91.6%).
CONCLUSION
TRE can be distinguished from TTP with good performance using machine learning-based imaging models. There remain issues with the quality of articles and the integration of models into clinical practice. Future studies should focus on the external validation of models and utilize standardized criteria such as CLAIM to allow for consistency in reporting.
Topics: Artificial Intelligence; Brain Neoplasms; Diagnostic Imaging; Glioma; Humans; Machine Learning
PubMed: 35599360
DOI: 10.1111/1754-9485.13436 -
Neurosurgery Aug 2022Circulating tumor DNA (ctDNA) has emerged as a promising noninvasive biomarker to capture tumor genetics in patients with brain tumors. Research into its clinical... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Circulating tumor DNA (ctDNA) has emerged as a promising noninvasive biomarker to capture tumor genetics in patients with brain tumors. Research into its clinical utility, however, has not been standardized because the sensitivity and specificity of ctDNA remain undefined.
OBJECTIVE
To (1) review the primary literature about ctDNA in adults with glioma to compare the sensitivity and specificity of ctDNA in the cerebrospinal fluid vs the plasma and (2) to evaluate the effect of tumor grade on detection of ctDNA.
METHODS
PRISMA-guided systematic review and meta-analysis was performed using published studies that assessed ctDNA in either plasma or cerebrospinal fluid among adult patients with confirmed glioma. Summary receiver operating characteristic curves were generated using the Rücker-Schumacher method, and area under the curve (AUC) was calculated.
RESULTS
Meta-analysis revealed improved biomarker performance for CSF (AUC = 0.947) vs plasma (AUC = 0.741) ctDNA, although this did not reach statistical significance ( P = .141). Qualitative analysis revealed greater sensitivities among single-allele PCR and small, targeted next-generation sequencing panels compared with broader panels. It additionally demonstrated higher sensitivity of ctDNA detection in high-grade vs low-grade gliomas, although these analyses were limited by a lack of specificity reporting in many studies.
CONCLUSION
ctDNA seems to be a highly sensitive and specific noninvasive biomarker among adults with gliomas. To maximize its performance, CSF should be studied with targeted genetic analysis platforms, particularly in high-grade gliomas. Further studies on ctDNA are needed to define its clinical utility in diagnosis, prognostication, glioblastoma pseudoprogression, and other scenarios wherein neoadjuvant therapies may be considered.
Topics: Adult; Biomarkers, Tumor; Circulating Tumor DNA; Glioma; High-Throughput Nucleotide Sequencing; Humans; Mutation
PubMed: 35535984
DOI: 10.1227/neu.0000000000001982