Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM)

Purpose: The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary malignant brain tumour and numerous outcomes derived from AI algorithms such as grading, survival, treatment-related effects and molecular status have been reported. The aim of the study is to evaluate the AI reporting methodology for outcomes relating to gliomas in magnetic resonance imaging (MRI) using the CLAIM criteria. Methods: A literature search was performed on three databases pertaining to AI augmentation of glioma MRI, published between the start of 2018 and the end of 2021 Results: A total of 4308 articles were identified and 138 articles remained after screening. These articles were categorised into four main AI tasks: grading (n= 44), predicting molecular status (n= 50), predicting survival (n= 25) and distinguishing true tumour progression from treatment-related effects (n= 10). The average CLAIM score was 20/42 (range: 10–31). Studies most consistently reported the scientific background and clinical role of their AI approach. Areas of improvement were identified in the reporting of data collection, data management, ground truth and validation of AI performance. Conclusion: AI may be a means of producing high-accuracy results for certain tasks in glioma MRI; however, there remain issues with reporting quality. AI reporting guidelines may aid in a more reproducible and standardised approach to reporting and will aid in clinical integration

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

Imaging for assessment of cancer treatment response to immune checkpoint inhibitors can be complementary in identifying hypophysitis

IntroductionHypophysitis is reported in 8.5%–14% of patients receiving combination immune checkpoint inhibition (cICI) but can be a diagnostic challenge. This study aimed to assess the role of routine diagnostic imaging performed during therapeutic monitoring of combination anti-CTLA-4/anti-PD-1 treatment in the identification of hypophysitis and the relationship of imaging findings to clinical diagnostic criteria.MethodsThis retrospective cohort study identified patients treated with cICI between January 2016 and January 2019 at a quaternary melanoma service. Medical records were reviewed to identify patients with a documented diagnosis of hypophysitis based on clinical criteria. Available structural brain imaging with magnetic resonance imaging (MRI) or computed tomography (CT) of the brain and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography with computed tomography (FDG-PET/CT) were assessed retrospectively. The main radiological outcome measures were a relative change in pituitary size or FDG uptake temporally attributed to cICI.ResultsThere were 162 patients (median age 60 years, 30% female) included. A total of 100 and 134 had serial CT/MRI of the brain and FDG-PET/CT, respectively. There were 31 patients who had a documented diagnosis of hypophysitis and an additional 20 who had isolated pituitary imaging findings. The pituitary gland enlargement was mild, and the largest absolute gland size was 13 mm, with a relative increase of 7 mm from baseline. There were no cases of optic chiasm compression. Pituitary enlargement and increased FDG uptake were universally transient. High-dose glucocorticoid treatment for concurrent irAEs prevented assessment of the pituitary–adrenal axis in 90% of patients with isolated imaging findings.ConclusionCareful review of changes in pituitary characteristics on imaging performed for assessment of therapeutic response to iICI may lead to increased identification and more prompt management of cICI-induced hypophysitis

The preoperative MRI assessment of adult intracranial diffuse gliomas

© 2018 Dr. Arian LasockiMRI (Magnetic Resonance Imaging), is an integral part of the management of intracranial diffuse gliomas, consisting of both astrocytomas and oligodendrogliomas, and ongoing improvements in MRI technology continually enhance its value. While advanced MRI techniques are adding to the functional information available, standard sequences remain the mainstay of the assessment, and are also being improved. Initially, MRI provides critical diagnostic information and aids neurosurgical planning, but its role is expanding beyond these primary functions, as outlined in this thesis. Improved identification and delineation of the tumour allows optimal management by surgical resection and adjuvant radiotherapy. MRI is also important for prognostication, by identifying features that may convey a better or worse prognosis. The addition of MRI information to clinical data and the histopathological assessment thus provides the most comprehensive assessment of the individual patient. The recent 2016 update to the WHO classification of central nervous system tumours has led to significant changes in the histological characterisation of gliomas, now providing an integrated genotypic and phenotypic classification1. The two steps in the genotypic classification of gliomas is testing for an isocitrate dehydrogenase (IDH) mutation, and if present, determination of 1p/19q status1. There are logistic and cost issues, however, and MRI can aid decision-making. The value of MRI in this context is greatest when access to definitive testing methods is limited, and the focus on the molecular phenotype in the new WHO classification increases the potential for a combined histologic-MRI assessment method at such centres. Due to its ability to assess the entire tumour and provide non-invasive characterisation over time, MRI also has the potential to overcome some of the limitations of both the phenotypic and genotypic assessment, including sampling error, intra-tumoural heterogeneity and monoallelic gene expression. Information obtained by MRI is also increasing our understanding of the disease, well demonstrated by the growing interest in the non-enhancing component of tumour. Gliomas are essentially incurable, with recurrence almost inevitable despite optimal therapy. There is thus growing interest in advanced surgical techniques aimed at more aggressive management, and accurate delineation of the tumour by MRI provides a critical guide to the surgeon

The role of 2-hydroxyglutarate magnetic resonance spectroscopy for the determination of isocitrate dehydrogenase status in lower grade gliomas versus glioblastoma: a systematic review and meta-analysis of diagnostic test accuracy

Purpose: Magnetic resonance spectroscopy (MRS) provides a non-invasive means of determining isocitrate dehydrogenase (IDH) status. Determination of 2-hydroxyglutarate (2-HG) presence through MRS is a means of determining IDH status; however, differences may be seen by grade. The goal of this paper is to perform a diagnostic test accuracy (DTA) meta-analysis on 2-HG MRS for IDH status in both lower-grade glioma (LGG) and glioblastoma (GBM) in preoperative patients. Methods: A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy guidelines. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2. The search was up to date as of 09/02/2021. Nine English-language journal articles were included. Results: The meta-analysis found a pooled sensitivity of 93% (95% CI 58–99%) and specificity of 84% (95% CI 51–96%) for LGG (n= 181). For GBM (n= 77), the pooled sensitivity was 84% (95% CI 25.0–99%) and the specificity was 97% (95% CI 43–100%). Conclusion: 2-HG MRS shows promise as a non-invasive means of determining IDH status, with specificity higher for GBM and sensitivity higher for LGG. The wide confidence intervals are notable, however, in particular related to the small number of IDH-mutant GBM studied. Diagnostic heterogeneity was particularly present for LGG, and the hierarchical summary receiver operator curves showed poor predictive accuracy in both groups. For more conclusive results, diagnostic test accuracy statistics need to be quantified with larger studies and more deliberate study design

Bevacizumab as a steroid-sparing agent during immunotherapy for melanoma brain metastases:A case series

Background: Brain metastases are common in advanced melanoma and often necessitate corticosteroids such as dexamethasone to control symptoms and reduce peritumoral edema. Immunotherapy improves survival in metastatic melanoma, but concomitant treatment with corticosteroids may reduce efficacy. Here, we report the use of bevacizumab, a vascular endothelial growth factor (VEGF) inhibitor, as a steroid-sparing agent in melanoma patients with brain metastases treated with immunotherapy. Methods: Medical records and imaging were retrospectively analyzed for melanoma patients with brain metastases who received bevacizumab at our institution between 2012 and 2017. Results: 12 melanoma patients with brain metastases received bevacizumab (5-7.5 mg/kg Q2-3 W; median 4 cycles, range 1-9). Patients were BRAF wild-type or resistant to BRAF/MEK inhibitor therapy. All had progressive intracranial disease after prior resection, stereotactic radiosurgery and/or whole brain radiotherapy, and up to four lines of previous systemic treatment. Prior to bevacizumab, all patients had radiologically defined peritumoral edema and nine required dexamethasone for symptom control. In 10 evaluable patients, six reduced their dexamethasone dose by more than 50%, and eight displayed reduced edema 4 weeks after bevacizumab. Seven patients experienced adverse events possibly related to bevacizumab, including intracranial hemorrhage, hypertension, and gastrointestinal bleeding. Ten patients received immunotherapy after bevacizumab. Five patients survived more than 6 months, including one who remained disease-free after 4 years and without neurological deficit despite being hemiplegic from edematous brain metastases prior to bevacizumab. Conclusion: In 12 very poor prognosis melanoma patients with brain metastases, bevacizumab was well-tolerated, associated with improved symptoms and reduced peritumoral edema despite weaning steroids, and facilitated treatment with immunotherapy that provided durable survival in a substantial proportion of cases

Driving innovation through collaboration: development of clinical annotation datasets for brain cancer biobanking

Background. A key component of cancer research is the availability of clinical samples with appropriately annotated clinical data. Biobanks facilitate research by collecting/storing various types of clinical samples for research. Brain Cancer Biobanking Australia (BCBA) was established to facilitate the networking of brain cancer biobanking operations Australia-wide. Maximizing biospecimen utility in a networked biobanking environment requires the standardization of procedures and data across different sites. The aim of this research was to scope and develop a recommended clinical annotation dataset both for pediatric and adult brain cancer biobanks.Methods. A multidisciplinary working group consisting of members from the BCBA Consortium was established to develop clinical dataset recommendations for brain cancer biobanks. A literature search was undertaken to identify any published clinical dataset recommendations for brain cancer biobanks. An audit of data items collected and stored by BCBA member biobanks was also conducted to survey current clinical data collection practices across the BCBA network.Results. BCBA has developed a clinical annotation dataset recommendation for pediatric and adult brain cancer biobanks. The clinical dataset recommendation has 5 clinical data categories: demographic, clinical and radiological diagnosis and surgery, neuropathological diagnosis, patient treatment, and patient follow-up. The data fields have been categorized into 1 of 3 tiers; essential, preferred, and comprehensive. This enables biobanks and researchers to prioritize appropriately where resources are limited.Conclusion. This dataset can be used to guide the integration of data from multiple existing biobanks for research studies and for planning prospective brain cancer biobanking activities

[18F]-fluoroethyl-L-tyrosine (FET) in glioblastoma (FIG) TROG 18.06 study: protocol for a prospective, multicentre PET/CT trial

Introduction Glioblastoma is the most common aggressive primary central nervous system cancer in adults characterised by uniformly poor survival. Despite maximal safe resection and postoperative radiotherapy with concurrent and adjuvant temozolomide-based chemotherapy, tumours inevitably recur. Imaging with O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) has the potential to impact adjuvant radiotherapy (RT) planning, distinguish between treatment-induced pseudoprogression versus tumour progression as well as prognostication.Methods and analysis The FET-PET in Glioblastoma (FIG) study is a prospective, multicentre, non-randomised, phase II study across 10 Australian sites and will enrol up to 210 adults aged ≥18 years with newly diagnosed glioblastoma. FET-PET will be performed at up to three time points: (1) following initial surgery and prior to commencement of chemoradiation (FET-PET1); (2) 4 weeks following concurrent chemoradiation (FET-PET2); and (3) within 14 days of suspected clinical and/or radiological progression on MRI (performed at the time of clinical suspicion of tumour recurrence) (FET-PET3). The co-primary outcomes are: (1) to investigate how FET-PET versus standard MRI impacts RT volume delineation and (2) to determine the accuracy and management impact of FET-PET in distinguishing pseudoprogression from true tumour progression. The secondary outcomes are: (1) to investigate the relationships between FET-PET parameters (including dynamic uptake, tumour to background ratio, metabolic tumour volume) and progression-free survival and overall survival; (2) to assess the change in blood and tissue biomarkers determined by serum assay when comparing FET-PET data acquired prior to chemoradiation with other prognostic markers, looking at the relationships of FET-PET versus MRI-determined site/s of progressive disease post chemotherapy treatment with MRI and FET-PET imaging; and (3) to estimate the health economic impact of incorporating FET-PET into glioblastoma management and in the assessment of post-treatment pseudoprogression or recurrence/true progression. Exploratory outcomes include the correlation of multimodal imaging, blood and tumour biomarker analyses with patterns of failure and survival.Ethics and dissemination The study protocol V.2.0 dated 20 November 2020 has been approved by a lead Human Research Ethics Committee (Austin Health, Victoria). Other clinical sites will provide oversight through local governance processes, including obtaining informed consent from suitable participants. The study will be conducted in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. Results of the FIG study (TROG 18.06) will be disseminated via relevant scientific and consumer forums and peer-reviewed publications.Trial registration number ANZCTR ACTRN1261900173514