Reirradiation versus systemic therapy versus combination therapy for recurrent high-grade glioma: a systematic review and meta-analysis of survival and toxicity.

PURPOSE: This review compares reirradiation (reRT), systemic therapy and combination therapy (reRT & systemic therapy) with regards to overall survival (OS), progression-free survival (PFS), adverse effects (AEs) and quality of life (QoL) in patients with recurrent high-grade glioma (rHGG). METHODS: A search was performed on PubMed, Scopus, Embase and CENTRAL. Studies reporting OS, PFS, AEs and/or QoL and encompassing the following groups were included; reirradiation vs systemic therapy, combination therapy vs systemic therapy, combination therapy vs reRT, and bevacizumab-based combination therapy vs reRT with/without non-bevacizumab-based systemic therapy. Meta-analyses were performed utilising a random effects model. Certainty of evidence was assessed using GRADE. RESULTS: Thirty-one studies (three randomised, twenty-eight non-randomised) comprising 2084 participants were included. In the combination therapy vs systemic therapy group, combination therapy improved PFS (HR 0.57 (95% CI 0.41-0.79); low certainty) and OS (HR 0.73 (95% CI 0.56-0.95); low certainty) and there was no difference in grade 3 + AEs (RR 1.03 (95% CI 0.57-1.86); very low certainty). In the combination therapy vs reRT group, combination therapy improved PFS (HR 0.52 (95% CI 0.38-0.72); low certainty) and OS (HR 0.69 (95% CI 0.52-0.93); low certainty). In the bevacizumab-based combination therapy vs reRT with/without non-bevacizumab-based systemic therapy group, adding bevacizumab improved PFS (HR 0.46 (95% CI 0.27-0.77); low certainty) and OS (HR 0.42 (95% CI 0.24-0.72; low certainty) and reduced radionecrosis (RR 0.17 (95% CI 0.06-0.48); low certainty). CONCLUSIONS: Combination therapy may improve OS and PFS with acceptable toxicities in patients with rHGG compared to reRT or systemic therapy alone. Particularly, combining bevacizumab with reRT prophylactically reduces radionecrosis. REGISTRATION: CRD42022291741.

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.

Return to work in patients with head and neck cancer: Systematic review and meta-analysis.

BACKGROUND: The aim of this review was to determine the prevalence of return to work (RTW) amongst head and neck cancer (HNC) survivors and to determine its impact on quality of life (QoL). METHODS: A literature search was conducted in PubMed, Scopus, Embase and CINAHL in March 2021. Articles were included if they reported the number of patients with HNC receiving definitive treatment who were working at the time of diagnosis and returned to work. RESULTS: There were 21 articles deemed eligible for inclusion. Meta-analysis suggested that 67% of patients with HNC who were employed at diagnosis RTW (95% CI 62%-73%, I2  = 97.53%). Patients who RTW were demonstrated to have lower levels of anxiety and depressive symptoms on the Hospital Anxiety and Depression Scale. CONCLUSIONS: Return to work is an important clinical outcome which must be considered in the survivorship care of patients with HNC.

Evaluating delays in patients treated with post-operative radiation therapy for head and neck squamous cell carcinoma.

INTRODUCTION: Delays in commencing post-operative radiation therapy (PORT) and prolongation of overall treatment times (OTT) are associated with reduced overall survival and higher recurrence rates in patients with head and neck squamous cell carcinoma (HNSCC). The objective of this study was to evaluate treatment delays, factors contributing to those delays and to explore strategies to mitigate them. METHODS: This retrospective study included patients with mucosal HNSCC at Townsville University Hospital treated with curative intent surgery and PORT between June 2011 and June 2019. The proportion of patients who experienced delays in commencing PORT (>6 weeks) and OTT were evaluated and reasons for these delays were explored. RESULTS: The study included 94 patients of which 70% experienced PORT delay. Surgery at an external facility (81% vs 56%, P = 0.006) and longer post-operative length of stay (P = 0.011) were significantly associated with a higher incidence of PORT delay. Aboriginal and Torres Strait Islander patients had a higher rate of PORT delay (89% vs 68.2%, P = 0.198). Significant delays were noted from time of surgery to radiation oncology (RO) consult and from RO consult to commencement of radiation treatment. CONCLUSION: This study demonstrates that the prevalence of PORT delay for patients with HNSCC remains high with room for improvement. Potential strategies to improve delays include developing effective care coordination, addressing specific needs of Indigenous patients, implementing reliable automated tracking and communication systems between teams and harnessing existing electronic referral systems.

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.