Purpose Low-grade glioma (LGG) patients may face health-related quality-of-life (HRQoL) impairments, due to the tumour, treatment and associated side-effects and prospects of progression. We systematically identified quantitative studies assessing HRQoL in adult LGG patients, for: aspects of HRQoL impacted; comparisons with non-cancer controls (NCC) and other groups; temporal trends; and factors associated with HRQoL. Methods MEDLINE, CINAHL, Embase, PubMed, and PsycINFO were systematically searched from inception to 14th September 2021. Following independent screening of titles and abstracts and full-texts, population and study characteristics, and HRQoL findings were abstracted from eligible papers, and quality appraised. Narrative synthesis was conducted. Results Twenty-nine papers reporting 22 studies (cross-sectional, n = 13; longitudinal, n = 9) were identified. Papers were largely good quality, though many excluded patients with cognitive and communication impairments. Comparators included high-grade gliomas (HGG) (n = 7); NCCs (n = 6) and other patient groups (n = 3). Nineteen factors, primarily treatment (n = 8), were examined for association with HRQoL. There was substantial heterogeneity in HRQoL instruments used, factors and aspects of HRQoL assessed and measurement timepoints. HRQoL, primarily cognitive functioning and fatigue, in adult LGG patients is poor, and worse than in NCCs, though better than in HGG patients. Over time, HRQoL remained low, but stable. Epilepsy/seizure burden was most consistently associated with worse HRQoL. Conclusion LGG patients experience wide-ranging HRQoL impairments. HRQoL in those with cognitive and communication impairments requires further investigation. These findings may help clinicians recognise current supportive care needs and inform types and timings of support needed, as well as inform future interventions.

Pelvic radiotherapy can damage surrounding tissue and organs, causing chronic conditions including bowel symptoms. We systematically identified quantitative, population-based studies of patient-reported bowel symptoms following pelvic radiotherapy to synthesize evidence of symptom type, prevalence, and severity. Medline, CINAHL, EMBASE, and PsychINFO were searched from inception to September 2022. Following independent screening of titles, abstracts, and full-texts, population and study characteristics and symptom findings were extracted, and narrative synthesis was conducted. In total, 45 papers (prostate, n = 39; gynecological, n = 6) reporting 19 datasets were included. Studies were methodologically heterogeneous. Most frequently assessed was bowel function (‘score’, 26 papers, ‘bother’, 19 papers). Also assessed was urgency, diarrhea, bleeding, incontinence, abdominal pain, painful hemorrhoids, rectal wetness, constipation, mucous discharge, frequency, and gas. Prevalence ranged from 1% (bleeding) to 59% (anal bleeding for >12 months at any time since start of treatment). In total, 10 papers compared radiotherapy with non-cancer comparators and 24 with non-radiotherapy cancer patient groups. Symptom prevalence/severity was greater/worse in radiotherapy groups and symptoms more common/worse post-radiotherapy than pre-diagnosis/treatment. Symptom prevalence varied between studies and symptoms. This review confirms that many people experience chronic bowel symptoms following pelvic radiotherapy. Greater methodological consistency, and investigation of less-well-studied survivor populations, could better inform the provision of services and support.

e13579 Background: Cutaneous squamous cell carcinoma (cSCC) are the most common form of metastasising skin cancer. Whilst rates of metastatic cSCC are low, they account for a significant proportion of skin cancer related morbidity and mortality, particularly within elderly cohorts, which poses a significant burden to healthcare services. Established cSCC tumour staging systems perform poorly at predicting metastatic risk. Additionally, we lack clinically validated prognostic biomarkers – highlighting the unmet need for novel risk stratification tools to guide clinical practice and improve outcomes for patients with advanced disease. We aimed to train four recognised machine learning (ML) algorithms on a large clinic-pathological dataset of primary cSCC, with the objective of optimising an ML strategy and developing a reliable and clinically useful risk stratification tool capable of accurately predicting metastatic events following primary cSCC. Methods: A dataset of primary cSCC registrations was derived from the Northern Cancer Registry, UK. This identified 7003 histologically confirmed primary cSCC registered between 2010–2020; providing a minimum of 2 years clinical follow-up. We conducted a retrospective analysis of standardised pathology datasets, recording clinical-pathological features. Primary outcome measure was regional and/or distant metastasis. Four machine learning algorithms, were trained based on these features, including: a Logistic Regression Trainer, a Decision Tree Classifier, a Random Forest Classifier and a fully connected artificial neural network (ANN). The algorithms were optimised on training data using five-fold cross validation. Subgroup analysis was performed using mean Shapley additive explanations (SHAP). Results: Accuracy scoring identified the ANN as the optimal predictor of metastasis (0.94), followed by: Logistic Regression Trainer (0.82), Random Forest Classifier (0.80), and Decision Tree Classifier (0.71). Preliminary subgroup analysis identified immunosuppression as most sensitive risk factor for developing metastatic disease (SHAP = 0.122). Conclusions: Significant heterogeneity in current morbidity and mortality data has limited the capacity of traditional statistical models and tumour staging systems to identify very high-risk cSSC. Our findings demonstrate that ML algorithms can accurately predict metastatic events in cSSC populations. Further development of a model user-interface is necessary to support the development of a useful risk stratification tool to guide clinical practice.