The Christie School of Oncology: Christie Research Publications Repository
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Patient-reported outcome measures in penile cancer: a narrative review
No full textPenile cancer is a relatively rare cancer globally. The cancer itself and its surgical excision can have detrimental physical and psychosocial implications on patients. Patients can share assessments of their health and quality of life using patient-reported outcome measures. These questionnaires are associated with more dynamic, patient-centred clinical decision-making, better patient symptom control and reduced need for hospitalisation. There is a drive for increased implementation and digitalisation of patient-reported outcome measures, especially in surgical settings, to allow real-time monitoring and adjustment of patient management. Consequently, this narrative review provides a contemporary exploration of the usage of patient-reported outcome measures in patients with penile cancer
Management of immune checkpoint inhibitor-associated toxicities in older adults with cancer: recommendations from the International Society of Geriatric Oncology (SIOG)
No full textImmune checkpoint inhibitors (ICIs) have substantially advanced the treatment landscape for a wide variety of malignancies. Older adults represent a large and rapidly growing demographic, among whom ICIs are widely prescribed. Management of ICI-associated toxicity among older adults, particularly in the presence of frailty and comorbidity, poses unique challenges. In this Policy Review, developed by the International Society of Geriatric Oncology (SIOG), we offer an evidence-based framework for health-care providers, caregivers, and policy makers for treating older adults with ICIs, focusing on unique considerations for this population that are not adequately addressed by existing guidelines, and expanding them to encompass geriatric oncology principles
Association of radiation-induced normal tissue toxicity with a high genetic risk for rheumatoid arthritis
No full textBACKGROUND: Overlapping genes are involved with rheumatoid arthritis (RA) and DNA repair pathways. Therefore, we hypothesized that patients with a high polygenic risk score for RA will have an increased risk of radiotherapy toxicity given the involvement of DNA repair. METHODS: Primary analysis was performed on 1494 prostate cancer, 483 lung cancer, and 1820 breast cancer patients assessed for development of radiotherapy toxicity in the REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) study. Validation cohorts were available from the Radiogenomics Consortium. All patients had undergone curative-intent radiotherapy and were assessed prospectively for toxicity. Germline genomic data was available for all patients, allowing a polygenic risk score to be calculated using 101 RA risk variants. Polygenic risk score was analyzed as a continuous variable and with a more than 90th percentile cutoff. Associations with acute and late standardized total average toxicity (STAT) scores and individual toxicity endpoints were analyzed in multivariable models with preselected adjustment variables. RESULTS: Increasing polygenic risk score for RA did not increase the risk of STAT-acute or STAT-late in any cohort. There was an increased risk of late esophagitis in the lung cancer cohort (coefficient = 0.018, P = .01), however this was not validated (P = .79). No individual acute or late toxicity endpoints were statistically significantly associated with polygenic risk score for the prostate or breast cohorts. No statistically significant results were found in the validation cohorts in multivariable models. CONCLUSIONS: Patients with a high genetic risk for RA do not show increased levels of toxicity after radiotherapy suggesting treatment planning does not need to be modified for such patients
ATM inhibition with AZD1390 and conventional radiotherapy in non-small cell lung cancer: interim report from the CONCORDE phase Ib trial (NCT04550104)
No full textComparing percent breast density assessments of an AI-based method with expert reader estimates: inter-observer variability
No full textPURPOSE: Breast density estimation is an important part of breast cancer risk assessment, as mammographic density is associated with risk. However, density assessed by multiple experts can be subject to high inter-observer variability, so automated methods are increasingly used. We investigate the inter-reader variability and risk prediction for expert assessors and a deep learning approach. APPROACH: Screening data from a cohort of 1328 women, case-control matched, was used to compare between two expert readers and between a single reader and a deep learning model, Manchester artificial intelligence - visual analog scale (MAI-VAS). Bland-Altman analysis was used to assess the variability and matched concordance index to assess risk. RESULTS: Although the mean differences for the two experiments were alike, the limits of agreement between MAI-VAS and a single reader are substantially lower at +SD (standard deviation) 21 (95% CI: 19.65, 21.69) -SD 22 (95% CI: - 22.71 , - 20.68 ) than between two expert readers +SD 31 (95% CI: 32.08, 29.23) -SD 29 (95% CI: - 29.94 , - 27.09 ). In addition, breast cancer risk discrimination for the deep learning method and density readings from a single expert was similar, with a matched concordance of 0.628 (95% CI: 0.598, 0.658) and 0.624 (95% CI: 0.595, 0.654), respectively. The automatic method had a similar inter-view agreement to experts and maintained consistency across density quartiles. CONCLUSIONS: The artificial intelligence breast density assessment tool MAI-VAS has a better inter-observer agreement with a randomly selected expert reader than that between two expert readers. Deep learning-based density methods provide consistent density scores without compromising on breast cancer risk discrimination
Recommendations for reporting and evaluating proton therapy beyond dose and constant relative biological effectiveness
No full textBACKGROUND AND PURPOSE: In proton therapy, a relative biological effectiveness (RBE) of 1.1 is used to convert proton dose into an equivalent photon dose. However, RBE varies with tissue type, fraction dose, and beam quality parameters beyond dose such as linear energy transfer (LET) raising concerns about increased local effectiveness and potential toxicity. This work aims to harmonize quantities used for clinical consideration of variable RBE for proton therapy. MATERIALS AND METHODS: A survey was distributed to proton centres to determine agreement on RBE-related concerns and clinical implementations. A subsequent clinical expert meeting facilitated by the European Particle Therapy Network was held to achieve consensus and to make clinical recommendations how to prescribe and report beyond using dose and constant RBE. RESULTS: The survey was answered by 17 out of 23 centres contacted (74%). For proton RBE, most concerns existed regarding toxicity in serial organs, while the assumption of an RBE of 1.1 was considered valid for targets. Most physicists intended to consider a physical quantity beyond dose in clinical decision making. CONCLUSIONS: A constant RBE of 1.1 was the consensus for prescribing dose. However, current practice of recording and reporting dose in proton therapy must be complemented: the recommended quantity beyond dose was the dose-averaged LET in water from primary and secondary protons, normalized to unit density. This will facilitate analyses of treatment data on effectiveness beyond dose and between centres. No consensus on a single variable RBE model was found. More clinical training on proton RBE is needed
Prostate R2*measurements on an MR-linac are repeatable and sensitive to treatment-induced changes
No full textManagement of menopausal symptoms following treatment for hormone receptor positive breast cancer
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