Factors associated with survival in small cell lung cancer: an analysis of real-world national audit, chemotherapy and radiotherapy data

Background: The mainstay of treatment for small cell lung cancer (SCLC) involves platinum doublet chemotherapy but the optimal duration, 4 vs. 6 cycles, is not known. Concurrent thoracic radiotherapy followed by prophylactic cranial irradiation (PCI) is recommended for fit individuals with limited stage. However, outside of clinical trials, the efficacy of sequential thoracic radiotherapy and PCI for extensive stage is uncertain.Methods: This retrospective, observational, cohort study used English national lung cancer data to determine the factors associated with survival for all people diagnosed with SCLC. More precisely, for individuals who received chemotherapy, we examined survival by the chemotherapy duration, thoracic radiotherapy dose and the use of PCI.Results: In total 6,438 people were diagnosed with SCLC. We identified that male sex (OR 0.7; 95% CI: 0.62–0.80), increasing age (P=0.01) greater comorbidity (P≤0.01), extensive stage (OR 0.21; 95% CI: 0.19–0.25) and worse performance status (PS2 vs. PS0 adjusted OR 0.38 95% CI: 0.31–0.48) were associated with reduced 1-year survival. Receipt of chemotherapy augmented survival. We analysed data for 1,761 people who had received chemotherapy. Thoracic radiotherapy (≥30 Gy for extensive stage and ≥40 Gy for limited stage) and PCI were independently associated with better survival (P≤0.01 for each), but 6 cycles of chemotherapy instead of 4 was not (limited stage adjusted OR 0.97; 95% CI: 0.48–1.97) extensive stage adjusted OR 1.34; 95% CI: 0.81–2.21).Conclusions: Extending chemotherapy beyond 4 cycles to 6 does not augment survival. Appropriately prescribed thoracic radiotherapy and PCI can prolong survival in both limited and extensive stage SCLC

Hypoxia imaging with [18F]HX4 PET in squamous cell head and neck cancer: a pilot study for integration into treatment planning

Background: Radical chemoradiotherapy is the primary treatment for head and neck cancers in many hospitals. Tumour hypoxia causes radiotherapy resistance, and is an indicator of poor prognosis for patients. Identifying hypoxia to select patients for intensified or hypoxia-modified treatment regimens is therefore of high clinical importance. Patients and Methods: We evaluated hypoxia in a group of patients with newly diagnosed squamous cell head and neck cancer using the hypoxia-selective radiotracer [18F]HX4. Patients underwent a single [18F]HX4 PET/CT scan prior to beginning chemoradiotherapy.Results: Three out of eight patients recruited were scanned with [18F]HX4. Two out of three had pre-treatment [18F]FDGPET/CT scans available for review. [18F]HX4 tumour uptake varied between patients, with tumour to mediastinal ratios ranging from1 to3.5. Conclusions: The spectrum of [18F]HX4 uptake in this small series of patients exemplifies the difference in oxygenation profiles between histologically similar tumours. Performing an additional PET scan with [18F]HX4 prior to chemoradiotherapy treatment was logistically challenging in a routine setting, and therefore validation of its clinical impact should be the focus of future studies

Toxicity profile of bevacizumab in the UK Neurofibromatosis Type 2 cohort

Bevacizumab is considered an established part of the treatment strategies available for schwannomas in patients with Neurofibromatosis Type 2(NF2). In the UK, it is available through NHS National Specialized Commissioning to NF2 patients with a rapidly growing target schwannoma. Regrowth of the tumour on suspension of treatment is often observed resulting in prolonged periods of exposure to bevacizumab to control the disease. Hypertension and proteinuria are common events with bevacizumab use and there are concerns with regards to the long-term risks of prolonged treatment. Dosing, demographic and adverse event(CTCAE 4.03) data from the UK NF2 bevacizumab cohort are reviewed with particular consideration of renal and cardiovascular complications. Eighty patients (48 male:32female), median age 24.5 years (range 11-66years), were followed for a median of 32.7 months (range 12.0–60.2months). The most common adverse events were fatigue, hypertension and infection. A total of 19/80 patients (24%) had either a grade 2 or grade 3 hypertension event and 14/80 patients (17.5%) had proteinuria. Of 36 patients followed for 36 months, 78% were free from hypertension and 86% were free of proteinuria. Logistic regression modeling identified age and induction dosing regime to be predictors of development of hypertension with dose of 7.5mg/kg three weekly and age >30years having higher rates of hypertension. Proteinuria persisted in one of three patients after cessation of bevacizumab. One patient developed congestive heart failure and the details of this case are described. Further work is needed to determine optimal dosing regimes to limit toxicity without impacting on efficacy

REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer

Purpose: REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data. Methods: An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated. Results: The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician-(47,025 forms) and patient-(54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade >= 2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung). Conclusion: The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers. Patient summary: Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short-and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity

Overcoming challenges of translating deep learning models for Glioblastoma: the ZGBM consortium.

OBJECTIVE To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep learning models. METHODS MR imaging data were analysed from a random sample of 5 patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. RESULTS All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced -weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. CONCLUSION The imaging protocol and scheduling varies across the UK, making it challenging to develop machine learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. ADVANCES IN KNOWLEDGE Successful translation of deep learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules

REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer

PURPOSE: REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data. METHODS: An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated. RESULTS: The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician- (47,025 forms) and patient- (54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade ≥2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung). CONCLUSION: The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers. PATIENT SUMMARY: Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short- and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity.status: publishe

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

Aim: To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). Materials and methods: Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in >= 10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). Results: Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. Conclusions: Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models. (C) 2021 The Authors. Published by Elsevier B.V