Clinical Research Directory

Using Breath, Cell Free DNA and Image Analysis to PRedIct Normal TissUe and Tumour Response During Prostate Cancer SBRT

Personalisation of radiotherapy dose based on real-time assessments of normal tissue and tumour response would maximise cure and minimise treatment related toxicity. During a 5 fraction course of prostate Stereotactic Body Radiotherapy (SBRT) this pilot study will assess whether a number of different biomarker approaches can predict for normal tissue and tumour response. Firstly the investigators will analyse volatile organic compounds released within the breath with each fraction of treatment. Secondly the investigators will analyse cell free normal tissue and tumour DNA released during treatment. Thirdly the investigators will develop imaging processing algorithms to look for imaging biomarkers predicting rectal wall toxicity using pre and post treatment cone beam CT verification images. Each of these approaches will be assessed against prostate specific antigen (PSA), Common Terminology Criteria for Adverse Events (CTCAE v4.0) criteria and Expanded Prostate Cancer Index Composite (EPIC-26) patient reported outcomes with a maximum of 24 months of follow up.

BREVOC Study: Exhaled VOCs for High-Risk Chest Pain in the ED

Study Objectives 1. Screening and identification of diagnostic biomarkers: To establish the exhaled volatile organic compound (VOC) profile of patients with acute high-risk chest pain and to differentiate high-risk chest pain patients. 2. Exploration of aldehyde detection: To investigate the role of exhaled aldehyde detection in high-risk chest pain patients; to establish and validate an early differential diagnostic model of exhaled VOCs for high-risk chest pain, thereby optimizing emergency triage procedures. 3. Prognostic evaluation: To assess the predictive value of VOC concentration changes for in-hospital mortality and major adverse cardiovascular events (MACE) in high-risk chest pain patients. 4. Novel diagnostic markers: To explore new biomarker combinations superior to conventional diagnostic indicators. Study Hypotheses 1. High-risk chest pain patients present a VOC profile distinct from that of healthy individuals and patients with other causes of chest pain. 2. Baseline levels and early changes of exhaled VOCs can achieve both rapid diagnosis and risk stratification. 3. Exhaled VOCs can predict the prognosis of high-risk chest pain patients. Sample Size Calculation This is an exploratory study, aiming to enroll all patients presenting with acute chest pain to the emergency department of our hospital between May 2025 and June 2026. Based on prior studies, the primary endpoint is assessed using area under the receiver operating characteristic curve (AUC-ROC) analysis, with α = 0.05 and 1-β = 0.90. The expected model AUC is 0.75, compared to a minimum acceptable AUC of 0.65. Assuming a group ratio of 1:2 (high-risk: non-high-risk), Power Analysis and Sample Size (PASS) software estimates a minimum sample size of approximately 1,320 patients. To enable subgroup analyses (e.g., acute coronary syndrome \[ACS\], pulmonary embolism \[PE\], aortic dissection \[AD\]) and the construction of multivariable predictive models, at least 150-300 patients per subgroup are required. According to preliminary investigation, the emergency department admits approximately 20 chest pain patients daily. To ensure model stability, cross-validation, and sufficient subgroup evaluation, a total of 6,000 patients will be prospectively enrolled, thereby enhancing scientific rigor and external validity. Primary Outcome Discrimination between high-risk and non-high-risk chest pain patients, assessed by AUC-ROC, sensitivity, and specificity. Secondary Outcomes 1. Missed diagnosis rate (the proportion of high-risk patients misclassified as low or intermediate risk by the model). 2. Average emergency department length of stay and medical costs under model-guided triage. 3. In-hospital mortality and incidence of major adverse cardiovascular events (MACE). Statistical Methods 1. Categorical variables will be expressed as frequencies or percentages; normally or approximately normally distributed continuous variables as mean ± standard deviation; and skewed data as median (P25, P75). Between-group comparisons will be performed using independent-samples t-tests, one-way analysis of variance (ANOVA), Mann-Whitney U tests, or Kruskal-Wallis tests for continuous variables, and chi-square (χ²) tests or Fisher's exact tests for categorical variables. 2. Feature selection of VOCs will be performed using methods such as least absolute shrinkage and selection operator (LASSO) regression, followed by the construction of a VOC scoring model. 3. Prognostic factors will be assessed using Cox proportional hazards models. 4. Trajectory analysis will be applied to evaluate changes in VOC concentrations over time.

Addressing Tetrachloroethylene Exposure in an Impacted Community

Groundwater in Martinsville, IN, is contaminated by volatile organic compounds (VOCs), tetrachloroethylene (PCE) and trichloroethylene (TCE). Indoor air in some residential and commercial buildings is also contaminated with PCE and TCE. This study is being conducted to better understand the impact of low-level exposures to these compounds on community members' health. Data collected in this study will be used to help the community identify a course of action.

Augmented Response of Volatile Biomarkers in Assessment of Oesophagogastric Cancer (AROMA 1 / BIORESOURCE)

Cancer of the stomach and oesophagus is among the world's top five cancers. Survival rates are very poor as the disease presents late and early symptoms are non-specific. The study team has developed a non-invasive test for cancers of the stomach and oesophagus based on the detection of volatile organic compounds in exhaled breath. These compounds are known to be produced by both cancers as well as cancer associated bacteria within the gut. The proposed innovation is to improve the accuracy of this test by investigating whether simple metabolic substrates can increase the production of these volatile organic compounds by both the tumour and its associated bacteria.

Breathomics in the Diagnosis and Prediction of Radiotherapy-Induced Oropharyngeal Mucositis in Head and Neck Tumors

Radiotherapy-Induced Oropharyngeal Mucositis (RIOM) is one of the most distressing side effects for patients with head and neck tumors during radiotherapy, requiring clinical physicians to manage it according to the severity of mucositis to alleviate symptoms and improve quality of life. However, traditional diagnosis of RIOM overly relies on subjective evaluation, lacks early sensitivity, and existing biomarker diagnostic methods suffer from insufficient efficacy, invasiveness, and inconsistent results. This study aims to explore the diagnostic and predictive value of exhaled breathomics in RIOM of head and neck tumors. By collecting exhaled breath samples from head and neck tumor patients undergoing radiotherapy and analyzing volatile organic compounds (VOCs) using breath detection technology, we aim to develop and validate a non-invasive diagnostic and predictive model based on exhaled breathomics. The study will identify specific VOCs as potential biomarkers, providing new tools for early diagnosis, timely prediction, and personalized treatment of RIOM.