16 results found
Owens CARA, Hindocha SUMEET, Lee RICHARD, et al., 2023, The lung cancers: staging and response, CT, 18F-FDG PET/CT, MRI, DWI: review and new perspectives, BRITISH JOURNAL OF RADIOLOGY, Vol: 96, ISSN: 0007-1285
Charlton TG, Hindocha S, 2023, Future Proofing the Clinical Oncology Workforce., Clin Oncol (R Coll Radiol), Vol: 35, Pages: 314-317
Hindocha S, Charlton TG, Linton-Reid K, et al., 2022, Author Correction: Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC, NPJ PRECISION ONCOLOGY, Vol: 6
Hindocha S, Charlton TG, Linton-Reid K, et al., 2022, Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC, npj Precision Oncology, Vol: 6, Pages: 1-11, ISSN: 2397-768X
Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592–0.832) and 0.685 (0.585–0.784), (2) RFS: 0.825 (0.733–0.916) and 0.750 (0.665–0.835), (3) Recurrence: 0.678 (0.554–0.801) and 0.673 (0.577–0.77). For the combined models: (1) OS: 0.702 (0.583–0.822) and 0.683 (0.586–0.78), (2) RFS: 0.805 (0.707–0.903) and 0·755 (0.672–0.838), (3) Recurrence: 0·637 (0.51–0.·765) and 0·738 (0.649–0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC.
Viggars A, Charlton T, Olsson-Brown A, et al., 2022, Acute Oncology: Increasing Engagement and Visibility in Acute Care Settings - The Trainee Perspective, CLINICAL ONCOLOGY, Vol: 34, Pages: 545-548, ISSN: 0936-6555
Hindocha S, Charlton T, Linton-Reid K, et al., 2022, Machine learning on clinical data for mortality risk-stratification after radiotherapy for NSCLC, Publisher: ELSEVIER IRELAND LTD, Pages: S773-S774, ISSN: 0167-8140
Hindocha S, Charlton T, Linton-Reid K, et al., 2022, A CT-radiomics model to predict recurrence post curative-intent radiotherapy for stage I-III NSCLC, Annual Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), Publisher: ELSEVIER IRELAND LTD, Pages: S319-S320, ISSN: 0167-8140
Hunter B, Hindocha S, Lee R, 2022, The role of artificial intelligence in cancer early diagnosis, Cancers, Vol: 14, Pages: 1-20, ISSN: 2072-6694
Improving the proportion of patients diagnosed with early-stage cancer is a key priority of the World Health Organisation. In many tumour groups, screening programmes have led to improvements in survival, but patient selection and risk stratification are key challenges. In addition, there are concerns about limited diagnostic workforces, particularly in light of the COVID-19 pandemic, placing a strain on pathology and radiology services. In this review, we discuss how artificial intelligence algorithms could assist clinicians in (1) screening asymptomatic patients at risk of cancer, (2) investigating and triaging symptomatic patients, and (3) more effectively diagnosing cancer recurrence. We provide an overview of the main artificial intelligence approaches, including historical models such as logistic regression, as well as deep learning and neural networks, and highlight their early diagnosis applications. Many data types are suitable for computational analysis, including electronic healthcare records, diagnostic images, pathology slides and peripheral blood, and we provide examples of how these data can be utilised to diagnose cancer. We also discuss the potential clinical implications for artificial intelligence algorithms, including an overview of models currently used in clinical practice. Finally, we discuss the potential limitations and pitfalls, including ethical concerns, resource demands, data security and reporting standards.
Hindocha S, Charlton TG, Linton-Reid K, et al., 2022, A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models., EBioMedicine, Vol: 77, ISSN: 2352-3964
BackgroundSurveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment.MethodsA retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed.FindingsMedian follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575–0·788) and 0·681 (0·597–0·766), 2) Recurrence: 0·687 (0·582–0·793) and 0·722 (0·635–0·81), and 3) OS: 0·759 (0·663–0·855) and 0·717 (0·634–0·8). Our models were superior to TNM stage and performan
Hindocha S, Charlton T, Linton-Reid K, et al., 2022, Combined CT radiomics of primary tumour and metastatic lymph nodes improves prediction of recurrence following radiotherapy for non-small cell lung cancer, Publisher: ELSEVIER IRELAND LTD, Pages: S59-S60, ISSN: 0169-5002
Hindocha S, Charlton T, Linton-Reid K, et al., 2022, A machine learning approach using clinical data for recurrence risk-stratification following radiotherapy for non-small cell lung cancer, Publisher: ELSEVIER IRELAND LTD, Pages: S57-S57, ISSN: 0169-5002
Hunter B, Reis S, Campbell D, et al., 2021, Development of a structured query language and natural language processing algorithm to identify lung nodules in a cancer centre, Frontiers in Medicine, Vol: 8, Pages: 1-10, ISSN: 2296-858X
Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy.Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.
Hindocha S, Campbell D, Ahmed M, et al., 2021, Immune checkpoint inhibitor and radiotherapy-related pneumonitis: an informatics approach to determine real-world incidence, severity, management & resource implications, Frontiers in Medicine, Vol: 8, Pages: 1-10, ISSN: 2296-858X
Pneumonitis is a well-described, potentially life-threatening adverse effect of immune checkpoint inhibitors (ICI) and thoracic radiotherapy. It can require additional investigations, treatment, and interruption of cancer therapy. It is important for clinicians to have an awareness of its incidence and severity, however real-world data are lacking and do not always correlate with findings from clinical trials. Similarly, there is a dearth of information on cost impact of symptomatic pneumonitis. Informatics approaches are increasingly being applied to healthcare data for their ability to identify specific patient cohorts efficiently, at scale. We developed a Structured Query Language (SQL)-based informatics algorithm which we applied to CT report text to identify cases of ICI and radiotherapy pneumonitis between 1/1/2015 and 31/12/2020. Further data on severity, investigations, medical management were also acquired from the electronic health record. We identified 248 cases of pneumonitis attributable to ICI and/or radiotherapy, of which 139 were symptomatic with CTCAE severity grade 2 or more. The grade ≥2 ICI pneumonitis incidence in our cohort is 5.43%, greater than the all-grade 1.3–2.7% incidence reported in the literature. Time to onset of ICI pneumonitis was also longer in our cohort (mean 4.5 months, range 4 days-21 months), compared to the median 2.7 months (range 9 days−19.2 months) described in the literature. The estimated average healthcare cost of symptomatic pneumonitis is £3932.33 per patient. In this study we use an informatics approach to present new real-world data on the incidence, severity, management, and resource burden of ICI and radiotherapy pneumonitis. To our knowledge, this is the first study to look at real-world incidence and healthcare resource utilisation at the per-patient level in a UK cancer hospital. Improved management of pneumonitis may facilitate prompt continuation of cancer therapy, and improved outcomes f
Hindocha S, Badea C, 2021, Moral exemplars for the virtuous machine: the clinician’s role in ethical artificial intelligence for healthcare, AI and Ethics, ISSN: 2730-5953
<jats:title>Abstract</jats:title><jats:p>Artificial Intelligence (AI) continues to pervade several aspects of healthcare with pace and scale. The need for an ethical framework in AI to address this has long been recognized, but to date most efforts have delivered only high-level principles and value statements. Herein, we explain the need for an ethical framework in healthcare AI, the different moral theories that may serve as its basis, the rationale for why we believe this should be built around virtue ethics, and explore this in the context of five key ethical concerns for the introduction of AI in healthcare. Some existing work has suggested that AI may replace clinicians. We argue to the contrary, that the clinician will not be replaced, nor their role attenuated. Rather, they will be integral to the responsible design, deployment, and regulation of AI in healthcare, acting as the moral exemplar for the virtuous machine. We collate relevant points from the literature and formulate our own to present a coherent argument for the central role of clinicians in ethical AI and propose ideas to help advance efforts to employ ML-based solutions within healthcare. Finally, we highlight the responsibility of not only clinicians, but also data scientists, tech companies, ethicists, and regulators to act virtuously in realising the vision of ethical and accountable AI in healthcare.</jats:p>
Hindocha S, Campbell D, Hunter B, et al., 2021, Cancer therapy pneumonitis incidence, severity and management: findings from a large UK cancer centre, Publisher: ELSEVIER IRELAND LTD, Pages: S37-S37, ISSN: 0169-5002
Hindocha S, Charlton T, Rayment M, et al., 2013, Feasibility and acceptability of routine human immunodeficiency virus testing in general practice: your views, Primary Health Care Research & Development, Vol: 14, Pages: 212-216, ISSN: 1463-4236
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.