Implementing streamlined radiology reporting and clinical results management in low-dose CT screening for lung cancer

Lung cancer kills more people in the UK than any other cancer. Mortality rates are poor, with fewer than 10% of people alive 10 years after diagnosis. Lung Cancer Screening (LCS) with low-dose CT (LDCT) is effective at reducing lung cancer mortality when employed in at-risk populations; because of this, in the US, LCS has been implemented as a national programme. The UK does not currently screen for lung cancer, but in 2019 NHS England announced a pilot scheme to implement lung health checks (LHC) in areas with the poorest lung cancer outcomes. Despite these advances in LCS in the UK, there are outstanding questions about how LCS could be implemented safely and effectively, which this thesis, based on experience and data from the SUMMIT Study, aims to investigate. To provide screening safely, implementation of any study or programme must focus on maintaining a favourable cost to benefit ratio. This is particularly true in LCS where high false positive and overdiagnosis rates, as well as considerable levels of incidental findings, lead to possible psychological stress, needless investigations and interventions, making provision challenging to both screenees and healthcare providers. The SUMMIT Study investigates how to deliver evidence-based LCS in a large population (25,000), and this thesis in particular focusses on how LCS can be streamlined through proformatisation of radiological data collection, clinical actioning of results and standardised communication with general practitioners (GPs) and participants. This thesis explains the approach to managing pulmonary nodules and incidental findings detected at LDCT in SUMMIT, and how these findings are collected, triaged, and communicated in a way that is both efficient and safe. Early data from SUMMIT is presented to understand how evidence-based proformas may enable streamlined clinical management, data collection and results communications, while decreasing the burden on healthcare professionals and participants alike

An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms

Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging.  These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy

Latest CT technologies in lung cancer screening:protocols and radiation dose reduction

The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in postprocessing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols

Automatic 3D pulmonary nodule detection in CT images: a survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks

Lung cancer screening

Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.</p

Low-Dose CT lung cancer screening:clinical evidence and implementation research

Lung cancer causes more deaths than breast, cervical, and colorectal cancer combined. Nevertheless, population-based lung cancer screening is still not considered standard practice in most countries worldwide. Early lung cancer detection leads to better survival outcomes: patients diagnosed with stage 1A lung cancer have a >75% 5-year survival rate, compared to < 5% at stage 4. LDCT thorax imaging for the secondary prevention of lung cancer has been studied at length, and has been shown to significantly reduce lung cancer mortality in high-risk populations. The US national lung screening trial reported 20% overall reduction in lung cancer mortality when comparing LDCT to chest x-ray, and the NELSON trial more recently reported 24% reduction when comparing LDCT to no screening. Hence, the focus has now shifted to implementation research. Consequently, the 4-IN-THE-LUNG-RUN consortium, based in 5 European countries, has set up a large-scale multi-center implementation trial. Successful implementation and accessibility of low-dose CT lung cancer screening are dependent on many factors, not limited to; population selection, recruitment strategy, CT-screening frequency, lung nodule management, participant compliance and cost-effectiveness. This review provides an overview of current evidence for LDCT lung cancer screening, and draws attention to major factors which need to be addressed to successfully implement standardized, effective, and accessible screening throughout Europe. Evidence shows that through the appropriate use of risk-prediction models and a more personalized approach to screening, efficacy could be improved. Further, extending the screening interval for low-risk individuals to reduce costs and associated harms is a possibility, and through the use of volumetric based measurement and follow-up, false positive results can be greatly reduced. Finally, smoking cessation programs could be a valuable addition to screening programs and artificial intelligence could offer the solution to the added workload pressures Radiologists are facing

Consensus statement on a screening programme for the detection of early lung cancer in Poland

Introduction: Lung cancer is the most common cancer in Poland and worldwide, and the leading cause of cancer-related deaths.Compared to the present day, the annual number of new cases of lung cancer will have increased by approximately 50%, by 2030.The overall ratio of mortality to incidence totals 0.87 and is among the highest. The five-year survival rate in Poland has recentlyachieved 13.4%. In 2015, lung cancer screening using low-dose computed tomography (LDCT) was introduced to routine clinicalpractice in the United States following the publication of the largest randomised study, The National Lung Screening Trial. Theimplementation of screening programmes in Poland and the rest of Europe also seems unavoidable. Due to the differences, bothin the socioeconomic considerations and healthcare funding, compared to that in the United States, the current approach comesdown to the awaited results of the European randomised study, NELSON. Material and methods: During the meeting of an expert panel at the “Torakoneptunalia 2016” conference in Jastarnia, Poland,a decision was made to summarise and publish the current data on LDCT lung cancer screening in the form of recommendations,or a position statement. The document was prepared by a team composed of a radiologist, thoracic surgeons, pulmonologists,clinical oncologists, epidemiologists, internists, health prevention specialists and pathologists. It reflects the current body ofknowledge about lung cancer, its diagnosis and treatment, and provides recommendations on early detection of lung cancer usingLDCT. The recommendations address the screening procedure, the requirements for the teams conducting the screening, and therequirements for radiologists, pathologists and surgeons involved in the diagnosis and treatment of patients. Results: While awaiting the results of the NELSON study and the European position statement on lung cancer screening methodology,the multidisciplinary group of experts presents their position, laying grounds for the development of an action plan forearly detection of lung cancer in the upcoming future in Poland. Conclusions: Primary and secondary prophylaxis are the principal ways to reduce lung cancer mortality. While smoking cessation is a taskof utmost importance, it must be accompanied by an effective screening programme if the outcome of the disease is to be improved