CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

The role of minimally invasive endoscopic techniques in the diagnosis, treatment and prevention of lung cancer

Squamous cell carcinoma of the lung arises from pre-invasive progenitors in the central airways. The archetypal model appears to be a stepwise morphological progression until there is invasion of the basement membrane. However, their natural history is not well understood and their treatment remains controversial, with radical therapies being offered to individuals who may never develop cancer. Autofluorescence bronchoscopy gives us the ability to follow the natural history of these lesions, with the prospect that early detection may improve survival. In this thesis, the natural history of pre-invasive disease is described in a prospective longitudinal cohort study. The data identifies a ‘high-risk’ cohort of patients with severe dysplasia and carcinoma in situ, in whom close surveillance detects multiple interval lung cancers at an early stage. The data from this indicates the need of a minimally invasive bronchoscopic treatment for these patients. A further prospective clinical trial evaluates the role of photodynamic therapy in individuals with early invasive carcinomas of the airway who were unfit for conventional lung cancer treatment. Photodynamic laser therapy (PDT) proved to be an effective therapy for patients with small and superficial lesions. However, PDT has not been tested in randomised controlled trials, so a randomised clinical trial (the PEARL trial) was designed to evaluate whether treating high-grade preinvasive lesions will avert progression into invasive carcinoma. Endoscopic laser resection of primary lung carcinoid tumours was also evaluated. This thesis demonstrates that laser can be used to effectively ablate carcinoid tumours. Treatment was particular effective in small intraluminal carcinoid tumours and may be an alternative to surgical resection. Finally, the role of sedation in interventional bronchoscopy was assessed in a prospective study for patients undergoing endobronchial ultrasound and transbronchial needle aspiration. This thesis demonstrates that endoscopist led sedation is comparable to anaesthetic led sedation, but identified the need for a randomised controlled trial

PREDICTION OF EARLY STAGE LUNG CANCER PROGNOSIS AFTER SURGERY USING A NEW CAD-GENERATED IMAGING MARKER

Due to cancer heterogeneity, identifying new clinical markers to more effectively predict prognosis of cancer patients plays an important role to improve efficacy of cancer treatment. Objective of this study is to develop and test a new quantitative imaging (QI) marker to predict prognosis of early stage non-small cell lung cancer (NSCLC) patients after surgery. For this purpose, this study includes following research tasks or steps. First, a new computer-aided detection (CAD) scheme was developed to automatically segment lung and tumor regions from the chest computed tomography (CT) images of all the slices simultaneously using an adaptive pixel value thresholding and/or region growing method. Next, CAD scheme was used to compute a large number of image features related to tumor shape, size, circularity, density heterogeneity, and lung background tissue patterns. Then, a machine learning approach was applied to build a multi-feature fusion based prediction model, which enables to produce a CAD-generated quantitative image (QI) marker for predicting diseases-free survival (DFS) of the NSCLC patients within 3 years after surgery. In order to achieve more robust result of training and testing the machine learning model, a leave-one-case-out (LOCO) cross-validation method was used. A feature selection process using a correlation-based feature subset evaluator and a synthetic minority oversampling technique (SMOTE) were embedded in LOCO based training process. Finally, prediction performance of the QI marker or prediction model was evaluated using the receiver operating characteristic (ROC) and other statistical data analysis. In summary, the goal of this study is to select more effective image features computed from both segmented lung tumors and emphysema related background regions for producing a new CAD-generated QI marker and demonstrate the feasibility of applying this new QI marker to yield higher performance in predicting prognosis of early stage NSCLC patient

Gene Expression Signature As A Diagnostic Test For Thoracic Aortic Aneurysms: A Validation Study

Thoracic aortic aneurysms (TAAs) are clinically-silent diseases that predispose individuals to life-threatening aortic dissection or rupture. If detected early, TAAs can be safely treated with elective surgery. Therefore, there is a great clinical need in screening for TAAs. However, no reliable screening programs exist, and radiographic imaging is too costly and harmful for screening entire at-risk populations. We hypothesize that a novel diagnostic blood test based on the gene-expression profiles of a previously-identified panel of 41 genes (a RNA signature) is greater than 70% sensitive in detecting TAAs. Using RNA extracted from peripheral blood cells of 40 individuals (24 TAA patients and 16 spousal controls), we performed real-time PCR using customized TaqMan Array Cards to analyze the relative expression levels of this panel of genes. A 10-fold cross-validation study based on these expression levels was used to predict whether each sample belonged to a TAA patient or a spousal control. When compared with each sample\u27s true clinical status, this RNA signature-based prediction model was 83% accurate, with a sensitivity of 88% and a specificity of 75%. Furthermore, the expression levels of the individual genes were largely consistent with their expression levels from a previous study of this RNA signature (r2 = 0.75 for TAA patient samples, and r2 = 0.73 for spousal control samples), supporting the reproducibility of this test. Altogether, these findings demonstrate that gene-expression profiling is an accurate, sensitive, and reliable method for detecting TAAs. If utilized as a clinical screening test, this RNA signature has the potential to detect silent TAAs, leading to earlier diagnosis and reduced mortality of this dangerous condition

Volume 21, issue 6

The mission of CJS is to contribute to the effective continuing medical education of Canadian surgical specialists, using innovative techniques when feasible, and to provide surgeons with an effective vehicle for the dissemination of observations in the areas of clinical and basic science research. Visit the journal website at http://canjsurg.ca/ for more.https://ir.lib.uwo.ca/cjs/1157/thumbnail.jp

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Dynamic contrast-enhanced CT compared with positron emission tomography CT to characterise solitary pulmonary nodules: the SPUtNIk diagnostic accuracy study and economic modelling

BACKGROUND: Current pathways recommend positron emission tomography-computerised tomography for the characterisation of solitary pulmonary nodules. Dynamic contrast-enhanced computerised tomography may be a more cost-effective approach. OBJECTIVES: To determine the diagnostic performances of dynamic contrast-enhanced computerised tomography and positron emission tomography-computerised tomography in the NHS for solitary pulmonary nodules. Systematic reviews and a health economic evaluation contributed to the decision-analytic modelling to assess the likely costs and health outcomes resulting from incorporation of dynamic contrast-enhanced computerised tomography into management strategies. DESIGN: Multicentre comparative accuracy trial. SETTING: Secondary or tertiary outpatient settings at 16 hospitals in the UK. PARTICIPANTS: Participants with solitary pulmonary nodules of ≥ 8 mm and of ≤ 30 mm in size with no malignancy in the previous 2 years were included. INTERVENTIONS: Baseline positron emission tomography-computerised tomography and dynamic contrast-enhanced computer tomography with 2 years' follow-up. MAIN OUTCOME MEASURES: Primary outcome measures were sensitivity, specificity and diagnostic accuracy for positron emission tomography-computerised tomography and dynamic contrast-enhanced computerised tomography. Incremental cost-effectiveness ratios compared management strategies that used dynamic contrast-enhanced computerised tomography with management strategies that did not use dynamic contrast-enhanced computerised tomography. RESULTS: A total of 380 patients were recruited (median age 69 years). Of 312 patients with matched dynamic contrast-enhanced computer tomography and positron emission tomography-computerised tomography examinations, 191 (61%) were cancer patients. The sensitivity, specificity and diagnostic accuracy for positron emission tomography-computerised tomography and dynamic contrast-enhanced computer tomography were 72.8% (95% confidence interval 66.1% to 78.6%), 81.8% (95% confidence interval 74.0% to 87.7%), 76.3% (95% confidence interval 71.3% to 80.7%) and 95.3% (95% confidence interval 91.3% to 97.5%), 29.8% (95% confidence interval 22.3% to 38.4%) and 69.9% (95% confidence interval 64.6% to 74.7%), respectively. Exploratory modelling showed that maximum standardised uptake values had the best diagnostic accuracy, with an area under the curve of 0.87, which increased to 0.90 if combined with dynamic contrast-enhanced computerised tomography peak enhancement. The economic analysis showed that, over 24 months, dynamic contrast-enhanced computerised tomography was less costly (£3305, 95% confidence interval £2952 to £3746) than positron emission tomography-computerised tomography (£4013, 95% confidence interval £3673 to £4498) or a strategy combining the two tests (£4058, 95% confidence interval £3702 to £4547). Positron emission tomography-computerised tomography led to more patients with malignant nodules being correctly managed, 0.44 on average (95% confidence interval 0.39 to 0.49), compared with 0.40 (95% confidence interval 0.35 to 0.45); using both tests further increased this (0.47, 95% confidence interval 0.42 to 0.51). LIMITATIONS: The high prevalence of malignancy in nodules observed in this trial, compared with that observed in nodules identified within screening programmes, limits the generalisation of the current results to nodules identified by screening. CONCLUSIONS: Findings from this research indicate that positron emission tomography-computerised tomography is more accurate than dynamic contrast-enhanced computerised tomography for the characterisation of solitary pulmonary nodules. A combination of maximum standardised uptake value and peak enhancement had the highest accuracy with a small increase in costs. Findings from this research also indicate that a combined positron emission tomography-dynamic contrast-enhanced computerised tomography approach with a slightly higher willingness to pay to avoid missing small cancers or to avoid a 'watch and wait' policy may be an approach to consider. FUTURE WORK: Integration of the dynamic contrast-enhanced component into the positron emission tomography-computerised tomography examination and the feasibility of dynamic contrast-enhanced computerised tomography at lung screening for the characterisation of solitary pulmonary nodules should be explored, together with a lower radiation dose protocol

Multimodality treatment for esophageal squamous cell carcinoma

This thesis aims to optimize multimodality treatments for locally advanced esophageal squamous cell carcinoma (ESCC), specifically in East-Asia, recognizing that ESCC may differ in biology and response to treatment in different parts of the world. Part I of the thesis introduces the subtype ESCC and its characteristics from different geographical perspectives followed by an exposition of the differences between ESCC in eastern and western worlds. Part II optimizes trimodal therapy of ESCC in Taiwan, as a representative region of East-Asia. Finally, in Part III, the accuracy of liquid biopsies in identifying patients who may not need surgery after trimodal therapy is assessed.