Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments

The lung cancers: staging and response, CT, 18F-FDG PET/CT, MRI, DWI: review and new perspectives.

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer deaths in both sexes combined. Recent years have seen major advances in the diagnostic and treatment options for patients with non-small-cell lung cancer (NSCLC), including the routine use of 2-deoxy-2[18F]-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) in staging and response evaluation, minimally invasive endoscopic biopsy, targeted radiotherapy, minimally invasive surgery, and molecular and immunotherapies. In this review, the central roles of CT and 18F-FDG PET/CT in staging and response in both NSCLC and malignant pleural mesothelioma (MPM) are critically assessed. The Tumour Node Metastases (TNM-8) staging systems for NSCLC and MPM are presented with critical appraisal of the strengths and pitfalls of imaging. Overviews of the Response Evaluation Criteria in Solid Tumours (RECIST 1.1) for NSCLC and the modified RECIST criteria for MPM are provided, together with discussion of the benefits and limitations of these anatomical-based tools. Metabolic response assessment (not evaluated by RECIST 1.1) will be explored. We introduce the Positron Emission Tomography Response Criteria in Solid Tumours (PERCIST 1.0) to include its advantages and challenges. The limitations of both anatomical and metabolic assessment criteria when applied to NSCLC treated with immunotherapy and the important concept of pseudoprogression are addressed with reference to immune RECIST (iRECIST). Separate consideration is given to the diagnosis and follow up of solitary pulmonary nodules with reference to the British Thoracic Society guidelines and Fleischner guidelines and use of the Brock (CT-based) and Herder (addition of 18F-FDG PET/CT) models for assessing malignant potential. We discuss how these models inform decisions by the multidisciplinary team, including referral of suspicious nodules for non-surgical management in patients unsuitable for surgery. We briefly outline current lung screening systems being used in the UK, Europe and North America. Emerging roles for MRI in lung cancer imaging are reviewed. The use of whole-body MRI in diagnosing and staging NSCLC is discussed with reference to the recent multicentre Streamline L trial. The potential use of diffusion-weighted MRI to distinguish tumour from radiotherapy-induced lung toxicity is discussed. We briefly summarise the new PET-CT radiotracers being developed to evaluate specific aspects of cancer biology, other than glucose uptake. Finally, we describe how CT, MRI and 18F-FDG PET/CT are moving from primarily diagnostic tools for lung cancer towards having utility in prognostication and personalised medicine with the agency of artificial intelligence

Design, tuning and performance evaluation of an automated pulmonary nodule detection system

Radiologists miss about 25-30% of all pulmonary nodules smaller than 1.0 cm. in mass screenings. A system for the automated detection of the pulmonary nodule based on that of Hallard has been designed, tuned, and tested on a 43 chest radiographs [Ballard, 1973). The goal of this system is to aid the radiologist in locating a pulmonary nodule by indicating a few sites in the radiograph that are most likely to be nodules. Computer image analysis programs that respond to specific types of anatomic features have been devised and are incorporated in a pattern recognizer, which uses linear discriminant analysis to classify the candidate nodule sites. Candidate nodule sites that are not classified as nodules are eliminated from the list of sites that are presented to the radiologist for inspection. The pattern recognizer was trained with the features from 2750 candidate nodules, which came from 37 films and another pattern recognizer was trained with the features from 402 candidate nodules from 9 films. This research demonstrates that pattern recognition techniques and procedurally driven image experts are capable of reducing the number of candidate nodule sites that a radiologist must inspect from at most 12 to at most 4 if he is to be 99% confident of having inspected any nodule detected by the system which was trained with 37 films. The radiologist must be willing to accept a film true positive rate of 88% (as opposed to a film true positive rate of 92%) for the convenience of having fewer points to inspect. These film true positive rates are derived from 37 films which contain nodules that were evaluated by the system. The particular contributions of this work lies in the implementation and testing of a spline filter, a preprocessing step, which removes background variations in the radiograph so that nodules are more visible; the development of Vascularity and Rib Experts which recognize these classes of candidate nodules; and in die implementation of the particular features that are extracted from the candidate nodule and used by the pattern classifier

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information of tumor spread. Such information improves cancer treatment in many ways. One important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information it provides helps radiation oncologists to better target the tumor region. However, currently most tumor delineations in radiotherapy planning are performed by manual segmentation, which consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In order to address this issue, a novel co-segmentation method is proposed in this work to enhance the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to differentiate and segment tumor regions from normal regions. On a combined dataset containing 29 patients with lung tumor, the combined method shows good segmentation results as well as good tumor recognition rate

The peripheral pulmonary lesion - bronchoscopic techniques to improve diagnosis

Lung cancer is a leading cause of cancer-related deaths worldwide. This is no different in Australia where it is the main cause of cancer-related mortality, and the fifth most commonly cancer diagnosed in Australians. The recent National Lung Screening Trial demonstrated an improvement in mortality when patients deemed high risk for lung cancer underwent annual screening with low dose computed tomography imaging. Nearly 25% of participants were shown to have imaging suspicious for lung cancer. In light of these results, and with the possibility of increased uptake of screening, it is very likely that the incidence of identified peripheral pulmonary lesions (PPL) will only continue to rise. In evaluating PPLs, standard bronchoscopic investigation involves obtaining transbronchial forceps biopsies (TB-FB). However TBFB has variable diagnostic sensitivity, influenced by factors such as lesion size and position. The introduction of radial endobronchial ultrasound (RP-EBUS) has helped improve diagnostic yields further. Ultrasound images obtained by the miniprobe reflect the underlying structure of the peripheral lesion being examined and RPEBUS is now a well-established technique in the evaluation of PPLs. The overall aim of this thesis was to examine innovative bronchoscopic techniques which could further aid diagnostic yield in investigating PPLs. Methods (i) Radial Endobronchial Ultrasound Greyscale Texture Analysis Using Whole-Lesion Analysis Can Characterise Benign and Malignant Lesions without Region-of-Interest Selection Bias Custom software was developed to analyse RP-EBUS images based on first and second order greyscale texture features. Unconstrained ROIs were mapped onto lesions. Features from expert and nonexpert defined ROIs were compared, as were results of image analysis to tissue histology. (ii) Radial Endobronchial Ultrasound with Transbronchial Cryobiopsy versus Radial EBUS alone for the Diagnosis of Peripheral Pulmonary Lesions Prospective, single-centre randomised controlled trials of patients with PPLs. Patients were randomised to receive either one transbronchial cryobiopsy (TB-CB) sample, or 5 TB-FB samples. Results (i) Greyscale texture analysis of RP-EBUS images using unconstrained regions of interest (ROIs) demonstrated 5 features which were significantly different between benign and malignant lesions. Highest positive predictive values were associated with maximal and range of pixel intensities. No significant differences were seen between expert and non-expert-defined ROIs. (ii) 28 lesions were evaluated with overall diagnostic yield 76.7%. Diagnostic yields of TB-CB and TB-FB were 91.7% and 68.8% respectively (p=0.14). Median size of TB-CB was 7.0mm compared to 2.55mm (p<0.0001). There were no major complications with either technique. Conclusion Timely diagnosis of PPLs is critical to enable disease staging and to guide initiation of appropriate definitive treatment. Greyscale image analysis and texture analysis using the whole RP-EBUS image as a ROI can assist in distinguishing between malignant and benign lesions. This is a potentially valuable additional clinical tool in the diagnosis of peripheral lesions. However further validation is required. Cryotherapy has provided an alternative method of obtaining transbronchial biopsies (TBBs). Not only does it provide significantly larger biopsy sample, which is advantageous for further immunohistochemical and molecular analysis, but it also could be superior in diagnosing lesions which are not easily accessible by TB-FB.Thesis (MPhil) -- University of Adelaide, Adelaide Medical School, 202

Evaluation of focal liver lesions by magnetic resonance imaging and correlation with pathology

 Background: The goals of imaging in focal liver lesions is to assess the number, size, location and characterize the lesions as benign / malignant with newer imaging modalities and confirmation of pathology by Fine needle aspiration cytology or by biopsy. This is essential for treatment planning and prognosis.Methods: A total of 42 patients detected to have focal lesions in liver on ultra-sonogram were characterized on MRI on the basis of morphology, signal characteristics, enhancement patterns. Extra hepatic spread is suggested by capsular breach, peritoneal metastases and lymph node enlargement. Tissue diagnosis was obtained by fine needle aspiration cytology/ Tru cut biopsy/ surgery. Hemangiomas and simple cysts were followed up for an average period of 7.5 months by imaging without biopsy.Results: Out of 42 patients, 28 were males (68%) and 14 were females (32%). The age range was 20 to 70 years with a mean age of 51 years for malignant lesions. The right lobe of liver was involved in 26 (62%), left lobe in 7 (17%) and both lobes in 9(21%) cases. There were 24 benign and 18 malignant lesions. The mean ADC value was 2.092 X 10-3 sec/ mm2 and 1.241 X 10-3 sec/ mm2 for benign and malignant lesions respectively. The difference in mean ADC values in both the groups was significant (p<0.0001).Conclusions: MRI was able to predict diagnosis in 38 of the 42 lesions (90%) which were proved on pathology or by follow up imaging. MRI could not provide specific diagnosis in two early abscess, one each of multifocal hepatocellular carcinoma and regenerative nodules. Thus MR imaging is a powerful tool for the evaluation of focal liver lesions