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

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

Implementation planning for lung cancer screening in China.

Lung cancer is the leading cause of cancer-related deaths in China, with over 690 000 lung cancer deaths estimated in 2018. The mortality has increased about five-fold from the mid-1970s to the 2000s. Lung cancer low-dose computerized tomography (LDCT) screening in smokers was shown to improve survival in the US National Lung Screening Trial, and more recently in the European NELSON trial. However, although the predominant risk factor, smoking contributes to a lower fraction of lung cancers in China than in the UK and USA. Therefore, it is necessary to establish Chinese-specific screening strategies. There have been 23 associated programmes completed or still ongoing in China since the 1980s, mainly after 2000; and one has recently been planned. Generally, their entry criteria are not smoking-stringent. Most of the Chinese programmes have reported preliminary results only, which demonstrated a different high-risk subpopulation of lung cancer in China. Evidence concerning LDCT screening implementation is based on results of randomized controlled trials outside China. LDCT screening programmes combining tobacco control would produce more benefits. Population recruitment (e.g. risk-based selection), screening protocol, nodule management and cost-effectiveness are discussed in detail. In China, the high-risk subpopulation eligible for lung cancer screening has not as yet been confirmed, as all the risk parameters have not as yet been determined. Although evidence on best practice for implementation of lung cancer screening has been accumulating in other countries, further research in China is urgently required, as China is now facing a lung cancer epidemic

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Computer Methods For Pulmonary Nodule Characterization From Ct Images

Computed tomography (CT) scans provide radiologists a non-invasive method of imaging internal structures of the body. Although CT scans have enabled the earlier detection of suspicious nodules, these nodules are often small and difficult to accurately classify for radiologists. An automated system was developed to classify a pulmonary nodule based on image features extracted from a single CT scan. Several critical issues related to performance evaluation of such systems were also examined. The image features considered in the system were: statistics from the density distribution, shape, curvature, and boundary features. The shape and density features were computed through moment analysis of the segmented nodule. Local curvature was computed from a triangle-tessellated surface of the nodule; the statistics of the distribution of curvatures were used as features in the system. Finally, the boundary of the nodule was examined to quantify the transition region between the nodule and lung parenchyma. This was accomplished by combining the grayscale information and 3D model to measure the gradient on the surface of the nodule. These methods resulted in a total of 43 features. For compari- son, 2D features were computed for the density and shape features, resulting in 26 features. Four feature classification schemes were evaluated: logistic regression, k-nearest-neighbors, distance-weighted nearest-neighbors, and support vector machines (SVM). These features and classifiers were validated on a large dataset of 259 nodules. The best performance, an area under the ROC curve (AUC) of 0.702, was achieved using 3D features and the logistic regression classifier. A major consideration when evaluating a nodule classification system is whether the system presents an improvement over a baseline performance. Since the majority of large nodules in many datasets are malignant, the impact of nodule size on the performance of the classification system was examined. This was accomplished by comparing the performance of the system with feature sets that included sizedependent features to feature sets that excluded those features.The performance of size alone, estimated using a size-threshold classifier, was an AUC of 0.653. For the SVM classifier, removing size-dependent features reduced the performance from an AUC of 0.69 to 0.61. To approximate the performance that might be obtained on a dataset without a size bias, a subset of cases was selected where the benign and malignant nodules were of similar sizes. On this subset, size was not a very powerful feature with an AUC of 0.507, and features that were not dependent on size performed better than size-dependent features for SVM, with an AUC of 0.63 compared to 0.52. While other methods have been proposed for performing nodule classification, this is the first study to comprehensively look at the performance impact from datasets with nodules that exhibit a bias in size

Open-source virtual bronchoscopy for image guided navigation

This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance

Evaluation of Risk Models and Biomarkers for the Optimization of Lung Cancer Screening

More deaths can be attributed to lung cancer, than to any other cancer type. Evidence collected over the last 10 years, from randomized trials in the USA and Europe, indicates that screening by means of low-dose computed tomography (LDCT) could reduce the number of lung cancer (LC) deaths by about 20%-24%. While these findings have led to the implementation of screening programs in the USA, South Korea and Poland, discussions on their optimal design and execution are still ongoing in various countries, including Germany. Optimizing screening means finding the right balance between mortality reduction and risks, harms, and monetary costs. LDCT-scans are expensive, expose participants to radiation and put them at risk for overdiagnosis, as well as at risk for unnecessary invasive and expensive confirmatory procedures triggered by false positive (FP) results. Minimizing the number of unnecessary screening and confirmatory examinations should be prioritized. While risk-based eligibility has been shown to best target candidates, questions regarding optimal screening frequency, accurate nodule evaluation, stop-screening criteria to reduce overdiagnosis, and the use of complementary non-invasive diagnostic methods, remain open. Statistical models and biomarkers have been developed to help answer these questions. However, there is limited evidence of their validity in data from screening contexts and populations other than those in which they were developed. The analyses presented in this thesis are based on data collected as part of the German Lung Cancer Screening Intervention (LUSI) trial in order to validate models that address the questions: 1) can candidates for biennial vs annual screening be identified on the basis of their LC risk? 2) can the number of FP test results be reduced by accurately estimating the malignancy of LDCT-detected nodules? 3) What was the extent of overdiagnosis in the LUSI trial and how does overdiagnosis risk relate to the age and remaining lifetime of participants? Additionally, blood samples from participants of the LUSI were measured to evaluate: 4) whether the well-validated diagnostic biomarker test EarlyCDT®-Lung is sensitive enough to detect tumors seen in LDCT images. The LCRAT+CT and Polynomial models predict LC risk based on subject characteristics and LDCT imaging findings. Results of this first external validation confirmed their ability to identify participants with LC detected within 1-2 years after first screening. Discrimination was higher compared to a criterion based on nodule size and, to a lesser degree, compared to a model based on smoking and subject characteristics (LCRAT). This suggested that while LDCT findings can enhance models, most of their performance can could be attributed to information on smoking. Skipping 50% of annual LDCT examinations (i.e., for participants with estimated risks <5th decile) would have caused <10% delayed diagnoses, indicating that candidates for biennial screening could be identified based on their predicted LC risks without compromising on early detection. Absolute risk estimates were, on average, below the observed LC rates, indicating poor calibration. Models developed using data from the Canadian screening study PanCan showed excellent ability to differentiate between tumors and non-malignant nodules seen on LDCT scans taken at first screening participation and to accurately predict absolute malignancy risk. However, they showed lower performance when applied on data of nodules detected in later rounds. In contrast, a model developed on data from the UKLS trial and models developed on data from clinical settings did not perform as well in any screening round. Excess incidence of screen-detected lung tumors, an estimator of overdiagnosis, was within the range of values reported by other trials after similar post-screening follow-up (ca. 5-6 years). Estimates of mean pre-clinical sojourn time (MPST) and LDCT detection sensitivity were obtained via mathematical modeling. The highest excess incidence and longest MPST estimates were found among adenocarcinomas. The proportion of tumors with long lead times predicted based on MPST estimates (e.g., 23% with lead times ≥8 years) suggested a substantial overdiagnosis risk for individuals with residual life expectancies shorter than these hypothetical lead times, for example for heavy smokers over the age of 75. The tumor autoantibody panel measured by EarlyCDT®-Lung, a test widely validated as a diagnostic tool in clinical settings and recently tested as a pre-screening tool in a large randomized Scottish trial (ECLS), was found to have insufficient sensitivity for the identification of lung tumors detected via LDCT and of participants with screen-detected pulmonary nodules for whom more invasive diagnostic procedures should be recommended. Overall, the findings presented in this thesis indicate that risk prediction models can help optimize LC screening by assigning participants to appropriate screening intervals, and by increasing the accuracy of nodule evaluation. However, there is a need for further external model validation and re-calibration. Additionally, while excess incidence can provide estimates of overdiagnosis risk at a population-level, a better approach would be to obtain model-based personalized estimates of tumor lead and residual lifetime. Better individualized decisions about whether to start or stop screening could be taken on the basis of the relationship between these estimates and the risk of overdiagnosis. Finally, although there is evidence for the potential of biomarkers to complement LC screening, the so far most promising candidate (EarlyCDT®-Lung) cannot be recommended as a pre-screening tool given its poor sensitivity for the identification of lung tumors detected via LDCT. In conclusion, while steps have been taken in the right direction, more research is required in order to answer all open questions regarding the optimal design of lung cancer screening programs