Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Discrepancy between radiological and pathological size of renal masses

<p>Abstract</p> <p>Background</p> <p>Tumor size is a critical variable in staging for renal cell carcinoma. Clinicians rely on radiological estimates of pathological tumor size to guide patient counseling regarding prognosis, choice of treatment strategy and entry into clinical trials. If there is a discrepancy between radiological and pathological measurements of renal tumor size, this could have implications for clinical practice. Our study aimed to compare the radiological size of solid renal tumors on computed tomography (CT) to the pathological size in an Australian population.</p> <p>Methods</p> <p>We identified 157 patients in the Westmead Renal Tumor Database, for whom data was available for both radiological tumor size on CT and pathological tumor size. The paired Student's <it>t</it>-test was used to compare the mean radiological tumor size and the mean pathological tumor size. Statistical significance was defined as <it>P </it>< 0.05. We also identified all cases in which post-operative down-staging or up-staging occurred due to discrepancy between radiological and pathological tumor sizes. Additionally, we examined the relationship between Fuhrman grade and radiological tumor size and pathological T stage.</p> <p>Results</p> <p>Overall, the mean radiological tumor size on CT was 58.3 mm and the mean pathological size was 55.2 mm. On average, CT overestimated pathological size by 3.1 mm (<it>P </it>= 0.012). CT overestimated pathological tumor size in 92 (58.6%) patients, underestimated in 44 (28.0%) patients and equaled pathological size in 21 (31.4%) patients. Among the 122 patients with pT1 or pT2 tumors, there was a discrepancy between clinical and pathological staging in 35 (29%) patients. Of these, 21 (17%) patients were down-staged post-operatively and 14 (11.5%) were up-staged. Fuhrman grade correlated positively with radiological tumor size (<it>P </it>= 0.039) and pathological tumor stage (<it>P </it>= 0.003).</p> <p>Conclusions</p> <p>There was a statistically significant but small difference (3.1 mm) between mean radiological and mean pathological tumor size, but this is of uncertain clinical significance. For some patients, the difference leads to a discrepancy between clinical and pathological staging, which may have implications for pre-operative patient counseling regarding prognosis and management.</p

3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images’ inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels’ appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool

Department of Radiology-Annual Executive Summary Report-July 1, 2009 to June 30, 2010

86 page Annual Executive Summary Report from Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, United States Table of Contents: Department of Radiology Chairman, Vice Chairmen 1 Divisions and Directors 1 Committees and Chairmen 1 Radiology Department Faculty Rank 2 Faculty with Secondary Appointments 3 Clinical Divisions 4 Radiology Residents and Fellows 5 Department Organizational Chart 6 Department Administration Chart 7 State of the Department 8 Appendix I: Publications Journal Articles 21 Books and Book Chapters 28 Abstracts 30 Appendix II: Formal Scientific Presentations 39 Appendix III: Honors, Editorial Activities, Service to Regional or National Organizations 59 Appendix IV: Active Grants 73 Appendix V: Pending Grants 7

Automated Decision Support System for Traumatic Injuries

With trauma being one of the leading causes of death in the U.S., automated decision support systems that can accurately detect traumatic injuries and predict their outcomes are crucial for preventing secondary injuries and guiding care management. My dissertation research incorporates machine learning and image processing techniques to extract knowledge from structured (e.g., electronic health records) and unstructured (e.g., computed tomography images) data to generate real-time, robust, quantitative trauma diagnosis and prognosis. This work addresses two challenges: 1) incorporating clinical domain knowledge into deep convolutional neural networks using classical image processing techniques and 2) using post-hoc explainers to align black box predictive machine learning models with clinical domain knowledge. Addressing these challenges is necessary for developing trustworthy clinical decision-support systems that can be generalized across the healthcare system. Motivated by this goal, we introduce an explainable and expert-guided machine learning framework to predict the outcome of traumatic brain injury. We also propose image processing approaches to automatically assess trauma from computed tomography scans. This research comprises four projects. In the first project, we propose an explainable hierarchical machine learning framework to predict the long-term functional outcome of traumatic brain injury using information available in electronic health records. This information includes demographic data, baseline features, radiology reports, laboratory values, injury severity scores, and medical history. To build such a framework, we peer inside the black-box machine learning models to explain their rationale for each predicted risk score. Accordingly, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. We demonstrate that imposing statistical and domain knowledge “checks and balances” not only does not adversely affect the performance of the machine learning classifier but also makes it more reliable. In the second project, we introduce a framework for detecting and assessing the severity of brain subdural hematomas. First, the hematoma is segmented using a combination of hand-crafted and deep learning features. Next, we calculate the volume of the injured region to quantitatively assess its severity. We show that the combination of classical image processing and deep learning can outperform deep-learning-only methods to achieve improved average performance and robustness. In the third project, we develop a framework to identify and assess liver trauma by calculating the percentage of the liver parenchyma disrupted by trauma. First, liver parenchyma and trauma masks are segmented by employing a deep learning backbone. Next, these segmented regions are refined with respect to the domain knowledge about the location and intensity distribution of liver trauma. This framework accurately estimated the severity of liver parenchyma trauma. In the final project, we propose a kidney segmentation method for patients with blunt abdominal trauma. This model incorporates machine learning and active contour modeling to generate kidney masks on abdominal CT images. The resultant of this component can provide a region of interest for screening kidney traumas in future studies. Together, the four projects discussed in this thesis contribute to diagnosis and prognosis of trauma across multiple body regions. They provide a quantitative assessment of traumas that is a more accurate measurement of the risk for adverse health outcomes as an alternative to current qualitative and sometimes subjective current clinical practice.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168065/1/negarf_1.pd

The Use of Preclinical Models to Improve the Treatment of Retinoblastoma

Rodent models play an essential role in the development of new chemotherapeutics and dosing regimes. It is often difficult to carryout a clinical study for pediatric cancers due to the small patient population. Retinoblastoma, a pediatric cancer of the eye, is one example of a pediatric cancer that can benefit from preclinical studies. Over the years various retinoblastoma rodent models have been developed used to test various combination of broad-spectrum systemic chemotherapy. It was found form these studies that the combination of topotecan and carboplatin was effective. However both drugs cause myelosuppression and therefore administrating both of these drugs systemically is not possible. An alternative effective therapy in the clinic was the use of a subconjunctival administration. We thought if we could administer both drugs, one by systemic and one by a subconjunctival injection, perhaps we could decrease the systemic exposure with good tumor response. Detailed pharmacokinetic studies were conducted to understand the subconjunctival injections of topotecan and carboplatin. It was found that both drugs could successfully penetrate the eye and increase drug exposure. In addition, in the presence of a tumor, drug exposure to the vitreous was greater. Additionally comparative pharmacodynamic studies combining topotecan subconjunctival injection with carboplatin intraperitoneal or carboplatin subconjunctival injection with topotecan intraperitoneal were conducted. The tumor response, systemic toxicity and local toxicity were studied. There was tumor response in both combinations and no ocular toxicity was seen with a single eye subconjunctival injection for either drug. However, rats that received the combination with topotecan subconjunctival injection and carboplatin intraperitoneal experienced great toxicity and morbidity. The data and observations suggest the death is due to dehydration. Therefore it was concluded that the alternative combination was better. The above data suggested an appropriate drug combination and schedule for a preclinical study. However, the noninvasive methods to follow tumor progression and choosing the correct genetic model needed to be determined. This was essential to ensure the preclinical study could be easily translated for future clinical studies. A characterization study of five modalities, retina camera, optomotry, tonometer, ultrasound and MRI, was done with retinoblastoma mice. We determined the feasibility of each technique. It was found that the retina camera could detect the tumor the earliest in a high throughput manner. Additionally, the tonometer and optomotry machines could assess ocular health. While the ultrasound and MRI could image the eye and tumor in one field of view, MRI could capture the posterior chamber in more detail along with the extraocular space. With different software programs, the tumor to eye ratio volume measurement were determined and compared to the gold standard of enucleation, embedding, serial sectioning and hand tracing. It was found that there was a better correlation between the ultrasound and hand tracing histological sections. Concurrently, the tumor progression of six different genotypes was assessed. The tumor progression depended on the number and different genes deleted. Additionally, based on genotypes, it was determined there was not a strong genotypic trend in the increase in IOP or the loss of vision. From the studies of tumor progression we have learn more about the influence of genes on tumor progression, which will benefit additional genetic studies in mouse model systems and human tumors

Colonoscopy and Colorectal Cancer Screening

Colorectal cancer (CRC) represents a major public health problem worldwide. Fortunately most CRCs originate from a precursor lesion, the adenoma, which is accessible and removable. This is the rationale for CRC screening programs, which are aimed to diagnose CRC at an early stage or even better to detect and resect the advanced adenoma before CRC has developed. In this background colonoscopy emerges as the main tool to achieve these goals with recent evidence supporting its role in CRC prevention. This book deals with several topics to be faced when implementing a CRC screening program. The interested reader will learn about the rationale and challenges of implementing such a program, the management of the detected lesions, the prevention of complications of colonoscopy, and finally the use of other screening modalities that are emerging as valuable alternatives. The relevance of the topics covered in it and the updated evidence included by the authors turn this book into a very useful tool to introduce the reader in this amazing and evolving field

A prospective study

Background: Conventional methods used for diagnosis of lung cancer are still inadequate for objectively estimating the exact depth of tumor invasion to bronchial wall, nodal staging, infiltration of mediastinal structures and extent of early lung cancer. Endobronchial ultrasound (EBUS) was introduced into clinical hospital practice in 2000, as a new diagnostic procedure visualizing bronchial and peribronchial tumors, mediastinal lymph nodes and adjacent vascular structures, with the aim of assessing bronchial wall and extraluminal pathology. Purpose: Since major European publications deal with EBUS application in general anesthesia, its use in routine bronchoscopy under topical anesthesia has been addressed more closely in this study. Hence the primary question we attempted to answer has been, is the addition of EBUS under topical anesthesia to bronchoscopy practicable and does it improve diagnosis in bronchial cancer beyond computer tomography (CT) and bronchoscopy alone? Patients, materials & methods: 50 consecutive patients were recruited with suspected lung cancer (suspicious shadow(s) and / or mediastinal adenopathy in chest CT) undergoing diagnostic and/or staging flexible fiberoptic bronchoscopy. In all patients, EBUS was performed as an adjuvant to bronchoscopy using midazolam sedation, lidocaine mucosal anesthesia and supplemental oxygen. A 20-mega Hz radial mechanical ultrasound probe integrated with a balloon, connected to ultrasound unit is advanced through the 2.8-millimeter working channel FFB to area of interest, where the balloon is inflated to provide a medium for ultrasound transmission. Agreement of EBUS findings with FFB/ CT and cyto-histology, additional information provided by EBUS, complications, patients tolerability under topical anesthesia were assessed. Results: Out of the 50 cases with suspected lung cancer, 36 cases were pathologically verified. In 36 lung cancer cases, EBUS findings coincided with those of FFB and CT in main features of the disease process. EBUS provided additional information in 25 cases (69%), in which 20 additive lymph nodes were detected; depth of tumor invasion was determined in 18 cases and compression of pulmonary arteries in 2 cases. In addition, it was helpful in explanation of bronchoscopic findings in 19 cases (53%) and exclusion of mediastinal structures infiltration. On the other hand, FFB and CT provided additional information in 7 cases (19%). In all studied cases, EBUS assisted transbronchial needle aspiration biopsy had diagnostic yield in extraluminal lesions up to 89% and 90% in mediastinal and intrapulmonary adenopathy. EBUS addition could change the nodal descriptors in 4 cases and patient stage in 2 cases, but without any subsequent therapeutic consequences. The complications encountered in all studied cases were either mild (6) or moderate (1) desaturation, mild (9) or moderate (3) cough and 2 cases of tachycardia. The procedure is completely tolerated by most of the patients (66%). There is an average increase in examination time of 15 minutes, constituting 44% of total time of bronchoscopy. Conclusions: EBUS application under topical anesthesia is a well-tolerated procedure, associated with mild infrequent side effects, providing valuable beneficial additional information to bronchoscopy and CT; hence its addition can improve the diagnosis and assessment of bronchial cancer. Further expected technical improvements are still needed, which may allow EBUS in the near future to play a more important role in diagnostic and interventional bronchoscopy. Recommendations: Prospective multicentre studies are needed for critical assessment of EBUS in operable lung cancer patients, correlating EBUS image findings with postoperative anatomical and pathological findings in same areas examined. A more applicable definition of depth of tumor invasion, the use of 30 MHz probes, the use of double lumen bronchoscopy, the cost effectiveness of procedure and the role of EBUS in peripheral lesions are recommended to be further studied. The sound indications of this new technology need to be settled, in the diagnostic investigation path of lung cancer