Predicting Pancreatic Cancer Using Support Vector Machine

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately

Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis

The key experimental results from this research are the viable and cost effective methods of diagnosing oral and pancreatic cancer with accuracies over 90%. Furthermore, development of the molecular windowing method to further narrow down the origins of those cancer biomarkers and further improve accuracy.Many papers are being published demonstrating how vibrational spectral biomarkers can be used to diagnose a whole variety of diseases, from cancers to colitis. However, much of the research, proposed as discovering a useful tool for clinical diagnosis, has not yet been widely utilised in clinical practice. This is due mainly to the lack or reproducibility of the findings and current lack of relating the spectral observation to a root biological cause. This thesis aims to highlight the inconsistencies between studies and propose an improved process for spectral biomarker identification, including suggestions for follow up studies to discover the foundation of the spectral change. This thesis reassesses, and adds to, ground covered by previous reviews regarding sample preparation, patient selection and multivariate analysis.Resultantly, this thesis brings light to the need, and suggests solutions, for:• a method to standardise results between detection devices,• knowledge of the additional requirements for using biomarkers for disease monitoring/prognosis,• understanding the biological root cause for the spectral shift.These promising results and suggestions for combined methodology improvements will provide guidance to enable this burgeoning research field to improve patient outcome in the clinical sphere

Targeted Molecular MR Imaging of HER2 and EGFR Using De Novo Designed Protein Contrast Agents

The application of magnetic resonance imaging (MRI) to non-invasively assess disease biomarkers has been hampered by lack of desired contrast agents with high relaxivity, targeting capability, and optimized pharmacokinetics. We developed a novel MRI probe which targets HER2, a biomarker for various cancers and a target for anti-cancer therapies. This multimodal HER2-targeted MRI probe integrates a rationally designed protein contrast agent with a high affinity HER2 affibody and near IR dye. Our probe can differentially monitor tumors with different HER2 levels in both cells and xenograft mice. In addition to its 10-fold higher dose efficiency compared to clinically-approved agent DTPA, our developed agent also exhibits advantages in crossing the endothelial boundary, tissue distribution, and tumor tissue retention as demonstrated by even distribution of the imaging probe across the entire tumor mass. Additionally, a second series of protein contrast agents that included affibody against EFGR developed with the capability to specifically target EGFR. These contrast agents have been utilized to monitor drug treatments and quantitatively analyze biomarker expression level. Furthermore, we anticipate these agents will provide powerful tools for quantitative assessment of molecular markers, and improved resolution for diagnosis, prognosis and drug discovery

Artificial intelligence in gastroenterology: a state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Computationally Enhanced Medical Decision Support for Pancreatic Cancer

This project investigated and applied computational techniques to enlarge a pancreatic cancer database and to enhance the medical decision-making process supported by this database. The database was previously developed by the Department of Surgical Oncology of the University of Massachusetts Medical School in conjunction with the Department of Computer Science at WPI. We substantially increased the number of patients included in the database, and conducted data mining experiments. These experiments compared the accuracies of predictions made by medical doctors and by data mining methods for two separate patient outcomes: tumor malignancy and survival time after surgery. The results of our experiments show that data mining techniques can be used to enhance the quality of medical decisions

Variabilities in global DNA methylation and β\beta-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer

Purpose: Knowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue. Methods: Here, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues. Results: The results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies. Conclusions: Molecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing

Ultrasound-based assessment and management of postmenopausal bleeding and endometrial polyps

This thesis has evaluated aspects of ultrasound-based assessment and management of women with postmenopausal bleeding and endometrial polyps. The efficacy of transrectal ultrasound scan (TRS) was assessed in 103 consecutive postmenopausal women with an axial uterus. TRS was accepted by two-thirds of the women and the proportion of satisfactory endometrial assessments was significantly higher on TRS compared to transvaginal scan (TVS), 91% (95% CI 84-98) vs 62% (95% CI 50-74), respectively. In the subgroup of 50 women with postmenopausal bleeding and an axial uterus, the endometrial thickness measured significantly thinner on TRS by a median of 1.2mm (IQR 0.4-3) compared to TVS. Furthermore, subjective pattern recognition for endometrial cancer was less accurate on TVS compared to TRS when the uterus is in an axial position. The interrater reliability of ultrasound subjective pattern recognition for endometrial cancer was prospectively assessed in 40 women with postmenopausal bleeding and a thickened endometrium (≥4.5mm); a good level of agreement (κ = 0.78, 95% CI 0.61-0.95) was found between an expert and an average operator. The diagnostic accuracy of ultrasound subjective pattern recognition for endometrial cancer was assessed in 240 consecutive women with postmenopausal bleeding and a thickened endometrium (≥4.5mm) and available histology. It performed well with a sensitivity and specificity of 88% (95% CI 77-95) and 97% (95% CI 94-99), respectively. The presence of focal malignancy within endometrial polyps was the most common cause of a false-negative diagnosis of endometrial cancer. Endometrial cancer was diagnosed on ultrasound by subjective pattern recognition and simultaneously assessed for the presence of deep myometrial invasion and cervical stromal invasion in 51 women. We found that the accuracy of ultrasound in the preoperative staging of endometrial cancer was comparable to MRI (sensitivity and specificity, 86% vs 77% and 66% vs 76%, respectively). A clinical model was presented to estimate the risk (low, intermediate, or high) of pre-malignancy or malignancy in postmenopausal endometrial polyps. The model included polyp size, the presence or absence of intralesional cystic spaces and the patient’s BMI as clinical variables. Accordingly, approximately one-third of postmenopausal polyps would be categorised as high- or intermediate-risk and they would account for over 90% of all premalignant/malignant polyps, while the remaining polyps would be categorised as low-risk with a 1/18 risk of pre-malignancy or malignancy. The overall accuracy of the model in predicting premalignant or malignant postmenopausal polyps was 92% (95% CI 86.0-97.4). The natural history of expectantly managed endometrial polyps was assessed retrospectively in 112 polyps over a median follow-up of 22.5 months (range 6-136). We found that polyps’ growth rates varied, and it was not possible to predict an individual polyp’s growth based on the patient’s clinical characteristics or polyp’s morphological features. Polyp’s growth rate was not associated with the risk of developing abnormal uterine bleeding (AUB). Some polyps underwent spontaneous regression (7/112, 6%) and this occurred more frequently among premenopausal women and those who were symptomatic of AUB