TexRAD is software that analyzes textures in existing medical scans to provide prognostic information and risk stratification to clinicians. It does this by measuring fine, medium, and coarse textures in scans of tumors like those in the liver, lungs, and other organs. This additional texture information can help predict factors like cancer stage, metastasis risk, and prognosis. TexRAD requires no new scanning procedures and can analyze routine clinical images, providing more information to clinicians to guide patient care decisions.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Short-course radiotherapy followed by neo-adjuvant chemotherapy in locally ad...Enrique Moreno Gonzalez
Current standard for most of the locally advanced rectal cancers is preoperative chemoradiotherapy, and, variably per institution, postoperative adjuvant chemotherapy. Short-course preoperative radiation with delayed surgery has been shown to induce tumour down-staging in both randomized and observational studies. The concept of neo-adjuvant chemotherapy has been proven successful in gastric cancer, hepatic metastases from colorectal cancer and is currently tested in primary colon cancer.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Short-course radiotherapy followed by neo-adjuvant chemotherapy in locally ad...Enrique Moreno Gonzalez
Current standard for most of the locally advanced rectal cancers is preoperative chemoradiotherapy, and, variably per institution, postoperative adjuvant chemotherapy. Short-course preoperative radiation with delayed surgery has been shown to induce tumour down-staging in both randomized and observational studies. The concept of neo-adjuvant chemotherapy has been proven successful in gastric cancer, hepatic metastases from colorectal cancer and is currently tested in primary colon cancer.
Liquid Biopsy: From Isolation to Downstream Applications 2018 Report by Yole ...Yole Developpement
How will liquid biopsy change cancer care?
More information on: https://www.i-micronews.com/category-listing/product/liquid-biopsy-from-isolation-to-downstream-applications-2018.html
Co-relation of multidetector CT scan based preoperative staging with intra-op...Apollo Hospitals
To assess the accuracy of CT scan in preoperative staging, to correlate preoperative findings with operative findings and with post-operative histopathological findings of colorectal carcinoma.
Radiation Oncology in 21st Century - Changing the ParadigmsApollo Hospitals
Since its inception radiation therapy has been used as one of
the essential treatment options in the management of malignant and some benign tumors. With better understanding of tumor biology many new molecules have been added to the armamentarium of an oncologist. There is continuous improvement in surgical techniques with more emphasis on minimally invasive, organ- and function-preserving techniques. Neoadjuvant chemotherapy with or without addition of radiation therapy has helped surgeon downsizing the tumor and obtaining clearer margins.
Liquid Biopsy: From Isolation to Downstream Applications 2018 Report by Yole ...Yole Developpement
How will liquid biopsy change cancer care?
More information on: https://www.i-micronews.com/category-listing/product/liquid-biopsy-from-isolation-to-downstream-applications-2018.html
Co-relation of multidetector CT scan based preoperative staging with intra-op...Apollo Hospitals
To assess the accuracy of CT scan in preoperative staging, to correlate preoperative findings with operative findings and with post-operative histopathological findings of colorectal carcinoma.
Radiation Oncology in 21st Century - Changing the ParadigmsApollo Hospitals
Since its inception radiation therapy has been used as one of
the essential treatment options in the management of malignant and some benign tumors. With better understanding of tumor biology many new molecules have been added to the armamentarium of an oncologist. There is continuous improvement in surgical techniques with more emphasis on minimally invasive, organ- and function-preserving techniques. Neoadjuvant chemotherapy with or without addition of radiation therapy has helped surgeon downsizing the tumor and obtaining clearer margins.
Basem AL Al Zahrany
How effective is CT Colonography in detecting colon cancer?
Introduction
The second cause leading to death in the United States is colorectal cancer in the same way colorectal cancer is the third common cancer in women and men. In the United States 135,260 people diagnosed with colorectal cancer in 2011. Colorectal cancer caused for 51,783 people from them the death. The number of colorectal cancer patients is predictable to rise in the future. Built on the present data, cost scenario and survival for colorectal cancer Yabroff et al expected the cost effect in 2020 for the primary $4.05 billion, making a significant cost load on the healthcare system. Colorectal cancer mortality was decreased by 18% to13 % with apply CT Colonography to detect colonic polyps before they developed to colorectal cancer (Trilisky et al, 2015). CT Colonography has been shown to have polyp discovery rates similar to the patient how use colonoscopy. CT Colonography is an exam for colorectal cancer screening which became generally effected for detecting polyps similar to those of colonoscopy. It has the prospective to improve colorectal cancer screening rates because of colorectal is come to be insignificant noninvasive ,faster patient in quantity ,no sedation requirement and potential for reduced cathartic examination . Appropriate program of a CT Colonography screening must implement and needs important announce to several aspects, counting proper patient preparation before the investigation, image acquisition, and post-processing of the developed images. A CT Colonography need workstation with special software and high quality monitors. Special software called Computer-Aided Detection CAD which is manipulate to reduce mistakes of spotting and showing polyps to the radiologist for images interpretation. These essay will discuss technique, advantages , disadvantages of CT Colonography and how CT Colonography can be affect for colorectal cancer .
Exam technique
Currently, patients go through intestine preparation procedure which has the purpose of avoid misdiagnosed of polyps by cleaning the colon. So far there is no agreement on a best program of food adjustment and releasing preparation of flexible period and amount. There is continuing studies aimed to rise patient relaxation and acceptance. To distinguish polyps from other residual the use usually fecal and fluid tagging with bowel laxative. Some time they do it with small quantity of both iodine-based contrast material and barium or one of them to improve the image and to eliminate the artifact of image which could be accrue ,which may rise the sensitivity of polyp detection, Digital subtraction is a post-acquisition processing technique use to improve the image quality(Trilisky,2015).
Advantages of CT Colonography
CT Colonography shows high sensitivit ...
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
Breast conserving surgery followed by adjuvant radiotherapy is adopted in the early detected cases and mastectomy followed by radiotherapy or chemotherapy in the advanced cases are the general practices.
This study aimed to compare the overall and disease specific survivals of patients who underwent laparoscopic and open resection of colorectal cancer in a high volume tertiary center.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidd...Christo Ananth
Christo Ananth, S. Amutha, K. Niha, Djabbarov Botirjon Begimovich, “Enhancing Segmentation Approaches from Super Pixel Division Algorithm to Hidden Markov Random Fields with Expectation Maximization (HMRF-EM)”, International Journal of Early Childhood Special Education, Volume 14, Issue 05, 2022,pp. 2400-2410.
Christo Ananth et al. discussed that In surgical planning and cancer treatment, it is crucial to segment and measure a liver tumor's volume accurately. Because it would involve automation, standardisation, and the incorporation of complete volumetric information, accurate automatic liver tumor segmentation would substantially affect the processes for therapy planning and follow-up reporting. Based on the Hidden Markov random field, Automatic liver tumor detection in CT scans is possible using hidden Markov random fields (HMRF-EM). A CT scan of the liver may be too low-resolution for this software. CT liver tissue segmentation is based on the HMRF model. When building an accurate HMRF model, an accurate initial image estimate is crucial. Adaptive K-means clustering is often used for initial estimation. HMRF's performance can be greatly improved by clustering. This project aims to segment liver tissue quickly. This paper proposes an adaptive K-means clustering approach for estimating liver images in the HMRF-EM model. The previous strategy had flaws, so this one fixed them. We compare the current and proposed methods. The proposed method outperforms the currently used method.
Controversies in the management of rectal cancersAjeet Gandhi
Management of rectal cancers have undergone a huge paradigm shift over the last decade. One the one hand, it has opened up new avenues; it also has thrown up new challenges and controversies
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Are There Any Natural Remedies To Treat Syphilis.pdf
Tex Rad.Pps
1. Home Welcome Imaging in Cancer Care Cancer care is becoming more sophisticated, increasing the need for accurate and detailed information about individual patients to inform treatment planning Increasing cancer incidence in an ageing population combined with constraints on health care expenditure places limitations on the number of additional procedures that patients can undergo TexRAD maximises the information that can be obtained from the diagnostic images routinely acquired in current clinical practice and does not require additional procedures HOW? 1 2 3 4 5 6 6 7 8
2. What is TexRAD? Measuring Tumour Complexity: TexRAD is a software application that analyses the textures in existing radiological scans to assist the clinician in assessing the prognosis of the cancer patient. This presentation will guide you through how TexRAD will improve patient care. Please use links on left hand side to go directly to particular sections. What is TexRAD? Screen shot of TexRAD software 1 2 3 4 5 6 6 7 8
7. Clinical Case Studies Clinical Application Colorectal cancer (Liver / Distant Metastatic Disease) Case Study | Clinical Demo | Evidence Lung cancer Case Study | Clinical Demo | Evidence Breast cancer Case Study | Clinical Demo | Evidence Prostate cancer Case Study | Clinical Demo | Evidence Renal cancer Case Study | Clinical Demo | Evidence Pulmonary Disorders | Schizophrenia | Dynamic Texture Analysis
9. Colorectal Case Study Colorectal Case Study Typical example: A patient visits a clinic after curative colorectal cancer surgery Undergoes a routine follow-up CT scan The Radiologist considers that the CT looks normal with no focal abnormalities However, 18 months later the patient relapses with focal metastatic disease of the liver – fatal consequence TexRAD could have assisted the radiologist to improve this scenario as part of routine clinical procedure 1 2 3 4 5 6 HOW? 6 7 8 Case Study | Demo | Evidence
10. Colorectal CS 2 Colorectal Case Study How TexRAD supports clinician & patient in case study From the routine CT scans taken in the clinic, TexRAD software uniquely extracts and measures fine , medium and coarse textures - in this example, from the Liver CT. TexRAD highlights texture anomalies which are not apparent to normal visual examination These texture anomalies can be used to predict the risk of metastatic disease From this additional information, radiologist may suggest alternative treatment pathways to the patient 1 2 3 4 5 6 6 7 8 Case Study | Demo | Evidence
11. Colorectal Demo 1 Colorectal - Demo 1 2 3 4 5 6 6 7 TexRAD screen shot of Liver Case Study | Demo | Evidence PACS workstation 8
12. Colorectal Demo 2 Screenshot - Liver TexRAD analysis of apparently normal appearing liver (after curative surgery of primary tumour) as seen on follow-up CT of a patient with colorectal cancer could predict the risk of metastatic disease Colorectal - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
13. Colorectal Demo 3 Colorectal - Demo 1 2 3 4 5 6 6 7 Screenshot - Work flow demonstration sequence for Liver Case Study | Demo | Evidence 8 Clinician’s Workflow
14. Colorectal Demo 4 STAGE 1 - Display the target clinical image of interest A TexRAD analysis is applied to the appropriate 2D CT image highlighting the liver (tissue of interest - TOI). The specialist clinical consultant (e.g. Radiologist) will select the image containing this TOI. Colorectal - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Conventional Abdominal CT Image
15. Colorectal Demo 5 STAGE 2 – Draw region of interest (ROI) to be analysed Using TexRAD’s graphical user interface tools, image window level/width can be altered to clearly delineate this TOI, interactive magnification/panning/centring can be used for better visualization of this TOI. Clinician can choose an appropriate ROI tool (e.g. Polygon ROI) from a list of options based on the application. This ROI is super-imposed on the TOI within the original image. Colorectal - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
16. Colorectal Demo 6 STAGE 3 – Texture Analysis TexRAD employs a novel algorithm (patent applied for) primarily to extract subtle but prognostic metrics currently not available in clinic. The software also graphically displays clinically relevant fine, medium and coarse liver textures separately (below) in addition to their fusion with the original CT image. Colorectal - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
17. Colorectal Demo 7 Colorectal - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence STAGE 4 – TexRAD Spectroscopy Summarises the entire texture results graphically for the colorectal cancer patient. Clinician can easily interpret the results for a quick assessment. 8
18. Colorectal Demo 8 STAGE 5 – Risk Stratification Report A risk stratification report specific to the colorectal cancer is generated, which should be used only to assist the clinician to make an accurate decision. The report contains patient ID and scan details, TexRAD analysis result, explanation and contact information. Colorectal - Demo 1 2 3 4 5 6 6 Case Study | Demo | Evidence 8 7 SAMPLE REPORT ONLY FOR ILLUSTRATION
19. Colorectal Background 1 Colorectal Background Facts about Colorectal Cancer Colorectal cancer is the second most common malignancy in Western societies [1]. 40% of the patients undergoing resection of the primary tumour will relapse and die of their disease, making colorectal cancer the second leading cause of death related to cancer [2]. The liver is the sole site of secondary tumour spread (i.e. metastasis) in 20-40% of patients [3] and therefore it is a common practice to follow-up patients after their curative resection [4]. There is an overall survival benefit for intensifying the follow-up of such patients with imaging of the liver being associated with reduced mortality (Odds ratio = 0.66, 95% confidence limits 0.46-0.95) [5]. 1 2 3 4 5 6 6 7 8 Case Study | Demo | Evidence
20. Colorectal Background 2 Colorectal Background Facts about Colorectal Cancer The American Society of Clinical Oncology (ASCO) now recommends annual CT of the chest and abdomen for 3 years after primary therapy for patients at higher risk of recurrence [6]. However, the risk of recurrence is not uniform for these patients and identification of predictive factors that are linked to outcomes may allow modification of surveillance strategies for particular sub-groups and provide effective and optimum patient care. ASCO has also highlighted the need for research in this area [6]. 1 2 3 4 5 6 6 7 8 Case Study | Demo | Evidence
21. Colorectal Background 3 Colorectal Background Comparison of Risk Stratification techniques Additional physiological imaging techniques such as Doppler ultrasound and quantitative analysis of liver contrast enhancement or perfusion on CT also have the potential to identify patients at higher risk of recurrence [7, 8] These techniques may reflect alterations of liver hemodynamics associated with occult metastases [9, 10]. However, the additional image acquisitions required by these techniques have been a barrier to their adoption into surveillance programs. (cost/additional radiation dosage) 1 2 3 4 5 6 6 Case Study | Demo | Evidence 7 8
22. Colorectal Evidence 1 Colorectal Evidence Why TexRAD is better - evidence TexRAD applied to routinely acquired CT images highlights subtle liver changes that may occur in association with alterations in liver physiology. Furthermore computer simulations and clinical studies have suggested that measurements of liver texture on CT may reflect liver vascularity [11-13]. The ability of Liver TexRAD to detect occult tumour on CT is further supported by studies demonstrating textural differences between normal livers and apparently normal areas of tissue within livers bearing tumours, areas also known to exhibit alterations in blood flow [13-15]. 1 2 3 4 5 6 6 Case Study | Demo | Evidence 7 8
23. Colorectal Evidence 2 Colorectal Evidence Why TexRAD is better - evidence Preliminary results of Liver TexRAD demonstrate the potential for liver texture on contrast-enhanced CT to provide a novel parameter that is not only insensitive to variations in acquisition parameters but can also act as a marker of survival for patients following resection of colorectal cancer [16]. 1 2 3 4 5 6 6 Case Study | Demo | Evidence 7 8
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27. Lung Homepage Lung Cancer Case Study | Demo | Evidence PACS workstation TexRAD screen shot of Lung lesion
28. Lung Case Study Lung Case Study Typical example: A patient diagnosed with lung cancer requires an accurate staging of the tumour and lymph node disease. Radiologists are unable to obtain a confident risk stratification from CT alone, which is critical for early prognosis and favourable patient outcome. The patient undergoes FDG PET-CT imaging, which overcomes the limitations of CT alone. However, this is an additional and expensive procedure. TexRAD can improve the performance of CT and potentially be employed for selection of patients for PET. 1 2 3 4 5 6 6 7 8 HOW? Case Study | Demo | Evidence
29. Lung CS 2 Lung Case Study How TexRAD supports clinician & patient in case study From routine CT scans taken in the clinic TexRAD software uniquely extracts and measures fine , medium and coarse textures - in this example, from the Lung lesion on CT - and classifies degree of adverse tumour biology These texture gradation can be used to predict tumour stage and the risk of metastatic and lymph node disease Based on this additional information, radiologist may optimally select patients who will benefit from FDG PET 1 2 3 4 5 6 6 7 8 Case Study | Demo | Evidence
30. Lung Demo 1 Lung TexRAD Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence PACS workstation TexRAD screen shot of Lung lesion 8
31. Lung Demo 2 Lung TexRAD analyses a focal cancerous lesion as seen on the conventional CT image of a patient with NSCLC could predict tumour stage, metabolism and lymph node disease involvement Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
33. Lung Demo 4 STAGE 1 - Display the target clinical image of interest A TexRAD analysis is applied to the appropriate 2D CT image highlighting the lung lesion (tissue of interest - TOI). The specialist clinical consultant (e.g. Radiologist) will select the image containing this TOI. Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Conventional Lung CT Image
34. Lung Demo 5 STAGE 2 – Draw region of interest (ROI) to be analysed Using TexRAD’s graphical user interface tools, image window level/width can be altered to clearly delineate this TOI, interactive magnification/panning/centring can be used for better visualisation of this TOI. Clinician can choose an appropriate ROI tool (e.g. Elliptical ROI, which encloses the TOI in an automated fashion) from a list of options based on the application. This ROI is super-imposed on the TOI within the original image. Lung - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Magnified CT Image For Drawing ROI
35. Lung Demo 6 Lung - Demo STAGE 3 – Texture Analysis TexRAD employs a novel algorithm (patent applied for) primarily to extracts subtle but prognostic metrics currently not available in clinic. The software also graphically displays clinically relevant fine, medium and coarse lung lesion textures separately (below) in addition to their fusion with the original CT image. 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Fine Lung Lesion Texture Medium Lung Lesion Texture Coarse Lung Lesion Texture
36. Lung Demo 7 Lung - Demo STAGE 4 – TexRAD Spectroscopy Summarises the entire texture results graphically for the lung cancer patient. Clinician can easily interpret the results for a quick assessment. 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
37. Lung Demo 8 Lung - Demo STAGE 5 – Risk Stratification Report A risk stratification report specific to the lung cancer is generated, which should be used only to assist the clinician to make an accurate decision. The report contains patient ID and scan details, TexRAD analysis result, explanation and contact information. 1 2 3 4 5 6 6 Case Study | Demo | Evidence 8 7 SAMPLE REPORT ONLY FOR ILLUSTRATION
38. Lung Background Lung - Background Facts about lung Cancer Lung cancer is the most common form of death related to cancer in men and second most common in woman [1, 2], accounting for 1.3 million deaths worldwide annually [3]. Non-small cell lung carcinoma (NSCLC) is the most common form of lung cancer prevalent in 80% of all cases [4]. Following initial diagnosis, patients with NSCLC undergo staging. The most common imaging staging procedure has been CT. However, due to low accuracy for CT staging, clinical guidelines now recommend Fluoro-deoxy-glucose (FDG) PET-CT unless the initial CT imaging shows evidence of inoperable disease. An improvement in the accuracy of CT could improve the selection of patients for FDG-PET. Case Study | Demo | Evidence 5 6 6 7 8 1 2 3 4
39. Lung Evidence 1 Lung Evidence Clinical Evidence – ICIS 2008 [5] 75 patients, PET identified tumour and nodal stages I, II, III & IV PET glucose uptake (SUV) was also measured Non-contrast CT images were employed for texture analysis Case Study | Demo | Evidence 5 6 6 7 8 1 2 3 4
40. Lung Evidence 2 Lung Evidence Case Study | Demo | Evidence Texture-Tumour Stage Association MGI (fine) vs Stage: rs =0.77, p=0.0002 Entropy (fine) vs Stage: rs =0.51, p=0.03 SUV vs Stage: rs =0.50, p=0.04 Texture-Nodal Stage Prediction Uniformity (fine) predicted node positive disease - area under the ROC curve = 0.7, p < 0.005, sensitivity=65%, specificity=76% In comparison with CT alone, sensitivity=43%, specificity=85% Combining CT and Texture, sensitivity=87%. Specificity=67% Texture-Metabolic Association Entropy (coarse) vs SUV: r=0.51, p=0.03 Uniformity (coarse) vs SUV: r=-0.52, p=0.03 5 6 6 7 8 1 2 3 4
41. Lung Evidence 3 Lung Evidence Clinical Evidence - Recent Findings accepted in ECR’10 [6] Case Study | Demo | Evidence Kaplan-Meier survival curves for NSCLC patients with lung lesions separated by (A) Texture analysis on CT and (B) PET glucose uptake (SUV). Survival curves were significantly different for Texture (p<0.001)]. 5 6 6 7 8 1 2 3 4
42. Breast Homepage Breast Cancer Case Study | Demo | Evidence TexRAD screen shot of Breast lesion
43. Breast Case Study Breast Case Study Typical example: A female participant within a breast screening program undergoes an initial mammogram examination. Further diagnostic mammogram confirms presence of cancerous tissue. Core biopsy identified non-invasive ductal carcinoma in situ (DCIS) and the standard breast conservation excision was performed. Surprisingly, the final excised specimen provided evidence of invasive focus, routinely underestimated by core biopsy, resulting in additional surgical procedure involving the axilla TexRAD could have predicted the risk of invasive disease preoperatively from mammographic lesions assisting in treatment planning and optimal selection of biopsy or surgery 1 2 3 4 5 6 6 7 8 HOW? Case Study | Demo | Evidence
44. Breast CS 2 Breast Case Study How TexRAD supports clinician & patient in case study From routine mammographic images taken in the clinic TexRAD software uniquely extracts and measures fine , medium and coarse textures - in this example, from the breast lesion - and characterises differences in calcification architecture These texture differences can be used to preoperatively estimate the likelihood of invasive focus from among patients with DCIS This additional information may assist the clinician in better treatment planning and optimal selection of sentinel node biopsy or axillary surgery 1 2 3 4 5 6 6 7 8 Case Study | Demo | Evidence
45. Breast Demo 1 Breast - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence TexRAD screen shot of Breast lesion 8
46. Breast Demo 2 Breast - Demo Breast TexRAD analysis of an obvious stellate mass as seen on a mammogram of a patient with core-biopsy proven breast cancer could estimate the risk of invasive disease 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
47. Breast Demo 3 Breast - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8 Clinician’s Workflow
48. Breast Demo 4 STAGE 1 - Display the target clinical image of interest A TexRAD analysis is applied to the appropriate 2D mammographic image highlighting the breast lesion (tissue of interest - TOI). The specialist clinical consultant (e.g. Radiologist) will select the image containing this TOI. Breast - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
49. Breast Demo 5 STAGE 2 – Draw region of interest (ROI) to be analysed Using TexRAD’s graphical user interface tools, image window level/width can be altered to clearly delineate this TOI, interactive magnification/panning/centring can be used for better visualisation of this TOI. Clinician can choose an appropriate ROI tool (e.g. Polygon ROI) from a list of options based on the application. This ROI is super-imposed on the TOI within the original image. Breast - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
50. Breast Demo 6 Breast - Demo STAGE 3 – Texture Analysis TexRAD employs a novel algorithm (patent applied for) primarily to extracts subtle but prognostic metrics currently not available in clinic. The software also graphically displays clinically relevant fine, medium and coarse breast lesion textures separately (below) in addition to their fusion with the original mammographic image. 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
51. Breast Demo 7 STAGE 4 – TexRAD Spectroscopy Summarises the entire texture results graphically for the breast cancer patient. Clinician can easily interpret the results for a quick assessment. Breast - Demo 1 2 3 4 5 6 6 7 Case Study | Demo | Evidence 8
52. Breast Demo 8 STAGE 5 – Risk Stratification Report A risk stratification report specific to the breast cancer is generated, which should be used only to assist the clinician to make an accurate decision. The report contains patient ID and scan details, TexRAD analysis result, explanation and contact information. Breast - Demo SAMPLE REPORT ONLY FOR ILLUSTRATION 1 2 3 4 5 6 6 Case Study | Demo | Evidence 8 7
53. Breast Background 1 Breast - Background Facts about Breast Cancer Invasive cancer of the breast and DCIS can both present as focal lesions on mammography. Pure DCIS is not an invasive process and rarely metastasises to regional lymph nodes [1]. The introduction of mammographic breast screening has resulted in a dramatic increase in the diagnosis of DCIS and its detection rate has reached 15-20% of all mammographically detected cancers [2-4]. Based on core biopsy specimens, the standard surgical planning for DCIS is to offer breast conservation excision. 1 2 3 7 8 Case Study | Demo | Evidence 4 5 6
54. Breast Background 2 Breast - Background Facts about Breast Cancer However, the detection of DCIS on core biopsy is quite frequently followed by evidence of invasion within the final excision specimen. This occurs in 11-44% of patients and results in the need of second operative procedure involving the axilla [4, 5]. Therefore the main concern is whether any kind of axillary staging is indicated in patients preoperatively diagnosed with DCIS only. 1 2 3 7 8 Case Study | Demo | Evidence 4 5 6
55. Breast Background 3 Breast - Background Facts about Breast Cancer The most often employed axillary treatment option for patients with DCIS is to perform a sentinel node biopsy at the time of initial excision. Nevertheless it may lead to reoperation in cases of proven invasive focus on excision. Therefore an effective way of estimating the likelihood of an invasive focus preoperatively in patients diagnosed with DCIS would assist in better treatment planning and optimal use of sentinel node biopsy or axillary surgery. 1 2 3 7 8 Case Study | Demo | Evidence 4 5 6
56. Breast Evidence 1 Breast Evidence Clinical Evidence – ICIS 2007 [6] 1 2 7 8 Case Study | Demo | Evidence Graph indicates the relationship between relative fine to medium uniformity value and extent of disease (tumour type) from mammographic focal region of interest 3 4 5 6
57. Breast Evidence 2 Breast Evidence Clinical Evidence – ICIS 2007 [6] 1 2 7 8 Case Study | Demo | Evidence Graph indicates the inverse relationship between relative fine to coarse mean grey-level intensity value and oestrogen receptor (ER) status from mammographic focal region of interest 3 4 5 6
58. Breast Evidence 3 Breast Evidence Clinical Evidence – ICIS 2007 [6] 1 2 7 8 Case Study | Demo | Evidence Graph indicates the inverse relationship between relative medium to coarse mean grey-level intensity value and progesterone receptor (PR) status from mammographic focal region of interest 3 4 5 6
59. Prostate Demo 1 Prostate Demo Case Study | Demo | Evidence Screenshot of the Prostate TexRAD highlighting the conventional CT image of the prostate (top-left), followed by the derived texture maps superimposed on the conventional CT image – fine (red), medium (green) and coarse (blue) texture 5 6 6 7 8 1 2 3 4
60. Prostate Evidence 1 Prostate Early Evidence Clinical Evidence – Submitted an abstract UKRC 2010 [1] Case Study | Demo | Evidence A medium texture (mean grey-level intensity) below 2.4 predicted the presence of tumour with an area under the ROC curve of 0.909, p = 0.0001, sensitivity = 84% and specificity = 84% Texture was significantly different between tumour & normal prostate tissue 5 6 6 7 8 1 2 3 4
61. Prostate Evidence 2 Prostate Early Evidence Clinical Evidence – Submitted an abstract UKRC 2010 [1] Case Study | Demo | Evidence Medium mean grey-level intensity correlates inversely with Prognostic risk factors (determined on the basis of gleason score, PSA level and clinical T stage) Texture correlates with prognostic risk factor (adverse tumour biology and/or patient outcome) 5 6 6 7 8 1 2 3 4
62. Renal Demo 1 Renal Metastases Demo TexRAD: Potential predictive imaging biomarker of response to treatment in metastatic renal cancer TexRAD PRE-TREATMENT Case Study | Demo | Evidence Screenshot of the Renal TexRAD highlighting ‘Right lobe liver’ metastases (pre-treatment) on the conventional CT image (top-left), followed by the derived texture maps superimposed on the conventional CT image – fine (red), medium (green) and coarse (blue) texture 5 6 6 7 8 1 2 3 4
63. Renal Demo 2 Renal Metastases Demo TexRAD: Potential predictive imaging biomarker of response to treatment in metastatic renal cancer TexRAD POST-TREATMENT Case Study | Demo | Evidence Screenshot of the Renal TexRAD highlighting ‘Right lobe liver’ metastases (post-treatment) on the conventional CT image (top-left), followed by the derived texture maps superimposed on the conventional CT image – fine (red), medium (green) and coarse (blue) texture 5 6 6 7 8 1 2 3 4
64. Renal Evidence 1 Renal Early Evidence Clinical Evidence – Recent findings a ccepted in ECR 2010 [1] Case Study | Demo | Evidence CT Texture significantly predicts patients with poor prognosis (treatment response) 5 6 6 7 8 1 2 3 4 p < 0.005
65. Renal Evidence 2 Renal Early Evidence Clinical Evidence – Recent findings a ccepted in ECR 2010 [1] Case Study | Demo | Evidence Existing anatomical imaging response criteria (RECIST) did not significantly predict patients with poor prognosis 5 6 6 7 8 1 2 3 4
66. Pulmonary Disorders 1 Pulmonary Disorders Clinical Evidence – Investigative Radiology 2008 [1] 5 6 6 7 8 3D volume rendered coronal, left and right sagittal views of (A) airway lung (fine) texture, (B) vascular lung (coarse) texture, and (C) the fusion of airway and vascular lung texture (intensity- and gradient-based) in a patient with no pulmonary disorders. 1 2 3 4
67. Pulmonary Disorders 2 Pulmonary Disorders Clinical Evidence – Investigative Radiology 2008 [1] 5 6 6 7 8 3D volume rendered coronal, left and right sagittal views of (A) airway lung (fine) texture, (B) vascular lung (coarse) texture, and (C) the fusion of airway and vascular lung texture (intensity- and gradient-based) in a patient with only pulmonary embolism. 1 2 3 4
68. Pulmonary Disorders 3 Pulmonary Disorders Clinical Evidence – Investigative Radiology 2008 [1] 5 6 6 7 8 3D volume rendered coronal, left and right sagittal views of (A) airway lung (fine) texture, (B) vascular lung (coarse) texture, and (C) the fusion of airway and vascular lung texture (intensity- and gradient-based) in a patient with pulmonary embolism and emphysema. 1 2 3 4
69. Pulmonary Disorders 4 Pulmonary Disorders Clinical Evidence – Investigative Radiology 2008 [1] The best distinction between emphysematous and non-emphysematous lung was observed for filtered fine airway texture quantified as MGI The best distinction between normal and patients with pulmonary disorders was observed for filtered coarse vascular texture quantified as entropy and uniformity A progressive percentage change from normal was observed for coarse vascular texture within PE and emphysematous lung Airway and vascular texture metrics demonstrated postural gradient consistent with known physiology 5 6 6 7 8 1 2 3 4
70. Brain Disorder 1 Brain Disorder-Schizophrenia Preliminary Clinical Evidence [1] 1 2 3 4 5 6 6 7 8 3D volume rendered left sagittal view of conventional MR whole brain GM (top row) and WM (bottom row) and corresponding textures (intensity- and gradient-based) respectively in a patient with schizophrenia. RAW GREY MATTER FINE GM TEXTURE MEDIUM GM TEXTURE COARSE GM TEXTURE RAW WHITE MATTER FINE WM TEXTURE MEDIUM WM TEXTURE COARSE WM TEXTURE
79. Project Team Principal Developer Dr. Balaji Ganeshan Clinical Collaborators Prof. Kenneth Miles Dr. Olga Strukowska Prof. Hugo Critchley Image Processing Collaborators Dr. Rupert Young Prof. Chris Chatwin Business Development Manager Mike Herd Mike Wylde Intellectual Property & Technology Transfer Office Russell Nicholls Medical Students & F2 Doctors Sandra Abaleke Elleny Panayiotou Ian Pressney Kate Burnand Mansi Rajpopat People
80. Contact Dr Balaji Ganeshan TexRAD Project Manager Research and Enterprise University of Sussex Falmer, Brighton UK BN1 9SB Tel: +44 (0) 7727228107 Email: TexRAD@sussex.ac.uk Contact
81. Disclaimer Notice TexRAD is meant to be used as an assistive risk stratification tool in clinical practice and should not form the basis for clinical decision making. The results obtained from TexRAD system should only be interpreted by a health care professional. IMPORTANT: TexRAD is being further evaluated and not yet available for routine clinical practice. Disclaimer