Incident cases of colorectal cancer, the third leading cause of cancer deaths worldwide, more than doubled between 1990 and 2019. For the Global Burden of Disease Study 2019, the Colorectal Cancer Collaborator Network, which includes Khalifa University’s Dr. Juan Acuna, investigated the global impact of colorectal cancer with the results published in.��

By Dr. Juan Acuna

In 2019, colorectal cancer was the third leading cause of cancer deaths worldwide and the second leading cause of disability-adjusted life years (DALYs) for cancer. Around 60 to 75 percent of colorectal cancer cases occur sporadically and are associated with modifiable risk and preventable factors – behaviors and exposures that can raise or lower a person’s risk of cancer – whereas 25 to 40 percent of cases are linked to non-modifiable risk factors, which include genetic factors, a personal history of polyps or adenoma, or a family history of colorectal cancer or hereditary risk.

Incident (new) cases are growing rapidly around the world, but particularly in low-income and middle-income countries. Part of this is due to the increased prevalence of modifiable risk factors, such as smoking, alcohol consumption, unhealthy diets, sedentary behavior, and obesity. However, at the same time, and in contrast, it is now much more affordable and simple to screen for colorectal cancer, with capacity for screening and increasing awareness contributing to the detection of more and earlier cases simply due to more people being screened and to massive programs aiming at early detection. Early detection dramatically improves the prognosis of colon cancer.

Target 3.4 of the UN Sustainable Development Goals focuses on reducing premature mortality from non-communicable diseases, including cancers, by a third by 2030. We can reach that goal by primary prevention (preventing the disease to happen in the first time) or by early detection, when the prognosis is substantially better, including allowing for complete cure. Colorectal cancer is one of those cancers that can be prevented (modifying behaviors or exposures) or detected early. By improving lifestyle and championing early detection we can address the global colorectal cancer burden, making it a key driver of progress towards meeting this goal.

To identify where progress is being made and where more work is needed, recent changes in the colorectal cancer burden should be tracked at the global, regional, and national levels. This global study with the Global Burden of Disease Collaborator Network (more than 700 investigators worldwide) investigated the burden of colorectal cancer in 204 countries and territories from 1990 to 2019. This work built on the previous study conducted in 2017, with another nine countries providing data, adding to the global picture.

In this study the GBD collaborators found that incident cases of colorectal cancer doubled or more than doubled in 16 of 21 world regions, and the number of deaths doubled or more than doubled in 15 of 21 world regions in the past three decades.

When standardized for age, the incidence and death rates either remained the same or decreased in countries measured as high sociodemographic index (SDI) countries, but increased in low SDI and middle SDI locations. Large increases in colorectal cancer incidence rates were observed in middle SDI countries, as well as in people aged 20 to 49 in high SDI countries.

We need further research into why younger people saw an increase in cases, but we can assume some of this is due to the main risk factors for colorectal cancer: obesity, physical inactivity, alcohol consumption, smoking, and an altered gut microbiome. This is where public health interventions can help, including increased screening and awareness and encouraging a reduction in risky behavior.

We can also assume that fast economic growth and rapid industrialization have an impact. A thriving middle class in developing countries adopting a westernized lifestyle characterized by an unhealthy diet and sedentary behavior have resulted in an increased incidence of lifestyle-related illnesses, including colorectal cancer, in the middle SDI countries.

All genders saw an increase in colorectal cancer incidence, but men experienced greater increases in incidence, deaths and disability-adjusted life years (DALYs) than women in terms of absolute counts. In 2019, men accounted for 57.2 percent of colorectal cancer incident cases, and 54.9 percent of deaths due to colorectal cancer. Additionally, when age was taken into account, the preponderance of colorectal cancer in men was more apparent in developed regions, including central Europe, high-income Asia Pacific and Australasia, but differences in the genders were smaller in South Asia and regions of Africa.

China, the USA, and Japan had the highest incident counts for all genders combined, but globally, the age-specific rates of colorectal cancer followed a bell-shaped distribution, with a peak in individuals aged 60 to 74. Incident cases were higher in men than in women in all age groups up to age 80 to 84, with a greater number of new cases in women aged over 85. Incident rates continued to increase with age, but all age groups experienced a rise in incident cases.

At the global level, a diet low in milk (15.6 percent), smoking (13.3 percent), a diet low in calcium (12.9 percent), and alcohol use (9.9 percent) were the main contributor to colorectal cancer DALYs, with the relevant contribution of different risk factors varying as per the region’s development status. A high BMI contributed only 8.3 percent of DALYs, but men with a higher BMI contributed significantly more to DALYs than women.

Colorectal cancer is clearly a global health concern and stemming the tide would be a key contributor to improving health and life quality around the world. We expect low and middle SDI countries to continue to see an increase in cases as a result of population ageing, increased life expectancy and improved screening and detection, so strategies such as dietary and lifestyle modifications are imperative to facing the challenge.

Studies like this one highlight the importance of population-based cancer registries for monitoring colorectal cancer incidence and providing an important resource for people and healthcare providers. Our findings can be used by policy makers and provide new perspectives for scientists and physicians around the world, informing efforts for equitable colorectal cancer control worldwide, with the larger goal of reducing the overall incidence and the specific global burden of cancer for all people.

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When data is sparse and medical knowledge of a disease is limited, combining modelling approaches can lead to more accurate predictions of clinical outcomes

Big data in healthcare is nothing new. Hospital records, medical records, results of medical examinations and biomedical research generate vast quantities of information that need to be handled carefully and accurately.

Sometimes, clinicians and researchers don’t understand how a disease progresses or the biochemical mechanisms behind a disease. Other times the data available is sparse because it depends on when the patient physically attends a clinic or appointment.

In the first case, the predictive accuracy of clinical outcomes for any mathematical model is limited as the underlying biological mechanisms are only partly understood. Machine learning techniques do not require knowledge of the underlying interactions in biomedical problems but infrequent data does impact their use, restraining any algorithm from accurately inferring the corresponding disease dynamics.����

Combining the two approaches could be the solution. Dr. Haralampos Hatzikirou, Associate Professor of Mathematics at Khalifa University, proposed a method to improve individualized predictions in cancer patients based on the Bayesian coupling of mathematical modelling and machine learning. This approach was tested on a simulated dataset for brain tumor patients and on two real cohorts of patients with leukemia and ovarian cancer. The results were closely aligned with the actual clinical data for individual patients, suggesting its potential use in enabling accurate personalized clinical predictions in healthcare.

Dr. Hatzikirou worked with Pietro Mascheroni, Michael Meyer-Hermann and Juan Carlos Lopez Alfonso from Braunschweig Integrated Center of Systems Biology and Helmholtz Centre for Infectious Research, Germany, and Dr. Symeon Savvopoulos, Mathematics Post-doctoral Fellow at Khalifa University.

The results were published in

In oncology, this clinical data is the cornerstone of providing personalized healthcare to the patient, but using the data is more challenging.

“Although mathematical models can be extremely powerful in proposing biological hypotheses, they require adequate knowledge of the underlying biological mechanisms of the analyzed system,” Dr. Hatzikirou explained. “Typically, this knowledge is not complete and we only know a limited amount of mechanistic interactions, such as molecular pathways, seen in cancer. Therefore, even though mathematical models provide a good description of an idealized version of what’s going on in cancer dynamics, they can’t always allow for accurate and quantitative predictions.

“On the other hand, machine learning techniques can handle the inherent complexity of biomedical problems. While mathematical models rely on causality, statistical learning methods identify correlations among data so they can systematically process large amounts of data and infer hidden data patterns. However, the data for each patient is limited to being collected whenever a patient is in the hospital or clinic. This doesn’t provide the algorithm with enough data to make meaningful individualized inferences.”

Dr. Hatzikirou and the research team proposed the first Bayesian method that combines the two techniques.

Bayes’ Theorem deals with probabilities based on prior experiences. These priors provide some information but once there is more data, the priors can be updated. It’s the law of probability governing the strength of evidence, saying how much to revise our probabilities when we observe new evidence. For example, if you know your patient has a positive cancer test result, and that’s all you know, you can look at how many people with a positive test result actually have cancer, and that is input to determining the probability that your patient has cancer.

The team first tested their approach on a simulated dataset of 500 virtual patients with brain cancer. Their model accounted for oxygen consumption by tumor cells, formation of new blood vessels due to the cancer spreading, and detecting the compression of other blood vessels by tumors growing and squashing them. For this demonstration, the team considered the tumor cell density to be the ‘modelable’ variable and the other variables as ‘unmodelable’ to represent the unknown mechanisms of disease progression. In the simulation, each patient attended appointments over a three-year period to serve as the sparse data collection opportunities.

First, a mathematical model for brain tumor growth was simulated, then a machine learning model, before finally the combined approach for comparison against the two individual models. The team found that their combined approach performs particularly well for prediction times larger than six months.

“Our method was able to correct the mathematical model predictions for most of the patients, particularly at later times,” Dr. Savvopoulos said. “We then tested our method on two cohorts of real life patients, using their data to more carefully test the effects of ‘unmodelable’ variables, or those unknowns. We used clinical datasets from patients with leukemia and patients with ovarian cancer.”

“Our proposed method aspires to solve a dire problem in personalized medicine that is related to the limited time-points of patient data collection and limited knowledge in cancer biology,” Dr. Hatzikirou said. “In all our tests, we found our model had excellent predictive capacity, but we did recognize some limitations that should be addressed when applying the methodology to real cases.”

Most importantly, this new combined approach is not restricted to Oncology. Most applications of clinical predictions concern data that is heterogeneous and sparse and there are always unknowns in our knowledge of disease mechanisms.

Jade Sterling
Science Writer
24 October 2021

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The features of a cell nucleus can reveal much about the health of a cell, but finding the nucleus among the background noise of a tissue sample can be laborious and time-consuming when done manually. Khalifa University researchers are exploring how AI can be leveraged to speed up the process of detecting a cell’s nucleus.

Known as the cell’s ‘command center’, the nucleus is a large organelle that stores the cell’s DNA. The nucleus controls all of the cell’s activities, such as growth and metabolism, using the DNA’s genetic information.

Pathologists use features of the cell nucleus to distinguish benign from malignant cells. They examine tissue samples for cells with increased nuclear size and irregularities in the nuclear membrane or abnormal distribution of chromatin, a substance within the chromosomes in the nucleus.

Artificial intelligence techniques are being investigated to streamline the process of nuclei detection. However, these techniques struggle when the tissue samples are noisy or when nuclei appear crowded.

Dr. Naoufel Werghi, Associate Professor, has collaborated with Dr. Sajid Javed, Assistant Professor, and Dr. Jorge Dias, Professor, to develop a machine learning solution that can sift through the background noise and more easily identify individual nuclei. Their results were published in.

In an ideal world, a sample of tissue would contain only that tissue, but most samples contain a rich mix of several other types. Most normal epithelial cells have nuclei which are round to oval-shaped, most lymphoid cells have round nuclei, while stromal cells have ovoid to spindle-shaped nuclei. Any machine learning algorithm would need to be trained on the specific nucleus shape for the nucleus it is tasked to detect, adding a further layer of training required if the algorithm were to be used in other applications.

“Nucleus detection is a challenging task because of the nuclear clutter and diverse shapes and sizes,” said Dr. Werghi. “Additionally, computation challenges arise because the images analyzed are multi-gigapixel images, and could contain billions of pixels and tens of thousands of cell nuclei.”

A number of potential methods have been reported for automatic detection of cell nuclei, including deep learning methods to train a convolutional neural network (CNN) to generate probability maps of where the cell nuclei are present.

Existing approaches are promising, but they require a significant amount of training data and expensive platforms to cater to the high computational requirements. The model proposed by the research team can be trained using much smaller training datasets that can be executed on a typical desktop computer.

“Our solution uses correlation filters to sort through the data and identify nuclei,” explained Dr. Werghi. “Compared to end-to-end deep learning in previous methods, correlation filters are computationally effective and require significantly less training data. The correlation filters are also flexible and can detect complex and irregular-shaped nuclei without requiring handcrafted features.”

These correlation filters help the algorithm to better discriminate different nuclear components from the non-nuclear regions and also to discern each nucleus from the remaining nuclei where they are clustered.

Constraints are placed on the spatial structure of the nucleus and its local contextual information in the correlation filter framework to handle varying nuclei shapes, texture, and clutter. The first considers the spatial structure of the nucleus, while the second discriminates between the nucleus and the non-nucleus region. Both of these help to reduce the noise in any given sample.

The team plans to explore the strength of correlation filters in analysis classifying cell nuclei and tissue phenotyping problems, such as cancer detection.

This research was funded by Khalifa University Center for Autonomous Robotic Systems (KUCARS).

Jade Sterling
Science Writer
5 July 2021

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Khalifa University researchers find a way to use convolutional neural networks to identify cancer in tissue samples, which could speed up diagnosis and improve outcomes in patients with colorectal cancer.

Colorectal cancer is the third most common cancer among men and women worldwide, and the second most common cause of cancer-related mortality. Most colorectal cancers are due to old age and lifestyle factors, with only a small number of cases due to underlying genetic disorders. It typically starts as a benign tumor, such as a polyp, which over time becomes cancerous. Like all forms of cancer, early diagnosis and differentiation of the tumor are crucial for a patient’s survival and wellbeing.

Colorectal cancer may be diagnosed by obtaining a sample of the colon and using histopathology – the study of changes in tissues caused by diseases – to determine the characteristics of the tumour tissue at the microscopic level.

Histology is the study of the microanatomy of cells, tissues and organs as seen through a microscope. The structure of each tissue in the body is directly related to its function and diseases affect tissues in distinctive ways. Studying the histology of a tissue can be very useful in making a diagnosis and determining the severity and progress of a condition.

Because of the great variety of tests that are available, and the high level of skill needed to carry out and interpret them, researchers are beginning to turn to computational pathology and artificial intelligence techniques to identify in tissue samples diseases like cancer.

Dr. Sajid Javed, Assistant Professor, and Dr. Naoufel Werghi, Associate Professor of Electrical Engineering and Computer Science, have collaborated with researchers from around the world to develop algorithms to identify samples of colorectal cancer tissue. A paper based on this research has been published in.

“Computational pathology is a fast-growing research area in cancer diagnosis and can play an instrumental role in helping medical professionals detect and classify tumors,” said Dr. Javed.

Cancer histology reveals underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes.

The phenotype is the set of observable characteristics or traits of an organism or a tissue. Image-based phenotyping aims to develop the computer vision techniques and tools needed to recover quantitative data from a wide range of images. But phenotyping presents challenging problems, particularly in images from colorectal cancer tissues.

Aided by advances in slide scanning microscopes and computing, convolutional neural networks (CNNs) have emerged as an important image analysis tool. CNNs use a network of interconnected layers of filters that highlight important patterns in the images and can continue to learn from previous results.

“Manual examination of tissue samples is time-consuming, highly subjective, and often affected by the observer,” explained Dr. Javed. “Meanwhile, algorithms analyzing digitized Whole Slide Images (WSIs) can examine hundreds of thousands of cells and billions of pixels to differentiate seven distinct tissue phenotypes.”

Deep learning methods require large amounts of annotated histology data for training, which may be tedious to obtain. Additionally, while these methods may be effective in determining tumor tissue, the tissues in colorectal cancer also contain a rich mix of several other types of tissue, including smooth muscle, inflammatory, necrotic, and benign tissue. Any algorithm must be taught to distinguish between these tissue types to be effective.

Texture analysis is a commonly used approach for tissue phenotyping, where texture features are computed to train classifiers, which are then used to predict distinct tissue types.

“Texture analysis may be attractive due to its simplicity but it does not fully capture the biological diversity of tissue components,” explained Dr. Javed.

“Recent methods have proposed integrating cellular connectivity features, which are used as a proxy to cellular interaction features. The notion of cellular connectivity features is based on the fact that spatially adjacent cells have a higher probability of receiving inter-cellular signals from each other than from cells that are farther away. It has also been shown that inter-cellular signals between various types of cells can influence the progression of cancer. However, a dynamic network of tumor growth cannot be adequately modelled by a single type of interaction. Our technique uses a multiplex network model to represent the intricate relationships between cell populations. We propose four different types of cellular networks integrating a variety of features representing tissue characteristics at different levels.”

In the researchers’ model, cells from a WSI are detected and classified into five distinct categories using a deep neural network. Then, four different types of cellular interaction features are computed and used to construct a four-layer multiplex graph. Since each slide contains thousands of cells, the slides are segmented into tiles or patches, which helps the algorithm determine the distribution of different types of tissues across the cells.

“There are many directions in which this work can be further extended,” added Dr. Javed. “Further cellular types such as blood cells could improve performance and also reveal more micro-level tissue communities. Additionally, our framework could be adapted to WSIs of different types of cancer. Of course, in clinical practice, our work can help medical practitioners understand the contents of the WSI and make more accurate and timely diagnoses.”

This work was funded by the Khalifa University of Science and Technology and the UK Medical Research Council. The collaborators were also supported by the PathLAKE digital pathology consortium, which is funded from the Data to Early Diagnosis and Precision Medicine strand of the UK government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation.��

Jade Sterling
Science Writer
5 July 2021

The post Leveraging AI to Detect Colorectal Cancer appeared first on Khalifa University.

Project Offers Training Opportunities at Postgraduate and Undergraduate Levels in Genome Science and Biological Computing using AI and Big Data Analysis

��Abu Dhabi-UAE: 4 March, 2019 – Khalifa University of Science and Technology, a research university dedicated to the advancement of learning through discovery and application of knowledge, and Sandooq Al Watan, the private sector initiative to boost the UAE’s social development, today announced they will collaborate on a project to study genetic predisposition to cancer in the UAE, thus saving lives, healthcare costs and benefiting organ transplants.

To be undertaken by the Khalifa University’s Center for Biotechnology (BTC), the project will study the DNA profiles of Emirati patients in an attempt to identify genetic markers relevant to the local population that will assist in the diagnosis and classification of cancers. An understanding of the genetic makeup of an Emirati patient will also provide an opportunity to customize therapeutic regimes to improve the treatment and management of disease. Sandooq Al Watan will sponsor this project through its platform that has supported 44 Emiratis through 16 projects so far across the UAE.

Dr. Arif Sultan Al Hammadi, Executive Vice-President, Khalifa University of Science and Technology, said: “Our collaboration with Sandooq Al Watan not only reflects our commitment to continue with advanced research but also discovery in areas that are relevant to the UAE and the region. As a research-intensive academic institution, Khalifa University prides itself in contributing to every segment of the UAE’s economic and social sectors including healthcare through scientific discoveries. We believe this research collaboration will benefit individuals who require chronic care as well as help governments prune healthcare outlays, which can be utilized for developmental purposes.”

Mohamed Taj Aldeen Al Qadi, Director General of Sandooq Al Watan, said: “We are keenly aware of the data bias in current DNA studies where people from Arab ancestries are underrepresented. This has caused genetic tests to be less relevant and less accurate in this region of the world. Our collaboration with Khalifa University will address this problem head on, leading to more accurate tests that can save lives and reduce billions in costs. It will also promote research in the UAE and position the country as an important creator of knowledge and technologies. We are very excited to kick this project off with such an esteemed organization and a great research team.

The project is led by Dr Habiba Al Safar, Director, Khalifa University Center for Biotechnology (BTC) and Associate Professor, Department of Biomedical Engineering, and Dr. Guan Tay, Associate Professor, Biomedical Engineering, Khalifa University, who have the resources and expertise in genome research. The project outcome will be in line with the UAE’s national agenda which mandates the development of training opportunities, underpinned by high quality research to fuel a diversified knowledge-based economy. It will also contribute towards the nation’s vision of establishing world-class medical services to cater to the UAE’s future healthcare requirements.

More importantly, the project will offer training opportunities in cutting-edge genome science as well as biological computing, incorporating artificial intelligence and big data analysis at the postgraduate as well as undergraduate levels to Khalifa University students. The research team recently welcomed PhD candidate Halima Al Naqbi, who completed her Master’s degree at the University of Pittsburgh. Master’s students, internships, as well as research positions are available through this Sandooq Al Watan-Khalifa University collaboration, providing opportunities to expand the pool of local experts in the field of genetics and personalized medicine.

Cancer remains one of the top challenges in medical practice because it remains a leading cause of mortality and morbidity worldwide. In addition, there are myriad different cancer types that continue to represent substantial economic and personal burden. The disease has a strong genetic component; however, the genomic factors that result in cancer susceptibility in the UAE remain largely unknown. This study is expected to yield discoveries that could lead to improvements in quality of life for cancer patients and potential reductions in healthcare costs.

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