Malignant mesothelioma is classified into three histological subtypes, Epithelioid, Sarcomatoid, and Biphasic according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Biphasic tumors display significant populations of both cell types. This subtyping is subjective and limited by current diagnostic guidelines and can differ even between expert thoracic pathologists when characterising the continuum of relative proportions of epithelioid and sarcomatoid components using a three class system. In this work, we develop a novel dual-task Graph Neural Network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score of all the cells in the sample. The proposed approach uses only core-level labels and frames the prediction task as a dual multiple instance learning (MIL) problem. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multi-centric test set from Mesobank, on which we demonstrate the predictive performance of our model. We validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score, finding that some of the morphological differences identified by our model match known differences used by pathologists. We further show that the model score is predictive of patient survival with a hazard ratio of 2.30. The code for the proposed approach, along with the dataset, is available at: https://github.com/measty/MesoGraph.

Despite major advances in treatment and diagnosis over the past decades, cancer still ranks as a leading cause of mortality and a major impediment to extending life expectancy worldwide. Artificial intelligence's future involvement in healthcare, particularly in the detection and treatment of cancer, is anticipated to take a variety of forms, ranging from identifying a specific form of cancer to evaluating which therapy method may best treat that particular instance. AI promises to increase customization of cancer care and help individuals live with the illness with a greater quality of life and fewer side effects. In order to discover cancer in its most treatable stage, screenings are meant to monitor patients who do not show any symptoms proactively. U.S. Preventative Services Task Force recommends screening for breast, cervical, colorectal, and lung cancer.

Background: Pleural Mesothelioma (PM) is an unusual, belligerent tumor that rapidly develops into cancer in the pleura of the lungs. Pleural Mesothelioma is a common type of Mesothelioma that accounts for about 75% of all Mesothelioma diagnosed yearly in the U.S. Diagnosis of Mesothelioma takes several months and is expensive. Given the risk and constraints associated with PM diagnosis, early identification of this ailment is essential for patient health. Objective: In this study, we use artificial intelligence algorithms recommending the best fit model for early diagnosis and prognosis of MPM. Methods: We retrospectively retrieved patients clinical data collected by Dicle University, Turkey, and applied multilayered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested using paired T-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers characteristic curve (ROC), and precision-recall curve (PRC). Results: In phase-1, SGD, AdaBoost. M1, KLR, MLP, VFDT generate optimal results with the highest possible performance measures. In phase 2, AdaBoost, with a classification accuracy of 71.29%, outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate Mesothelioma. Conclusion: This study confirms that data obtained from Biopsy and imagining tests are strong predictors of Mesothelioma but are associated with a high cost; however, they can identify Mesothelioma with optimal accuracy.

Leicester [England], March 26 (ANI): International genomics research led by the University of Leicester has used artificial intelligence (AI) to study an aggressive form of cancer, which could improve patient outcomes. Mesothelioma is caused by breathing asbestos particles and most commonly occurs in the linings of the lungs or abdomen. Currently, only seven per cent of people survive five years after diagnosis, with a prognosis averaging 12 to 18 months. New research undertaken by the Leicester Mesothelioma Research Programme has now revealed, using AI analysis of DNA-sequenced mesotheliomas, that they evolve along similar or repeated paths between individuals. These paths predict the aggressiveness and possible therapy of this otherwise incurable cancer.

The Data Lab has today released details of a new project using AI to assess and treat mesothelioma, or'asbestos cancer'. Scottish medical imaging software firm Canon Medical Research Europe and the University of Glasgow are set to publish clinical findings from a study evaluating a new cancer assessment tool, developed as part of the Cancer Innovation Challenge. A study team have created a prototype that can automatically find and measure mesothelioma on CT scans. These scans are then used by the trained AI to assess patient's response to drug treatments like chemotherapy. The AI was trained by showing it over 100 CT scans, on which an expert clinician had drawn around all areas of the tumour – showing the AI what to look for.

International genomics research led by the University of Leicester has used artificial intelligence (AI) to study an aggressive form of cancer, which could improve patient outcomes. Mesothelioma is caused by breathing asbestos particles and most commonly occurs in the linings of the lungs or abdomen. Currently, only seven per cent of people survive five years after diagnosis, with a prognosis averaging 12 to 18 months. New research undertaken by the Leicester Mesothelioma Research Programme has now revealed, using AI analysis of DNA-sequenced mesotheliomas, that they evolve along similar or repeated paths between individuals. These paths predict the aggressiveness and possible therapy of this otherwise incurable cancer.

Leicester [UK], March 28 (ANI): A new international genomics research led by the University of Leicester used artificial intelligence (AI) to study an aggressive form of cancer, which could be helpful in improving patient outcomes. Mesothelioma is caused by breathing asbestos particles and most commonly occurs in the linings of the lungs or abdomen. Currently, only seven per cent of people survive five years after diagnosis, with a prognosis averaging 12 to 18 months. New research undertaken by the Leicester Mesothelioma Research Programme has now revealed, using AI analysis of DNA-sequenced mesotheliomas, that they evolve along similar or repeated paths between individuals. These paths predict the aggressiveness and possible therapy of this otherwise incurable cancer.

Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology.

This technology has enabled doctors to better detect conditions like epilepsy and Alzheimer's disease, and may transform the field of oncology more than any prior advancement. Although there is still much work to be done before AI becomes mainstream within medicine, professionals are considering this opportunity to be a major step toward advancing cancer care in the future. A challenge oncologists often face is learning how to effectively treat tumors over time. In an effort to combat this issue, a team of scientists from the University of Edinburgh have recently developed an approach known as "REVOLVER," which directly addresses evolving tumors that can become resistant to treatment over time. Through the use of AI, they have discovered a connection between repeated tumor mutations and survival rate, suggesting that specific patterns of DNA mutations could predict how cancers may progress in the future.

From the invention of smartphones to digital assistants like Siri or Alexa breakthrough technology has been an extremely hot topic in recent years. Many aspects of life revolve around technology, but some of the most incredible innovations, however, are unseen by the majority of us in our daily lives. Generally speaking, it was much more grim to receive a cancer diagnosis roughly 50 years ago than it is today. The 5-year survival rate for childhood leukemia was just 14 percent from 1960 to 1963, for example, but has more than quadrupled to 61.4 percent. There has been a number of significant advancements in the world of cancer care and more breakthroughs have been made since the start of the 21st century than any period of time prior.