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Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases.

Melanoma is by far the most serious form of skin cancer. But when it’s caught and treated early, the disease is almost always curable. That’s why it’s important to develop more effective ways to detect melanoma in its earliest stages — a key focus of research at Memorial Sloan Kettering and elsewhere. One approach that’s showing promise is the creation of artificial intelligence (AI) tools. For the past five years, investigators from MSK have led an annual Grand Challenge for the development of AI algorithms that can accurately distinguish between spots that are melanoma and those that are not.

El evento tendrá lugar el día lunes 25 de abril a las 19:30. Participan: Dra. Ana Santaballa Beltrán Jefa de la unidad de Cáncer de Mama y Tumores Ginecológicos, Hospital Universitari y Politècnic La Fe, Valencia. Dra. Virginia Martínez Marín MD PhD, Servicio de Oncología Médica, Hospital U. La Paz, Madrid Dr. Luis Ignacio Martínez Alcorta Servicio de Medicina Preventiva, Hospital Universitario Donostia. Una sesión moderada por Alipio Gutierrez, Responsable de Contenidos de Salud de Telemadrid.

Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice.

 

A trained artificial intelligence model predicted treatment outcomes before surgery of women with high-grade serous ovarian cancer, according to study results. Researchers presented the findings of the pilot study in a plenary session during the Society of Gynecologic Oncology 2022 Annual Meeting on Women’s Cancer.

Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes – prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

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Artificial intelligence (AI) has captured society’s imagination and generated enthusiasm for its potential to improve our quality of life, especially in the health-care area. The availability of high-dimensionality data sets along with innovations in high-performance computing and deep-learning architectures has created an explosion of AI use in various aspects of oncology; these uses range from detection and classification of cancer to molecular characterization of tumors and their microenvironment, leading to drug discovery and predicting treatment outcomes for patients. Yet, although AI is rapidly being incorporated into oncologic research, work remains to be done to translate these studies into real-world, clinically meaningful applications.

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