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AI and machine learning could improve cancer diagnosis through biomarker discovery


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.

Can Artificial Intelligence Detect Melanoma?


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.

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer


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.


Artificial intelligence model predicts treatment response in ovarian cancer


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.

An overview of artificial intelligence in oncology


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.

Emerging Issues Regarding Artificial Intelligence in Cancer Research and Clinical Practice


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.

Artificial Intelligence Comes of Age in the Fight Against Cancer


Artificial intelligence, machine learning and deep learning applied to high-quality datasets, which ideally would be large and from diverse groups of individuals, will be increasingly used to interpret medical imaging, automate analyses, build predictive models, transform written text into coded data, and improve population health.

Artificial intelligence for diagnosis and gleason grading of prostate cance


A global artificial intelligence (AI) competition, the Prostate cANcer graDe Assessment (PANDA) challenge, compiled and publicly released a European cohort for AI development, the largest publicly available dataset of prostate biopsies to date and fully reproduced top-performing algorithms and externally validated their generalisation to independent United States and European cohorts and compared them with the reviews of pathologists. Through such a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalogue of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology according to an article published on 13 January 2022 in the Nature Medicine.

An artificial intelligence‐assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions


The present study created an artificial intelligence (AI)‐automated diagnostics system for uterine cervical lesions and assessed the performance of these images for AI diagnostic imaging of pathological cervical lesions. AI‐assisted image diagnosis was able to improve gynecologist diagnosis accuracy significantly (P<0.01) for invasive cancer and tended to improve their accuracy for CIN2‐3 (P=0.14).

Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/ postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting


The first prospective clinical trial to evaluate the safety and performance of an artificial intelligence (AI) diagnostic aid for skin cancer detection and management in the real-world clinical setting. Participants are recruited on a consecutive basis from routine attendance at melanoma and skin cancer assessment clinics, forming a representative sample of patients and lesion phenotypes from which to evaluate AI algorithm performance. AI performance will be compared with teledermatologists' assessment, as well as to face-to-face assessors of varying clinical experience (registrars and consultant dermatologists), and with histopathology results for biopsied lesions.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters


The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed data from a cohort of 303 patients with reverse transcription-polymerase chain reaction (RT-PCR) confirmed COVID-19, during the first phase of the COVID-19 global pandemic from 14 March to 10 September 2020. Statistical methods and survival analysis, together with the development of machine learning classifiers, were carried out on these data, with the purpose of identifying hematochemical parameters that better reflect and contribute to the risk assessment.


Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach


Studies correlating specific genetic mutations and treatment response are ongoing to establish an effective treatment strategy for gastric cancer (GC). To facilitate this research, a cost- and time-effective method to analyze the mutational status is necessary. Deep learning (DL) has been successfully applied to analyze hematoxylin and eosin (H and E)-stained tissue slide images.

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