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Artificial Intelligence May Help Oncologists Better Risk-Stratify Patients With Prostate Cancer.


A multi-modal artificial intelligence model may have the ability to accurately stratify patients with prostate cancer into risk subgroups.

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography


Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.

Machine learning-based behavioral intervention can improve end-of-life cancer care


Electronic nudges delivered to health care clinicians based on a machine learning algorithm that predicts mortality risk quadrupled rates of conversations with patients about their end-of-life care preferences, according to the long-term results of a randomized clinical trial published by Penn Medicine investigators in JAMA Oncology today. The study also found that the machine learning-triggered reminders significantly decreased use of aggressive chemotherapy and other systemic therapies at end of life, which research shows is associated with poor quality of life and side effects that can lead to unnecessary hospitalizations in their final days.

AI-calculated biomarker could predict how lung cancer patients respond to immunotherapy


Too often, searching for the right cancer treatment is a guess-and-check process, as doctors move through a list of costly therapies that may or may not work until they find one that sticks. But a newly discovered biomarker that can be calculated by an artificial intelligence algorithm from a routine CT scan could one day make that process much more efficient.

Machine-Learning Models Predict 6-Month Ovarian Cancer Mortality


A new study shows that an ensemble of machine-learning models can predict six-month mortality for patients with ovarian cancer using patient-reported outcome data.

Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group


Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.

AI tools for earlier pancreatic cancer diagnosis may yield ‘increasingly better outcomes’


Use of artificial intelligence in medical imaging has increased rapidly across health care specialties, and oncology is no exception. Researchers have developed AI tools to assist in detection and diagnosis of several cancers, such as those of the breast, colon, liver, lung and prostate.

Exscientia, MD Anderson Collaborate on AI-Driven Oncology Drug Discovery and Development


AI-driven pharmatech company Exscientia announced today that it has commenced an oncology drug discovery and development collaboration with the University of Texas MD Anderson Cancer Center to identify novel anti-cancer, cell-intrinsic small-molecule compounds based on jointly identified therapeutic targets. The researchers at MD Anderson’s Therapeutics Discovery division will use Exscientia’s expertise in building machine learning models to help identify druggable targets and in what it terms a “data-agnostic” approach to drug design.

Machine Learning Helps Predict Response to Immunotherapy


Scientists at the Johns Hopkins Kimmel Comprehensive Cancer Center and its Bloomberg~Kimmel Institute for Cancer Immunotherapy report that they successfully trained a machine learning algorithm to predict, in hindsight, which patients with melanoma would respond to treatment and which would not respond.

Artificial intelligence may help predict cardiotoxicity in renal cell carcinoma


Artificial intelligence models can help predict cardiotoxicity risk among patients with renal cell carcinoma treated with VEGF receptor inhibitors, according to study results. Integration of artificial intelligence (AI) models into electronic medical records can help oncologists and other members of the clinical care team identify those who may benefit from cardio-oncology monitoring and treatment, findings presented at International Kidney Cancer Symposium: North America showed.

Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer


Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database.

How “the most advanced machine learning approach” is finding new cancer-causing mutational signatures.


This is the most advanced machine learning approach for analysing mutational signatures. We know that because we’ve extensively compared it with everything else that exists.

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