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Researchers Use Machine Learning To Repair Genetic Damage


DNA damage is constantly occurring in cells, either due to external sources or as a result of internal cellular metabolic reactions and physiological activities. Accurate repair of such DNA damages is critical to avoid mutations and chromosomal rearrangements linked to diseases including cancer, immunodeficiencies, neurodegeneration, and premature aging. A team of researchers at Massachusetts General Hospital and the National Cancer Research Centre have identified a way to repair genetic damage and prevent DNA alterations using machine learning techniques. The researchers state that it is possible to learn more about how cancer develops and how to fight it if we understand how DNA lesions originate and repair. Therefore, they hope that their discovery will help create better cancer treatments while also protecting our healthy cells. To combat challenges to DNA integrity, cells have evolved systems that detect DNA lesions and initiate a signaling cascade that promotes DNA repair, referred to as the DNA damage response (DDR).

Could Fruit Flies Help Match Patients With Cancer Treatments?


Joel Silverman is facing down a nightmarish cancer prognosis. What he'd thought was a benign cyst in his jaw was actually a rare cancer that grew stealthily, supplanting the bone. And even after the tumor was excised, an undetectable remnant in his bloodstream seeded metastatic lesions in his lungs. His doctors can do little beyond removing the lesions as they appear. This cancer, myoepithelial carcinoma, doesn't have a standard chemotherapy treatment.

Machine learning identifies breast lesions likely to become cancer


A new study reveals that a machine learning tool can help to identify which breast lesions, already classified as "high-risk," are likely to become cancerous. The researchers behind the study believe that the technology could eliminate unnecessary surgeries. Breast lesions are classified as high-risk after a biopsy reveals they have a higher chance of developing into cancer. Surgical removal is typically the recommended treatment option for these lesions due to the increased risk, even though many of these lesions do not pose an immediate threat. With "less immediate" cases, surgery may be deemed unnecessary and follow up imaging or other treatments may be found to be the preferred course of action -- but only if there is a reliable way of differentiating between the lesions.

Could 'AI' Become a Partner in Breast Cancer Care?


TUESDAY, Oct. 17, 2017 (HealthDay News) -- Machines armed with artificial intelligence may one day help doctors better identify high-risk breast lesions that might turn into cancer, new research suggests.

Machine Learning Applied To Predicting High-Risk Breast Lesions May Reduce Unnecessary Surgeries


What is the background for this study? What are the main findings? Response: Image-guided biopsies that we perform based on suspicious findings on mammography can yield one of three pathology results: cancer, high-risk, or benign. Most high-risk breast lesions are noncancerous, but surgical excision is typically recommended because some high-risk lesions can be upgraded to cancer at surgery. Currently, there are no imaging or other features that reliably allow us to distinguish between high-risk lesions that warrant surgery from those that can be safely followed, which has led to unnecessary surgery of high-risk lesions that are not associated with cancer.