Living near toxic sites is linked to aggressive breast cancer. Can AI improve mammography interpretation? Glioblastoma cells that “unstick” from neighboring cells become more deadly, and patients who receive remote monitoring after cancer surgery have better outcomes. All of these story ideas and more are in this month’s tip sheet from Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine.

Breast cancer is increasingly affecting younger women globally, often before the screening guidelines recommend testing age. Young patients with breast cancer have a worse prognosis than older women.

Early screening through AI-enhanced mammography and high-throughput sequencing-powered genetic tests can identify high-risk individuals, offering a critical time frame for prevention and intervention.

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the main causes of cancer-related mortality. Precisely predicting whether this type of tumour will reappear remains one of the key challenges in oncology. To try and make progress in this field, an international team led by the Universitat Rovira i Virgili has developed an artificial intelligence model that brings together medical imaging data and clinical information to calculate the risk of tumour recurrence in a much more accurate and interpretative way.

The Comparative Immunotherapy Program led by Nicola Mason of the School of Veterinary Medicine at the University of Pennsylvania is redefining how therapies are tested, uniting human and veterinary medicine to move promising immunotherapies forward to fight cancer and autoimmune diseases. “Pet dogs develop diseases spontaneously in the same way as humans do,” Mason says. The research that laid the groundwork for the program is featured in the documentary “Shelter Me: The Cancer Pioneers,” which has been nominated for two Daytime Emmys.