Another step in the battle against cancer has been accomplished by researchers from the Hebrew University of Jerusalem (HU). They have introduced a novel method that combines nano informatics and machine learning to precisely predict cancer-cell behaviors.According to the researchers, this will make it possible to identify subpopulations of cells with distinct characteristics, such as drug sensitivity and the danger of spreading (metastasizing) to other parts of the body.Doctoral student Yoel Goldstein and Prof. Ofra Benny from the School of Pharmacy in the HU Faculty of Medicine collaborated with Prof. Tommy Kaplan, head of the computational biology department at the School of Engineering and Computer Science.They said their research could transform cancer diagnosis and treatment, enhancing personalized medicine by making possible speedy and accurate testing of cancer-cell behaviors from patient biopsies. This could lead to the development of new clinical tests to monitor disease progression and the effectiveness of treatment.The researchers recently published their study in the Nature Group’s prestigious journal Science Advances under the title “Magnetism and photo dual-controlled supramolecular assembly for suppression of tumor invasion and metastasis.”
Lacking accuracy and efficiency
Current tools for predicting and detecting cancer often lack accuracy and efficiency. Traditional methods such as imaging scans and tissue biopsies can be invasive, costly, and time-consuming, leading to delays in treatment and potential misdiagnoses.These approaches may not capture the dynamic nature of cancer progression and can result in limited insights into the disease’s behavior at a cellular level. As a result, patients may face delays in diagnosis, suboptimal treatment outcomes, and increased psychological distress.The study highlighted the urgent need for more effective and noninvasive diagnostic tools, such as the HU discovery, which presents a significant advancement in personalized medicine, providing hope for more effective and customized treatment strategies for cancer patients.