Researchers at Rice University, UTHealth School of Dentistry, and The University of Texas M.D. Anderson Cancer Center have developed a smartphone-based imaging system, mDOC, to help dental professionals identify patients who may need referral for oral cancer evaluation. The device captures white light and autofluorescence images of the mouth and uses a machine learning algorithm to assess risk. In a study of 50 patients at community dental clinics, the system demonstrated 60 percent sensitivity and 88 percent specificity in identifying lesions requiring expert review—outperforming standard clinical exams. The technology offers a fast, low-cost tool to support early detection and timely referral in routine dental care settings.

Researchers from The University of Texas MD Anderson Cancer Center have discovered a previously unknown mechanism that explains how bacteria can drive treatment resistance in patients with oral and colorectal cancer.

Researchers Eduard Porta and Daniela Grases have just published the first practical guide on the use of Spatial Transcriptomics. Thanks to their experience, resulting from processing of more than 1,000 tissue samples in the laboratory, the Spatial Biology program at the Josep Carreras Leukemia Research Institute is now a leading expert in the use of this technology, essential for better understanding blood cancers and advancing in the development of new therapies. This is a fundamental aspect of the Josep Carreras Institute's new mission, which seeks to transform the way patients with hematologic cancer are diagnosed and treated.

Healing doesn’t end with treatment. As cancer survivorship grows, many continue to struggle with anxiety and depression – often in silence. A new review highlights how caring-healing modalities (CHMs) like mindfulness and peer support can build resilience and reduce emotional distress. These human-centered approaches shift focus from treatment to healing, helping survivors reclaim their sense of self. Rooted in empathy and emotional expression, CHMs offer a whole-person approach that meets the deep psychological needs often overlooked in survivorship care –especially among breast cancer survivors.

Controlling the state of a cell in a desired direction is one of the central challenges in life sciences, including drug development, cancer treatment, and regenerative medicine. However, identifying the right drug or genetic target for that purpose is extremely difficult. To address this, researchers at KAIST have mathematically modeled the interaction between cells and drugs in a modular “Lego block” manner—breaking them down and recombining them—to develop a new AI technology that can predict not only new cell–drug reactions never before tested but also the effects of arbitrary genetic perturbations.