Photothermal therapy, as an emerging cancer treatment method, has attracted significant attention due to its advantages such as minimal invasiveness, low toxicity, and strong spatiotemporal control. It overcomes the limitations of traditional therapies, which often involve large wounds and systemic toxicity. Recently, a study published in Biofunctional Materials reported the successful development of a novel nanomaterial. This material demonstrates excellent photothermal conversion efficiency and good biocompatibility, showing promising potential as a long-lasting and highly effective photothermal agent in experiments, thus offering new possibilities for precise tumor treatment.

For the first time, researchers have used machine learning – a type of artificial intelligence (AI) – to identify the most important drivers of cancer survival in nearly all the countries in the world. The study, which is published in the leading cancer journal Annals of Oncology, provides information on which improvements or policy changes can be made in each country that would have the greatest impact on improving cancer survival. By going to the online tool created by the researchers, anyone can find their country and scroll down to see which factors, such as national wealth, access to radiotherapy and universal health coverage, are most associated with cancer outcomes.

Nearly 15 percent of U.S. adults continue to smoke, often because long-term health risks feel too abstract compared with the immediate rewards. Jeff Stein, an addiction researcher at Virginia Tech’s Fralin Biomedical Research Institute, is leading a National Cancer Institute–funded study testing whether episodic future thinking — a strategy that helps people vividly imagine their personal future — can reduce impulsivity and support quitting. The fully remote study will enroll 128 participants nationwide and combine the intervention with counseling and nicotine patches.

Deep learning model trained with stage II colorectal cancer whole slide images identifies features associated with risk of recurrence – with higher success rate than clinical prognostic parameters

Large language models (LLM) can generate treatment recommendations for straightforward cases of hepatocellular carcinoma (HCC) that align with clinical guidelines but fall short in more complex cases, according to a new study by Ji Won Han from The Catholic University of Korea and colleagues publishing January 13^th in the open-access journal PLOS Medicine.

According to a Phase I study led by researchers at The University of Texas MD Anderson Cancer Center, published today in Nature Medicine, the novel monoclonal antibody linavonkibart demonstrated the potential to overcome treatment resistance to anti-PD-1 immune checkpoint inhibitors in multiple cancer types.