The vaccine is personalized to a patient's tumor, and results from the Phase 3 trial are yet another example of the promise of mRNA vaccines in treating cancer. The new mRNA vaccine is waiting on FDA approval but has shown great promise in Phase 3 clinical trials. An experimental vaccine offers new hope to patients with melanoma, one of the most aggressive forms of skin cancer . The pharmaceutical companies Merck and Moderna released the results of a Phase 3 clinical trial that evaluated the efficacy of a personalized mRNA-based vaccine that, when combined with immunotherapy, reduced the risk of disease recurrence or death by 42 percent compared to treatment with the immunotherapy drug alone. Georgina Long, medical director of the Australian Melanoma Institute and the study's principal investigator, said in a statement that these results are "transformative" and "have the potential to make a foundational shift in how we treat patients with earlier-stage melanoma."

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The startup Coherence Neuro is now testing a brain-computer interface that could one day use electrical stimulation to prevent tumors from growing. A San Francisco startup with ties to Elon Musk's Neuralink has started testing its brain implant to detect and treat cancer in humans. Coherence Neuro says it temporarily placed its coin-sized implant in the brains of three people undergoing surgery to have brain tumors removed at the Royal Melbourne Hospital in Australia. The implant was in place for roughly 30 minutes before being removed, providing an important safety check before the device can be implanted long-term in patients with brain cancer. Known as a brain-computer interface, the Coherence Neuro device is designed to sense the unique electrical signals of tumors and deliver mild electrical stimulation to prevent their growth.

This comparative summary underscores the breadth, depth, and clinical relevance of PanTS relative to existing public datasets. While a number of prior datasets were incorporated into our training partition, our team made substantial and transformative contributions. Specifically, 23 board-certified radiologists independently annotated and rigorously validated previously unlabeled pancreatic tumors as well as over 25 additional abdominal and thoracic anatomical structures, many of which were not comprehensively labeled in the source datasets. This effort significantly elevates the clinical utility and completeness of the dataset. Scale: With 36,390 CT scans, PanTS is over 8.5 larger than the most extensive existing dataset dedicated to pancreatic tumor detection, setting a new benchmark for scale in abdominal imaging datasets.

PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993,000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation than those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16 larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.

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Radiation therapy, treating over half of all cancer patients, involves using specialized machines to direct high-energy beams at tumors, aiming to damage cancer cells while minimizing harm to nearby healthy tissues. Customizing the shape and intensity of radiation beams for each patient leads to solving large-scale constrained optimization problems that need to be solved within tight clinical time-frame. At the core of these challenges is a large matrix that is commonly sparsified for computational efficiency by neglecting small elements. Such a crude approximation can degrade the quality of treatment, potentially causing unnecessary radiation exposure to healthy tissues--this may lead to significant radiation-induced side effects--or delivering inadequate radiation to the tumor, which is crucial for effective tumor treatment. In this work, we demonstrate, for the first time, that randomized sketch tools can effectively sparsify this matrix without sacrificing treatment quality. We also develop a novel randomized sketch method with desirable theoretical guarantees that outperforms existing techniques in practical application. Beyond developing a novel randomized sketch method, this work emphasizes the potential of harnessing scientific computing tools, crucial in today's big data analysis, to tackle computationally intensive challenges in healthcare. The application of these tools could have a profound impact on the lives of numerous cancer patients.

Physical models in the form of partial differential equations serve as important priors for many under-constrained problems. One such application is tumor treatment planning, which relies on accurately estimating the spatial distribution of tumor cells within a patient's anatomy. While medical imaging can detect the bulk of a tumor, it cannot capture the full extent of its spread, as low-concentration tumor cells often remain undetectable, particularly in glioblastoma, the most common primary brain tumor. Machine learning approaches struggle to estimate the complete tumor cell distribution due to a lack of appropriate training data. Consequently, most existing methods rely on physics-based simulations to generate anatomically and physiologically plausible estimations. However, these approaches face challenges with complex and unknown initial conditions and are constrained by overly rigid physical models. In this work, we introduce a novel method that integrates data-driven and physics-based cost functions, akin to Physics-Informed Neural Networks (PINNs).