Technology Innovation

Pancreatic cancer is one of the deadliest cancers because it is usually found late and is difficult to treat. Most patients cannot have surgery, and even those who do often see the cancer return. The tumor is surrounded by a dense, fibrous barrier that blocks drugs from reaching cancer cells and creates an environment that […]

Researchers have developed a low-cost, non-invasive cancer treatment that uses LED light and tin-based nanoflakes to destroy tumors without the harsh side effects of chemotherapy. The treatment works by giving patients ultra-thin tin oxide (SnOx) nanoflakes that target cancer cells. When exposed to simple LED light, the nanoflakes heat up and kill the cancer cells

Researchers have created ALFA-K, a computer-based tool that predicts how cancer changes over time by tracking whole chromosomes. Instead of just observing tumors at one moment, this approach helps forecast how they will evolve. Cancer cells often gain or lose entire chromosomes, which can change thousands of genes at once. Until now, it was difficult

Researchers have developed a new imaging method that rapidly predicts how cancer drugs will work in patient-derived tumor models. Patient-derived cancer organoids (PDCOs) are 3D models grown from a patient’s tumor, providing a realistic way to study treatment response, but current tests often miss resistant cells and early changes. The technique, Wide-Field Optical Redox Imaging

TAR-200, a novel bladder implant delivering gemcitabine, shows striking results for high-risk, non-muscle-invasive bladder cancer that failed prior treatments. The pretzel-shaped device is inserted via catheter and slowly releases chemotherapy over three weeks, allowing prolonged contact with tumors compared with standard liquid therapy. In the phase 2 SunRISe-1 trial, 82% of 85 patients achieved complete

Artificial Intelligence (AI) is emerging as a powerful tool in detecting interval breast cancers—tumors that appear after a normal screening but before the next scheduled exam. These cancers are often more aggressive and harder to treat. AI helps in two key ways. First, it can reassess prior images, identifying about one-third of cancers that radiologists

Recent research using artificial intelligence (AI) on routine low-dose CT scans suggests that body composition may help predict lung cancer risk. These scans can measure muscle, fat, and bone without extra testing. One key finding is that low muscle density (myosteatosis)—when fat infiltrates muscle tissue—strongly correlates with higher lung cancer risk. Low overall muscle mass (sarcopenia) may

The Phase 3 COMPETE trial (NCT03049189) demonstrates that the radioligand therapy ¹⁷⁷Lu-edotreotide (ITM-11) offers substantial benefits for patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs). Patients receiving ITM-11 had a median progression-free survival (PFS) of 23.9 months, compared with 14.1 months for those treated with everolimus. The therapy showed consistent benefit across both gastrointestinal and pancreatic tumors

A new study shows that combining MRI with PET/CT can stage esophageal cancer (EC) as accurately as the current standard methods while being less invasive and reducing radiation exposure. Currently, staging relies on contrast-enhanced CT, endoscopic ultrasound (EUS), and PET/CT, which require radiation, anesthesia, and may be limited if the tumor obstructs the esophagus. In a

Recent study evaluated the AI deep-learning model Mirai for predicting interval breast cancers (ICs)—cancers that appear between scheduled mammogram screenings—in a screening program for women aged 50–70. The study analyzed 134,217 negative screening mammograms, including 524 ICs. Mirai’s 3-year risk scores were significantly higher in women who later developed ICs, with an overall C index