Cancer Diagnostics – News and Features

An experimental blood test detects early-stage pancreatic cancer more effectively than other available tests. The findings pave are an important step toward approval as a potential diagnostic method for pancreatic cancer.

UCLA researchers developed a non-invasive imaging method using 89Zr-TLX250 to detect clear-cell renal cell carcinoma. This technique demonstrated high sensitivity (85.5%) and specificity (87.0%), effectively identifying cancer in small renal masses.

Researchers have developed an AI model that can perform an array of diagnostic tasks for multiple forms of cancer, improving upon existing AI systems.

Researchers have developed a new mechanism for predicting the aggressiveness of triple-negative breast cancer (TNBC). This method uses patient-derived xenograft models to more accurately forecast cancer recurrence.

A identified 813 potential cancer driver genes via splicing, expanding targets beyond traditional mutation-centric models. Using the "spotter" algorithm, researchers found significant non-overlap with existing mutation databases.

Researchers have pioneered a light-based method for analyzing dried blood crystals, enhancing cancer detection. The technique shows 90% accuracy in diagnosing and classifying cancer with less pain and risk compared to traditional methods.

Researchers found a new class of RNA in blood nanoparticles, potentially paving the way for less invasive cancer diagnosis. They used next-generation RNA sequencing and other innovative tools to develop a cost-effective liquid biopsy test.

A new study has identified diet-derived metabolites, associated with red and processed meats, that drive young-onset colorectal cancer risk.

A deep learning model can effectively detect prostate cancer from MRI images, a new study reports.

The results of routine blood tests could be used to speed up cancer diagnosis among people with stomach pain or bloating, a new study suggests.