Cancer Diagnostics – News and Features

A study reveals that 3% of normal breast cells in healthy women have chromosomal abnormalities linked to invasive breast cancer, challenging current cancer detection methods. Aneuploid cells accumulate with age and mimic invasive cancer profiles.

Precision diagnostics for diseases that affect the brain and other organs brought closer by new ability to exclusively access contents of organ-derived extracellular vesicles in blood.

The INAVO120 study reveals that the three-drug therapy inavolisib, palbociclib and fulvestrant significantly delays cancer progression in PIK3CA-mutated breast cancer. The therapy has received FDA approval and shows promise as a new standard of care.

An international team has developed a new imaging method that analyzes cancer cells and their surrounding tissue at a single-cell level. This innovative approach identifies distinct patient groups based on prognosis.

A team at The University of Texas at El Paso has developed a microfluidic device that can quickly and affordably detect colorectal and prostate cancer. This innovative biochip eliminates the need for expensive equipment.

An AI model designed to detect skin cancer outperformed existing methods in a new study.

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.