Joann G. Elmore, MD, MPH, on Cancer Diagnosis: When Pathologists Disagree, Artificial Intelligence May Help
AACR Annual Meeting 2021
Joann G. Elmore, MD, MPH, of the UCLA Fielding School of Public Health, discusses previous studies that show wide variability in cancer diagnoses, the uncertainties introduced by computer-aided detection tools, and new research on artificial intelligence and machine learning that may lead to more consistent and accurate diagnoses and prognoses, potentially improving treatment (Abstract SY01-03).
The ASCO Post Staff
Jessica C. Hassel, MD, of University Hospital Heidelberg, discusses phase III results of a study that compared tebentafusp, a bispecific fusion protein, with investigator’s choice in patients with metastatic uveal melanoma. Tebentafusp nearly halved the risk of death among patients in the trial with this rare eye cancer (Abstract CT002).
The ASCO Post Staff
Linda T. Vahdat, MD, MBA, of Memorial Sloan Kettering Cancer Center, discusses results of a phase II trial designed to test the concept that targeting the tumor microenvironment by depleting copper may prevent metastases, essentially disrupting the infrastructure that contributes to tumor spread.
The ASCO Post Staff
Brian I. Rini, MD, of Vanderbilt University, discusses the IMmotion151 trial results on overall survival and the association of gene expression and clinical outcomes with atezolizumab plus bevacizumab vs sunitinib in patients with locally advanced or metastatic renal cell carcinoma (Abstract CT188).
The ASCO Post Staff
Rita Nanda, MD, of the University of Chicago, discusses the latest data on novel treatment strategies for triple-negative breast cancer, including immune checkpoint, PARP, and ATK inhibitors; antibody-drug conjugates; and targeting the androgen receptor.
The ASCO Post Staff
Jeanne Tie, MD, MBChB, of the Peter MacCallum Cancer Centre, discusses how to improve the current, somewhat imprecise, approach based on pathologic staging alone, used to select patients for adjuvant treatment. Circulating tumor DNA analysis after curative-intent treatment may detect minimal residual disease and might be used to predict recurrence and adjuvant treatment efficacy across multiple tumor types.
