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
Charlotte E. Ariyan, MD, PhD, of Memorial Sloan Kettering Cancer Center, discusses improved outcomes with metastasectomy in the setting of checkpoint inhibitors, with the removal of residual disease and “escape” lesions. Surgical outcomes may also be better than targeted treatments, although long-term data and biomarkers are needed to confirm these findings.
The ASCO Post Staff
Katelyn T. Byrne, PhD, of the Perelman School of Medicine at the University of Pennsylvania, discusses the first in-depth analysis of the impact of selicrelumab, an anti-CD40 antibody, which was found to enrich T cells in pancreatic tumors, activate the immune system, and alter the tumor stroma (Abstract CT005).
The ASCO Post Staff
Carey K. Anders, MD, of the Duke Cancer Center, discusses the ways in which treatment of brain metastases arising from solid tumors has moved into a new era of patient care and how the field may advance.
The ASCO Post Staff
Richard S. Finn, MD, of UCLA Medical Center, discusses updated efficacy and safety data from the IMbrave150 trial of patients receiving atezolizumab plus bevacizumab vs sorafenib as first-line treatment for unresectable hepatocellular carcinoma (Abstract CT009).
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.
