Findings illustrate promise of technology to lower misdiagnosis rate for deadliest form of skin cancer
PHILADELPHIA – November 1, 2022 – Proscia®, a leading provider of digital and computational pathology solutions, has released study results on new artificial intelligence (AI) that predicts diagnostic agreement for melanoma, the deadliest form of skin cancer. The findings, which were presented at the European Conference on Computer Vision (ECCV) 2022, highlight the potential of the technology to improve diagnostic accuracy for melanoma and other diseases with low pathologist concordance.
Conducted at the University of Florida and Thomas Jefferson University, Proscia’s retrospective study “Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression” demonstrated the AI’s performance on 1,412 whole slide images of skin biopsies. Each image was assessed by three to five dermatopathologists to establish a concordance rate. The R2 correlation between the technology’s predictions and the dermatopathologists’ concordance rates was 0.51.[1]
In addition to this study, Proscia plans to conduct additional research illustrating the potential benefits of AI in helping pathologists to diagnose melanoma, including:
- Lowering the misdiagnosis rate for difficult cases. Melanoma often presents like benign mimickers, causing pathologists to disagree on its diagnosis 40% of the time.[2] As cases are often evaluated by only one pathologist, AI that predicts concordance with multiple experts could help to improve diagnostic accuracy by serving as a second set of eyes.
- Accelerating turnaround times for critical results. Over 15 million skin biopsies are taken annually in the United States[3], each of which may display one of hundreds of diagnoses. AI that predicts diagnostic agreement could flag cases that were likely to be challenging, driving efficiency gains by suggesting additional testing to provide a more complete look prior to pathologist review.
- Reducing costs and distress for patients. Frequent over-diagnosis of melanoma not only results in additional costs for health systems but also leads patients to pay for unnecessary treatment and cope with the stress of believing they have a life-threatening disease.[4] Increased diagnostic accuracy could help to eliminate these burdens.
“Melanoma can be very challenging to diagnose,” said Dr. Kiran Motaparthi, Director of Dermatopathology and Clinical Associate Professor of Dermatology at the University of Florida. “Proscia’s technology signals an exciting advancement as pathologists increasingly turn to AI to deliver on their commitment to excellent patient care.”
Proscia’s research also indicates that the same AI could be extended to other diagnoses that demonstrate low pathologist agreement. This includes breast cancer staging[5] as well as Gleason grading of prostate cancer[6], which is used to evaluate the aggressiveness of the disease. Both often play an important role in informing treatment decisions.
“With this study, we have laid the groundwork for a new use case of AI in pathology that could have a tremendous impact on patient outcomes,” said Sean Grullon, Proscia’s Lead AI Scientist and lead author of the study. “Our technology relies on self-supervised learning to recognize incredibly subtle patterns, demonstrating the power of one of the most advanced approaches in AI.”
About Proscia
Proscia is a software company that is accelerating pathology’s digital transformation to change the way we understand diseases like cancer. Its Concentriq digital pathology platform and powerful AI applications are advancing the 150-year-old standard of research and diagnosis towards a data-driven discipline, unlocking new insights that accelerate discovery, improve patient outcomes, and fulfill the promise of precision care. Leading diagnostic laboratories and over 10 of the top 20 pharmaceutical companies rely on Proscia’s software each day. For more information, visit proscia.com.
[1] Grullon, S. Spurrier, V., Zhao, J. , Chivers, C., Jiang, Y. Motaparthi, K. Lee, J., Bonham, M. and Ianni, J. Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression. Accepted to the European Conference of Computer Vision (ECCV) 2022 AI in Medical Imaging Workshop. arXiv:2210.04803
[2] Elmore, J.G., Barnhill, R.L., Elder, D.E., Longton, G.M., Pepe, M.S., Reisch, L.M., Carney, P.A., Titus, L.J., Nelson, H.D., Onega, T., et al.: Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study. BMJ 357 (2017)
[3] Klipp, J. (2019). The U.S. Anatomic Pathology Market: Forecast & Trends 2019-2021. Laboratory Economics. Poughkeepsie, NY.
[4] Welch, H.G., Mazer, B.L., Adamson, A.S.: The rapid rise in cutaneous melanoma diagnoses. The New England journal of medicine 384(1), 72–79 (2021)
[5] Plichta, J.K., Thomas, S.M., Sergesketter, A.R., Greenup, R.A., Fayanju, O.M., Rosenberger, L.H., Tamirisa, N., Hyslop, T., Hwang, E.S.: Clinical and pathological stage discordance among 433,514 breast cancer patients. Am J Surg 218(4), 669– 676 (10 2019)
[6] Coard, K.C., Freeman, V.L.: Gleason grading of prostate cancer: level of concordance between pathologists at the University Hospital of the West Indies. Am J Clin Pathol 122(3), 373–376 (Sep 2004)
