Share Email Print
cover

Proceedings Paper

Prediction of tumour mutational burden of squamous cell carcinoma using histopathology images of surgical specimens
Author(s): Salma Dammak; Matthew Cecchini; Katherina Baranova; David Palma; Keith Kwan; Aaron D. Ward
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Immunotherapy is a novel anti-cancer treatment that shows significant improvements in outcomes for lung cancer patients. However, this treatment has the potential for substantial side effects in a minority of patients, and many lung cancer patients do not benefit from it. Programmed-death ligand-1 expression in tumour cells is currently the main biomarker used to identify those who might benefit but it is not very accurate. Tumour mutational burden (TMB) is a promising alternative, with lung cancers having more than 10 mutations/megabase being more likely to respond to immunotherapy. However, the cost and time it takes to obtain TMB makes it difficult to implement in the clinic. In this study, we used the deep learning technique of transfer learning with Alexnet to obtain a model that can estimate whether a cancer is highly mutated or not based on digitized hematoxylin and eosin histology slides that are routinely obtained from surgical resection of squamous cell carcinoma. The system was developed using images from 20 patients obtained through The Cancer Genome Atlas, five of which were reserved for validation. On this validation set, the system had an area under the receiver operator characteristic curve of 0.80, error rate of 24%, false negative rate of 26%, and false positive rate of 22%. This motivates additional work in this direction to build a system that can be used in the future to inform physicians as to which patients with squamous lung carcinoma would benefit from immunotherapy.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132005 (16 March 2020);
Show Author Affiliations
Salma Dammak, Western Univ. (Canada)
Matthew Cecchini, Western Univ. (Canada)
Katherina Baranova, Western Univ. (Canada)
David Palma, Western Univ. (Canada)
Keith Kwan, Western Univ. (Canada)
Aaron D. Ward, Western Univ. (Canada)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray