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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, and many lung cancer patients do not benefit from it. Programmed-death ligand-1 expression in tumor cells is currently the main biomarker used to identify those who might benefit but it is not very accurate. Tumor 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 our previous work, we have shown that TMB status can be predicted from digitized hematoxylin and eosin histology slides that are routinely obtained from surgical resection of squamous cell carcinoma from a single center using the deep learning technique of transfer learning. In this study, we have shown that this is possible across different centers and explored various design parameters for such a system using 30 slides from 20 different centers from the Cancer Genome Atlas. The parameters exploration yielded 108 models with a median area under the receiver operator characteristic curve of 0.70, and an interquartile range of 0.30 on the validation set (n=7). The model we selected for further testing had perfect classification. 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.
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