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. 2022 Jun 8;12:927426. doi: 10.3389/fonc.2022.927426

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Copyright © 2022 Niu, Wang, Zhang, Han, Yang, Bai, Huang, Ren, Tian, Yin, Zhao, Wang, Shi and Zhang

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Computational pipeline for predicting TMB in lung cancer. (A) The tumor area was annotated by a professional pathologist and cut into 512*512 image blocks. (B) Image processing includes noise reduction (discarding image blocks with a blank rate greater than 30%) and color normalization (Macenko method). (C) Divide TMB high and low with 10 as the threshold. (D) The prediction model was constructed with residual network, the model was tested with 5-fold cross-validation, and receiver operating characteristic (ROC) curve was used to evaluate the model.