Dataset Information

Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning.

ABSTRACT: Colorectal cancer (CRC) is one of the most prevalent malignancies, and immunotherapy can be applied to CRC patients of all ages, while its efficacy is uncertain. Tumor mutational burden (TMB) is important for predicting the effect of immunotherapy. Currently, whole-exome sequencing (WES) is a standard method to measure TMB, but it is costly and inefficient. Therefore, it is urgent to explore a method to assess TMB without WES to improve immunotherapy outcomes. In this study, we propose a deep learning method, DeepHE, based on the Residual Network (ResNet) model. On images of tissue, DeepHE can efficiently identify and analyze characteristics of tumor cells in CRC to predict the TMB. In our study, we used ×40 magnification images and grouped them by patients followed by thresholding at the 10th and 20th quantiles, which significantly improves the performance. Also, our model is superior compared with multiple models. In summary, deep learning methods can explore the association between histopathological images and genetic mutations, which will contribute to the precise treatment of CRC patients.

SUBMITTER: Liu Y

PROVIDER: S-EPMC9171017 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning.

Liu Yongguang Y Huang Kaimei K Yang Yachao Y Wu Yan Y Gao Wei W

Frontiers in oncology 20220524

Colorectal cancer (CRC) is one of the most prevalent malignancies, and immunotherapy can be applied to CRC patients of all ages, while its efficacy is uncertain. Tumor mutational burden (TMB) is important for predicting the effect of immunotherapy. Currently, whole-exome sequencing (WES) is a standard method to measure TMB, but it is costly and inefficient. Therefore, it is urgent to explore a method to assess TMB without WES to improve immunotherapy outcomes. In this study, we propose a deep le ...[more]

PMID: 35686098

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Project description:BackgroundThe current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data.MethodsA total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes.ResultsThe proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC.ConclusionsThe proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.

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Dataset Information

Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning.

Publications

Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning.

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