Survival prediction of stomach cancer using expression data and deep learning models with histopathological images

Wei, Ting; Yuan, Xin; Gao, Ruitian; Johnston, Luke J.; Zhou, Jie; Wang, Yifan; Kong, Weiming; Xie, Yujing; Zhang, Yue; Xu, Dakang; Yu, Zhangsheng

doi:10.1111/cas.15592

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…For the survival prediction task, we improved the final output layer of CLAM based on the survival model proposed by Wei et al, and adopted the COX proportional risk model to meet the requirements of survival prediction 46,47 , named BEPH-CLAMSurvival (Fig. 1d).…”

Section: Wsi Survival Prediction-beph-clam Survivalmentioning

confidence: 99%

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Yang,

Wei,

Liang

et al. 2024

Preprint

Self Cite

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Computational pathology, utilizing whole slide image (WSI) for pathological diagnosis, has advanced the development of intelligent healthcare. However, the scarcity of annotated data and histological differences hinder the general application of existing methods. Extensive histopathological data and the robustness of self-supervised models in small-scale data demonstrate promising prospects for developing foundation pathology models. In this work, we propose the BEPH (BEiT-based model Pre-training on Histopathological image), a general method that leverages self-supervised learning to learn meaningful representations from 11 million unlabeled histopathological images. These representations are then efficiently adapted to various tasks, including patch-level cancer recognition, WSI-level cancer classification, and survival prediction for multiple cancer subtypes. Experimental results demonstrate that our model consistently outperformsseveral comparative models, even with limited training data reduced to 50%. Especially when the downstream structure is the same, the model can improve ResNet and DINO by up to a maximum increase of 8.8% and 7.2% (WSI level classification), and 6.44% and 3.28% on average (survival prediction), respectively. Therefore, BEPH offers a universal solution to enhance model performance, reduce the burden of expert annotations, and enable widespread clinical applications of artificial intelligence. The code and models can be obtained at https://github.com/Zhcyoung/BEPH. And currently, online fine-tuning of WSI classification tasks is available for use on http://yulab-sjtu.natapp1.cc/BEPH.

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Section: Wsi Survival Prediction-beph-clam Survivalmentioning

confidence: 99%

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Yang,

Wei,

Liang

et al. 2024

Preprint

Self Cite

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“…Another study by Wei et al [ 31 ] introduced a DL-based model, called MultiDeepCox-SC, that predicts OS in GC patients by integrating histopathological images with clinical and gene expression data. They trained the model using 10-fold cross-validation with 382 WSIs from the TCGA dataset, and the model calculated the risk score using H&E images, age, gender, tumor grade, tumor stage, and gene expression profiles to predict the patient’s risk of death.…”

Section: Prediction Of the Clinical Outcome And Biomarker Detectionmentioning

confidence: 99%

Artificial Intelligence in the Pathology of Gastric Cancer

Choi

Kim

2023

J Gastric Cancer

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Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

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A Novel High-Dimensional Kernel Joint Non-Negative Matrix Factorization With Multimodal Information for Lung Cancer Study

Shi,

Jin,

Deng

et al. 2024

IEEE J. Biomed. Health Inform.

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Survival prediction of stomach cancer using expression data and deep learning models with histopathological images

Cited by 4 publications

References 47 publications

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Artificial Intelligence in the Pathology of Gastric Cancer

A Novel High-Dimensional Kernel Joint Non-Negative Matrix Factorization With Multimodal Information for Lung Cancer Study

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