Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy

Qi, Lin; Liang, Jie-ying; Li, Zhong-wu; Xi, Shao-yan; Lai, Yu-ni; Gao, Feng; Zhang, Xian-rui; Wang, De-shen; Hu, Ming-tao; Cao, Yi; Xu, Li-jian; Chan, Ronald C.K.; Xing, Bao-cai; Wang, Xin; Li, Yu-hong

doi:10.1016/j.isci.2023.107702

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…Other reported predictors included vitality (percentage in pathology report), Ki-67, pMMR (mismatch repair proficiency) and SOFs (spatial organization features from histology), each in one model. The latter were identified using deep learning techniques through fully automated tissue classification and quantification of SOFs that were correlated with poorer outcomes [65].…”

Section: Histopathological Predictorsmentioning

confidence: 99%

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

Kokkinakis,

Ziogas,

Llaque Salazar

et al. 2024

Cancers

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Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of “high-risk” patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.

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Section: Histopathological Predictorsmentioning

confidence: 99%

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

Kokkinakis,

Ziogas,

Llaque Salazar

et al. 2024

Cancers

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Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology

Mubarak,

Rashid,

Sapna

et al. 2024

Artif Intell Gastroenterol

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Digital pathology (DP) and its subsidiaries including artificial intelligence (AI) are rapidly making inroads into the area of diagnostic anatomic pathology (AP) including gastrointestinal (GI) pathology. It is poised to revolutionize the field of diagnostic AP. Historically, AP has been slow to adopt digital technology, but this is changing rapidly, with many centers worldwide transitioning to DP. Coupled with advanced techniques of AI such as deep learning and machine learning, DP is likely to transform histopathology from a subjective field to an objective, efficient, and transparent discipline. AI is increasingly integrated into GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care. Specifically, AI in GI pathology enhances diagnostic accuracy, streamlines workflows, provides predictive insights, integrates multimodal data, supports research, and aids in education and training, ultimately improving patient care and outcomes. This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology. The main aim was to provide updates and create awareness among the pathology community.

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Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy

Cited by 2 publications

References 51 publications

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology

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