Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response DOI Creative Commons
Xiao Fu, Erik Sahai, Anna Wilkins

и другие.

The Journal of Pathology, Год журнала: 2023, Номер 260(5), С. 578 - 591

Опубликована: Авг. 1, 2023

Abstract In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun radically change how images are used in clinic. Alongside recognition that tumour microenvironment (TME) a profound impact on phenotype, technical development highly multiplexed immunofluorescence platforms enhanced biological complexity can be captured TME with high precision. AI an increasingly powerful role quantitation features association such clinically important outcomes, as occurs distinct stages conventional machine learning. Deep‐learning algorithms able elucidate patterns inherent input data minimum levels human and, hence, have potential achieve relevant predictions discovery features. Furthermore, diverse repertoire deep‐learning interrogate extends beyond convolutional neural networks include attention‐based models, graph networks, multimodal models. To date, models largely been evaluated retrospectively, outside well‐established rigour prospective clinical trials, part because traditional trial methodology may not always suitable for assessment technology. However, enable pathology‐based analytics meaningfully care, specific measures ‘added benefit’ current standard care validation setting important. This will need accompanied by adequate explainability interpretability. Despite challenges, combination expanding datasets, increased computational power, possibility integration pre‐clinical experimental insights into model means there is exciting future progress these applications. © 2023 The Authors. Journal Pathology published John Wiley & Sons Ltd behalf Pathological Society Great Britain Ireland.

Язык: Английский

Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution DOI Creative Commons
Jingyang Qian, Xin Shao,

Hudong Bao

и другие.

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 16, 2025

Deciphering the features, structure, and functions of cell niche in tissues remains a major challenge. Here, we present scNiche, computational framework to identify characterize niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated biological datasets, demonstrate that can effectively robustly while outperforming other existing methods. In proteomics human triple-negative breast cancer, reveals influence microenvironment on cellular phenotypes, further dissects patient-specific distinct compositions or phenotypic characteristics. By analyzing mouse liver transcriptomics across normal early-onset failure donors, uncovers disease-specific injury niches, delineates remodeling failure. Overall, enables decoding data. authors develop characterise

Язык: Английский

Процитировано

0
Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data DOI
Jimin Tan, Hortense Le, Jiehui Deng

и другие.

Nature Biomedical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

0
STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes DOI Creative Commons
Ran Wang, Qian Yan, Xiaojing Guo

и другие.

Genome Medicine, Год журнала: 2025, Номер 17(1)

Опубликована: Март 3, 2025

Abstract Here we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features various cell types, which facilitate downstream analysis provide insights into tumor microenvironments, disease mechanisms, treatment development, histological organization captures broader spectrum biological compared other methods detects novel components. The characterized by gene sets demonstrate greater robustness transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git .

Язык: Английский

Процитировано

0
Analyzing Spatial Point Patterns in Digital Pathology: Immune Cells in High-Grade Serous Ovarian Carcinomas DOI
Jonatan A. González, Julia Wrobel, Simon Vandekar

и другие.

The American Statistician, Год журнала: 2025, Номер unknown, С. 1 - 26

Опубликована: Март 28, 2025

Язык: Английский

Процитировано

0
Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response DOI Creative Commons
Xiao Fu, Erik Sahai, Anna Wilkins

и другие.

The Journal of Pathology, Год журнала: 2023, Номер 260(5), С. 578 - 591

Опубликована: Авг. 1, 2023

Abstract In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun radically change how images are used in clinic. Alongside recognition that tumour microenvironment (TME) a profound impact on phenotype, technical development highly multiplexed immunofluorescence platforms enhanced biological complexity can be captured TME with high precision. AI an increasingly powerful role quantitation features association such clinically important outcomes, as occurs distinct stages conventional machine learning. Deep‐learning algorithms able elucidate patterns inherent input data minimum levels human and, hence, have potential achieve relevant predictions discovery features. Furthermore, diverse repertoire deep‐learning interrogate extends beyond convolutional neural networks include attention‐based models, graph networks, multimodal models. To date, models largely been evaluated retrospectively, outside well‐established rigour prospective clinical trials, part because traditional trial methodology may not always suitable for assessment technology. However, enable pathology‐based analytics meaningfully care, specific measures ‘added benefit’ current standard care validation setting important. This will need accompanied by adequate explainability interpretability. Despite challenges, combination expanding datasets, increased computational power, possibility integration pre‐clinical experimental insights into model means there is exciting future progress these applications. © 2023 The Authors. Journal Pathology published John Wiley & Sons Ltd behalf Pathological Society Great Britain Ireland.

Язык: Английский

Процитировано

10