Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Leong, Tze Ker Matthew; Lo, Wen Shern; Lee, Wei En Zen; Tan, Benedict; Lee, Xing Zhao; Lee, Li Wen Justina Nadia; Lee, Jia-Ying Joey; Suresh, Nivedita; Loo, Lit‐Hsin; Szu, Evan; Yeong, Joe Poh Sheng

doi:10.1016/j.addr.2021.113959

Cited by 8 publications

(11 citation statements)

References 98 publications

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“…[ 117 ] Furthermore, while pathologists often have difficulty differentiating squamous cell carcinoma from adenocarcinoma in NSCLC (particularly in the case of poorly differentiated tumors), the application of ML to IHC data yielded promising results. [ 1 ] In particular, a decision tree model trained on 30 small biopsy cases of NSCLC demonstrated that a combination of two markers, namely p63 and CK5/6, in addition to a three‐marker IHC panel (TTF‐1, Napsin A, and p40) yielded an accuracy of 72%. Importantly, applying the capacity of ML to learn and predict based on the large datasets obtained from histological slides means that the process of histopathological analysis can be streamlined.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…Tumors are characterized by a high degree of heterogeneity, with their characteristics depending on cell interactions within the tumor microenvironment (TME). [ 1 ] Whilst tissue homeostasis can be maintained by interactions between normal cells and the TME, interactions between tumor cells and the TME can result in cellular reprogramming, leading to the evolvement of the TME to support tumorigenesis and drug resistance. [ 1–3 ] With a heterogeneous milieu of tumor and immune cells, an in‐depth understanding and characterization of the TME may lead to the discovery of mechanisms and biomarkers to improve patient management strategies.…”

Section: Introductionmentioning

confidence: 99%

“…[ 1 ] Whilst tissue homeostasis can be maintained by interactions between normal cells and the TME, interactions between tumor cells and the TME can result in cellular reprogramming, leading to the evolvement of the TME to support tumorigenesis and drug resistance. [ 1–3 ] With a heterogeneous milieu of tumor and immune cells, an in‐depth understanding and characterization of the TME may lead to the discovery of mechanisms and biomarkers to improve patient management strategies. As such, there have been continuous efforts to develop tumor models that mimic the TME in cancer biology.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment

Lee

Goh

et al. 2023

Adv Healthcare Materials

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In vitro tumor models have played vital roles in enhancing the understanding of the cellular and molecular composition of tumors, as well as their biochemical and biophysical characteristics. Advances in technology have enabled the evolution of tumor models from two-dimensional cell cultures to three-dimensional printed tumor models with increased levels of complexity and diverse output parameters. With the increase in complexity, the new generation of models is able to replicate the architecture and heterogeneity of the tumor microenvironment more realistically than their predecessors. In recent years, artificial intelligence (AI) has been used extensively in healthcare and research, and AI-based tools have also been applied to the precise development of tumor models. The incorporation of AI facilitates the use of high-throughput systems for real-time monitoring of tumorigenesis and biophysical tumor properties, raising the possibility of using AI alongside tumor modeling for personalized medicine. Here, the integration of AI tools within tumor modeling is reviewed, including microfluidic devices and cancer-on-chip models.

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Section: Artificial Intelligencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment

Lee

Goh

et al. 2023

Adv Healthcare Materials

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“…Further research is thus warranted, to enable us to harness the full potential of bringing together in vitro models and AI in the study of the TME and to enable us to extract and maximize the vast quantity of information stored within the intricacies of the TME. This translates into improved diagnostic, prognostic, and therapeutic outcomes for patients [ 122 ].…”

Section: Coupling Cancer On Chip and Artificial Intelligence For Future Cancer Managementmentioning

confidence: 99%

Organ on Chip Technology to Model Cancer Growth and Metastasis

2022

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Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell–cell and cell–matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented.

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“…However, in clinical research, a typical study cohort for target protein expression ranges from tens to hundreds; such a small number of datasets is insufficient for high performance model development, and the shortage of public datasets provides limited support in transfer learning. The data requirement sets a high entry barrier for adopting AI-DL technology in clinical research with special stains such as fluorescent stain [ 19 , 20 ]. One conceivable approach for filling this gap is to use color normalization algorithms to compensate for model variation in different staining methodologies, but previous studies have mostly focused on solving the variation in the same colored detection methods [ [21] , [22] , [23] ].…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based breast cancer detection for cross-staining histopathology images

Huang¹,

Ouyang²,

Hsu³

et al. 2023

Heliyon

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Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space

Cited by 8 publications

References 98 publications

Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment

Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment

Organ on Chip Technology to Model Cancer Growth and Metastasis

Deep-learning based breast cancer detection for cross-staining histopathology images

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