Deep learning in digital pathology for personalized treatment plans of cancer patients

Wen, Zhe; Wang, Shidan; Yang, Donghan M.; Xie, Yang; Chen, Mingyi; Bishop, Justin A.; Xiao, Guanghua

doi:10.1053/j.semdp.2023.02.003

Cited by 8 publications

(3 citation statements)

References 129 publications

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“…Furthermore, external validation would be challenging because of the lack of reproducibility of HE staining across pathology laboratories, or even within the same laboratory, notably because of variations in fixation time. 44 , 45 …”

Section: Discussionmentioning

confidence: 99%

Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts

Lecuelle,

Truntzer,

Basile

et al. 2024

eBioMedicine

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Section: Discussionmentioning

confidence: 99%

Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts

Lecuelle,

Truntzer,

Basile

et al. 2024

eBioMedicine

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“…Automatic digital machine learning presents a promising tool for assessing TILs precisely and consistently. This advanced technology allows for the simultaneous evaluation of complex TIL composition and localization using multiple defining markers while minimizing interobserver variability commonly encountered with manual assessment techniques (187). The use of an automated signature of CD8xPD-L1 has been reported as a predictive marker in nonsmall-cell lung cancer patients (188).…”

Section: Future Directions Toward Precision Medicinementioning

confidence: 99%

The molecular mechanism of actions and clinical utilities of tumor infiltrating lymphocytes in gastrointestinal cancers: a comprehensive review and future prospects toward personalized medicine

Piroozkhah,

Gholinezhad,

Piroozkhah

et al. 2023

Front. Immunol.

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Gastrointestinal (GI) cancers remain a significant global health burden, accounting for a substantial number of cases and deaths. Regrettably, the inadequacy of dependable biomarkers hinders the precise forecasting of patient prognosis and the selection of appropriate therapeutic sequencing for individuals with GI cancers, leading to suboptimal outcomes for numerous patients. The intricate interplay between tumor-infiltrating lymphocytes (TILs) and the tumor immune microenvironment (TIME) has been shown to be a pivotal determinant of response to anti-cancer therapy and consequential clinical outcomes across a multitude of cancer types. Therefore, the assessment of TILs has garnered global interest as a promising prognostic biomarker in oncology, with the potential to improve clinical decision-making substantially. Moreover, recent discoveries in immunotherapy have progressively changed the landscape of cancer treatment and significantly prolonged the survival of patients with advanced cancers. Nonetheless, the response rate remains constrained within solid tumor sufferers, even when TIL landscapes appear comparable, which calls for the development of our understanding of cellular and molecular cross-talk between TIME and tumor. Hence, this comprehensive review encapsulates the extant literature elucidating the TILs’ underlying molecular pathogenesis, prognostic significance, and their relevance in the realm of immunotherapy for patients afflicted by GI tract cancers. Within this review, we demonstrate that the type, density, and spatial distribution of distinct TIL subpopulations carries pivotal implications for the prediction of anti-cancer treatment responses and patient survival. Furthermore, this review underscores the indispensable role of TILs in modulating therapeutic responses within distinct molecular subtypes, such as those characterized by microsatellite stability or programmed cell death ligand-1 expression in GI tract cancers. The review concludes by outlining future directions in TIL-based personalized medicine, including integrating TIL-based approaches into existing treatment regimens and developing novel therapeutic strategies that exploit the unique properties of TILs and their potential as a promising avenue for personalized cancer treatment.

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“…Deep learning (DL) is a branch of artificial intelligence (AI) that relies on artificial neural networks (ANN) capable of learning from large amounts of data and extracting information relevant to various tasks. DL has found numerous applications in the medical field, particularly in imaging diagnosis [1], clinical and drug research [2], disease classification and prediction [3], personalized therapy design [4], and public health monitoring [5]. It can offer significant advantages over traditional data analysis methods, both in terms of performances and automation [6].…”

Section: Introductionmentioning

confidence: 99%

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

et al. 2023

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Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.

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Deep learning in digital pathology for personalized treatment plans of cancer patients

Cited by 8 publications

References 129 publications

Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts

Machine learning evaluation of immune infiltrate through digital tumour score allows prediction of survival outcome in a pooled analysis of three international stage III colon cancer cohorts

The molecular mechanism of actions and clinical utilities of tumor infiltrating lymphocytes in gastrointestinal cancers: a comprehensive review and future prospects toward personalized medicine

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

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