Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

By K. Bera, K. Schalper, David L. Rimm, V. Velcheti, Anant Madabhushi. Nature Reviews Clinical Oncology, 2019

1,383 citations recorded by the source corpus.

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and ‘hand-crafted’ feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development. We discuss some of the challenges relating to the use of AI, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies. Finally, we present potential future opportunities for precision oncology.The authors of this Perspective critically evaluate various artificial intelligence (AI)-based computational approaches used for digital pathology and provide a broad framework to incorporate these tools into clinical oncology, discussing challenges such as the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.

Subfields: Artificial Intelligence, Cancer Research, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/847ada64e867bd2a6ac272b5efb89c914d4b4375

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Citations in the corpus

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “As an example, deep learning neural networks have significantly advanced molecular diagnostics in clinical oncology, leading to a new era in digital pathology and precision medicine [101].”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, methodology): “With the advent of whole slide scanning and the development of large digital data sets of tissue slide images, there has been a significant increase in applications of DL approaches to digital pathology data [213].”
AI in health and medicine (2022, background): “In the field of pathology, AI has made major strides in diagnosing cancers and providing new disease insights 28–33 , largely through the use of whole-slide imaging.”
Early detection of cancer (2022, methodology): “The application of ML techniques to digitized slides can increase sensitivity; reduce subjectivity and inter-reader variation; and predict prognosis, recurrence, and tumor susceptibility to treatment (120).”
Artificial intelligence for digital and computational pathology (2023, background): “Reaching the goal of precision medicine requires that increasingly different assays are carried out, each with a finer spatial and temporal resolution, thereby drastically increasing the amount of data available per patient.”
Harnessing multimodal data integration to advance precision oncology (2021, methodology): “Unimodal machine learning methods Cancer imaging data have been exploited to predict molecular features of tumours and to discover new prognostic associations with clinical outcomes, and we refer readers to a number of excellent reviews in these areas 13–15 .”
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, methodology): “, modifying data with missing features or extreme values) in order to learn effectively.(4) Additionally, the input features must be amenable to the type of model.”
Deep neural network models for computational histopathology: A survey (2019, background): “The success of DL depends on the availability of highquality training sets to achieve the desired predictive performance (Madabhushi and Lee, 2016; Bera et al., 2019; Niazi et al., 2019).”