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Informatics, digital & computational pathology

Computational pathology

Digitization and precision oncology


Monika Lamba Saini, M.D., Ph.D.
John D. Cochran, M.D.

Last author update: 3 November 2023
Last staff update: 3 November 2023

Copyright: 2023, PathologyOutlines.com, Inc.

PubMed Search: Digitization and precision oncology

Monika Lamba Saini, M.D., Ph.D.
John D. Cochran, M.D.
Page views in 2023: 308
Page views in 2024 to date: 61
Table of Contents
Definition / general | Essential features | Terminology | Applications | Implementation | Advantages | Limitations | Software | Additional references | Board review style question #1 | Board review style answer #1 | Board review style question #2 | Board review style answer #2
Cite this page: Saini ML, Cochran JD. Digitization and precision oncology. PathologyOutlines.com website. https://www.pathologyoutlines.com/topic/informaticsdigitazationoncology.html. Accessed May 5th, 2024.
Definition / general
  • Convergence of digitization, artificial intelligence and precision medicine methodologies to extract meaningful, actionable insights from histological imaging and subsequent analysis, enhancing quality of cancer detection, quantification, classification (e.g., tumor group and type) and prognostic capabilities
Essential features
  • Increased productivity of expert pathology teams via accelerated digital slide review and analysis for enhanced research and patient care purposes
  • Capability to hold large scale digital slide libraries that can be beneficial for extracting meaningful insights for future clinical research and accessed by expert pathologists regardless of location
  • Discovery and presentation of insights that validate prescribed oncology treatments or support a change in treatment course based on an individual's diagnostic data, facilitating improved care through genomic and proteomic characterization and spatial profiling
  • Creating new approaches for cancer detection, screening, diagnosis and categorization
  • Potential for expanded development of modeling approaches to better understand biological cause of cancer
Terminology
  • Often used interchangeably with digital pathology, artificial intelligence (AI), machine learning, deep learning and computational pathology
Applications
  • Decentralized pathologic review
  • Mining of subvisual morphometric phenotypes (Nat Rev Clin Oncol 2019;16:703)
  • Mitosis detection and quantification for grading and prognostication of tumors
  • Extract insights from pathologic images using AI driven algorithms and machine learning techniques (World J Transl Med 2021;9:1)
  • Improving the cancer diagnostic workflow, including diagnosis, grading, staging, prognosis and treatment response predictive outcomes for cancer subtypes
  • Automated calculation and quantification of diagnostic, proliferation and immune biomarkers, which help in stratification and prognostication of cancer patients (Mod Pathol 2022;35:23)
  • Quick detection of mutations directly from digital histology
  • Quantifying and localizing proteins in the tumor microenvironment
  • Predict treatment response of particular drug, including immunotherapy and targeted therapy, from biomarkers or even directly from pathology slides
  • Predict established treatment response markers including microsatellite instability for cancers such as gastric, colorectal and endometrial and gene expression signatures in immunotherapies
  • Predict patient survival in multiple tumor types based on histology slide analysis
  • Capability to predict patient outcomes or phenotypes based on comprehensive analysis leveraging multiple data, including multiomics and imaging (Nat Cancer 2022;3:1026)
Implementation
  • Expert insights and oversight, including pathologists, imaging scientists, data scientists, data management experts, technologists, clinical trial leaders, lab service providers and regulatory experts (Nat Rev Clin Oncol 2019;16:703)
  • Imaging technology, including scanners, image management systems and imaging systems to quantify data
  • Technology infrastructure, including network, storage and security strategy
  • Digital workflow strategy, including clear outline of roles and responsibilities of full team
  • Regulatory compliance, including adhering to imaging system validation guidelines set by College of American Pathologists and other professional pathology associations and regulatory authorities, including the U.S. Food and Drug Administration (FDA), to secure certification of system quality and reliability (Nat Rev Clin Oncol 2019;16:703)
  • Training staff, including histotechnicians, histotechnologists, pathology assistants, cytotechnologists, cytogenetic technologists and pathologists
  • Collaboration among healthcare professionals and industry to share best practices and key learnings in digital pathology imaging system use and integration, enhancing interoperability (Healthcare in Europe: Unleashing the Power of Digital Pathology for Precision Medicine [Accessed 13 September 2023])
  • Tumor boards, interdisciplinary clinical conferences focusing discussion on individual patient cases
Advantages
  • May aid in the discovery and presentation of insights that validate prescribed oncology treatments or highlight the need for changes to treatment regimens for improved personalized care (Br J Cancer 2022;126:4)
  • Early detection of cancer or risk predictors
  • Capability to derive new phenotypes from real world data sources and high throughput genotyping
  • Classification of cancers with unknown primary origin (Br J Cancer 2022;126:4)
  • Supports the use of a singular test, regardless of tissue type, to secure richer data insights on a granular level, allowing for a wider range of potential treatments and related care plans
  • Potential to develop new diagnostics, prognostics and companion diagnostics to reference for existing or emerging health conditions (Br J Cancer 2022;126:4)
  • Rapid automated collection, processing, storage and interpretation of both large sets of structured and unstructured data, allowing pathologists to further understand treatment entities, relationships, dosage and more with evidence based insights
  • Ability to collect and store a large scale digital imaging repository with rapid accessibility regardless of location, reducing process delays and glass slide damage
  • Reducing pathologist workload (Nat Rev Clin Oncol 2019;16:703)
  • Potential cost savings based on less time and resources to retrieve archived slides for reference and reduced time in image analysis given parallel pathologist reviews
  • Evolving class of modeling techniques that can build future models that optimize clinical applications for oncology and other therapeutic areas
  • Growing use cases for digital pathology and slide analysis as a biomarker of genomic data (Br J Cancer 2022;126:4)
  • Ability to examine diverse tumor properties on a highly molecular level with deeper context on tumor characterization and microenvironment (Nat Cancer 2022;3:1026)
Limitations
  • Data sources may be invalid, subject to biased evaluation or not representative for AI based model design (Nat Rev Clin Oncol 2019;16:703)
  • Potential for inaccurate or unfair diagnosis for some patients if datasets used in training AI model are not diverse
  • AI powered models can be difficult to interpret, leading to challenges for pathologists to understand how diagnosis was decided and potential reduced trust in AI based result
  • Digitized model needs performance validation regardless of patient variability (Mod Pathol 2022;35:23)
  • Enabling appropriate implementation as digitization requires advanced instruments, equipment and networks to effectively connect to centralized server
  • Need for technologies to be supported by appropriate expertise, including data scientists, technologists, clinical trial experts and pathologists, to interpret outputs of complex models and oncology experts who understand the science behind the disease
  • Requires assurance that AI models are validated, accredited by regulatory authorities and compliant with data protection standards before clinical use
  • Potential for lack of access to large amounts of high quality datasets in precision oncology (Br J Cancer 2022;126:4)
  • Current lack of industry standard of value determination and reimbursement structures for digital pathology use
  • Concerns relative to reproducibility, interpretability, accuracy of competing devices, financial burden of image processing hardware and regulatory approvals necessary for clinical trials
  • Variability in histological data due to differing laboratory sources and operational processes, including methods of slide preparation (e.g., sectioning, fixation, staining and mounting), scoring algorithms and inherent observer variability (Mod Pathol 2022;35:23)
Software
Board review style question #1
Which of the following is true about AI in pathology?

  1. It aids in detecting biases in the data during algorithm training
  2. It aids in detection, quantification and classification of tumor group and type
  3. It aids in protecting sensitive patient information
  4. It will replace the pathologist
Board review style answer #1
B. It aids in detection, quantification and classification of tumor group and type. Artificial intelligence in pathology is capable of recognizing and quantifying cellular features, histological characteristics, morphological structures and their patterns and biologically distinct regions (e.g., tumor or stroma regions), thus enabling the detection, quantitative analysis and categorization of tumor categories and types. Answer A is incorrect because AI is not able to detect biases in data during algorithm training. Answer C is incorrect because AI doesn't deal directly with patient information. Answer D is incorrect because AI will not replace the pathologist but act like an adjunct to the pathologist.

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Reference: Digitization and precision oncology
Board review style question #2
AI can augment pathology data to be used in oncology by

  1. Combining clinical and genomic information
  2. Combining data acquisition and data inclusion
  3. Combining diversity data and predictive data
  4. Combining quality control and image acquisition data
Board review style answer #2
A. Combining clinical and genomic information. The combination of clinical and genomic medicine with the use of artificial intelligence in pathology has the potential to enhance patient healthcare in oncology. Genomic medicine technologies and pathology data are being employed for patients with not so robust therapeutic response or unique healthcare needs. AI offers insights through sophisticated computational and inferential processes, allowing the system to think and learn while improving physician decision making. Answers B, C and D are incorrect because pathology data is not affected by these factors.

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Reference: Digitization and precision oncology
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