Cited by 28AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

One label is all you need: Interpretable AI-enhanced histopathology for oncology

Open access
Tavolara, Thomas E., Su, Ziyu, Gurcan, Metin N., Niazi, M. Khalid Khan·2023·Seminars in Cancer Biology

Abstract

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

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Registry

Crossref · 10.1016/j.semcancer.2023.09.006

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  • Region-of-interest (ROI)-level annotations remain essential for localization and segmentation tasks, including tumor boundary identification and mitotic figure detection.

    “In contrast, region-of-interest (ROI)-level annotations remain essential for localization and segmentation tasks, including tumor boundary identification and mitotic figure detection [46,47].”

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Authors
Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K.
Affiliations
Wake Forest University Health Sciences (US)
Corresponding author
Niazi, M. Khalid Khan
Venue
Seminars in Cancer Biology
Publisher
Elsevier BV
Published
2023-12
Volume
97
Pages
70-85
ISSN
1044-579X (ISSN-L)
OpenAlex
W4387521690
Cited by
28
References
106
Citations by year
2026: 6 · 2025: 11 · 2024: 9 · 2023: 2
FWCI
0.58
Type
journal article
Language
en
Open access
Locations
  • Seminars in Cancer Biology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion)
Funders
National Institutes of Health (GR125886, R01CA276301, R01DC020715, R21CA273665, R21EB029493)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
explainable AI, whole slide images, H&E staining, tumor detection, metastasis detection, tumor grade, molecular markers, gene expression, molecular subtyping, genetic markers, mutational burden, microsatellite instability, chromosomal instability, survival prediction, explainability, oncology
MeSH terms
Artificial Intelligence, Eosine Yellowish-(YS), Humans, Medical Oncology, Reproducibility of Results, Chromosomal Instability

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