If molecular classification defines the future of endometrial cancer (EC) care, artificial intelligence (AI) may determine how equitably that future is realized.
The advent of molecular classification has transformed the diagnosis and prognostication of EC. The 4 molecular subtypes defined by the Cancer Genome Atlas—
POLE ultramutated, mismatch repair–deficient, p53-abnormal (p53abn), and no specific molecular profile (NSMP)—have substantial prognostic significance and are incorporated into the current World Health Organization and International Federation of Gynecology and Obstetrics classification systems [
1,
2]. Despite its clinical value, the implementation of molecular subtyping remains uneven, largely because of barriers to genetic testing in resource-limited settings, particularly in low- and middle-income countries [
2].
In this context, AI applied to hematoxylin and eosin (H&E)–stained histopathological slides may serve as an effective bridge between morphology and molecular characterization. Advances in deep learning enable algorithms to identify complex architectural and cytological patterns associated with specific genomic alterations. Recent studies have demonstrated that AI can predict molecular subtypes from routine histopathology, indicating that morphologic correlates of genomic changes are reproducible and detectable [
1,
3]. Notably, these models extend beyond classification, showing potential to identify novel subgroups within established categories, particularly within the NSMP subtype [
2,
4]. However, the clinical utility of AI in EC depends not only on predictive performance but also on its integration into real-world workflows. To date, most studies have emphasized algorithmic accuracy, whereas implementation strategies remain insufficiently explored. To address this gap, we propose a structured approach in which AI is used to triage EC cases through computational pathology as a decision-support layer.
Under this framework, all EC cases would undergo an initial AI-based assessment of digital H&E-stained slides would generate outputs including probable molecular subtype and key biomarker status, such as mismatch repair status. Based on these outputs, cases can be stratified operationally according to prediction confidence. For cases with high-confidence predictions (probability >0.85), a provisional diagnosis may be assigned, with confirmatory testing reserved for situations in which treatment decisions require validation. For cases with intermediate confidence (0.60–0.85), targeted diagnostic testing, such as immunohistochemistry for mismatch repair deficiency and p53, can be performed. For cases with low-confidence predictions (probability <0.60), AI-based classification should be considered indeterminate, and comprehensive molecular evaluation using standard diagnostic methods should be undertaken to establish the molecular subtype.
Tiered diagnostic strategies offer several advantages, including more efficient resource allocation, reduced use of unnecessary tests, and shorter turnaround times. Importantly, diagnostic accuracy is preserved by ensuring that indeterminate cases undergo comprehensive molecular evaluation. Preliminary evidence suggests that such triage approaches may substantially reduce the number of molecular tests required without compromising clinical accuracy, particularly in high-volume centers [
5-
7].
This framework may be particularly applicable in resource-constrained healthcare systems, where AI could function as a cost-effective gatekeeper by selectively directing the use of expensive molecular diagnostics. Implementation could involve a digital pathology platform integrated with a cloud-based AI system. In addition, affordable imaging solutions, such as portable slide scanners, may help address infrastructure limitations [
8,
9].
Several challenges must be addressed before clinical implementation of AI. First, the predominantly retrospective design of existing studies limits generalizability. Prospective multicenter trials are needed to validate real-world performance. Second, variability in tissue processing, staining, and slide digitization can introduce distributional shifts that adversely affect model performance on external datasets. Finally, model interpretability remains a critical concern [
8,
10]. Many deep learning systems function as “black boxes,” generating predictions without transparent reasoning. Recent developments in interpretable AI aim to address this limitation and may facilitate clinical adoption [
6].
Ethical and regulatory considerations are equally important. The use of AI in clinical diagnosis necessitates clear frameworks for accountability, validation, and oversight. The role of the pathologist must remain central, with AI functioning strictly as a decision-support tool rather than a replacement. Furthermore, equitable access to AI technologies must be ensured to prevent widening disparities based on patients’ financial resources.
Table 1 summarizes representative studies evaluating the role of AI in EC classification [
1-
4,
6].
The proposed probability thresholds in this triage system should be considered flexible rather than fixed. These thresholds should be calibrated using institution-specific datasets that reflect local tumor characteristics and technical factors. Integration of AI algorithms with laboratory information systems and multidisciplinary tumor boards may further enhance diagnostic utility and facilitate clinical decision-making.
In summary, AI represents a promising and pragmatic adjunct to molecular classification in EC. Its primary value lies in enabling scalable, cost-effective risk stratification, particularly in settings where molecular testing is limited. A structured AI-assisted triage approach may optimize diagnostic workflows without compromising accuracy. Future efforts should prioritize implementation strategies that ensure equitable access and avoid exacerbating disparities in cancer care.
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Authors’ contribution
Conception and design of the work: GR, PP. Conceptualization: GR. Literature review: GR, PP. Writing–original draft: GR, PP. Writing–review & editing: GR, PP. Accountability: GR, PP. Final approval of the article: GR, PP.
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Conflict of interest
No potential conflict of interest relevant to this article was reported.
-
Funding
None.
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Data availability
Not applicable.
-
Acknowledgments
None.
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Supplementary materials
None.
Table 1.Representative studies on AI-assisted molecular classification in endometrial cancer
|
Author (year) |
Study design/cohort |
AI approach |
Molecular target |
Key findings |
Clinical implication |
|
Qi et al. [1] (2025) |
Multicenter cohort study (China) |
Clinical-grade deep learning on whole-slide images |
TCGA molecular subtypes |
Achieved high accuracy for molecular subtyping using routine H&E slides |
Demonstrates the feasibility of AI-assisted triage before molecular testing |
|
Darbandsari et al. [2] (2024) |
Multi-institutional retrospective study |
AI-based histopathological image analysis |
AI-defined molecular subsets |
Identified a distinct, prognostically relevant AI-defined EC subgroup |
Suggests that AI may uncover novel biological risk groups |
|
Hong et al. [3] (2021) |
Retrospective cohort |
Multi-resolution deep learning models |
Molecular features and EC subtypes |
Successfully predicted molecular features from histopathological images |
Provides early evidence supporting morphology-based molecular inference |
|
Umemoto et al. [4] (2024) |
Diagnostic accuracy study |
Deep learning–based slide analysis |
MMR status |
Reliably predicted MMR deficiency from H&E slides |
May serve as a screening tool for Lynch syndrome and molecular testing prioritization |
|
Fremond et al. [6] (2023) |
Multi-institutional validation study |
Interpretable deep learning model |
TCGA molecular classification |
Generated explainable AI predictions from H&E slides |
May improve clinical trust and adoption |
References
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