Molecular and Pathological Heterogeneity of Synchronous Small and Large Duct Intrahepatic Cholangiocarcinoma—A Case Series
Bibliographic record
Abstract
Background: Synchronous small- and large-duct intrahepatic cholangiocarcinoma (iCCA) represents a rare and heterogeneous entity, posing challenges for diagnosis, prognosis, and treatment selection. The pathological and molecular diversity between these subtypes influences tumor behavior and therapeutic response, necessitating a personalized approach. This study investigates the molecular and pathological heterogeneity of synchronous iCCA and its clinical implications. Methods: This prospective case series included six patients diagnosed with synchronous small- and large-duct iCCA at the Military Medical Academy, Sofia, between January 2023 and January 2025, with a median follow-up of 15 months. Tumor classification was based on histopathological examination, immunohistochemical analysis, and next-generation sequencing (NGS)-based genomic profiling. Radiological and clinical data were analyzed to assess tumor growth patterns, treatment response, and progression-free survival (PFS). Results: Small-duct-predominant iCCA was associated with IDH1/2 mutations and FGFR2 fusions, a mass-forming growth pattern, and longer PFS. In contrast, large-duct-predominant iCCA exhibited KRAS, TP53, and NF1 mutations, an infiltrative periductal growth pattern, and a more aggressive clinical course with shorter PFS. Tumor mutational burden-high (TMB-H) and microsatellite instability-high (MSI-H) were observed in a subset of large-duct iCCA cases, suggesting potential benefit from immune checkpoint inhibitors (ICIs). Conclusions: Synchronous small- and large-duct iCCA demonstrates distinct molecular, histopathological, and clinical features, necessitating individualized treatment strategies. Targeted therapies for IDH1/2- and FGFR2-altered small-duct iCCA have shown efficacy, whereas large-duct iCCA remains more aggressive and treatment-resistant, requiring novel therapeutic approaches. Future research should focus on adaptive treatment strategies that account for tumor heterogeneity and dominant molecular drivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".