ArticleEuropean journal of cancer (Oxford, England : 1990)2024
A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas.
Article in European journal of cancer (Oxford, England : 1990), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.European journal of cancer (Oxford, England : 1990) · 2026Article
- A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.Scientific reports · 2026Article
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
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6 authors.
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Abstract
introductionPapillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with the classical and follicular variants representing most cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post-surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored.
objectivesWe introduce a new computational pathology approach to develop prognostic gene signatures for PTC that is informed by quantitative features of tumor and immune cell morphology.
methodsWe quantified nuclear and immune-related features of tumor morphology to develop a pathomic signature, which was then used to inform an RNA-expression signature model provides a notable advancement in risk stratification compared to both standalone and pathology-informed gene-expression signatures.
resultsThere was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).
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