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TRV-2026-0915Certified recordPeer-reviewed

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroid carcinoma (IEFVPTC) are diagnostically challenging thyroid neoplasms with overlapping clinical and molecular characteristics. Although artificial intelligence has shown promise for diagnostic support in radiology and histopathology, its application to gross pathology remains unexplored. This study analyzed gross pathology photographs from 87 patients (43 with NIF…

Health · G Space — documented gain · certified 2026-08-28 · v1 · article view · machine-readable

Current reading — gain

A prespecified EfficientNet plus Random Forest model classified gross pathology photographs to distinguish NIFTP from IEFVPTC with pooled AUC 0.788, sensitivity 0.588 and specificity 0.944 in 87 patients.

What this doesn’t fix

Proof-of-concept with only 87 patients, modest discriminative performance, and no statistically significant differences among backbone-classifier combinations after multiple-testing correction, limiting generalizability.

Evidence

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Truvace Impact Record TRV-2026-0915, v1: “Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images.” Truvace, 2026-08-28. /record/TRV-2026-0915 (accessed at citation time). sha256 abdae84d55dac4cb…

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