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Artificial intelligence-assisted screening for NTRK fusion-positive salivary gland tumors: A novel digital pathology
Ryutaro Onaga1, Shingo Sakashita2, Daisuke Komura3
1Department of Head and Neck Medical Oncology, National Cancer Center Hospital East, Kashiwa, Japan; Department of Otolaryngology and Head and Neck Surgery, Jichi Medical University, Tochigi, Japan; Department of Clinical Oncology, Jichi Medical University, Tochigi, Japan.
Abstract:
NTRK fusion is a promising therapeutic target for salivary gland cancer (SGC). However, the diagnostic complexity of the histological SGC subtype and low NTRK fusion-positive SGC incidence (5%), except for secretory carcinoma (90%), limit targeted therapy. Despite recommended pan-TRK immunohistochemistry (IHC) screening, evaluation remains unstandardized. Artificial intelligence (AI) has now enabled the prediction of genetic and molecular information. We aimed to evaluate the usefulness of the AI model, a histopathological image retrieval system-Luigi-Oral-in detecting NTRK fusion-positive cases. All 273 primary salivary gland tumors surgically obtained between 2005 and 2023 were retrospectively and blindly assessed using fluorescence in situ hybridization (FISH) for ETV6 rearrangements, pan-TRK IHC (EPR17341), and differential diagnosis with Luigi-Oral for hematoxylin-eosin staining queries in December 2024 (https://luigi-pathology.com/). Luigi-Oral defined inclusion of secretory carcinoma among the top five diagnoses as positive differential indication. Overall, 273 salivary gland tumors were identified, including 104 benign and 169 malignant tumors, with 7 samples pathologically diagnosed as secretory carcinomas. FISH, pan-TRK IHC, and Luigi-Oral identified 7, 7, and 24 positive cases, respectively. All the FISH-positive cases were pathologically diagnosed as secretory carcinoma. Luigi-Oral exhibited a sensitivity, specificity, false-positive rate, and false-negative rate of 85.7%, 93.2%, 6.8%, and 14.3% for detecting FISH-positive cases, respectively. The false-positive rate was 13.3% in acinic cell carcinoma, histologically similar to secretory carcinoma. Luigi-Oral might be a useful alternative to IHC for screening NTRK fusion-positive rare SGC cases, and advances in digital pathology can facilitate implementation of an AI model in the NTRK screening workflow.
Insights
Artificial intelligence (AI) shows promise in detecting NTRK fusion-positive salivary gland cancers (SGC). The AI model Luigi-Oral may serve as an alternative to immunohistochemistry (IHC) for screening rare SGC cases.
Area of Science:
- Oncology
- Pathology
- Artificial Intelligence
Background:
- NTRK fusion is a key target in salivary gland cancer (SGC), but low incidence and diagnostic complexity hinder targeted therapy.
- Current screening methods like pan-TRK immunohistochemistry (IHC) lack standardization.
- Artificial intelligence (AI) offers potential for predicting molecular information from histopathological images.
Purpose of the Study:
- To evaluate the efficacy of an AI-powered histopathological image retrieval system, Luigi-Oral, in identifying NTRK fusion-positive SGC cases.
- To compare the performance of AI with traditional methods like fluorescence in situ hybridization (FISH) and IHC.
Main Methods:
- Retrospective analysis of 273 primary salivary gland tumors (2005-2023).
- Assessment using FISH for ETV6 rearrangements, pan-TRK IHC, and AI-based differential diagnosis (Luigi-Oral) on H&E staining.
- Luigi-Oral identified secretory carcinoma within the top five diagnoses as a positive indicator.
Main Results:
- Luigi-Oral identified 24 NTRK fusion-positive cases, compared to 7 each for FISH and IHC.
- The AI model demonstrated a sensitivity of 85.7% and specificity of 93.2% for detecting FISH-positive cases.
- A higher false-positive rate was observed in acinic cell carcinoma, which resembles secretory carcinoma.
Conclusions:
- AI, specifically the Luigi-Oral system, shows potential as an effective alternative to IHC for screening rare NTRK fusion-positive SGC.
- Digital pathology and AI integration can enhance NTRK fusion screening workflows for targeted therapies.