Virchows Archiv : an international journal of pathology · 2026
A multimodal explainable ai framework to assist in the differential diagnosis of head and neck reactive follicular hyperplasia and follicular lymphoma: an international multicentre study.
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de Souza Lucas Lacerda, Chen Zhiyang, de Cáceres Cinthia Veronica Bardález López, Rodrigues Nathalia Gomes, Pontes Hélder Antônio Rebelo, Soares Ciro Dantas, Coracin Fábio Luiz, Kimura Igor, Siqueira Sheila Aparecida Coelho, Mariano Fernanda Viviane, Kimura Talita de Carvalho, Bonfitto João Felipe Leite, de Freitas Leandro Luiz Lopes, de Aquino Sibele Nascimento, Hankinson Paul, Mahmood Hanya, Walsh Hannah, Torres Torres Itzia Araceli, Mosqueda-Taylor Adalberto, Aleixo Pedro Bandeira, Martins Manoela Domingues, Menna-Barreto Carolina Louzada, Hagag Ahmed, Nakamura Thaís Cerqueira Reis, Dos Santos Giovanna Calabrese, Moraes Matheus Cardoso, Lopes Marcio Ajudarte, Santos-Silva Alan Roger, Fonseca Felipe Paiva, Khurram Syed Ali, Vargas Pablo Agustin
Аннотация
The differential diagnosis between reactive follicular hyperplasia (RFH) and follicular lymphoma (FL) in head and neck tissues represents a diagnostic challenge, particularly given the rarity of extranodal FL at these anatomical sites. We developed and evaluated an explainable multimodal artificial intelligence framework to assist in this differential diagnosis. This international multicentre study assembled 108 cases (54 RFH, 54 FL) from 10 centres across 4 continents, representing one of the largest collections of head and neck follicular lesions. We developed a multimodal framework integrating convolutional neural networks (CNNs: AlexNet, VGG16, ResNet18), vision transformers (CellViT + +), graph neural networks (Cell-GNN for spatial analysis), and gradient-boosted decision trees (XGBoost) with morphometric features. Explainability was achieved through Grad-CAM visualisation and SHAP analysis. The best-performing model (CellViT + +) achieved 95.7% accuracy on the internal test set. External validation demonstrated accuracy of 80.5% (ResNet18, Cohort 1) and 69.0% (VGG16-Seg, Cohort 2), with performance variation reflecting the challenge of generalisation across centres. Explainability analysis revealed that the multimodal framework integrated morphometric features (nuclear area, eccentricity) with epidemiological context (patient age, consistent with known FL demographics). Cell-GNN spatial analysis identified significant architectural reorganisation in FL, quantified by Hedges' g = -6.65, representing the loss of normal follicular polarity. This study demonstrates the feasibility of a multimodal explainable AI framework for assisting in the differential diagnosis of rare head and neck follicular lesions. The integration of morphological, spatial, and clinical features provides a foundation for future collaborative validation studies and potential diagnostic support in specialised pathology settings.