Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis

• Published in: Computers in biology and medicine Vol. 178; p. 108774
• Main Authors: Iwamoto, Ryuta, Nishikawa, Toui, Musangile, Fidele Yambayamba, Matsuzaki, Ibu, Sagan, Kanako, Nishikawa, Mizuki, Mikasa, Yurina, Takahashi, Yuichi, Kojima, Fumiyoshi, Hori, Yoshikazu, Hosoi, Hiroki, Mori, Hideo, Sonoki, Takashi, Murata, Shin-ichi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2024; Elsevier Limited
• Subjects: Adult; Aged; Algorithms; Artificial Intelligence; Chemotherapy; Clinical outcomes; Female; Humans; Internal Medicine; Lymphoma; Lymphoma, Follicular - pathology; Male; Medical prognosis; Middle Aged; Morphology; Object recognition; Other; Patients; Photonics; Physical characteristics; Prognosis; Regression analysis; Technology assessment; Japan
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108774

Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular lymphoma, but there is substantial observer variation in assessment among hematopathologists. We aimed to perform quantitative morphological analysis of centroblasts in follicular lymphoma using new artificial intelligence technology in relation to the clinical prognosis. Hematoxylin and eosin slides of lesions were prepared from 36 cases of follicular lymphoma before initial chemotherapy. Cases were classified into three groups by clinical course after initial treatment. The ‘excellent prognosis’ group were without recurrence or progression of follicular lymphoma within 60 months, the ‘poor prognosis’ group were those that had relapse, exacerbation, or who died due to the follicular lymphoma within 60 months, and the ‘indeterminate prognosis’ group were those without recurrence or progression but before the passage of 60 months. We created whole slide images and image patches of hematoxylin and eosin sections for all cases. We designed an object detection model specialized for centroblasts by fine-tuning YOLOv5 and segmented all centroblasts in whole slide images. The morphological characteristics of centroblasts in relation to the clinical prognosis of follicular lymphoma were analyzed. Centroblasts in follicular lymphoma of the poor prognosis group were significantly smaller in nuclear size than those in follicular lymphoma of the excellent prognosis group in the following points: median of nuclear area (p = 0.013), long length (p = 0.042), short length (p = 0.007), nuclear area of top 10 % cells (p = 0.024) and short length of top 10 % cells (p = 0.020). Cases with a mean nuclear area of <55 μm2 had poorer event-free survival than those with a mean nuclear area of ≥55 μm2 (p < 0.0123). AI methodology is suggested to be able to surpass pathologist's observation in capturing morphological features. Small-sized centroblasts will likely become a new prognostic factor of follicular lymphoma. [Display omitted] •We validated follicular lymphoma based on newly developed artificial intelligence analysis.•Results reveals that small centroblasts correlated with poor prognosis.•New histopathological standard for prognostic implications of follicular lymphoma was provided.