D'Abbronzo Giuseppe, D'Antonio Antonio, De Chiara Annarosaria, Panico Luigi, Sparano Lucianna, Diluvio Anna, Sica Antonello, Svanera Gino, De Chiara Giovanni, Fuggi Mariano, Russo Ferdinando, Franco Renato, Ronchi Andrea
Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università Degli Studi Della Campania "Luigi Vanvitelli", Via Luciano Armanni 5, 80138, Naples, Italy.
Pathology Unit, Hospital "Ospedale del Mare", 80147, Naples, Italy.
Virchows Arch. 2025 May 13. doi: 10.1007/s00428-025-04122-5.
Bone marrow fibrosis plays a critical role in the diagnosis, prognosis, and management of haematological disorders, particularly myeloproliferative neoplasms like primary myelofibrosis. Accurate assessment of fibrosis, typically graded through histochemical techniques such as reticulin and trichrome staining, is essential but remains highly dependent on the pathologist's experience. To address the challenges of variability in interpretation and the increasing demand for standardized evaluations, we developed a digital pathology system for automated bone marrow reticulin fibrosis grading. This study utilized 86 bone marrow biopsy specimens from patients diagnosed with Philadelphia chromosome-negative myeloproliferative neoplasms, collected between 2018 and 2023. A fully convolutional network based on the InceptionV3 architecture was trained to assess fibrosis grades (MF0-MF3) from whole slide images of reticulin-stained sections. The model was trained using 3814 annotated images and validated using a separate set of 40 BMBs. The algorithm's performance was evaluated by comparing its fibrosis grading to expert hematopathologists' assessments, yielding a Cohen's kappa coefficient of 0.831, indicating excellent agreement. The algorithm showed strong concordance in fibrosis grading, especially for MF0 (k = 0.918) and MF3 (k = 0.886), and substantial agreement for intermediate grades (MF1 and MF2). Further validation across multiple institutions and scanning platforms confirmed the algorithm's robustness, with an overall agreement of 0.816. These results demonstrate the potential of digital pathology tools to provide standardized, reproducible fibrosis grading, thereby aiding pathologists in clinical decision-making and training.
骨髓纤维化在血液系统疾病的诊断、预后及管理中起着关键作用，尤其是在原发性骨髓纤维化等骨髓增殖性肿瘤中。纤维化的准确评估通常通过组织化学技术（如网硬蛋白和三色染色）进行分级，这至关重要，但仍高度依赖病理学家的经验。为应对解读变异性的挑战以及对标准化评估日益增长的需求，我们开发了一种用于自动骨髓网硬蛋白纤维化分级的数字病理系统。本研究使用了2018年至2023年期间收集的86例被诊断为费城染色体阴性骨髓增殖性肿瘤患者的骨髓活检标本。基于InceptionV3架构的全卷积网络被训练用于从网硬蛋白染色切片的全切片图像中评估纤维化分级（MF0 - MF3）。该模型使用3814张标注图像进行训练，并使用另一组40张骨髓活检标本进行验证。通过将算法的纤维化分级与血液病理专家的评估进行比较来评估算法的性能，得到的科恩kappa系数为0.831，表明一致性极佳。该算法在纤维化分级方面表现出很强的一致性，尤其是对于MF0（k = 0.918）和MF3（k = 0.886），对于中间分级（MF1和MF2）也有高度一致性。在多个机构和扫描平台上的进一步验证证实了该算法的稳健性，总体一致性为0.816。这些结果表明数字病理工具在提供标准化、可重复的纤维化分级方面的潜力，从而有助于病理学家进行临床决策和培训。
