D'Abbronzo Giuseppe, D'Antonio Antonio, De Chiara Annarosaria, Panico Luigi, Sparano Lucianna, Diluvio Anna, Sica Antonello, Svanera Gino, Franco Renato, Ronchi Andrea
Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "Luigi Vanvitelli", 80138 Naples, Italy.
Pathology Unit, Hospital "Ospedale del Mare", 80147 Naples, Italy.
Cancers (Basel). 2024 Apr 26;16(9):1687. doi: 10.3390/cancers16091687.
The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University "Luigi Vanvitelli" in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as .tiff images and .png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists' evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners.
用于诊断费城染色体（Ph）阴性骨髓增殖性肿瘤（MPN）的骨髓活检（BMB）中的细胞密度评估是一项关键的诊断特征，通常由人眼通过光学显微镜进行，从而导致观察者间和观察者内的变异性。因此，使用自动化工具可能会减少变异性，提高评估的一致性。这项工作的目的是开发一种基于人工智能的准确工具，用于自动量化BMB组织学中的细胞密度。从意大利那不勒斯“路易吉·万维泰利”大学病理科档案中选取了2018年1月至2023年6月期间诊断为Ph阴性MPN的55张BMB组织学切片，在Ventana DP200或Epredia P1000上进行扫描，并导出为全切片图像（WSI）。随机选择15张BMB切片以获得基于人工智能工具的训练集。一名专家病理学家和一名经过培训的住院医师对造血组织和脂肪组织进行注释，并将注释导出为.tiff图像和带有两种颜色（造血组织为黑色，脂肪组织为黄色）的.png标签。随后，我们开发了一种用于造血组织和脂肪组织的语义分割模型。其余40张BMB切片用于模型验证。我们将模型的性能与五位血液病理专家和三位实习生对细胞密度的评估进行了比较；我们的模型与专家病理学家的评估之间获得了最佳一致性，实习生的一致性较差。两种不同扫描仪之间的细胞密度评估没有显著差异。
