Fauzi Mohammad Faizal Ahmad, Pennell Michael, Sahiner Berkman, Chen Weijie, Shana'ah Arwa, Hemminger Jessica, Gru Alejandro, Kurt Habibe, Losos Michael, Joehlin-Price Amy, Kavran Christina, Smith Stephen M, Nowacki Nicholas, Mansor Sharmeen, Lozanski Gerard, Gurcan Metin N
Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia.
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.
BMC Med Inform Decis Mak. 2015 Dec 30;15:115. doi: 10.1186/s12911-015-0235-6.
Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL cases are stratified into three histological grades based on the average centroblast count per high power field (HPF). The centroblast count is performed manually by the pathologist using an optical microscope and hematoxylin and eosin (H&E) stained tissue section. Although this is the current clinical practice, it suffers from high inter- and intra-observer variability and is vulnerable to sampling bias.
In this paper, we present a system, called Follicular Lymphoma Grading System (FLAGS), to assist the pathologist in grading FL cases. We also assess the effect of FLAGS on accuracy of expert and inexperienced readers. FLAGS automatically identifies possible HPFs for examination by analyzing H&E and CD20 stains, before classifying them into low or high risk categories. The pathologist is first asked to review the slides according to the current routine clinical practice, before being presented with FLAGS classification via color-coded map. The accuracy of the readers with and without FLAGS assistance is measured.
FLAGS was used by four experts (board-certified hematopathologists) and seven pathology residents on 20 FL slides. Access to FLAGS improved overall reader accuracy with the biggest improvement seen among residents. An average AUC value of 0.75 was observed which generally indicates "acceptable" diagnostic performance.
The results of this study show that FLAGS can be useful in increasing the pathologists' accuracy in grading the tissue. To the best of our knowledge, this study measure, for the first time, the effect of computerized image analysis on pathologists' grading of follicular lymphoma. When fully developed, such systems have the potential to reduce sampling bias by examining an increased proportion of HPFs within follicle regions, as well as to reduce inter- and intra-reader variability.
滤泡性淋巴瘤（FL）是西方世界最常见的淋巴恶性肿瘤之一。根据每个高倍视野（HPF）中的中心母细胞平均计数，FL病例被分为三个组织学等级。中心母细胞计数由病理学家使用光学显微镜和苏木精-伊红（H&E）染色的组织切片手动进行。尽管这是目前的临床实践，但它存在较高的观察者间和观察者内变异性，并且容易受到抽样偏差的影响。
在本文中，我们提出了一种名为滤泡性淋巴瘤分级系统（FLAGS）的系统，以协助病理学家对FL病例进行分级。我们还评估了FLAGS对专家和经验不足的读者准确性的影响。FLAGS通过分析H&E和CD20染色自动识别可能需要检查的HPF，然后将它们分为低风险或高风险类别。病理学家首先被要求根据当前的常规临床实践审查幻灯片，然后通过颜色编码图呈现FLAGS分类。测量有和没有FLAGS协助的读者的准确性。
四名专家（获得委员会认证的血液病理学家）和七名病理住院医师在20张FL幻灯片上使用了FLAGS。使用FLAGS提高了读者的整体准确性，住院医师的提高最为明显。观察到平均AUC值为0.75，这通常表明诊断性能“可接受”。
本研究结果表明，FLAGS有助于提高病理学家对组织分级的准确性。据我们所知，本研究首次测量了计算机图像分析对病理学家滤泡性淋巴瘤分级的影响。当充分开发时，此类系统有可能通过检查卵泡区域内增加比例的HPF来减少抽样偏差，并减少读者间和读者内的变异性。
