胆管刷片全玻片图像中与恶性肿瘤相关的细胞簇的计算机形态特征的自动分析。
Automated analysis of computerized morphological features of cell clusters associated with malignancy on bile duct brushing whole slide images.
机构信息
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
Department of Pathology, Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
出版信息
Cancer Med. 2023 Mar;12(5):6365-6378. doi: 10.1002/cam4.5365. Epub 2022 Oct 24.
BACKGROUND
Bile duct brush specimens are difficult to interpret as they often present inflammatory and reactive backgrounds due to the local effects of stricture, atypical reactive changes, or previously installed stents, and often have low to intermediate cellularity. As a result, diagnosis of biliary adenocarcinomas is challenging and often results in large interobserver variability and low sensitivity OBJECTIVE: In this work, we used computational image analysis to evaluate the role of nuclear morphological and texture features of epithelial cell clusters to predict the presence of pancreatic and biliary tract adenocarcinoma on digitized brush cytology specimens.
METHODS
Whole slide images from 124 patients, either diagnosed as benign or malignant based on clinicopathological correlation, were collected and randomly split into training (S , N = 58) and testing (S , N = 66) sets, with the exception of cases diagnosed as atypical on cytology were included in S . Nuclear boundaries on cell clusters extracted from each image were segmented via a watershed algorithm. A total of 536 quantitative morphometric features pertaining to nuclear shape, size, and aggregate cluster texture were extracted from within the cell clusters. The most predictive features from patients in S were selected via rank-sum, t-test, and minimum redundancy maximum relevance (mRMR) schemes. The selected features were then used to train three machine-learning classifiers.
RESULTS
Malignant clusters tended to exhibit lower textural homogeneity within the nucleus, greater textural entropy around the nuclear membrane, and longer minor axis lengths. The sensitivity of cytology alone was 74% (without atypicals) and 46% (with atypicals). With machine diagnosis, the sensitivity improved to 68% from 46% when atypicals were included and treated as nonmalignant false negatives. The specificity of our model was 100% within the atypical category.
CONCLUSION
We achieved an area under the receiver operating characteristic curve (AUC) of 0.79 on S , which included atypical cytological diagnosis.
背景
由于狭窄部位的局部影响、非典型反应性改变或先前安装的支架，胆管刷取标本常呈现出炎症和反应性背景，且通常具有低至中等的细胞密度，因此难以解释。结果，胆管腺癌的诊断具有挑战性，往往导致观察者间差异较大且敏感性较低。目的：本研究采用计算图像分析来评估上皮细胞簇的核形态和纹理特征对预测数字化刷取细胞学标本中胰腺和胆道腺癌的作用。
方法
收集了 124 例患者的全切片图像，根据临床病理相关性诊断为良性或恶性，并随机分为训练集（S ，N=58）和测试集（S ，N=66），除外细胞学诊断为不典型的病例归入 S 。通过分水岭算法对每个图像中提取的细胞簇的核边界进行分割。从细胞簇内提取了总共 536 个与核形状、大小和聚集簇纹理相关的定量形态特征。通过秩和检验、t 检验和最小冗余最大相关性（mRMR）方案从 S 中的患者中选择最具预测性的特征。然后，使用所选特征对三种机器学习分类器进行训练。
结果
恶性细胞簇的核内纹理均匀性较低，核膜周围纹理熵较大，短轴长度较长。细胞学单独诊断的敏感性为 74%（不包括不典型病例）和 46%（包括不典型病例）。当纳入不典型病例并将其视为非恶性假阴性时，使用机器诊断可将敏感性从 46%提高至 68%。我们的模型在不典型病例类别中的特异性为 100%。
结论
在包含不典型细胞学诊断的 S 中，我们获得了 0.79 的受试者工作特征曲线下面积（AUC）。
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