Departments of Pathology and Laboratory Medicine, University of Calgary and Alberta Precision Laboratories.
Alberta Health Services, Calgary, AB.
Appl Immunohistochem Mol Morphol. 2022 Feb 1;30(2):91-98. doi: 10.1097/PAI.0000000000000988.
The International Endocervical adenocarcinoma Criteria and Classification (IECC) categorizes tumors into human papilloma virus (HPV) associated (HPVA), not associated (NHPV), and invasive adenocarcinoma not otherwise specified (IA NOS). HPVA and NHPV encompass 11 histotypes and an algorithm of mucin content, HPV ribonucleic acid (RNA), estrogen receptor and GATA3 is proposed for the diagnosis of most. In this study, the IECC algorithm's diagnoses were compared with hematoxylin and eosin (H&E) based IECC histotyping. Kappa statistics measured performance agreement. With additional markers, hierarchical clustering by random forest (RF) classification identified the most discriminating between tumor types, and investigated other algorithms. Three pathologists independently reviewed digitized H&E images of n=152 primary cervical adenocarcinomas for IECC histotype and mucin content, and tissue microarrays for expression of HPV RNA by in situ hybridization and 16 antibodies by immunohistochemistry. Results were finalized by consensus. There were n=113 HPVA, n=22 NHPV, and n=17 IA NOS. Mucin was obvious in n=36 and limited in n=116. Among n=124 with satisfactory test results, HPV RNA was positive in n=96, estrogen receptor in n=72, and GATA3 in n=15. The IECC algorithm diagnosed n=99 which agreed with H&E histotyping in n=64 for a fair κ of 0.36 (95% confidence interval, 0.21-0.50): n=12 were undiagnosed and n=13 were IA NOS. Small sample sizes restricted RF to HPVA versus NHPV which were discriminated by p16, HPV RNA, and MUC6 with an area under the curve of 0.74 (95% confidence interval, 0.58-0.90). The IECC algorithm for histotyping under-performed. The RF algorithmin for categorization was favorable, but validation in larger studies and investigation of additional algorithms to discriminate between all IECC histotypes are needed.
国际宫颈内膜腺癌分类（IECC）将肿瘤分为人乳头瘤病毒（HPV）相关（HPVA）、不相关（NHPV）和未另行指定的浸润性腺癌（IA NOS）。HPVA 和 NHPV 包括 11 种组织类型，并提出了一种基于粘蛋白含量、HPV 核糖核酸（RNA）、雌激素受体和 GATA3 的算法来诊断大多数肿瘤。在这项研究中，IECC 算法的诊断结果与基于苏木精和伊红（H&E）的 IECC 组织学类型进行了比较。Kappa 统计测量了性能一致性。通过随机森林（RF）分类的附加标记，层次聚类确定了肿瘤类型之间最具区分性的标记，并研究了其他算法。三名病理学家独立审查了 n=152 例原发性宫颈腺癌的数字化 H&E 图像，以进行 IECC 组织学类型和粘蛋白含量的评估，并通过原位杂交和免疫组织化学检查组织微阵列中 HPV RNA 和 16 种抗体的表达。结果通过共识最终确定。其中 n=113 例为 HPVA，n=22 例为 NHPV，n=17 例为 IA NOS。n=36 例有明显的粘蛋白，n=116 例有有限的粘蛋白。在 n=124 例具有满意检测结果的患者中，n=96 例 HPV RNA 阳性，n=72 例雌激素受体阳性，n=15 例 GATA3 阳性。IECC 算法诊断 n=99 例，与 H&E 组织学类型一致 n=64 例，kappa 值为 0.36（95%置信区间，0.21-0.50）：n=12 例未诊断，n=13 例为 IA NOS。样本量小限制了 RF 仅用于区分 HPVA 与 NHPV，其鉴别特征为 p16、HPV RNA 和 MUC6，曲线下面积为 0.74（95%置信区间，0.58-0.90）。IECC 组织学分类算法表现不佳。RF 分类算法是有利的，但需要在更大的研究中验证，并研究其他算法来区分所有 IECC 组织学类型。
