Evidence map›Paper›PMID 34593968›Full record

ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022

Grading of endocervical adenocarcinoma: a novel prognostic system based on tumor budding and cell cluster size.

Haiyan Shi, Lei Ye, Weiguo Lu, Bingjian Lu

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Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Haiyan ShiDepartment of Surgical Pathology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.
Lei YeDepartment of Surgical Pathology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.
Weiguo LuCenter for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province and Department of Gynecological Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China.
Bingjian LuDepartment of Surgical Pathology and Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China. lbj@zju.edu.cn.ORCID http://orcid.org/0000-0001-7309-1116

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A novel 3-tiered grading system based on tumor budding activity and cell nest size has been validated to be highly prognostic in organ-wide squamous cell carcinomas. In this study, we applied a similar grading system with slight modification to assess the prognostic value in an institutional cohort of well annotated endocervical adenocarcinomas (EAC) consisting of 398 consecutive cases with surgical resection, no neoadjuvant chemotherapy, and higher than stage pT1a. Each case was reviewed by the International Endocervical Adenocarcinoma Criteria and Classification (IECC) and Silva pattern classification, and scored on tumor budding activity and cell cluster size to form the basis of a novel grading system. High budding activity, small tumor cell cluster size, and novel grade 3 were more frequently associated with a decreased overall survival time and tumor recurrence time (p < 0.001), and several other clinicopathologic factors including HPV-independent adenocarcinoma, lymphovascular invasion, lymph node metastasis, advanced FIGO stage, and Silva pattern C (p < 0.05). Moreover, the novel grading system was helpful in stratifying overall survival in HPV-associated adenocarcinoma (p = 0.036) and gastric-type adenocarcinoma (p = 0.033). On multivariate analysis, novel grade 3 was an adverse indicator for overall survival and tumor recurrence independently of age and FIGO stage (p < 0.05). By comparison, Silva pattern C was only associated with tumor relapse (p = 0.020) in HPV-associated adenocarcinomas whereas the conventional FIGO system was not associated with overall survival and tumor recurrence in EAC (p > 0.05). In conclusion, our study demonstrates that the grading system based on tumor budding activity and cell cluster size is robust in prognostic assessment that outperforms the conventional FIGO grading and Silva pattern classification in EAC. The novel grading system, if further validated, could be applicable in routine pathologic descriptions of EAC by providing useful information in clinical decision-making.

Indexed as

AdenocarcinomaCarcinoma, Squamous CellPapillomavirus InfectionsUterine Cervical NeoplasmsFemaleHumansNeoplasm GradingNeoplasm Recurrence, LocalPrognosisRetrospective Studies

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.