Uterine corpus invasion in cervical cancer: a multicenter retrospective case–control study

ARCHIVES OF GYNECOLOGY AND OBSTETRICS(2021)

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Abstract
Objective To determine the accuracy of uterine corpus invasion (UCI) diagnosis in patients with cervical cancer and identity risk factors for UCI and depth of invasion. Methods Clinical data of patients with cervical cancer who underwent hysterectomy between 2004 and 2016 were retrospectively reviewed. UCI was assessed on uterine pathology. Independent risk factors for UCI and depth of invasion were identified using binary and ordinal logistic regression models, respectively. Results A total of 2,212 patients with cervical cancer from 11 medical institutions in China were included in this study. Of these, 497 patients had cervical cancer and UCI, and 1,715 patients had cervical cancer and no UCI, according to the original pathology reports. Retrospective review of the original pathology reports revealed a missed diagnosis of UCI in 54 (10.5%) patients and a misdiagnosis in 36 (2.1%) patients. Therefore, 515 patients with cervical cancer and UCI (160 patients with endometrial invasion, 176 patients with myometrial invasion < 50%, and 179 patients with myometrial invasion ≥ 50%), and 1697 patients with cervical cancer without UCI were included in the analysis. Older age, advanced stage, tumor size, adenocarcinoma, parametrial involvement, resection margin involvement, and lymph node metastasis were independent risk factors for UCI. These risk factors, except resection margin involvement, were independently associated with depth of UCI. Conclusions UCI may be missed or misdiagnosed in patients with cervical cancer on postoperative pathological examination. Older age, advanced stage, tumor size, adenocarcinoma, parametrial involvement, resection margin involvement, and lymph node metastasis were independent risk factors for UCI and depth of UCI, with the exception of resection margin involvement.
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Key words
Cervical cancer,Uterine corpus,Invasion depth,Pathological review,Diagnostic accuracy
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