Novel Strategy for the Management of Cervical Multicystic Diseases

Author:

Yoshino Ai,Kobayashi EijiORCID,Tsuboyama Takahiro,Fukui Hideyuki,Tomiyama Noriyuki,Sato Kazuaki,Morii Eiichi,Nakatani Eiji,Komura Naoko,Sawada Ikuko,Tanaka Yusuke,Hori Kensuke,Yoshimura Akihiko,Takahashi Ryoko,Iwamiya Tadashi,Hisa Tsuyoshi,Nishimura Sadako,Kitai Toshihiro,Yokota Hiromi,Shindo Mariko,Miyata Hiromi,Hashimoto Namiko,Sakiyama Kanako,Abe Hazuki,Ueda Yutaka,Kimura Tadashi

Abstract

Abstract Purpose To investigate the clinical practices of diagnosing multicystic cervical lesions as a means to develop a more appropriate diagnostic algorithm for gastric-type adenocarcinoma (GAS) and its precursors. Methods Clinical information for 159 surgically treated patients for multicystic disease of the uterine cervix was collected from 15 hospitals. We performed a central review of the MRI and pathological findings. The MRI findings were categorized into four types including two newly proposed imaging features based on the morphology and distribution of cysts, and the diagnosis accuracy was assessed. Among the four MRI types, types 1 and 2 were categorized as benign lesions that included LEGH; type 3 were precancerous lesions (with an assumption of atypical LEGH); and type 4 were malignant lesions. Results The central pathological review identified 56 cases of LEGH, seven with GAS, four with another form of carcinoma, and 92 with benign disease. In clinical practice, over-diagnosis of malignancy (suspicion of MDA) occurred for 12/19 cases (63.2%) and under-diagnosis of malignancy occurred for 4/11 (36%). Among the 118 patients who had a preoperative MRI and underwent a hysterectomy, type 3 or 4 MRI findings in conjunction with abnormal cytology were positively indicative of premalignancy or malignancy, with a sensitivity and specificity of 61.1% and 96.7%, respectively. Conclusions Although the correct preoperative diagnosis of cervical cancer with a multicystic lesion is challenging, the combination of cytology and MRI findings creates a more appropriate diagnostic algorithm that significantly improves the diagnostic accuracy for differentiating benign disease from premalignancy and malignancy.

Funder

Osaka University

Publisher

Springer Science and Business Media LLC

Subject

Oncology,Surgery

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