Suppr超能文献

磁共振成像对基于超声的ADNEX模型错误分类的恶性卵巢肿瘤的诊断价值。

Diagnostic value of magnetic resonance imaging for malignant ovarian tumors mis-subclassified by the ultrasound-based ADNEX model.

作者信息

Jiang Meijiao, Yuan Congcong, Lu Siwei, Zhu Yunkai, Chu Caiting, Li Wenhua

机构信息

Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Front Oncol. 2025 Feb 25;15:1406735. doi: 10.3389/fonc.2025.1406735. eCollection 2025.

Abstract

OBJECTIVE

Accurately predicting metastatic cancer to the adnexa, stage I and advanced ovarian cancer before surgery is crucial. The ADNEX model, based on ultrasound, is currently the only prediction model that can differentiate between these types. This study aims to analyze MRI features and diagnostic value in malignant ovarian tumors mis-subclassified by the ADNEX model, considering their diverse histopathologic types.

METHODS

From January 2018 to September 2022, 164 patients with pathologically confirmed ovarian malignancies were selected from those who were examined by ultrasound. The clinical and MRI characteristics of 51 patients mis-subclassified by the ADNEX model were compared with histopathological types.

RESULTS

A total of 30 were confirmed with primary ovarian cancer (5 with HGSOC, 14 with CCC, 2 with EC, 4 with MC, 2 with GCT, 1 with YST, 1 with immature teratoma, and 1 with dysgerminoma). There were 21 patients who had metastatic ovarian tumors (10 with colorectal cancer, 4 with gastric cancer, 2 with uterine cervical cancer, 3 with endometrial cancer, 1 with breast cancer, and 1 with LAMN). The only significant difference between the two groups was in CEA. The mean diameters of the primary and metastatic ovarian tumors were 10.29 cm (range: 3.61 cm-26.02 cm) and 8.58 cm (range: 3.10 cm-20.30 cm), respectively. A total of 42 masses were lobulated (82.35%, 42/51), and 26 masses were solid-cystic (26/51, 50.98%). There was a significant difference between CCC and other tumors, with mean ADC values of 1.01 × 10 mm/s (range: 0.68-1.28×10 mm/s) and 0.74×10 mm/s (range: 0.48-0.99×10 mm/s), respectively (P=0.000). A total of 50 masses presented isointense-T1, hyperintense-T2, and hyperintense-DWI signal on MRI (50/51,98.04%). There were 33 masses that showed intensive enhancement (33/51,64.71%). There were 17 masses who had necrosis (17/51, 33.33%), with the majority being HGSOC and ovarian metastases from colorectal and gastric cancers (12/17, 70.59%). There were 19 masses that presented hemorrhage (19/51,37.25%), with the majority being CCC (10/19, 52.63%). A total of 46 masses were diagnosed correctly by MRI (46/51,90.20%). There were 35 and 15 masses that were rated as O-RADS score 5 and score 4, respectively. One mass was rated as score 3.

CONCLUSIONS

DWI signal, ADC value, degree of enhancement, and characteristic components within the mass on MRI can provide supplementary information for malignant ovarian tumors mis-subclassified by the ADNEX model.

摘要

目的

术前准确预测附件转移癌、Ⅰ期和晚期卵巢癌至关重要。基于超声的ADNEX模型是目前唯一能够区分这些类型的预测模型。本研究旨在分析ADNEX模型误分类的恶性卵巢肿瘤的MRI特征及其诊断价值,并考虑其不同的组织病理学类型。

方法

选取2018年1月至2022年9月间经超声检查且病理确诊为卵巢恶性肿瘤的164例患者。将ADNEX模型误分类的51例患者的临床和MRI特征与组织病理学类型进行比较。

结果

共确诊原发性卵巢癌30例(高级别浆液性卵巢癌5例、透明细胞癌14例、子宫内膜样癌2例、黏液性癌4例、生殖细胞肿瘤2例、卵黄囊瘤1例、未成熟畸胎瘤1例、无性细胞瘤1例)。有21例患者发生卵巢转移瘤(结直肠癌转移10例胃癌转移4例、子宫颈癌转移2例、子宫内膜癌转移3例、乳腺癌转移1例、低级别阑尾黏液性肿瘤转移1例)。两组之间唯一显著差异在于癌胚抗原(CEA)。原发性和转移性卵巢肿瘤的平均直径分别为10.29cm(范围:3.61cm - 26.02cm)和8.58cm(范围:3.10cm - 20.30cm)。共有42个肿块呈分叶状(82.35%,42/51),26个肿块为实性 - 囊性(26/51,50.98%)。透明细胞癌与其他肿瘤之间存在显著差异,平均表观扩散系数(ADC)值分别为1.01×10⁻³mm²/s(范围:0.68 - 1.28×10⁻³mm²/s)和0.74×10⁻³mm²/s(范围:0.48 - 0.99×10⁻³mm²/s)(P = 0.000)。共有50个肿块在MRI上表现为T1等信号-T2高信号-DWI高信号(50/51,98.04%)。有33个肿块表现为强化明显(33/51,64.71%)。有17个肿块存在坏死(17/51,33.33%),其中大多数为高级别浆液性卵巢癌以及结直肠癌和胃癌的卵巢转移瘤(12/17,70.59%)。有19个肿块出现出血(19/51,37.25%),其中大多数为透明细胞癌(10/19,52.63%)。MRI正确诊断46个肿块(46/51,90.20%)。有35个和15个肿块分别被评为O-RADS 5类和4类。1个肿块被评为3类。

结论

MRI上的DWI信号、ADC值、强化程度及肿块内特征性成分可为ADNEX模型误分类的恶性卵巢肿瘤提供补充信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e713/11893382/8552cfa6ed32/fonc-15-1406735-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验