Dataset Information

Preoperative magnetic resonance imaging predicts clinicopathological parameters and stages of endometrial carcinomas.

ABSTRACT:

Background

We investigated the agreement and accuracy of preoperative magnetic resonance imaging (MRI) with postoperative pathological characteristics and stages of endometrial endometrioid carcinoma (EEC).

Methods

We recruited 527 women with EEC who underwent staging surgery at a single medical institution. The preoperative MRI, stages, and clinical and pathological parameters, including myometrial invasion (MI), cervical invasion (CI), adnexal metastasis (AM), intra-abdominal metastasis, and pelvic and/or para-aortic nodal metastasis, were recorded and analyzed. The agreement and accuracy between the preoperative MRI findings and these parameters and stages were assessed.

Results

The rate of the preoperative MRI-based clinical stage matching the postoperative surgical stage was 85.2% in International Federation of Gynecology and Obstetrics stage IA, 51.9% in stage IB, 35.5% in stage II, 5.3% in stage IIIA, 33.3% in stage IIIB, 28.6% in stage IIIC1, 64.3% in stage IIIC2, and 93.8% in stage IVB. The consistency between radiologists and pathologists was 80.5% for deep MI, 91.5% for cervical invasion, 92.2% for adnexal metastasis, 98.9% for intra-abdominal metastasis, and 87.5% and 92.2% for pelvic and para-aortic nodal metastases, respectively. The negative predictive value of intra-abdominal metastasis was the highest with 99.8%.

Conclusions

Preoperative MRI could be an excellent tool for routine preoperative assessment to predict pathological parameters and stages of EEC, especially in excluding intra-abdominal metastatic disease.

SUBMITTER: Wu CY

PROVIDER: S-EPMC8855918 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

Preoperative magnetic resonance imaging predicts clinicopathological parameters and stages of endometrial carcinomas.

Wu Chia-Ying CY Tai Yi-Jou YJ Shih I-Lun IL Chiang Ying-Cheng YC Chen Yu-Li YL Hsu Heng-Cheng HC Cheng Wen-Fang WF

Cancer medicine 20211230 4

<h4>Background</h4>We investigated the agreement and accuracy of preoperative magnetic resonance imaging (MRI) with postoperative pathological characteristics and stages of endometrial endometrioid carcinoma (EEC).<h4>Methods</h4>We recruited 527 women with EEC who underwent staging surgery at a single medical institution. The preoperative MRI, stages, and clinical and pathological parameters, including myometrial invasion (MI), cervical invasion (CI), adnexal metastasis (AM), intra-abdominal me ...[more]

PMID: 34967506

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Project description:BackgroundAppropriate preoperative identification of iCCA subtype is essential for personalized management, so the aim of this study is to investigate the role of MR imaging features in preoperatively differentiating the iCCA subtype.MethodsNinety-three patients with mass-forming intrahepatic cholangiocarcinoma (iCCA, 63 small duct type and 30 large duct type) were retrospectively enrolled according to the latest 5th WHO classification (mean age, males vs. females: 60.66 ± 10.53 vs. 61.88 ± 12.82, 50 men). Significant imaging features for differentiating large duct iCCA and small duct iCCA were identified using univariate and multivariate logistic regression analyses, and a regression-based predictive model was then generated. Furthermore, diagnostic performance parameters of single significant imaging features and the predictive model were obtained, and corresponding receiver operating characteristic (ROC) curves were subsequently presented.ResultsThe univariate analysis showed that tumor in vein, arterial phase hypoenhancement, intrahepatic duct dilatation, lack of targetoid restriction and lack of targetoid appearance in T2 were predictors of large duct type iCCA. Arterial phase hypoenhancement, intrahepatic duct dilatation and lack of targetoid restriction were independent predictors for large duct type iCCA in multivariate analysis. The regression-based predictive model has achieved the best preoperative prediction performance in iCCA subcategorization so far. The area under the ROC curve of the regression-based predictive model was up to 0.91 (95% CI: 0.85, 0.98), and it was significantly higher than every single significant imaging feature.ConclusionsArterial phase hypoenhancement, intrahepatic duct dilatation and lack of targetoid restriction could be considered reliable MR imaging indicators of large duct type iCCA. MR imaging features can facilitate noninvasive prediction of iCCA subtype with satisfactory predictive performance.

Project description:Endometrial carcinoma (EC) molecular classification based on four molecular subclasses identified in The Cancer Genome Atlas (TCGA) has gained relevance in recent years due to its prognostic utility and potential to predict benefit from adjuvant treatment. While most ECs can be classified based on a single classifier (POLE exonuclease domain mutations - POLEmut, MMR deficiency - MMRd, p53 abnormal - p53abn), a small but clinically relevant group of tumours harbour more than one molecular classifying feature and are referred to as 'multiple-classifier' ECs. We aimed to describe the clinicopathological and molecular features of multiple-classifier ECs with abnormal p53 (p53abn). Within a cohort of 3518 molecularly profiled ECs, 107 (3%) tumours displayed p53abn in addition to another classifier(s), including 64 with MMRd (MMRd-p53abn), 31 with POLEmut (POLEmut-p53abn), and 12 with all three aberrations (MMRd-POLEmut-p53abn). MMRd-p53abn ECs and POLEmut-p53abn ECs were mostly grade 3 endometrioid ECs, early stage, and frequently showed morphological features characteristic of MMRd or POLEmut ECs. 18/28 (60%) MMRd-p53abn ECs and 7/15 (46.7%) POLEmut-p53abn ECs showed subclonal p53 overexpression, suggesting that TP53 mutation was a secondary event acquired during tumour progression. Hierarchical clustering of TCGA ECs by single nucleotide variant (SNV) type and somatic copy number alterations (SCNAs) revealed that MMRd-p53abn tumours mostly clustered with single-classifier MMRd tumours (20/23) rather than single-classifier p53abn tumours (3/23), while POLEmut-p53abn tumours mostly clustered with single-classifier POLEmut tumours (12/13) and seldom with single-classifier p53abn tumours (1/13) (both p ≤ 0.001, chi-squared test). Finally, the clinical outcome of patients with MMRd-p53abn and POLEmut-p53abn ECs [stage I 5-year recurrence-free survival (RFS) of 92.2% and 94.1%, respectively] was significantly different from single-classifier p53abn EC (stage I RFS 70.8%, p = 0.024 and p = 0.050, respectively). Our results support the classification of MMRd-p53abn EC as MMRd and POLEmut-p53abn EC as POLEmut. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Dataset Information

Preoperative magnetic resonance imaging predicts clinicopathological parameters and stages of endometrial carcinomas.

Background

Methods

Results

Conclusions

Publications

Preoperative magnetic resonance imaging predicts clinicopathological parameters and stages of endometrial carcinomas.

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