Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis

Wei, Mingxiang; Fan, Bo; Cao, Hui; Zhou, Wei; Shan, Wenli; Bai, Genji

doi:10.1177/0284185120944916

Cited by 5 publications

(4 citation statements)

References 65 publications

(74 reference statements)

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“…Conventional magnetic resonance imaging (cMRI) plays a significant role in the diagnosis of pelvic diseases due to its high soft-tissue resolution ( 13 , 14 ). It can clearly reveal the lesion characteristics (cystic, bleeding, edema, fibrosis, and so on), the relationship between the tumor and surrounding tissues, and the status of lymph node disease (metastasis or inflammation).…”

Section: Discussionmentioning

confidence: 99%

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma

Wang

Chen

et al. 2022

Front. Oncol.

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ObjectiveTo compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs).MethodsIn this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical factors, combined with radiomics features, were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC.ResultsThe AUC of the clinical model and multisequence radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002).ConclusionThe nomogram models of clinical factors combined with MRI multisequence radiomics signatures can help identifying low- and high-grade SOCs and a provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.

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Section: Discussionmentioning

confidence: 99%

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma

Wang

Chen

et al. 2022

Front. Oncol.

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“…The characteristics of neovascularization are that the tube wall is relatively thin, without smooth muscle, and it is only composed of endothelial cells, so its elasticity is poor, and its distribution is also relatively disorder, and the tube lumen is relatively thickened. Therefore, if a large number of new blood vessels are found in the lesion, it often indicates that the lesion is malignant ( 22 , 23 ).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Value of Two-Dimensional Transvaginal Ultrasound Combined with Contrast-Enhanced Ultrasound in Ovarian Cancer

Shahai

Liu

et al. 2022

Front. Surg.

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ObjectiveExplore the value of two-dimensional transvaginal ultrasound combined with contrast-enhanced ultrasound in the differential diagnosis of ovarian cancer, so as to provide the basis for clinical diagnosis and treatment of ovarian cancer.MethodsA total of 100 suspected ovarian cancer patients who were admitted to our hospital from January 2019 to December 2021 were selected as the research subjects, including 62 ovarian cancer patients (ovarian cancer group) and 38 ovarian benign tumor patients (benign group). Two-dimensional vaginal ultrasound and contrast-enhanced ultrasound were performed in both groups. The differences in PI, RI, EDV, PSV, and VM parameters of the two groups as well as those of patients with ovarian cancer of different grades were compared. Record the contrast-enhanced ultrasound parameters such as AT, TTP and IMAX, and determine the diagnostic value.ResultsThe PI and RI of the ovarian cancer group were lower than those of the benign ovarian tumor group, and the EDV, PSV and VM of the ovarian cancer group were higher than those of the benign ovarian tumor group (p < 0.05). The PI and RI of the patients in stage I–II of the ovarian cancer group were higher than those in stage III–IV, and the EDV, PSV and VM were lower than those in the patients in stage III–IV, with statistical significance (p < 0.05). The results of contrast-enhanced ultrasound showed that the AT and TTP values in the ovarian cancer group were significantly shorter than those in the benign group, and the peak intensity was significantly higher than that in the benign group, and the differences were statistically significant (p < 0.05). The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of two-dimensional ultrasound combined with contrast-enhanced ultrasound in the diagnosis of ovarian cancer were high, 95.16%(59/62), 86.84%(33/38), 92.19%(59/64), 91.67%(33/36) and 92.00%(92/100), respectively.ConclusionContrast-enhanced ultrasound to some extent makes up for the deficiencies of conventional ultrasound, is helpful to detect early ovarian cancer, and can be used for the differential diagnosis of small ovarian tumors with difficult two-dimensional ultrasound diagnosis. Two-dimensional ultrasound combined with contrast-enhanced ultrasound can effectively improve the detection rate and differential diagnosis value of ovarian cancer, which is of great significance in the early diagnosis and differentiation of ovarian cancer.

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“…Conventional magnetic resonance imaging (cMRI) plays a significant role in the diagnosis of pelvic diseases due to its high soft-tissue resolution [ 9 , 10 ]. It can clearly reveal the lesion morphology, characteristics (edema, hemorrhage, fibrosis, and so on), the relationship between the lesion and surrounding tissues, and even the status of lymph nodes (metastatic or inflammatory).…”

Section: Introductionmentioning

confidence: 99%

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

Wang

Chen

et al. 2022

Journal of Oncology

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Objective. To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC). Methods. This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS). Results. One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group. Conclusion. By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

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Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis

Cited by 5 publications

References 65 publications

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma

Diagnostic Value of Two-Dimensional Transvaginal Ultrasound Combined with Contrast-Enhanced Ultrasound in Ovarian Cancer

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma

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