Imaging before cytoreductive surgery in advanced ovarian cancer patients

Rizzo, Stefania; Grande, Maria Del; Manganaro, Lucia; Papadia, Andrea; Grande, Filippo Del

doi:10.1136/ijgc-2019-000819

Cited by 26 publications

(25 citation statements)

References 45 publications

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“…It can scan different sections to display the anatomical structure and condition of the body under examination, which is conducive to the early detection of lesions. In addition, MRI has high soft tissue resolution and high qualitative diagnostic value (23,24). Thus, MRI plays an important role in the diagnosis and differential diagnosis of ovarian tumors.…”

Section: Discussionmentioning

confidence: 99%

“…As CT scans only show the defect of the cross-sectional image, it is difficult to distinguish between endometriotic cysts, the uterine serosal layer or ovarian tumors (25,26). Conversely, MRI uses multi-directional and multi-level imaging, obtains a larger amount of information, a higher resolution of soft tissues, and clearly defines the range of edema, inflammation, tumors, etc., and thus provides certain biochemical and pathological information (21,24,26).…”

Section: Discussionmentioning

confidence: 99%

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Systematic review and meta-analysis of imaging differential diagnosis of benign and malignant ovarian tumors

Wang¹,

Zheng²,

Gao³

et al. 2022

Gland Surg

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Background: With the increasing incidence of gynecological ovarian tumors, the differential diagnosis of benign and malignant ovarian tumors is of great significance for subsequent treatment. Currently, ovarian examinations commonly use computed tomography (CT) or magnetic resonance imaging (MRI). This study sought to compare the value of CT and MRI in differentiating between benign and malignant ovarian tumors. Methods:The PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, and Weipu databases were searched for published articles using the following terms "CT" or "Computed Tomography" or "MRI" or "Magnetic Resonance imaging" and "ovarian cancer" or "ovarian tumor" or "ovarian neoplasm" or "adnexal mass" or "adnexal lesion". The articles were screened and the data were extracted based on the inclusion and exclusion criteria. The Quality Assessment of Diagnostic Accuracy Studies-2 recommended by the Cochrane Collaboration was used to assess the methodological quality of the included studies, and the network meta-analysis was performed by Stata 15.0. Results:The results showed that the overall sensitivity and specificity of CT were 0.79 [95% confidence intervals (CI): 0.70-0.87] and 0.87 (95% CI: 0.80-0.92), respectively. The overall sensitivity and specificity of MRI were 0.94 (95% CI: 0.91-0.95) and 0.91 (95% CI: 0.90-0.93), respectively. The area under the curve of the CT and MRI summary receiver operating characteristics were 0.9016 and 0.9764, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Systematic review and meta-analysis of imaging differential diagnosis of benign and malignant ovarian tumors

Wang¹,

Zheng²,

Gao³

et al. 2022

Gland Surg

View full text Add to dashboard Cite

show abstract

“…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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“…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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Imaging before cytoreductive surgery in advanced ovarian cancer patients

Cited by 26 publications

References 45 publications

Systematic review and meta-analysis of imaging differential diagnosis of benign and malignant ovarian tumors

Systematic review and meta-analysis of imaging differential diagnosis of benign and malignant ovarian tumors

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

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

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