scholarly journals Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion

2022 ◽  
Vol 11 ◽  
Author(s):  
Gang Huang ◽  
Yaqiong Cui ◽  
Ping Wang ◽  
Jialiang Ren ◽  
Lili Wang ◽  
...  

BackgroundDetection of lymphovascular space invasion (LVSI) in early cervical cancer (CC) is challenging. To date, no standard clinical markers or screening tests have been used to detect LVSI preoperatively. Therefore, non-invasive risk stratification tools are highly desirable.ObjectiveTo train and validate a multi-parametric magnetic resonance imaging (mpMRI)-based radiomics model to detect LVSI in patients with CC and investigate its potential as a complementary tool to enhance the efficiency of risk assessment strategies.Materials and MethodsThe model was developed from the tumor volume of interest (VOI) of 125 patients with CC. A total of 1037 radiomics features obtained from conventional magnetic resonance imaging (MRI), including a small field-of-view (sFOV) high-resolution (HR)-T2-weighted MRI (T2WI), apparent diffusion coefficient (ADC), T2WI, fat-suppressed (FS)-T2WI, as well as axial and sagittal contrast-enhanced T1-weighted MRI (T1c). We conducted a radiomics-based characterization of each tumor region using pretreatment image data. Feature selection was performed using the least absolute shrinkage and selection operator method on the training set. The predictive performance was compared with single variates (clinical data and single-layer radiomics signatures) analyzed using a receiver operating characteristic (ROC) curve. Three-fold cross-validation performed 20 times was used to evaluate the accuracy of the trained classifiers and the stability of the selected features. The models were validated by using a validation set.ResultsFeature selection extracted the six most important features (3 from sFOV HR-T2WI, 1 T2WI, 1 FS-T2WI, and 1 T1c) for model construction. The mpMRI-combined radiomics model (area under the curve [AUC]: 0.940) reached a significantly higher performance (better than the clinical parameters [AUC: 0.730]), including any single-layer model using sFOV HR-T2WI (AUC: 0.840), T2WI (AUC: 0.770), FS-T2WI (AUC: 0.710), ADC maps (AUC: 0.650), sagittal, and axial T1c values (AUC: 0.710, 0.680) in the validation set.ConclusionBiomarkers using multi-parametric radiomics features derived from preoperative MR images could predict LVSI in patients with CC.

2013 ◽  
Vol 128 (3) ◽  
pp. 449-453 ◽  
Author(s):  
Elisabeth Epstein ◽  
Antonia Testa ◽  
Adrius Gaurilcikas ◽  
Alessia Di Legge ◽  
Liveke Ameye ◽  
...  

Author(s):  
Sahar Mahmoud Abd elsalam ◽  
Omnia Mokhtar ◽  
Lamia Adel ◽  
Reda Hassan ◽  
Manal Ibraheim ◽  
...  

Radiotherapy ◽  
10.5772/67382 ◽  
2017 ◽  
Author(s):  
Kenji Yoshida ◽  
Ryo Nishikawa ◽  
Daisuke Miyawaki ◽  
Yasuhiko Ebina ◽  
Ryohei Sasaki

2018 ◽  
Vol 60 (5) ◽  
pp. 670-676
Author(s):  
Ji Zhang ◽  
Weizhong Tian ◽  
Xinhua Bu ◽  
Xiulan Wang ◽  
Fangzheng Tian ◽  
...  

Background Patients with uterine cervical cancer suffer high mortality. Accurate detection of a residual tumor by magnetic resonance imaging (MRI) during and after directed brachytherapy (BCT) is crucial for the success of cancer treatment and is a significant predictor of patient survival. Purpose To determine the diagnostic significance of MRI in detecting residual tumor tissue after BCT. Material and Methods The Web of Knowledge, Cochrane Library, and PubMed were systematically searched (January 1997 to December 2016) for post-brachytherapy MRI studies that measured residual tumors in patients with uterine cervical cancer. All data were analyzed using the Meta-Disc 1.4 program. Results Four clinical studies consisting of 163 patients (147 of whom were included in the present analysis) who were diagnosed with uterine cervical cancer according to the International Federation of Gynecology and Obstetrics (FIGO) staging system were included in the study. All the patients received BCT and underwent MRI detection of residual tumors tissue. In studies where the accuracy of MRI detection was confirmed by histological tests or gynecological tests, the summary estimates of specificity, sensitivity, positive predictive value, negative predictive value, and accuracy were 88.5%, 83.5%, 53.5%, 97.1%, and 84.3%, respectively. Conclusion MRI-directed BCT is commonly used for cervical cancer patients. Based on our investigation of four independent studies, MRI showed better prediction of positive results than negative results in patients with cervical cancer after BCT. However, more data on the greater numbers of patients are needed to establish the accuracy of MRI detection of cervical cancer after BCT.


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