MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

Wang, Mandi; Perucho, Jose Angelo Udal; Tse, Ka Yu; Chu, Mandy Man Yee; Ip, Philip; Lee, Elaine

doi:10.1007/s00330-020-06913-7

Cited by 20 publications

(22 citation statements)

References 40 publications

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“…This retrospective study was approved by the Institutional Ethics Review Board, which waived the requirement for written informed patient consent. The study was conducted in accordance with the Declaration of Helsinki ( 18 ). The main cohort included 203 women with CC, confirmed by histopathological analysis after reviewing the institutional database of medical records from June 2012 to December 2018.…”

Section: Methodsmentioning

confidence: 99%

“…If the tumor region was not ascertained, the area was not included in the segmentation. After standardized pre-processing, 1037 radiomics features were extracted from the original and filtered images using PyRadiomics packages, including the shape features ( 14 ), first-order statistics ( 18 ), and texture features (including 24 gray-level co-occurrence matrix [GLCM] features, 16 gray-level run-length matrix [GLRLM] features, 16 gray-level size zone matrix [GLSZM] features, 14 gray level dependence matrix [GLDM] features, and 5 neighboring gray-tone difference matrix [NGTDM] features) ( 24 ). One month later, 30 patients were randomly selected for lesion definition and radiomics feature extraction by another radiologist (Y. J., with 14 years of experience in pelvic radiological diagnosis), who was blinded to the clinical and pathological findings.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…However, the potential of ADC values to differentiate the cervical carcinoma subtypes remains controversial. Some studies reported that the ADC values of SCC were significantly lower than those of ACA ( 6 , 25 ), whereas Winfield et al. showed that ADC values could not differentiate SCC from ACA ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

Shao

Liu

et al. 2022

Front. Oncol.

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ObjectiveThe objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma.MethodsSixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons.ResultsOnly the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829).ConclusionThe parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.

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“…TA by mathematical methods of quantitative imaging image pixel gray level statistics and spatial distribution and structure information, to extract the texture feature which cannot be identified by the naked eye, revealing the heterogeneity of tumor histologic features and certain genes, and by using the quantitative information of the differential diagnosis of the disease, grading, classification and evaluation of curative effect (7,17). At present, TA technology has been widely used in the diagnosis, effect evaluation and prognosis prediction of tumors in the brain, lung, breast, liver and pelvic cavity (18)(19)(20)(21). It is mainly used in the staging of rectal cancer and the evaluation of neoadjuvant chemotherapy, etc.…”

Section: Application Of Ta In Emvi Evaluation Of Rectal Adenocarcinomamentioning

confidence: 99%

The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma

et al. 2022

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PurposeThis study aims to evaluate the value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) for evaluating extramural vascular invasion (EMVI) of rectal adenocarcinoma.MethodsNinety-six patients with pathologically confirmed rectal adenocarcinoma after surgical resections were collected. Patients were divided into the EMVI positive group (n=39) and the EMVI negative group (n=57). We measured the IVIM-DWI parameters and TA parameters of rectal adenocarcinoma. We compare the differences of the above parameters between the two groups and establish a prediction model through multivariate logistic regression analysis. the ROC curve was performed for parameters with each individual and in combination.ResultsADC, D, D* value between the two groups were statistically significant (P= 0.015,0.031,0). Six groups of texture parameters were statistically significant between the two groups (P=0.007,0.037,0.011,0.005,0.007,0.002). Logistic regression prediction model shows that GLCM entropy_ALL DIRECTION_offset7_SD and D* are important independent predictors, and the AUC of the regression prediction model was 0.821, the sensitivity was 92.98%, the specificity was 61.54%, and the Yoden index was 0.5452. The AUC was significantly higher than that of other single parameters.Conclusion3.0T MRI IVIM-DWI parameters combined with texture analysis can provide valuable information for EMVI evaluation of rectal adenocarcinoma before the operation.

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MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

Cited by 20 publications

References 40 publications

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

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

Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma

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