CT texture analysis in histological classification of epithelial ovarian carcinoma

An, He; Wang, Yiang; Wong, Esther Man Fung; Lyu, Shanshan; Han, Lujun; Perucho, Jose Angelo Udal; Cao, Peng; Lee, Elaine

doi:10.1007/s00330-020-07565-3

Cited by 19 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To date, as far as we know, there have been no CTTA related studies on testicular tumors. Previous research has focused on tumors such as epithelial ovarian carcinoma, renal cell carcinoma or lung carcinoma ( 26 – 29 ). In the study of An et al.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based CT texture analysis in the differentiation of testicular masses

Hu,

Qiao,

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

PurposeTo evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and identifying seminoma in testicular germ cell tumors, respectively.MethodsWe retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers.Results985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively.ConclusionUntil now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning-based CT texture analysis in the differentiation of testicular masses

Hu,

Qiao,

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…An et al. have determined the histological subtype classification in epithelial ovarian carcinoma using CT texture features extracted from the portal venous phase of contrast-enhanced CT images ( 42 ). Yu et al.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Zhang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

show abstract

“… 12 Previous studies reported the ability of computed tomography (CT)–based radiomics in the evaluation of EOC, including differentiating between EOC and non-EOC, as well as predicting clinical outcome or survival. 13 , 14 , 15 Recent studies showed promising results of CT-based texture features in classifying HGSC and non-HGSC 16 and excellent performance of multiple parametric magnetic resonance imaging (MRI)–based radiomics in distinguishing type 1 (non-HGSC) and type 2 (HGSC) tumors. 17 , 18 , 19 Nonetheless, to date, none of the studies focused on the discriminative ability of CT radiomics in histologic subtyping using multicenter data sets.…”

Section: Introductionmentioning

confidence: 99%

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

Wang

Perucho

et al. 2022

JAMA Netw Open

Self Cite

View full text Add to dashboard Cite

ImportanceEpithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication.ObjectiveTo assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets.Design, Setting, and ParticipantsIn this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non–high-grade serous carcinoma.ExposuresContrast-enhanced CT-based radiomics.Main Outcomes and MeasuresIntraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve.ResultsIn this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non–high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort.Conclusions and RelevanceIn this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

show abstract

CT texture analysis in histological classification of epithelial ovarian carcinoma

Cited by 19 publications

References 45 publications

Machine learning-based CT texture analysis in the differentiation of testicular masses

Machine learning-based CT texture analysis in the differentiation of testicular masses

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

Contact Info

Product

Resources

About