A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography

Abstract

This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.

Application of Enhanced T1WI of MRI Radiomics in Glioma Grading

“…Manual delineation is susceptible to other nonobjective factors. To reduce errors and obtain stable features, based on previous experience [ 9 , 13 , 14 ], the current study used two doctors for segmentation and the addition of interference noise.…”

“…Nowadays, radiomics methods are more and more widely used in the evaluation of various diseases. Some researchers have tried to apply it to meningioma, lung cancer, cervical cancer, and gastrointestinal stromal tumors [ 9 , 10 , 13 , 14 , 17 ]. Some researchers have also used similar methods to actively study glioma [ 18 , 19 ].…”

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

“…Therefore, observing the new blood vessels can judge the benign and malignant tumor. In contrast, the blood flow signals in or around benign ovarian tumors such as fibromas are not rich, and the RI is high, so there is usually no obvious blood flow signal ( 16 ). In this study, the criteria for the diagnosis of ovarian cancer by contrast-enhanced ultrasound were as follows: After contrast-enhanced ultrasound was performed, bleeding and necrotic areas were observed, the lesion morphology was irregular, and the contrast process showed fast forward and fast reverse.…”

A radiomics-based machine learning pipeline to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest

“…Depending on the endpoint of interest, various ML classifiers may be used in a radiomics pipeline. Support vector machine (SVM), Bayesian network (BN), multivariate logistic regression (MLR), k-nearest neighbor (kNN), decision trees (DT), random forests (RF), neural network (NNet), and convolutional neural networks (CNN) are among the ML classifiers that are most commonly used in radiomics-based ML pipelines [8][9][10][11][12][13][14][15][16][17][18][19][20] . The feasibility of using radiomics-based ML pipelines to distinguish between benign and malignant bone lesions has been reported in previous studies 1-4, 6, 7 .…”

“…In recent years, radiomics-based machine learning (ML) classifiers have shown great potential for use in the early detection of bone metastases (BM) and in assessing response of BM to radiotherapy (RT) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] . However, in order to be clinically acceptable, radiomics models must be trained on large data sets of real-world images.…”