ArticleThe Journal of international medical research2023
Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms.
Article in The Journal of international medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.Diagnostics (Basel, Switzerland) · 2024Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms.
methodsThis retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters.
resultsNonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished.
conclusionsComputed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.
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