Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors

Qi, Lisha; Chen, Dandan; Li, Chunxiang; Li, Jinghan; Wang, Jingyi; Chao, Zhang; Li, Xiaofeng; Qiao, Ge; Wu, Haixiao; Zhang, Xiaofang; Ma, Wenjuan

doi:10.3389/fgene.2021.753948

Cited by 22 publications

(20 citation statements)

References 32 publications

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“…In this study, a large cohort of 600 patients was analyzed and more pathological subtypes were included, except for AML and clear cell RCC. Sex was the only clinical signature in the nomogram of this study, suggesting that clinical factors in renal masses may contribute minimally to the differentiation of benign from malignant renal masses as reported in other cancers including ovarian, breast, and lung cancers (38)(39)(40). This observation merits further investigation.…”

Section: Discussionmentioning

confidence: 62%

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

Qiao

et al. 2022

Front. Oncol.

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ObjectivesThis study was conducted in order to develop and validate an ultrasonic-based radiomics nomogram for diagnosing solid renal masses.MethodsSix hundred renal solid masses with benign renal lesions (n = 204) and malignant renal tumors (n = 396) were divided into a training set (n = 480) and a validation set (n = 120). Radiomics features were extracted from ultrasound (US) images preoperatively and then a radiomics score (RadScore) was calculated. By integrating the RadScore and independent clinical factors, a radiomics nomogram was constructed. The diagnostic performance of junior physician, senior physician, RadScore, and radiomics nomogram in identifying benign from malignant solid renal masses was evaluated based on the area under the receiver operating characteristic curve (ROC) in both the training and validation sets. The clinical usefulness of the nomogram was assessed using decision curve analysis (DCA).ResultsThe radiomics signature model showed satisfactory discrimination in the training set [area under the ROC (AUC), 0.887; 95% confidence interval (CI), 0.860–0.915] and the validation set (AUC, 0.874; 95% CI, 0.816–0.932). The radiomics nomogram also demonstrated good calibration and discrimination in the training set (AUC, 0.911; 95% CI, 0.886–0.936) and the validation set (AUC, 0.861; 95% CI, 0.802–0.921). In addition, the radiomics nomogram model showed higher accuracy in discriminating benign and malignant renal masses compared with the evaluations by junior physician (DeLong p = 0.004), and the model also showed significantly higher specificity than the senior and junior physicians (0.93 vs. 0.57 vs. 0.46).ConclusionsThe ultrasonic-based radiomics nomogram shows favorable predictive efficacy in differentiating solid renal masses.

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Section: Discussionmentioning

confidence: 62%

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

Qiao

et al. 2022

Front. Oncol.

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“…[ 36 ] Low Low Low Low Low Qi et al . [ 37 ] Low Low Low Low High Stefan et al . [ 63 ] Unclear Low Low Low High Wang et al [ 38 ] Low Low Low Unclear Unclear Martinez-Mas et al [ 39 ] High Low Low High High Zhang et al [ 40 ] High Unclear High High Low Acharya et al [ 41 ] High Low Low High High Aramendia-Vidaurreta et al [ 46 ] Unclear Low Low Low Unclear Khazendar et al [ 47 ] Unclear Low Low Low …”

Section: Resultsmentioning

confidence: 99%

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

et al. 2023

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Objectives Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. Methods We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. Results In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6–100% and 66.7–100% and specificities ranged from 76.3–100%; 69–100% and 77.8–100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. Conclusion Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.

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“…In addition, the current study also showed that HE-4 level was an independent predictor for distinguishing benign and malignant ovarian tumors. Unlike previous studies ( 33 , 37 ), the level of CA-125 and HE4 were evaluated simultaneously in our study. This study showed that only the HE-4 level could be used as an independent predictor.…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

et al. 2022

Front. Oncol.

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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.

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Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors

Cited by 22 publications

References 32 publications

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

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

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