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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.

SUBMITTER: Li S

PROVIDER: S-EPMC8062553 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSerous cystic neoplasm (SCN), mucinous cystic neoplasm (MCN), and intraductal papillary mucinous neoplasm (IPMN) comprise a large proportion of pancreatic cystic neoplasms (PCNs). Patients with MCN and IPMN require surgery due to the potential of malignant transformation, whereas those with SCN require periodic surveillance. However, the differential diagnosis of patients with PCNs before treatment remains a great challenge for all surgeons. Therefore, the establishment of a reliable diagnostic tool is urgently required for the improvement of precision diagnostics.MethodsBetween February 2015 and December 2020, 143 consecutive patients with PCNs who were confirmed by postoperative pathology were retrospectively included in the study cohort, then randomized into development and test cohorts at a ratio of 7:3. The predictors of preoperative clinical-radiologic parameters were evaluated by univariate and multivariable logistic regression analyses. A total of 1,218 radiomics features were computationally extracted from the enhanced computed tomography (CT) scans of the tumor region, and a radiomics signature was established by the random forest algorithm. In the development cohort, multi- and binary-class radiomics models integrating preoperative variables and radiomics features were constructed to distinguish between the 3 types of PCNs. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the predictive efficiency of the model. An independent internal test cohort was applied to validate the classification models.ResultsAll preoperative prediction models were built by integrating the radiomics signature with 13 diagnosis-related radiomics features and 3 important clinical-radiologic parameters: age, sex, and tumor diameter. The multiclass prediction model presented an overall accuracy of 0.804 in the development cohort and 0.707 in the test cohort. The binary-class prediction models displayed higher overall accuracies of 0.853, 0.866, and 0.928 in the development dataset and 0.750, 0.839, and 0.889 in the test dataset. In the test cohort, the binary-class radiomics models showed better predictive performances {AUC =0.914 [95% confidence interval (CI): 0.786 to 1.000], 0.863 (95% CI: 0.714 to 0.941), and 0.926 (95% CI: 0.824 to 1.000)} than the multiclass radiomics model [AUC =0.850 (95% CI: 0.696 to 1.000)], with a large net benefit in the decision curve analysis (DCA). The radiomics-based nomogram provided the correct predicted probability for the diagnosis of PCNs.ConclusionsThe proposed radiomics models with clinical-radiologic parameters and radiomics features help to predict the accurate diagnosis among PCNs to advance personalized medicine.

Project description:Background: Serous cystadenoma (SCA), mucinous cystadenoma (MCN), and intraductal papillary mucinous neoplasm (IPMN) are three subtypes of pancreatic cystic neoplasm (PCN). Due to the potential of malignant-transforming, patients with MCN and IPMN require radical surgery while patients with SCA need periodic surveillance. However, accurate pre-surgery diagnosis between SCA, MCN, and IPMN remains challenging in the clinic. Methods: This study enrolled 164 patients including 76 with SCA, 40 with MCN and 48 with IPMN. Patients were randomly split into a training cohort (n = 115) and validation cohort (n = 41). We performed statistical analysis and Boruta method to screen significantly distinct clinical factors and radiomics features extracted on pre-surgery contrast-enhanced computed tomography (CECT) images among three subtypes. Three reliable machine-learning algorithms, support vector machine (SVM), random forest (RF) and artificial neural network (ANN), were utilized to construct classifiers based on important radiomics features and clinical parameters. Precision, recall, and F1-score were calculated to assess the performance of the constructed classifiers. Results: Nine of 547 radiomics features and eight clinical factors showed a significant difference among SCA, MCN, and IPMN. Five radiomics features (Histogram_Entropy, Histogram_Skeweness, LLL_GLSZM_GLV, Histogram_Uniformity, HHL_Histogram_Kurtosis), and four clinical factors, including serum carbohydrate antigen 19-9, sex, age, and serum carcinoembryonic antigen, were identified important by Boruta method. The SVM classifier achieved an overall accuracy of 73.04% in training cohort and 71.43% in validation cohort, respectively. The RF classifier achieved overall accuracy of 84.35 and 79.59%, respectively. The constructed ANN model showed an overall accuracy of 77.39% in the training dataset and 71.43% in the validation dataset. All the three classifiers showed high F1 score for differentiation among the three subtypes. Conclusion: Our study proved the feasibility and translational value of CECT-based radiomics classifiers for differentiation among SCA, MCN, and IPMN.

Project description:BackgroundSurvival prediction is crucial for patients with gastric neuroendocrine neoplasms (gNENs) to assess the treatment programs and may guide personalized medicine. This study aimed to develop and evaluate a deep learning (DL) radiomics model to predict the overall survival (OS) in patients with gNENs.MethodsThe retrospective analysis included 162 consecutive patients with gNENs from two hospitals, who were divided into a training cohort, internal validation cohort (The First Affiliated Hospital of Zhengzhou University; n=108), and an external validation cohort (The Henan Cancer Hospital; n=54). DL radiomics analysis was applied to computed tomography (CT) images of the arterial phase and venous phase, respectively. Based on pretreatment CT images, two DL radiomics signatures were developed to predict OS. The combined model incorporating the radiomics signatures and clinical factors was built through the multivariable Cox proportional hazards (CPH) method. The combined model was visualized into a radiomics nomogram for individualized OS estimation. Prediction performance was assessed with the concordance index (C-index) and the Kaplan-Meier (KM) estimator.ResultsThe DL-based radiomics signatures based on two phases were significantly correlated with OS in the training (C-index: 0.79-0.92; P<0.01), internal validation (C-index: 0.61-0.86; P<0.01), and external validation (C-index: 0.56-0.75; P<0.01) cohorts. The combined model integrating radiomics signatures with clinical factors showed a significant improvement in predictive performance compared to the clinical model in the training (C-index: 0.86 vs. 0.80; P<0.01), internal validation (C-index: 0.77 vs. 0.71; P<0.01), and external validation (C-index: 0.71 vs. 0.66; P<0.01) cohorts. Moreover, the combined model classified patients into high-risk and low-risk groups, and the high-risk group had a shorter OS compared to the low-risk group in the training cohort [hazard ratio (HR) 3.12, 95% confidence interval (CI): 2.34-3.93; P<0.01], which was validated in the internal (HR 2.51, 95% CI: 1.57-3.99; P<0.01) and external validation cohort (HR 1.77, 95% CI: 1.21-2.59; P<0.01).ConclusionsDL radiomics analysis could serve as a potential and noninvasive tool for prognostic prediction and risk stratification in patients with gNENs.

Project description:BackgroundWe evaluated malignancy according to the characteristics of pericardial fluid in symptomatic Japanese patients undergoing pericardiocentesis and computed tomography (CT).MethodsThis was a retrospective, single-center, observational study of 125 symptomatic patients undergoing pericardiocentesis. The patients were classified into two groups: a malignancy group and a non-malignancy group, according to the primary disease and cytology of the pericardial effusion (PE). We compared the pericardial fluid sample and CT measurements between both groups.ResultsAll patients were diagnosed as having exudative PE by Light's criteria. PE with malignant cells was demonstrated in 76.8% of the malignancy group patients. Pericardial to serum lactate dehydrogenase (LDH) ratio > 0.6, as one of Light's criteria, was associated with malignancy (p = 0.017). Lower serum brain natriuretic peptide (BNP) concentration was also associated with malignancy (BNP: 126.9 ± 89.8 pg/ml vs 409.2 ± 97.7 pg/ml, malignancy vs non-malignancy groups, respectively; p = 0.037). A significant difference was observed in pericardial fluid glucose level between the malignancy and non-malignancy groups (pericardial fluid glucose: 78.24 ± 48.29 mg/dl vs 98.41 ± 44.85, respectively; p = 0.048). Moreover, CT attenuation values (Hounsfield units (HU)) tended to be higher in the malignancy group vs the non-malignancy group (22.7 [interquartile range (IQR), 17.4-26.0] vs 17.4 [IQR, 13.7-26.4], respectively; p = 0.08). The sensitivity and specificity of pericardial fluid glucose level ≤ 70 mg/dl and CT attenuation values > 20 HU were 40.9% and 89.6%, respectively, in the malignancy group. The positive- and negative predictive values of pericardial fluid glucose level ≤ 70 mg/dl and CT attenuation values > 20 HU were 85.7% and 50.0%, respectively, in the malignancy group. Pericardial fluid glucose level ≤ 70 mg/dl and CT attenuation values > 20 HU were cutoff values associated with malignancy (p = 0.012).ConclusionsLower pericardial fluid glucose level with higher CT attenuation values may suggest malignancy-related PE.

Project description:Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination. Intra-patient agreement and inter-patient agreement were compared using the overall concordance correlation coefficient (OCCC). To further evaluate the corrections, hierarchical clustering was used to identify patient scans before and after correction. To assess the general applicability of the corrections, they were applied to 17 CT scans of a radiomics phantom. The reduction in the inter-scanner variability relative to non-small cell lung cancer patient scans was quantified. The variation in pixel sizes caused the intra-patient variability to be large (OCCC <95%) relative to the inter-patient variability in 79% of the features. However, with the resampling and filtering corrections, the intra-patient variability was relatively large in only 10% of the features. With the filtering correction, 8 of 8 patients were correctly clustered, in contrast to only 2 of 8 without the correction. In the phantom study, resampling and filtering the images of a rubber particle cartridge substantially reduced variability in 61% of the radiomics features and substantially increased variability in only 6% of the features. Surprisingly, resampling without filtering tended to increase the variability. In conclusion, applying a correction based on resampling and Butterworth low-pass filtering in the frequency domain effectively reduced variability in CT radiomics features caused by variations in pixel size. This correction may also reduce the variability introduced by other CT scan acquisition parameters.

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

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