Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules.

BACKGROUND: A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. The purpose of this study was to compare the classification performance of linear and nonlinear machine-learning algorithms for the evaluation of thyroid nodules using pathological reports as reference standard. METHODS: Ethical approval was obtained for this retrospective analysis, and the informed consent requirement was waived. A total of 1179 thyroid nodules (training cohort, n = 700; validation cohort, n = 479) were confirmed by pathological reports or fine-needle aspiration (FNA) biopsy. The following ultrasonography (US) featu res were measured for each nodule: size (maximum diameter), margins, shape, aspect ratio, capsule, hypoechoic halo, composition, echogenicity, calcification pattern, vascularity, and cervical lymph node status. We analyzed five nonlinear and three linear machine-learning algorithms. The diagnostic performance of each algorithm was compared by using the area under the curve (AUC) of the receiver operating characteristic curve. We repeated this process 1000 times to obtain the mean AUC and 95% confidence interval (CI). RESULTS: Overall, nonlinear machine-learning algorithms demonstrated similar AUCs compared with linear algorithms. The Random Forest and Kernel Support Vector Machines algorithms achieved slightly greater AUCs in the validation cohort (0.954, 95% CI: 0.939-0.969; 0.954 95%CI: 0.939-0.969, respectively) than other algorithms. CONCLUSIONS: Overall, nonlinear machine-learning algorithms share similar performance compared with linear algorithms for the evaluation the malignancy risk of thyroid nodules.

A nomogram for individual prediction of vascular invasion in primary breast cancer.

OBJECTIVES: To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. METHODS: We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. RESULTS: VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89-0.99, the sensitivity was 78.9% (95%CI: 72.4%-89.1%), the specificity was 95.3% (95%CI: 89.1%-100.0%), the accuracy was 86.0% (95%CI: 82.0%-92.0%), the PPV was 95.7% (95%CI: 90.0%-100.0%), and the NPV was 77.4% (95%CI: 67.8%-87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83-0.95), the sensitivity was 70.3% (95%CI: 60.7%-79.2%), the specificity was 88.9% (95%CI: 80.0%-97.1%), the accuracy was 77.0% (95%CI: 70.0%-83.0%), the PPV was 91.8% (95%CI: 85.3%-98.0%), and the NPV was 62.7% (95%CI: 51.7%-74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. CONCLUSION: The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.

Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules.

OBJECTIVES: The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules. METHODS: A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI). RESULTS: The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918-0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915-0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776-0.789). CONCLUSIONS: This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules. KEY POINTS: • Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules. • However, ultrasound accuracy relies heavily on examiner experience. • A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration.

Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma.

There is no consensus on specific prognostic biomarkers potentially improving survival of nasopharyngeal carcinoma (NPC), especially in advanced-stage disease. The prognostic value of MRI-based radiomics signature is unclear. A total of 970 quantitative features were extracted from the tumor of 100 untreated NPC patients (stage III-IVb) (discovery set: n = 70, validation set: n = 30). We then applied least absolute shrinkage and selection operator (lasso) regression to select features that were most associated with progression-free survival (PFS). Candidate prognostic biomarkers included age, gender, overall stage, hemoglobin, platelet counts and radiomics signature. We developed model 1 (without radiomics signature) and model 2 (with radiomics signature) in the discovery set and then tested in the validation set. Multivariable Cox regression analysis was used to yield hazard ratio (HR) of each potential biomarker. We found the radiomics signature stratified patients in the discovery set into a low or high risk group for PFS (HR = 5.14, p < 0.001) and was successfully validated for patients in the validation set (HR = 7.28, p = 0.015). However, the other risk factors showed no significantly prognostic value (all p-values for HR, > 0.05). Accordingly, pretreatment MRI-based radiomics signature is a non-invasive and cost-effective prognostic biomarker in advanced NPC patients, which would improve decision-support in cancer care.

Development of a preprocedure nomogram for predicting contrast-induced acute kidney injury after coronary angiography or percutaneous coronary intervention.

Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for postcontrast exposure prediction, thus have limited values in practice. We aimed to develop a novel nomogram based on preprocedural features for early prediction of CI-AKI in patients after coronary angiography (CAG) or percutaneous coronary intervention (PCI). A total of 245 patients were retrospectively reviewed from January 2015 to January 2017. Least absolute shrinkage and selection operator (Lasso) regression model was applied to select most strong predictors for CI-AKI. The CI-AKI risk score was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. The discrimination of nomogram was assessed by C-statistic. The occurrence of CI-AKI was 13.9% (34 out of 245). We identified ten predictors including sex, diabetes mellitus, lactate dehydrogenase level, C-reactive protein, years since drinking, chronic kidney disease (CKD), stage of CKD, stroke, acute myocardial infarction, and systolic blood pressure. The CI-AKI prediction nomogram obtained good discrimination (C-statistic, 0.718, 95%CI: 0.637-0.800, p = 7.23 × 10-5). The cutoff value of CI-AKI risk score was -1.953. Accordingly, the novel nomogram we developed is a simple and accurate tool for preprocedural prediction of CI-AKI in patients undergoing CAG or PCI.

Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.

The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient boosting tree (GBT) was then used to determine the relative importance (RI) and interaction effects of the variables. Results indicated that elevated white blood cell (WBC) count > 15 × 109/L (RI: 49.47, p < 0.001) was the top predictor of severe HFMD, followed by spinal cord involvement (RI: 26.62, p < 0.001), spinal nerve roots involvement (RI: 10.34, p < 0.001), hyperglycemia (RI: 3.40, p < 0.001), and brain or spinal meninges involvement (RI: 2.45, p = 0.003). Interactions between elevated WBC count and hyperglycemia (H statistic: 0.231, 95% CI: 0-0.262, p = 0.031), between spinal cord involvement and duration of fever ≥3 days (H statistic: 0.291, 95% CI: 0.035-0.326, p = 0.035), and between brainstem involvement and body temperature (H statistic: 0.313, 95% CI: 0-0.273, p = 0.017) were observed. Therefore, GBT is capable to identify the predictors for severe HFMD and their interaction effects, outperforming conventional regression methods.

CT and MRI findings in focal eosinophilic infiltration of the liver.

OBJECTIVES: To investigate the findings of computed tomography (CT) and magnetic resonance imaging (MRI) of focal eosinophilic infiltration (FEI) of the liver. METHODS: A retrospective study including 29 patients with confirmed FEI of the liver was performed. We evaluated the lesions' number, distribution, size, shape, margin, attenuation or signal intensity characteristics, the enhancement pattern, and some special features. Spearman correlation analysis was used to analyze the correlation between the number of lesions and the eosinophil counts in peripheral blood. RESULTS: In all, 108 lesions were detected in 29 cases, including two cases with single lesion and the remaining 27 cases with multiple lesions. The mean size of all lesions was 34 mm (range, from 3 to 61 mm). 95 (88%) lesions were located in subcapsular parenchyma or surrounding the portal vein. Most (66%) subcapsular lesions were wedge shaped and all lesions surrounding portal vein were round shaped. However, the hepatic parenchymal lesions were irregular or round shaped. All lesions showed ill-defined margins. On pre-contrast CT images, the lesions showed slightly low attenuation or iso-attenuating. On T1-weighted and T2-weighted images, the lesions were slightly iso-/hypointense and hyperintense, respectively. A total of 23 (79.3%) cases were gradually enhanced. Branches of portal vein went through the lesions in all cases; 12 had 'stripe sign' and 16 had 'halo ring sign.' Spearman analysis indicated a significant correlation between the number of lesions and the increased eosinophils in peripheral blood (r = 0.627, p = 0.0003). CONCLUSIONS: Special CT and MRI features and increased eosinophils may strongly suggest the diagnosis of FEI of the liver.