An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson's disease.

Cognitive impairment in Parkinson's disease (PD) severely affects patients' prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008-July 2017, follow-up >5 years) were included. MRI radiomic features (n = 1284) were extracted from bilateral caudate and putamen. Two models were developed to predict dementia development: (1) a clinical model-age, disease duration, and cognitive composite scores, and (2) a combined clinical and radiomics model. The area under the receiver operating characteristic curve (AUC) were calculated for each model. The models' interpretabilities were studied. Among total 262 PD patients (mean age, 68 years ± 8 [standard deviation]; 134 men), 51 (30.4%), and 24 (25.5%) patients developed dementia within 5 years of PD diagnosis in the training (n = 168) and test sets (n = 94), respectively. The combined model achieved superior predictive performance compared to the clinical model in training (AUCs 0.928 vs. 0.894, P = 0.284) and test set (AUCs 0.889 vs. 0.722, P = 0.016). The cognitive composite scores of the frontal/executive function domain contributed most to predicting dementia. Radiomics derived from the caudate were also highly associated with cognitive decline. Multiparametric MRI radiomics may have an incremental prognostic value when integrated with clinical profiles to predict future cognitive decline in PD.

A Radiomics-Based Model with the Potential to Differentiate Growth Hormone Deficiency and Idiopathic Short Stature on Sella MRI.

PURPOSE: We hypothesized that a radiomics approach could be employed to classify children with growth hormone deficiency (GHD) and idiopathic short stature (ISS) on sella magnetic resonance imaging (MRI). Accordingly, we aimed to develop a radiomics prediction model for differentiating GHD from ISS and to evaluate the diagnostic performance thereof. MATERIALS AND METHODS: Short stature pediatric patients diagnosed with GHD or ISS from March 2011 to July 2020 at our institution were recruited. We enrolled 312 patients (GHD 210, ISS 102) with normal sella MRI and temporally split them into training and test sets (7:3). Pituitary glands were semi-automatically segmented, and 110 radiomic features were extracted from the coronal T2-weighted images. Feature selection and model development were conducted by applying mutual information (MI) and a light gradient boosting machine, respectively. After training, the model's performance was validated in the test set. We calculated mean absolute Shapley values for each of the selected input features using the Shapley additive explanations (SHAP) algorithm. Volumetric comparison was performed for GHD and ISS groups. RESULTS: Ten radiomic features were selected by MI. The receiver operating characteristics curve of the developed model in the test set was 0.705, with an accuracy of 70.6%. When analyzing SHAP plots, root mean squared values had the highest impact in the model, followed by various texture features. In volumetric analysis, sagittal height showed a significant difference between GHD and ISS groups. CONCLUSION: Radiomic analysis of sella MRI may be able to differentiate between GHD and ISS in clinical practice for short-statured children.

An interpretable radiomics model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas.

OBJECTIVES: This study investigated whether radiomic features can improve the prediction accuracy for tumor recurrence over clinicopathological features and if these features can be used to identify high-risk patients requiring adjuvant radiotherapy (ART) in WHO grade 2 meningiomas. METHODS: Preoperative magnetic resonance imaging (MRI) of 155 grade 2 meningioma patients with a median follow-up of 63.8 months were included and allocated to training (n = 92) and test sets (n = 63). After radiomic feature extraction (n = 200), least absolute shrinkage and selection operator feature selection with logistic regression classifier was performed to develop two models: (1) a clinicopathological model and (2) a combined clinicopathological and radiomic model. The probability of recurrence using the combined model was analyzed to identify candidates for ART. RESULTS: The combined clinicopathological and radiomics model exhibited superior performance for the prediction of recurrence compared with the clinicopathological model in the training set (area under the curve [AUC] 0.78 vs. 0.67, P = 0.042), which was also validated in the test set (AUC 0.77 vs. 0.61, P = 0.192). In patients with a high probability of recurrence by the combined model, the 5-year progression-free survival was significantly improved with ART (92% vs. 57%, P = 0.024), and the median time to recurrence was longer (54 vs. 17 months after surgery). CONCLUSIONS: Radiomics significantly contributes added value in predicting recurrence when integrated with the clinicopathological features in patients with grade 2 meningiomas. Furthermore, the combined model can be applied to identify high-risk patients who require ART.

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation.

The heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model's generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization associated with the inter-institutional heterogeneity of MRI, the smaller image set style of the external validation was translated into the larger image set style of the institutional training set using CycleGAN. On external validation before CycleGAN application, the performance of the combined T2 and T1C models showed an area under the curve (AUC), accuracy, and F1 score of 0.77 (95% confidence interval 0.63-0.91), 70.7%, and 0.54, respectively. After applying CycleGAN, the performance of the combined T2 and T1C models increased, with an AUC, accuracy, and F1 score of 0.83 (95% confidence interval 0.70-0.97), 73.2%, and 0.59, respectively. Quantitative metrics (by Fréchet Inception Distance) showed that CycleGAN can decrease inter-institutional image heterogeneity while preserving predictive information. In conclusion, leveraging CycleGAN may be helpful to increase the generalizability of a radiomics model in differentiating meningioma grade on external validation.

An interpretable radiomics model for the diagnosis of panic disorder with or without agoraphobia using magnetic resonance imaging.

BACKGROUND: Early and accurate diagnosis of panic disorder with or without agoraphobia (PDA) is crucial to reducing disease burden and individual suffering. However, its diagnosis is challenging for lack of validated biomarkers. This study aimed to investigate whether radiomic features extracted from T1-weighted images (T1) of major fear-circuit structures (amygdala, insula, and anterior cingulate cortex [ACC]) could differentiate patients with PDA from healthy controls (HCs). METHODS: The 213 participants (93 PDA, 120 HCs) were allocated to training (n = 149) and test (n = 64) sets after undergoing magnetic resonance imaging. Radiomic features (n = 1498) were extracted from T1 of the studied structures. Machine learning models were trained after feature selection and then validated in the test set. SHapley Additive exPlanations (SHAP) explored the model interpretability. RESULTS: We identified 29 radiomic features to differentiate participants with PDA from HCs. The area under the curve, accuracy, sensitivity, and specificity of the best performing radiomics model in the test set were 0.84 (95% confidence interval: 0.74-0.95), 81.3%, 75.0%, and 86.1%, respectively. The SHAP model explanation suggested that the energy features extracted from the bilateral long insula gyrus and central sulcus of the insula and right ACC were highly associated with the risk of PDA. LIMITATIONS: This was a cross-sectional study with a relatively small sample size, and the causality of changes in radiomic features and their biological and clinical meanings remained to be elucidated. CONCLUSIONS: Our findings suggest that radiomic features from the fear-circuit structures could unveil hidden microstructural aberrations underlying the pathogenesis of PDA that could help identify PDA.

A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from high-grade gliomas.

OBJECTIVES: To develop a fully automatic radiomics model to differentiate adult pilocytic astrocytomas (PA) from high-grade gliomas (HGGs). METHODS: This retrospective study included 302 adult patients with PA (n = 62) or HGG (n = 240). The patients were randomly divided into training (n = 211) and test (n = 91) sets. Clinical data were obtained, and radiomic features (n = 372) were extracted from multiparametric MRI with automatic tumour segmentation. After feature selection with F-score, a Light Gradient Boosting Machine classifier with subsampling was trained to develop three models: (1) clinical model, (2) radiomics model, and (3) combined clinical and radiomics model. Human performance was also assessed. The performance of the classifier was validated in the test set. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. RESULTS: A total of 15 radiomic features were selected. In the test set, the combined clinical and radiomics model (area under the curve [AUC], 0.93) showed a significantly higher performance than the clinical model (AUC, 0.79, p = 0.037) and had a similar performance to the radiomics model (AUC, 0.92, p = 0.828). The combined clinical and radiomics model also showed a significantly higher performance than humans (AUC, 0.76-0.81, p < 0.05). The model explanation by SHAP suggested that lower intratumoural heterogeneity from T2-weighted images was highly associated with PA diagnosis. CONCLUSIONS: The fully automatic combined clinical and radiomics model may be helpful for differentiating adult PAs from HGGs. KEY POINTS: • Differentiating adult PAs from HGGs is challenging because PAs may manifest a large spectrum of imaging presentations, often including aggressive imaging features. • The fully automatic combined clinical and radiomics model showed a significantly higher performance than the clinical model or humans. • The model explanation by SHAP suggested that second-order features from T2-weighted imaging were important in diagnosis and might reflect the underlying pathophysiology that PAs have lesser tissue heterogeneity than HGGs.

An interpretable multiparametric radiomics model for the diagnosis of schizophrenia using magnetic resonance imaging of the corpus callosum.

There is a growing need to develop novel strategies for the diagnosis of schizophrenia using neuroimaging biomarkers. We investigated the robustness of the diagnostic model for schizophrenia using radiomic features from T1-weighted and diffusion tensor images of the corpus callosum (CC). A total of 165 participants [86 schizophrenia and 79 healthy controls (HCs)] were allocated to training (N = 115) and test (N = 50) sets. Radiomic features of the CC subregions were extracted from T1-weighted, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) images (N = 1605). Following feature selection, various combinations of classifiers were trained, and Bayesian optimization was adopted in the best performing classifier. Discrimination, calibration, and clinical utility of the model were assessed. An online calculator was constructed to offer the probability of having schizophrenia. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model. We identified 30 radiomic features to differentiate participants with schizophrenia from HCs. The Bayesian optimized model achieved the highest performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.81-0.98), 80.0, 83.3, and 76.9%, respectively, in the test set. The final model offers clinical probability in an online calculator. The model explanation by SHAP suggested that second-order features from the posterior CC were highly associated with the risk of schizophrenia. The multiparametric radiomics model focusing on the CC shows its robustness for the diagnosis of schizophrenia. Radiomic features could be a potential source of biomarkers that support the biomarker-based diagnosis of schizophrenia and improve the understanding of its neurobiology.

A diagnostic tree for differentiation of adult pilocytic astrocytomas from high-grade gliomas.

BACKGROUND: To develop a diagnostic tree analysis (DTA) model based on demographical information and conventional MRI for differential diagnosis of adult pilocytic astrocytomas (PAs) and high-grade gliomas (HGGs; World Health Organization grade III-IV). METHODS: A total of 357 adult patients with pathologically confirmed PA (n = 65) and HGGs (n = 292) who underwent conventional MRI were included. The patients were randomly divided into training (n = 250) and validation (n = 107) datasets to assess the diagnostic performance of the DTA model. The DTA model was created using a classification and regression tree algorithm on the basis of demographical and MRI findings. RESULTS: In the DTA model, tumor location (on cerebellum, brainstem, hypothalamus, optic nerve, or ventricle), cystic mass with mural nodule appearance, presence of infiltrative growth, and major axis (cutoff value, 2.9 cm) were significant predictors for differential diagnosis of adult PAs and HGGs. The AUC, accuracy, sensitivity, and specificity were 0.94 (95% confidence interval 0.86-1.00), 96.2%, 89.5%, and 97.7%, respectively, in the test set. The accuracy of the DTA model was significantly higher than the no-information rate in the test (96.2 % vs 85.0%, P < 0.001) set. CONCLUSION: The DTA model based on MRI findings may be useful for differential diagnosis of adult PA and HGGs.

Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma.

CONTEXT: Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning. OBJECTIVE: To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients. DESIGN: Retrospective study. SETTING: Severance Hospital, Seoul, Korea. PATIENTS: A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA nonresponders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set. RESULTS: The ensemble classifier showed an area under the curve (AUC) of 0.81 [95% confidence interval (CI), 0.74-0.87] in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95% CI, 0.67-0.96), 77.8%, 78.6%, and 77.3%, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set. CONCLUSIONS: Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.

Bawu decoction () ameliorates benign prostatic hyperplasia in rats.

OBJECTIVE: To evaluate the efficacy of Bawu Decoction (, BWD, Palmul-tang in Korean) against benign prostatic hyperplasia (BPH). METHODS: Twenty-four male Wistar rats were divided into 4 groups, with 6 rats in each group. The 4 study groups included sham-operated group (CON), BPH model group, fifinasteride-treated group, and BWD-treated group. All the groups except CON group received a subcutaneous injection of 10 mg/kg of testosterone, while CON group received saline. Finasteride at a dose of 5 mg/kg was administered to the finasteride-treated group for a period of 4 weeks. BWD group received BWD at a dose of 200 mg/kg for 4 weeks. The prostatic weight, prostate weight to body weight ratio, relative prostate weight ratio, serum testosterone and dihydrotestosterone (DHT) level, and histological analysis of prostatic tissue were analyzed. RESULTS: Compared to BPH model group, BWD administration was associated with reductions in prostatic weight, prostate and relative prostate weight ratio weight to body weight ratio (P<0.05). The concentration of serum testosterone and DHT were higher in BPH group compared with CON group (P<0.05). Administration of finasteride and BWD suppressed the elevation of serum testosterone and DHT levels signifificantly (both P<0.05). In addition, BWD suppressed the growth of prostatic tissue (P<0.05). CONCLUSION: BWD has suppressant effects on development of BPH through inhibition of serum testosterone and DHT.

Scapulothoracic Muscle Activity during Use of a Wall Slide Device (WSD), a Comparison with the General Wall Push up Plus.

[Purpose] The purpose of this study was to evaluate the effect of the wall slide device on activation of the scapulothoracic musculature. [Subjects] We recruited 15 healthy male subjects. [Methods] The subjects performed the general wall push-up plus (WPUP) and the wall slide with device (WSD) exercises. During the exercises, the muscle activities of the upper and lower trapezius (UT, LT), middle and lower serratus anterior (MSA, LSA), and pectoralis major (PM) were measured. [Results] The normalized muscle activity data of the WSD were significantly higher in UT, MSA and LSA than the WPUP. [Conclusion] Our results suggest that exercise using the WSD can more effectively activate the scapulothoracic musculature than the general WPUP.