An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma.

The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643-0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.

Performance comparison of 2D and 3D MRI radiomics features in meningioma grade prediction: A preliminary study.

Objectives: The objective of this study was to compare the predictive performance of 2D and 3D radiomics features in meningioma grade based on enhanced T1 WI images. Methods: There were 170 high grade meningioma and 170 low grade meningioma were selected randomly. The 2D and 3D features were extracted from 2D and 3D ROI of each meningioma. The Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The 2D and 3D predictive models were constructed by naive Bayes (NB), gradient boosting decision tree (GBDT), and support vector machine (SVM). The ROC curve was drawn and AUC was calculated. The 2D and 3D models were compared by Delong test of AUCs and decision curve analysis (DCA) curve. Results: There were 1143 features extracted from each ROI. Six and seven features were selected. The AUC of 2D and 3D model in NB, GBDT, and SVM was 0.773 and 0.771, 0.722 and 0.717, 0.733 and 0.743. There was no significant difference in two AUCs (p=0.960, 0.913, 0.830) between 2D and 3D model. The 2D features had a better performance than 3D features in NB models and the 3D features had a better performance than 2D features in GBDT models. The 2D features and 3D features had an equal performance in SVM models. Conclusions: The 2D and 3D features had a comparable performance in predicting meningioma grade. Considering the issue of time and labor, 2D features could be selected for radiomics study in meningioma. Key points: There was a comparable performance between 2D and 3D features in meningioma grade prediction. The 2D features was a proper selection in meningioma radiomics study because of its time and labor saving.

Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and 3D deep learning models.

Purpose: To evaluate and compare the predictive performance of different deep learning models using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma. Methods: The data of 233 patients with pathologically confirmed hepatocellular carcinoma (HCC) treated at our hospital from June 2016 to June 2021 were retrospectively analyzed. Three deep learning models were constructed based on three different delineate methods of the region of interest (ROI) using the Darwin Scientific Research Platform (Beijing Yizhun Intelligent Technology Co., Ltd., China). Manual segmentation of ROI was performed on the T1-weighted axial Hepatobiliary phase images. According to the ratio of 7:3, the samples were divided into a training set (N=163) and a validation set (N=70). The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of three models, and their sensitivity, specificity and accuracy were assessed. Results: Among 233 HCC patients, 109 were pathologically MVI positive, including 91 men and 18 women, with an average age of 58.20 ± 10.17 years; 124 patients were MVI negative, including 93 men and 31 women, with an average age of 58.26 ± 10.20 years. Among three deep learning models, 2D-expansion-DL model and 3D-DL model showed relatively good performance, the AUC value were 0.70 (P=0.003) (95% CI 0.57-0.82) and 0.72 (P<0.001) (95% CI 0.60-0.84), respectively. In the 2D-expansion-DL model, the accuracy, sensitivity and specificity were 0.7143, 0.739 and 0.688. In the 3D-DL model, the accuracy, sensitivity and specificity were 0.6714, 0.800 and 0.575, respectively. Compared with the 3D-DL model (based on 3D-ResNet), the 2D-DL model is smaller in scale and runs faster. The frames per second (FPS) for the 2D-DL model is 244.7566, which is much larger than that of the 3D-DL model (73.3374). Conclusion: The deep learning model based on Gd-EOB-DTPA-enhanced MRI could preoperatively evaluate MVI in HCC. Considering that the predictive performance of 2D-expansion-DL model was almost the same as the 3D-DL model and the former was relatively easy to implement, we prefer the 2D-expansion-DL model in practical research.

Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA.

Objective: To develop a radiomics nomogram for predicting microvascular invasion (MVI) before surgery in hepatocellular carcinoma (HCC) patients. Materials and Methods: The data from a total of 189 HCC patients (training cohort: n = 141; validation cohort: n = 48) were collected, involving the clinical data and imaging characteristics. Radiomics features of all patients were extracted from hepatobiliary phase (HBP) in 15 min. Least absolute shrinkage selection operator (LASSO) regression and logistic regression were utilized to reduce data dimensions, feature selection, and to construct a radiomics signature. Clinicoradiological factors were identified according to the univariate and multivariate analyses, which were incorporated into the final predicted nomogram. A nomogram was developed to predict MVI of HCC by combining radiomics signatures and clinicoradiological factors. Radiomics nomograms were evaluated for their discrimination capability, calibration, and clinical usefulness. Results: In the clinicoradiological factors, gender, alpha-fetoprotein (AFP) level, tumor shape and halo sign served as the independent risk factors of MVI, with which the area under the curve (AUC) is 0.802. Radiomics signatures covering 14 features at HBP 15 min can effectively predict MVI in HCC, to construct radiomics signature model, with the AUC of 0.732. In the final nomogram model the clinicoradiological factors and radiomics signatures were integrated, outperforming the clinicoradiological model (AUC 0.884 vs. 0.802; p <0.001) and radiomics signatures model (AUC 0.884 vs. 0.732; p < 0.001) according to Delong test results. A robust calibration and discrimination were demonstrated in the nomogram model. The results of decision curve analysis (DCA) showed more significantly clinical efficiency of the nomogram model in comparison to the clinicoradiological model and the radiomic signature model. Conclusions: Depending on the clinicoradiological factors and radiological features on HBP 15 min images, nomograms can effectively predict MVI status in HCC patients.

A radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images.

OBJECTIVES: The objective of this study was to develop a radiomics nomogram for predicting the meningioma grade based on enhanced T1 weighted imaging (T1WI) images. METHODS: 188 patients with meningioma were analyzed retrospectively. There were 94 high-grade meningioma to form high-grade group and 94 low-grade meningioma were selected randomly to form low-grade group. Clinical data and MRI features were analyzed and compared. The clinical model was built by using the significant variables. The least absolute shrinkage and selection operator regression was used to select the most valuable radiomics feature. The radiomics signature was built and the Rad-score was calculated. The radiomics nomogram was developed by the significant variables of the clinical factors and Rad-score. The calibration curve and the Hosmer-Lemeshow test were used to evaluate the radiomics nomogram. Different models were compared by Delong test and decision curve analysis curve. RESULTS: The sex, size and surrounding invasion were used to build clinical model. The area under the receiver operator characteristic curve (AUC) of clinical model was 0.870 (95% CI: 0.782-0.959). Nine features were used to construct the radiomics signature. The AUC of the radiomics signature was 0.885 (95% CI: 0.802-0.968). The AUC of radiomics nomogram was 0.952 (95% CI: 0.904-1). The AUC of radiomics nomogram was higher than that of clinical model and radiomics signature with a significant difference (p＜0.05). The decision curve analysis curve showed that the radiomics nomogram had a larger net benefit than the clinical model and radiomics signature. CONCLUSION: The radiomics nomogram based on enhanced T1 weighted imaging images for predicting the meningioma grade showed high predictive value and might contribute to the diagnosis and treatment of meningioma. ADVANCES IN KNOWLEDGE: 1. We first constructed a radiomic nomogram to predict the meningioma grade.2. We compared the results of the clinical model, radiomics signature and radiomics nomogram.

Clinical study of recurrent mild encephalitis/encephalopathy with a reversible splenial lesion in two cases.

BACKGROUND: Mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) has been reported worldwidely. However, the data about recurrent cases is limited. We aimed to analyze the clinical and radiographic features of recurrent MERS, and its possible mechanisms. CASE PRESENTATION: Two patients with clinically recurrent MERS were reported here, exhibiting neurological symptoms such as limbs weakness and numbness, stand/walk unsteadily, slurred speech and irritability, and typical lesions in the corpus callosum and white matter. One of them experienced another four episodes with a similar clinical course and magnetic resonance imaging findings over a period of 10 years. The Na levels in the present two patients were normal. DISCUSSION AND CONCLUSION: Combined with the patients reported previously, recurrence could be seen in both MERS type 1 and type 2 patients, from two to multiple times, with the latter possibly more common. It suggested that some genetic factors might be involved in MERS, especially for MERS type 2 or familial MERS.

Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.

PURPOSE: The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison. METHOD: A total of 108 patients with intracerebral hemorrhage were retrospectively evaluated. Images of baseline non-contrast computed tomography (NCCT) and follow-up NCCT scan within 24 h were retrospectively reviewed. An HE was defined as a volume increase of more than 33% or an increase greater than 12.5 mL from the volume of the baseline NCCT. Texture parameters of the baseline NCCT images were selected by the least absolute shrinkage and selection operator (LASSO) regression. We used support vector machine (SVM), decision tree (DT), conditional inference trees (CIT), random forest (RF), k­nearest neighbors (KNN), back-propagation neural network (BPNet) and Bayes to build models. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) was performed and compared among models. RESULTS: Every model had a relatively high AUC (all > 0.75), SVM and KNN had the highest AUC of 0.91. There were significant differences between SVM and CIT (Z > 2.266, p = 0.02345), KNN and CIT (Z = 2.4834, p = 0.01301), RF and CIT (Z = 2.6956, p = 0.007027), KNN and BPNet (Z = 2.0122, p = 0.0442), RF and BPNet (Z = 1.9793, p = 0.04778). There was no significant difference among SVM, DT, RF, KNN and Bayes (p > 0.05). The SVM obtained the largest net benefit when the threshold probability was less than 0.33, while KNN obtained the largest net benefit when the threshold probability was greater than 0.33. Combined with ROC and DCA, SVM and KNN performed better in all the models for predicting HE. CONCLUSION: Radiomic models based on different machine learning methods can be used to predict HE and the models generated by SVM and KNN performed best.

The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma.

Objectives: To non-invasively predict the coexistence of isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas using apparent diffusion coefficient (ADC) histogram and direct ADC measurements and compare the diagnostic performances of the two methods. Materials and methods: A total of 118 patients with adult-type diffuse glioma who underwent preoperative brain magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) were included in this retrospective study. The patient group included 40 patients with coexisting IDH mutation and MGMT promoter methylation (IDHmut/MGMTmet) and 78 patients with other molecular status, including 32 patients with IDH wildtype and MGMT promoter methylation (IDHwt/MGMTmet), one patient with IDH mutation and unmethylated MGMT promoter (IDHmut/MGMTunmet), and 45 patients with IDH wildtype and unmethylated MGMT promoter (IDHwt/MGMTunmet). ADC histogram parameters of gliomas were extracted by delineating the region of interest (ROI) in solid components of tumors. The minimum and mean ADC of direct ADC measurements were calculated by placing three rounded or elliptic ROIs in solid components of gliomas. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performances of the two methods. Results: The 10th percentile, median, mean, root mean squared, 90th percentile, skewness, kurtosis, and minimum of ADC histogram analysis and minimum and mean ADC of direct measurements were significantly different between IDHmut/MGMTmet and the other glioma group (P < 0.001 to P = 0.003). In terms of single factors, 10th percentile of ADC histogram analysis had the best diagnostic efficiency (AUC = 0.860), followed by mean ADC obtained by direct measurements (AUC = 0.844). The logistic regression model combining ADC histogram parameters and direct measurements had the best diagnostic efficiency (AUC = 0.938), followed by the logistic regression model combining the ADC histogram parameters with statistically significant difference (AUC = 0.916) and the logistic regression model combining minimum ADC and mean ADC (AUC = 0.851). Conclusion: Both ADC histogram analysis and direct measurements have potential value in predicting the coexistence of IDHmut and MGMTmet in adult-type diffuse glioma. The diagnostic performance of ADC histogram analysis was better than that of direct ADC measurements. The combination of the two methods showed the best diagnostic performance.

A cohort study of mild encephalitis/encephalopathy with a reversible splenial lesion in children.

PURPOSE: To investigate the clinical features, imaging features, and prognosis of mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) in children METHODS: The clinical and imaging data of a cohort of 28 children diagnosed as MERS from January 2019 to October 2020 were retrospectively analyzed RESULTS: Of the 28 patients, 17 were males and 11 were females. The onset age ranged from 8 months to 12 years old, with an average age of 4 years and 2 months. All children developed normally before onset, and three of them had a history of febrile convulsion. More than half of the patients (62.9%) had preceding infections of gastrointestinal tract. All the cases developed seizures, and most (71.4%) had more than one time. Other neurological symptoms included dizziness/headache, consciousness disorder, limb weakness, blurred vision, and dysarthria. Cranial magnetic resonance imaging (MRI) showed lesions in the splenium of the corpus callosum in all, extending to other areas of the corpus callosum, bilateral semi-ovoid center, and adjacent periventricular in two cases. The clinical symptoms were relieved after steroids, intravenous immunogloblin, and symptomatic treatment, without abnormal neurodevelopment during the followed-up (2 months-2 years). Complete resolution of the lesions was observed 8-60 days after the initial MRI examinations CONCLUSION: MERS in children is related to prodromal infection mostly, with a wide spectrum of neurologic symptoms, characteristic MRI manifestations, and good prognosis.

Brain magnetic resonance imaging findings and radiologic review of maple syrup urine disease: Report of three cases.

BACKGROUND: Maple syrup urine disease (MSUD) is a rare autosomal-recessive disorder that affects branched-chain amino acid (BCAA) metabolism and is named after the distinctive sweet odor of affected infants' urine. This disease is characterized by the accumulation of BCAAs and corresponding branched-chain ketoacids of leucine, isoleucine, and valine in the plasma, urine, and cerebrospinal fluid. However, the mechanisms of MSUD-induced brain damage remain poorly defined. The accumulation of BCAAs in the brain inhibits the activity of pyruvate dehydrogenase and α-ketoglutarate, disrupting the citric acid cycle and consequently impacting the synthesis of amino acids, causing cerebral edema and abnormal myelination. CASE SUMMARY: We report three neonates admitted to our hospital with the classic subtype of MSUD. All three patients, with a transient normal period, presented with poor feeding, vomiting, poor weight gain, and increasing lethargy after birth. Laboratory testing revealed metabolic acidosis. The serum tandem mass spectrometry amino acid profile showed elevated plasma levels of BCAAs (leucine, isoleucine, and valine). Brain magnetic resonance imaging (MRI) presented abnormal signals mainly involving the globus pallidus, thalamus, internal capsule, brainstem, and cerebellar white matter, which represent the typical myelinated areas in normal full-term neonates. CONCLUSION: In our patients, MRI showed typical features, in concordance with the available literature. Early detection and timely treatment are very helpful for the prognosis of MSUD patients. Therefore, we discuss the neuroimaging features of MSUD to enhance the knowledge of pediatricians about this disease.

Radiomics Analysis of MR Imaging with Gd-EOB-DTPA for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Investigation and Comparison of Different Hepatobiliary Phase Delay Times.

PURPOSE: To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHOD: A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. RESULTS: The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. CONCLUSIONS: Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.

Imaging manifestations of congenital mesoblastic nephroma.

OBJECTIVE: Congenital mesoblastic nephroma (CMN) is a rare renal tumor mainly observed in infants and young children. This study aims to analyze the imaging manifestations of CMN to improve the understanding of the disease. METHODS: The imaging manifestations and clinical records of all pediatric patients with CMN admitted to our hospital over the last 7 years were retrospectively analyzed. The diagnosis of CMN was confirmed by postoperative pathology. All patients underwent computed tomography (CT) scans; 2 patients additionally underwent magnetic resonance imaging (MRI) scans (including one prenatal MRI scan). RESULTS: We evaluated 10 pediatric patients (6 males and 4 females) aged 7 days to 12 months (median age: 4 months) with CMN located on the left kidney in six cases and the right kidney in four cases. The CT imaging manifested as solid lesions (5 cases), solid-cystic lesions with solid predominance (4 cases), or solid-multicystic lesions with cystic predominance (1 case). Enhanced CT showed moderately and heterogeneously enhanced solid component and intracystic septations at the corticomedullary phase that were further enhanced at the nephrographic phase, although their CT values were still lower than those of the renal parenchyma. The "double-layer sign" were seen in 4 cases of classic type of CMN, and the "intratumor pelvis sign" were seen in 9 cases that include 5 classic, 3 cellular and 1 mixed type of CMN. In the 2 patients who underwent MRI, the scans showed solitary masses. The lesions had hypointense signals on the T1WI sequence and isointensity or slightly lower-intensity signals than the surrounding renal parenchyma on the fluid-sensitive sequences, whereas the lesions showed hyperintense signals on the diffusion-weighted imaging (DWI) sequence. CONCLUSIONS: The imaging manifestations of CMN are closely correlated with the pathological subtype and have certain characteristics. The "double-layer sign" was seen with most classic type CMN, and "intratumor pelvis sign" was seen in 90% cases.

The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas.

BACKGROUND: The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. PURPOSE: To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. MATERIALS AND METHODS: A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. RESULTS: The radiomic model showed good discrimination in both the primary dataset (AUC = 0.87, 95% CI: 0.754 to 0.855, ACC = 0.798, sensitivity = 85.5%, specificity = 75.4%, positive predictive value = 0.734, and negative predictive value = 0.867) and the validation dataset (AUC = 0.86, 95% CI: 0.690 to 0.913, ACC = 0.789, sensitivity = 91.3%, specificity = 69.0%, positive predictive value = 0.700, and negative predictive value = 0.909). CONCLUSION: The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.

Multimodal MR Features of 8 Cases of Epithelioid Glioblastoma.

PURPOSE: The MRI features of epithelioid glioblastoma (eGBM) were analyzed. The apparent diffusion coefficient (ADC), MR perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS) findings were quantitatively analyzed. METHODS: The MRI images of 8 cases of eGBM were analyzed retrospectively. The location and edge, signal, peritumoral edema, adjacent meningeal invasion, and enhancement of the lesions were observed. The ADC value, relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and N-acetylaspartate/acetylcholine (NAA/Cho) value were analyzed. RESULTS: Among the 8 patients, the tumors were mainly located in the temporal lobe (n = 3), frontal lobe (n = 3), and parietal lobe (n = 2). The lesion boundary was clear in 6 cases and unclear in 2. The lesions were superficial in 5 cases and in the deep white matter in 3. Internal hemorrhage was observed in 4 cases. There was cystic necrosis in 7 cases, and only 1 case was solid without cystic necrosis. There was no edema around the lesion in 1 case, severe edema in 5, and moderate edema in 2. In 4 cases, the adjacent meninges were involved, and in 1 case, the ependyma was involved. Two patients developed leptomeningeal metastasis within 2 months after the operation. The average ADC value of the tumor parenchyma among all 8 patients was7.15 × 10-4 mm2/s,which was 17.6% lower than that of the contralateral side. The Cho/NAA metabolite ratio was 5.27 and 0.81 in the lesions of 2 patients. The rCBV was 3.51 ml/100 g and 3.32 ml/100 g of lesions in 2 patients; these values were 36% and 29% higher, respectively, than those of the contralateral side. The rCBF was 31.5 ml/100 g/min and 82.1 ml/100 g/min of lesions in two patients; these values were 49% and 203% higher, respectively, than those of the contralateral side. CONCLUSION: eGBM characteristics include a superficial location, easy cyst degeneration, easy necrosis and hemorrhage, and clear boundaries. It easily invades adjacent meninges and shows cerebrospinal fluid dissemination and metastasis. Combining new MR techniques, such as ADC values, PWI, and MRS, could be helpful for improving diagnostic accuracy.

CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes.

PURPOSE: The objective of this study was to investigate whether computed tomography texture analysis can be used to differentiate papillary renal cell carcinoma (PRCC) subtypes. METHOD: Sixty-two PRCC tumors were retrospectively evaluated, with 30 type 1 tumors and 32 type 2 tumors. Texture parameters quantified from three-phase contrast-enhanced CT images were compared with least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for each parameter. The selected texture parameters of each phase were used to generate support vector machine (SVM) classifiers. Decision curve analysis (DCA) of the classification was performed. RESULTS: The two texture parameters with the top two AUC values were - 333-7 Correlation (AUC = 0.772) and 45-7 Entropy (AUC = 0.753) in the corticomedullary phase, 333-4 Correlation (AUC = 0.832) and 45-7 Entropy (AUC = 0.841) in the nephrographic phase, and 135-7 Entropy (AUC = 0.858) and - 333-1 InformationMeasureCorr2 (AUC = 0.849) in the excretory phase. Entropy and Correlation have a high correlation with the two types of PRCC and are increased in type 2 PRCC. A model incorporating the texture parameters with the top two AUC values in each phase produced an AUC of 0.922 with an accuracy of 84% (sensitivity = 89% and specificity = 80%). The nephrographic-phase model and the model combining the texture parameters of the three phases can differentiate the two types with the largest net benefit. CONCLUSIONS: Computed tomography texture analysis can be used to distinguish type 2 PRCC from type 1 with high accuracy, which may be clinically important.

Differentiation Researches on the Meningioma Subtypes by Radiomics from Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study.

BACKGROUND: Meningioma subtypes are one of the most common key points to the treatment and prognosis of patients. The purpose of this study was to investigate the differential diagnostic value of radiomics features on meningioma. METHODS: A total of 241 patients with meningioma who had undergone tumor resection were randomly selected including 80 with meningothelial meningioma, 80 with fibrous meningioma, and 81 with transitional meningioma. These meningiomas were divided into 4 groups including: meningothelial versus fibrous (group 1), fibrous versus transitional (group 2), meningothelial versus transitional (group 3), and meningothelial versus fibrous versus transitional (group 4). All patients were examined using the same magnetic resonance scanner (GE 3.0 T) and the preoperative contrast-enhanced T1-weighted images were available. Radiomics features from the contrast-enhanced T1-weighted images of 241 patients were evaluated by 2 experienced radiology specialists. RESULTS: A total of 385 radiomics features were extracted from the images of each patient. Several preprocessing methods were applied on the radiomics dataset to reduce the redundancy and highlight differences between different meningioma before the Fisher discrimination analysis was adopted and leave one out cross validation methods were used for the model validation. The differentiation accuracies of the Fisher discriminant analysis model for groups 1, 2, 3, and 4 were 99.4%, 98.8%, 100% and 100%, respectively; leave one out cross validation method was achieved for group 1, 2, 3, and 4 with the accuracies of 91.3%, 95.0%, 100%, and 94.2%, respectively. CONCLUSIONS: Radiomics features and the combined Fisher discriminant analysis could provide satisfactory performance in the preoperative differential diagnosis of meningioma subtypes and enable the potential ability for clinical application.