Conventional Imaging Features Research Articles

Added value of arterial enhancement fraction derived from dual-energy computed tomography for preoperative diagnosis of cervical lymph node metastasis in papillary thyroid cancer: initial results.

To explore the added value of arterial enhancement fraction (AEF) derived from dual-energy computed tomography CT (DECT) to conventional image features for diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC). A total of 273 cervical LNs (153 non-metastatic and 120 metastatic) were recruited from 92 patients with PTC. Qualitative image features of LNs were assessed. Both single-energy CT (SECT)-derived AEF (AEFS) and DECT-derived AEF (AEFD) were calculated. Correlation between AEFD and AEFS was determined using Pearson's correlation coefficient. Multivariate logistic regression analysis with the forward variable selection method was used to build three models (conventional features, conventional features + AEFS, and conventional features + AEFD). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Abnormal enhancement, calcification, and cystic change were chosen to build model 1 and the model provided moderate diagnostic performance with an area under the ROC curve (AUC) of 0.675. Metastatic LNs demonstrated both significantly higher AEFD (1.14 vs 0.48; p < 0.001) and AEFS (1.08 vs 0.38; p < 0.001) than non-metastatic LNs. AEFD correlated well with AEFS (r = 0.802; p < 0.001), and exhibited comparable performance with AEFS (AUC, 0.867 vs 0.852; p = 0.628). Combining CT image features with AEFS (model 2) and AEFD (model 3) could significantly improve diagnostic performances (AUC, 0.865 vs 0.675; AUC, 0.883 vs 0.675; both p < 0.001). AEFD correlated well with AEFS, and exhibited comparable performance with AEFS. Integrating qualitative CT image features with both AEFS and AEFD could further improve the ability in diagnosing cervical LN metastasis in PTC. Arterial enhancement fraction (AEF) values, especially AEF derived from dual-energy computed tomography, can help to diagnose cervical lymph node metastasis in patients with papillary thyroid cancer, and complement conventional CT image features for improved clinical decision making. • Metastatic cervical lymph nodes (LNs) demonstrated significantly higher arterial enhancement fraction (AEF) derived from dual-energy computed tomography (DECT) and single-energy CT (SECT)-derived AEF (AEFS) than non-metastatic LNs in patients with papillary thyroid cancer. • DECT-derived AEF (AEFD) correlated significantly with AEFS, and exhibited comparable performance with AEFS. • Integrating qualitative CT images features with both AEFS and AEFD could further improve the differential ability.

Prediction of Parkinson’s disease pathogenic variants using hybrid Machine learning systems and radiomic features

PurposeIn Parkinson’s disease (PD), 5–10% of cases are of genetic origin with mutations identified in several genes such as leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA). We aim to predict these two gene mutations using hybrid machine learning systems (HMLS), via imaging and non-imaging data, with the long-term goal to predict conversion to active disease. MethodsWe studied 264 and 129 patients with known LRRK2 and GBA mutations status from PPMI database. Each dataset includes 513 features such as clinical features (CFs), conventional imaging features (CIFs) and radiomic features (RFs) extracted from DAT-SPECT images. Features, normalized by Z-score, were univariately analyzed for statistical significance by the t-test and chi-square test, adjusted by Benjamini-Hochberg correction. Multiple HMLSs, including 11 features extraction (FEA) or 10 features selection algorithms (FSA) linked with 21 classifiers were utilized. We also employed Ensemble Voting (EV) to classify the genes. ResultsFor prediction of LRRK2 mutation status, a number of HMLSs resulted in accuracies of 0.98 ± 0.02 and 1.00 in 5-fold cross-validation (80% out of total data points) and external testing (remaining 20%), respectively. For predicting GBA mutation status, multiple HMLSs resulted in high accuracies of 0.90 ± 0.08 and 0.96 in 5-fold cross-validation and external testing, respectively. We additionally showed that SPECT-based RFs added value to the specific prediction of of GBA mutation status. ConclusionWe demonstrated that combining medical information with SPECT-based imaging features, and optimal utilization of HMLS can produce excellent prediction of the mutations status in PD patients.

Multimodality MRI Radiomics Based on Machine Learning for Identifying True Tumor Recurrence and Treatment-Related Effects in Patients with Postoperative Glioma.

Conventional magnetic resonance imaging (MRI) features have difficulty distinguishing glioma true tumor recurrence (TuR) from treatment-related effects (TrE). We aimed to develop a machine-learning model based on multimodality MRI radiomics to help improve the efficiency of identifying glioma TuR. A total of 131 patients were enrolled and randomly divided into the training set (n=91) and the test set (n=40). Radiomic features were extracted from the postoperative enhancement (PoE) region and edema (ED) region from four routine MRI sequences. After analyses of Spearman's rank correlation coefficient, and least absolute shrinkage and selection operator, the key radiomic features were selected to construct support vector machine (SVM) and k-nearest neighbor (KNN) models. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to analyze the performance. The PoE model had a significantly higher area under curve (AUC) than the ED model (p<0.05). Among the models constructed with a single sequence, the model using PoE regional features from CE-T1WI was superior to other models, with an AUC of 0.905 for SVM and 0.899 for KNN. In multimodality models, the PoE model outperformed the ED model with an AUC of 0.931 for SVM and 0.896 for KNN. The multimodality model, which combined routine sequences and the whole regional features, showed a slightly better performance with an AUC of 0.965 for SVM and 0.955 for KNN. Decision curve analysis showed the good clinical utility of multimodal radiomics models. Multimodality radiomics can identify glioma TuR and TrE, potentially aiding clinical decision-making for individualized treatment. And edematous regions may provide useful information for recognizing recurrence. 2021.04.15, No:2020039.

Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps.

Preoperative differentiation between angiomatous meningioma (AM) and atypical meningioma (ATM) is related to treatment planning. In this study, we explored the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating AM and ATM, and further assess the correlations between these parameters and the Ki-67 proliferation index. Thirty AM and 35 ATM patients were enrolled and their clinical and conventional magnetic resonance imaging (MRI) features were analyzed in this study. Nine ADC histogram parameters [mean, variance, skewness, and kurtosis, as well as the 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentile of ADC] were selected and compared by independent t-test or Mann-Whitney U test. Diagnostic performance analysis was performed by receiver operating characteristic (ROC) curves. The relationship between ADC histogram parameters and the Ki-67 proliferation index was assessed by Spearman's correlation coefficient. AM group showed a significantly higher mean [median (interquartile range): 124.07 (22.66) vs. 112.12 (16.04), P<0.001], ADC1 [107.50 (17.00) vs. 82.00 (20.33), P<0.001], ADC10 (mean ± standard deviation: 115.80±12.09 vs. 96.86±9.86, P<0.001), and ADC50 [124.00 (21.13) vs. 109.00 (15.17), P<0.001], compared to the ATM group. Significant correlations were identified between the mean (r=-0.428, P<0.001), ADC1 (r=-0.549, P<0.001), ADC10 (r=-0.529, P<0.001), ADC50 (r=-0.483, P<0.001), and the Ki-67 proliferation index. ROC analysis showed that the best diagnostic performance was achieved by ADC1 (AUC =0.900). Whereas, no differences were found between variance, skewness, kurtosis, ADC90, and ADC99 (P=0.067-0.787). AM and ATM exhibit overlapping conventional MRI features. ADC histogram analysis, especially ADC1, maybe a reliable quantitative imaging biomarker for differentiation between AM and ATM.

Utility of Apparent Diffusion Coefficient Histogram Analysis in Differentiating Microcystic Meningioma from Intracranial Solitary Fibrous Tumor

To investigate the possibility of histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating microcystic meningioma (MM) from intracranial solitary fibrous tumor (SFT). Eighteen patients with MM and 23 patients with SFT were enrolled in this retrospective study. Conventional magnetic resonance imaging (MRI) features and 9 ADC histogram parameters (including mean, first (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentiles ADC, as well as variance, skewness, and kurtosis) between MM and SFT were compared. The diagnostic performance of the optimal parameter was determined by the receiver operating characteristic analysis. SFT showed a significantly lower mean, ADC1, ADC10, ADC50, ADC90, and ADC99 than MM (all P<0.05), while no significant difference was found in conventional MRI features or other ADC histogram parameters (all P>0.05). ADC1 was identified as the optimal parameter in differentiating between MM and SFT, which achieved an area under the curve of 0.861, with sensitivity, specificity, and accuracy of 78.26%, 88.89%, and 82.93%, respectively. MM and SFT show overlapping conventional MRI features. ADC histogram analysis helps to differentiate between MM and SFT, with ADC1 being the optimal parameter with the best discrimination performance.

Whole-tumor histogram analysis of apparent diffusion coefficients for predicting lymphovascular space invasion in stage IB-IIA cervical cancer.

To investigate the value of apparent diffusion coefficient (ADC) histogram analysis based on whole tumor volume for the preoperative prediction of lymphovascular space invasion (LVSI) in patients with stage IB-IIA cervical cancer. Fifty consecutive patients with stage IB-IIA cervical cancer were stratified into LVSI-positive (n = 24) and LVSI-negative (n = 26) groups according to the postoperative pathology. All patients underwent pelvic 3.0T diffusion-weighted imaging with b-values of 50 and 800 s/mm2 preoperatively. Whole-tumor ADC histogram analysis was performed. Differences in the clinical characteristics, conventional magnetic resonance imaging (MRI) features, and ADC histogram parameters between the two groups were analyzed. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of ADC histogram parameters in predicting LVSI. ADCmax, ADCrange, ADC90, ADC95, and ADC99 were significantly lower in the LVSI-positive group than in the LVSI-negative group (all P-values < 0.05), whereas no significant differences were reported for the remaining ADC parameters, clinical characteristics, and conventional MRI features between the groups (all P-values > 0.05). For predicting LVSI in stage IB-IIA cervical cancer, a cutoff ADCmax of 1.75×10-3 mm2/s achieved the largest area under ROC curve (Az) of 0.750, followed by a cutoff ADCrange of 1.36×10-3 mm2/s and ADC99 of 1.75×10-3 mm2/s (Az = 0.748 and 0.729, respectively), and the cutoff ADC90 and ADC95 achieved an Az of <0.70. Whole-tumor ADC histogram analysis has potential value for preoperative prediction of LVSI in patients with stage IB-IIA cervical cancer. ADCmax, ADCrange, and ADC99 are promising prediction parameters.

The incremental value of CCTA-derived myocardial radiomics signature for ischemia diagnosis with reference to CT myocardial perfusion imaging.

To explore the incremental value of myocardial radiomics signature derived from static coronary computed tomography angiography (CCTA) for identifying myocardial ischemia based on stress dynamic CT myocardial perfusion imaging (CT-MPI). Patients who underwent CT-MPI and CCTA were retrospectively enrolled from two independent institutions, one used as training and the other as testing. Based on CT-MPI, coronary artery supplying area with relative myocardial blood flow (rMBF) value <0.8 was considered ischemia. Conventional imaging features of target plaques which caused the most severe narrowing of the vessel included area stenosis, lesion length (LL), total plaque burden, calcification burden, non-calcification burden, high-risk plaque (HRP) score, and CT fractional flow reserve (CT-FFR). Myocardial radiomics features were extracted at three vascular supply areas from CCTA images. The optimized radiomics signature was added to the conventional CCTA features to build the combined model (radiomics + conventional). There were 168 vessels from 56 patients enrolled in the training set, and the testing set consisted of 135 vessels from 45 patients. From either cohort, HRP score, LL, stenosis ≥50% and CT-FFR ≤0.80 were associated with ischemia. The optimal myocardial radiomics signature consisted of nine features. The detection of ischemia using the combined model was significantly improved compared with conventional model in both training and testing set (AUC 0.789 vs 0.608, p < 0.001; 0.726 vs 0.637, p = 0.045). Myocardial radiomics signature extracted from static CCTA combining with conventional features could provide incremental value to diagnose specific ischemia. Myocardial radiomics signature extracted from CCTA may capture myocardial characteristics and provide incremental value to detect specific ischemia when combined with conventional features.

A multiparameter radiomic model for accurate prognostic prediction of glioma

AbstractAn accurate prediction of prognosis is important for clinical treatments of glioma. In this study, a multiparameter radiomic model is proposed for accurate prognostic prediction of glioma. Three kinds of region of interest were extracted from preoperative postcontrast T1‐weighted images and T2 fluid‐attenuated inversion recovery images acquired from 140 glioma patients. Radiomics score (Radscore) was calculated and the conventional image features and clinical molecular characteristics that may be related to progression‐free survival (PFS) were evaluated. Five uniparameter and various combinations of biparameter and multiparameter models based on above characteristics were built. The performance of these models was evaluated by concordance index (C index), and the nomogram of the multiparameter radiomic model was constructed. The results show that the proposed multiparameter radiomic model has a better prediction performance than other models. In the training and validation sets, the calibration curves of the multiparameter radiomic model for the 1‐, 2‐, and 3‐year PFS probability demonstrate a high consistence between predictions and observations. In conclusion, this study demonstrates that the multiparameter radiomic model based on Radscore, conventional image features and clinical molecular characteristics can improve the prediction accuracy of glioma prognosis, which could be informative for individualized treatments.

Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed

BackgroundTo investigate the value of preoperative computed tomography (CT) texture features, routine imaging features, and clinical features in the prognosis of non-small cell lung cancer (NSCLC) after radical resection.MethodsDemographic parameters and clinically features were analyzed in 107 patients with stage I-IIIB NSCLC, while 73 of these patients received CT scanning and radiomic characteristics for prognosis assessment. Texture analysis features include histogram, gray size area matrix and gray co-occurrence matrix features. The clinical risk features were identified using univariate and multivariate logistic analyses. By incorporating the radiomics score (Rad-score) and clinical risk features with multivariate cox regression, a combined nomogram was built. The nomogram performance was assessed by its calibration, clinical usefulness and Harrell’s concordance index (C-index). The 5-year OS between the dichotomized subgroups was compared using Kaplan–Meier (KM) analysis and the log-rank test.ResultsConsisting of 4 selected features, the radiomics signature showed a favorable discriminative performance for prognosis, with an AUC of 0.91 (95% CI: 0.84 ~ 0.97). The nomogram, consisting of the radiomics signature, N stage, and tumor size, showed good calibration. The nomogram also exhibited prognostic ability with a C-index of 0.91 (95% CI, 0.86–0.95) for OS. The decision curve analysis indicated that the nomogram was clinically useful. According to the KM survival curves, the low-risk group had higher 5-year survival rate compared to high-risk.ConclusionThe as developed nomogram, combining with preoperative radiomics evidence, N stage, and tumor size, has potential to preoperatively predict the prognosis of NSCLC with a high accuracy and could assist to treatment for the NSCLC patients in the clinic.

Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade

This study aims to evaluate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) values in differentiating oligodendroglioma of various grades and explore the correlation between ADC and Ki-67. The preoperative MRI data of 99 patients with World Health Organization (WHO) grades 2 (n = 42) and 3 (n = 57) oligodendroglioma confirmed by surgery and pathology were retrospectively analyzed. Conventional MRI features, ADCmean, ADCmin, and normalized ADC (nADC) were compared between the two groups. A receiver operating characteristic curve was used to evaluate each parameter's diagnostic efficacy in differentiating the two tumor types. Each tumor's Ki-67 proliferation index was also measured to explore its relationship with the ADC value. Compared with WHO2 grade tumors, WHO3 grade tumors had a larger maximum diameter and more significant cystic degeneration/necrosis, edema, and moderate/severe enhancement (all P < 0.05). The ADCmin, ADCmean, and nADC values of the WHO3 and WHO2 grade tumors were significantly different, and the ADCmin value most accurately distinguished the two tumor types, yielding an area under the curve value of 0.980. When 0.96 × 10-3 mm2/s was used as the differential diagnosis threshold, the sensitivity, specificity, and accuracy of the two groups were 100%, 93.00%, and 96.96%, respectively. The ADCmin (r = -0.596), ADCmean (r = - 0.590), nADC (r = - 0.577), and Ki-67 proliferation index values had significantly negative correlations (all P < 0.05). Conventional MRI features and ADC values are beneficial in the noninvasive prediction of the WHO grade and tumor proliferation rate of oligodendroglioma.

327 Radiopathomics: Integration of Advanced Neuroimaging and Molecular Pathology Features in Meningiomas

OBJECTIVES/GOALS: Our overall objective is to investigate the relationship between radiologic features of meningioma with recently identified histopathological and molecular biomarkers, and to apply a machine learning (ML) approach to further demonstrate their utility in predicting clinical outcomes. METHODS/STUDY POPULATION: We have enrolled a cohort of 84 patients with meningioma diagnosed on the basis of conventional gadolinium-enhanced MRI imaging features since September 2019. Each patient has demographic and clinical data, Ga-68-DOTATATE MRI/PET SUV and dynamic metrics, DCE-MRI perfusion parameters, and histopathologic data. Various tumor subregions will be segmented semi-automatically and later confirmed by experienced neuroradiologist. Histopathologic data will include histologic grade, mitotic rate, Ki67 proliferative index, and presence of WHO established atypical histologic features, immunohistochemical parameters, and established high-grade molecular features. We will use supervised learning techniques to develop algorithms for predicting molecular features from imaging phenotypes. RESULTS/ANTICIPATED RESULTS: Anticipated results - advancements in understanding the molecular biomarkers of meningiomas has uncovered genetic alterations and epigenetic changes that more accurately determine tumor behavior. Currently, the imaging correlates of these molecular biomarkers are unknown, and utilizing radiographic data to predict prognosis and imaging-based classifications of meningiomas have not yet been investigated. Validated imaging correlates of molecular biomarkers not only provide an in-vivo assessment of tumor biology, but can also be integrated with histopathologic features ( radiopathomics models’) for more accurate disease prognostication. We anticipate that our results will identify surrogate imaging features for some of the recently emerged molecular biomarkers of meningioma. DISCUSSION/SIGNIFICANCE: There is a paucity of data on the importance of imaging phenotypes in determining tumor biology. This work has the potential of significant clinical impact by enabling a priori molecular characterization of meningiomas at the time of new diagnosis or recurrence, thereby allowing a personalized medicine approach to treatment planning.

Texture analysis of diffusion kurtosis imaging for differentiating malignant from benign sinonasal lesions: added value to conventional imaging features.

To evaluate the performance of texture analysis (TA) of diffusion kurtosis imaging (DKI) in differentiating malignant from benign sinonasal lesions, and its added value to the conventional imaging features. Fifty-eight patients with malignant and 40 patients with benign sinonasal lesions were retrospectively enrolled. Conventional CT and MRI features were reviewed. Texture parameters were obtained and compared between two groups. Multivariate logistic regression analysis was used to identify the most valuable variables. Receiver operating characteristic curves were performed to assess the differentiating performance of independent variables and their combination. There were significant differences in tumor necrosis, bone erosion and soft tissue invasion between the two groups (all p < 0.05). There were significant differences in the 10th and entropy of Apparent diffusion coefficient map, the mean, 10th and entropy of D map, the mean and 90th of K map between the two groups (all p < 0.002). The bone erosion, entropy of D, and mean of K were independent variables associated with malignant tumors. Receiver operating characteristic analyses indicated that the combination of three features possessed better differentiating performance than bone erosion alone (p = 0.003). TA of DKI could supply incremental value to conventional imaging features for pre-operative differential diagnosis between benign and malignant sinonasal lesions. The present study is the first to combine conventional imaging features and the TA of DKI in the differential diagnosis between benign and malignant sinonasal lesions. Our findings suggest that TA of DKI could supply incremental value to conventional imaging features.

Characterization of spatially mapped volumetric molecular ultrasound signals for predicting response to anti-vascular therapy

Quantitative three-dimensional molecular ultrasound is a promising technology for longitudinal imaging applications such as therapy monitoring; the risk profile is favorable compared to positron emission tomography and computed tomography. However, clinical translation of quantitative methods for this technology are limited in that they assume that tumor tissues are homogeneous, and often depend on contrast-destruction events that can produce unintended bioeffects. Here, we develop quantitative features (henceforth image features) that capture tumor spatial information, and that are extracted without contrast destruction. We compare these techniques with the contrast-destruction derived differential targeted enhancement parameter (dTE) in predicting response to therapy. We found thirty-three reproducible image features that predict response to antiangiogenic therapy, without the need for a contrast agent disruption pulse. Multiparametric analysis shows that several of these image features can differentiate treated versus control animals with comparable performance to post-destruction measurements, suggesting that these can potentially replace parameters such as the dTE. The highest performing pre-destruction image features showed strong linear correlations with conventional dTE parameters with less overall variance. Thus, our study suggests that image features obtained during the wash in of the molecular agent, pre-destruction, may replace conventional post-destruction image features or the dTE parameter.

Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease

Objectives. Parkinson’s disease (PD) is a complex neurodegenerative disorder, affecting 2%–3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different cognitive dysfunctionality aspects. Early MoCA prediction may facilitate better temporal therapy and disease control. Radiomics features (RF), in addition to clinical features (CF), are indicated to increase clinical diagnoses, etc, bridging between medical imaging procedures and personalized medicine. We investigate the effect of RFs, CFs, and conventional imaging features (CIF) to enhance prediction performance using hybrid machine learning systems (HMLS). Methods. We selected 210 patients with 981 features (CFs, CIFs, and RFs) from the Parkinson’s Progression-Markers-Initiative database. We generated 4 datasets, namely using (i), (ii) year-0 (D1) or year-1 (D2) features, (iii) longitudinal data (D3, putting datasets in years 0 and 1 longitudinally next to each other), and (iv) timeless data (D4, effectively doubling dataset size by listing both datasets from years 0 and 1 separately). First, we directly applied 23 predictor algorithms (PA) to the datasets to predict year-4 MoCA, which PD patients this year have a higher dementia risk. Subsequently, HMLSs, including 14 attribute extraction and 10 feature selection algorithms followed by PAs were employed to enhance prediction performances. 80% of all datapoints were utilized to select the best model based on minimum mean absolute error (MAE) resulting from 5-fold cross-validation. Subsequently, the remaining 20% was used for hold-out testing of the selected models. Results. When applying PAs without ASAs/FEAs to datasets (MoCA outcome range: [11,30]), Adaboost achieved an MAE of 1.74 ± 0.29 on D4 with a hold-out testing performance of 1.71. When employing HMLSs, D4 + Minimum_Redundancy_Maximum_Relevance (MRMR)+K_Nearest_Neighbor Regressor achieved the highest performance of 1.05 ± 0.25 with a hold-out testing performance of 0.57. Conclusion. Our study shows the importance of using larger datasets (timeless), and utilizing optimized HMLSs, for significantly improved prediction of MoCA in PD patients.

Precise differentiation between jugular foramen paragangliomas and metastases: utility of diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging.

This study tested the utility of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced imaging (DCE-MRI) in differentiating paragangliomas and metastases in the jugular foramen in combination with conventional imaging. Forty-nine consecutive patients with paragangliomas or metastases between January 2015 and April 2022 were included in this retrospective study. All patients had pretreatment DWI and DCE-MRI. Between paragangliomas and metastases, normalized apparent diffusion coefficient (nADCmean) and DCE-MRI parameters were compared along with conventional imaging features (enhancement pattern, presence of flow voids, cystic/necrotic change, and bone erosion). The diagnostic performance was tested using receiver operating characteristic (ROC) analysis. Thirty-five paragangliomas (5 male; median 49years) and 14 metastases (9 male; median 61years) were analyzed. The most common 3 primary cancers included 4 lung cancers, 3 breast cancers, and 3 melanomas. The presence of flow void was significantly different between paragangliomas and metastases (21/35 vs 2/14; P = 0.0047) in conventional imaging features, while fractional plasma volume (Vp) was significantly different between the two tumor types (median 0.46 vs 0.19; P < 0.001) in DWI and DCE-MRI parameters. The areas under the ROC curves (AUCs) of the presence of flow void and Vp were 0.72 and 0.93, respectively. The AUC of the combination of the presence of flow void and Vp was 0.95 and significantly improved compared to that of the presence of flow void (P < 0.001). Adding DCE-MRI to the head and neck protocol can aid in the precise differentiation between jugular foramen paragangliomas and metastases.

Utility of dynamic susceptibility contrast MRI for differentiation between paragangliomas and meningiomas in the cerebellopontine angle and jugular foramen region

PurposeDifferentiation of paragangliomas and meningiomas can be a challenge. This study aimed to assess the utility of dynamic susceptibility contrast perfusion MRI (DSC-MRI) to distinguish paragangliomas from meningiomas. MethodsThis retrospective study included 40 patients with paragangliomas and meningiomas in the cerebellopontine angle and jugular foramen region between March 2015 and February 2022 in a single institution. Pretreatment DSC-MRI and conventional MRI were performed in all cases. Normalized relative cerebral blood volume (nrCBV), relative cerebral blood flow (nrCBF), relative mean transit time (nrMTT), and time to peak (nTTP) as well as conventional MRI features were compared between the 2 tumor types and between meningioma subtypes as appropriate. Receiver operating characteristic curve and multivariate logistic regression analysis were performed. ResultsTwenty-eight meningiomas including 8 WHO grade II meningiomas (12 males, 16 females; median age 55 years) and 12 paragangliomas (5 males, 7 females; median age 35 years) were included in this study. Paragangliomas had a higher rate of cystic/necrotic changes (10/12 vs 10/28; P = 0.014), a higher rate of internal flow voids (9/12 vs 8/28; P = 0.013), higher nrCBV (median 9.78 vs 6.64; P = 0.04), and shorter nTTP (median 0.78 vs 1.06; P < 0.001) than meningiomas. There was no difference in conventional imaging features and DSC-MRI parameters between meningioma subtypes. nTTP was identified as the most significant parameter for the 2 tumor types in the multivariate logistic regression analysis (P = 0.009). ConclusionsIn this small retrospective study, DSC-MRI perfusion differences were observed between paragangliomas and meningiomas, but not between grade I and II meningiomas.

MRI Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional MR Imaging Features.

Prognostic evaluation is important for personalized treatment in children with medulloblastoma (MB). Limited data are available for risk stratification using a radiomics-based model. To evaluate the incremental value of an MRI radiomics signature in stratifying the risk of pediatric MB in terms of overall survival (OS). Retrospective. A total of 111 children (mean age 5.82 years) with pathologically confirmed MB divided into training and validation cohorts (77 and 34 children, respectively). A 3 T, contrast-enhanced T1-weighted imaging with inversion recovery. The study endpoint was OS defined as the time between the preoperative MRI study and death or last follow-up. The radiomics signature model and a clinical-MRI model were developed for personalized OS prediction. An integrative model, which combined the radiomics signature and clinical-MRI features, was also built using multivariable Cox regression model. The performance of the three models was evaluated with the C-index. The performance of integrative model was assessed by calibration curve and decision curve analysis (DCA). Independent T-test, Mann-Whitney U test, Fisher's exact tests or chi-square test, logistic regression analysis, Kaplan-Meier survival analysis, C-index, intraclass correlation coefficients (ICC). P < 0.05 was considered statistically significant. The media OS was 2.83 years (3.87 ± 1.85 years). Two clinical and one conventional MR imaging features (remnant, adjuvant treatment, and peritumoral edema) were selected for clinical-MRI model building. The integrative model evaluated OS (C-index 0.823) better than either the radiomics signature (C-index 0.702) or the clinical-MRI model (C-index 0.771). And it also showed good performance in the validation cohort (C-indices: 0.786, 0.756, 0.721), which was validated by the good calibration (P >0.05) and more benefit. This study demonstrated that the integrative model, which combined radiomics signature, clinical, and conventional MRI features, showed best performance in OS evaluation for children with MB. The radiomics signature may confer incremental value over clinical-MRI features. 3. Stage 2.

NIMG-90. DIFFERENTIATION OF INTRACRANIAL SOLITARY FIBROUS TUMOR AND MENINGIOMA ON CONVENTIONAL, DIFFUSION-WEIGHTED, AND DYNAMIC SUSCEPTIBILITY CONTRAST PERFUSION MRI

Abstract BACKGROUND AND PURPOSE To evaluate the differences in the imaging findings of intracranial solitary fibrous tumor (SFT) and meningioma on dynamic susceptibility contrast (DSC) perfusion MRI, apparent diffusion coefficient (ADC), and conventional sequences. METHODS Between January 2016 and April 2022, the study enrolled 14 (median, 46 years [range, 25–74]; six females; seven with DSC-MRI) and 27 patients (median, 53 years [21–83]; 23 females; all with DSC-MRI) with pathologically proven SFT and meningioma, respectively. The normalized relative cerebral blood volume and flow (nrCBV and nrCBF), percentage signal recovery (PSR), normalized mean ADC (nADCmean), and conventional imaging features, including T1-weighted hyperintensity, were evaluated and statistically compared using Bonferonni correction. RESULTS PSR was significantly lower in SFT compared with that in meningioma (median, 22.5 [range, 6.4–48.8] vs. 70.1 [range, 18.6–104.2], p&amp;lt; 0.001). The majority of SFT showed mainly T1-weighted hyperintensities compared to the gray matter, while only one meningioma showed the finding (8/14 [57.1%] vs. 1/27 [3.7%], p&amp;lt; 0.001). No significant difference was observed in nrCBV, nrCBF, or nADCmean. CONCLUSIONS PFS was significantly lower in intracranial SFT compared with that in meningioma, indicating the difference in capillary permeability between the tumors. Comparing the hyperintensity on T1WI of the solid components with that of the cerebral cortex was a simple and useful characteristic to differentiate SFT from meningioma.

Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review.

The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.

The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses.

PurposeTo compare the value of contrast-enhanced CT (CECT) and non-contrast-enhanced CT (NCECT) radiomics models in differentiating tuberculosis (TB) from non-tuberculous infectious lesions (NTIL) presenting as solid pulmonary nodules or masses, and develop a combine radiomics model (RM).Materials and methodsThis study was a retrospective analysis of 101 lesions in 95 patients, including 49 lesions (from 45 patients) in the TB group and 52 lesions (from 50 patients) in the NTIL group. Lesions were randomly divided into training and test sets in the ratio of 7:3. Conventional imaging features were used to construct a conventional imaging model (IM). Radiomics features screening and NCECT or CECT RM construction were carried out by correlation analysis and gradient boosting decision tree, and logistic regression. Finally, conventional IM, NCECT RM, and CECT RM were used for combine RM construction. Additionally, we recruited three radiologists for independent diagnosis. The differential diagnostic performance of each model was assessed using the areas under the receiver operating characteristic curve (AUCs).ResultsThe CECT RM (training AUC, 0.874; test AUC, 0.796) outperformed the conventional IM (training AUC, 0.792; test AUC, 0.708), the NCECT RM (training AUC, 0.835; test AUC, 0.704), and three radiologists. The diagnostic efficacy of the combine RM (training AUC, 0.922; test AUC, 0.833) was best in the training and test sets.ConclusionsThe diagnostic efficacy of the CECT RM was superior to that of the NCECT RM in identifying TB from NTIL presenting as solid pulmonary nodules or masses. The combine RM had the best performance and may outperform expert radiologists.