Multimodality radiomics analysis based on [18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis.

OBJECTIVES: To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients. METHODS: One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA). RESULTS: The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539-0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619-0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit. CONCLUSIONS: Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients. KEY POINTS: • Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention.

Machine learning-based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle.

OBJECTIVES: To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles. METHODS: A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists. RESULTS: The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts. CONCLUSIONS: The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance. KEY POINTS: • The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.

External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.

OBJECTIVES: The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction. METHODS: This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. CONCLUSIONS: The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. KEY POINTS: • A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study.

Inflow-based vascular-space-occupancy (iVASO) might potentially predict IDH mutation status and tumor grade in diffuse cerebral gliomas.

PURPOSE: The aim of the study is to assess the diagnostic performance of inflow-based vascular-space-occupancy (iVASO) MR imaging for differentiating glioblastomas (grade IV, GBM) and lower-grade diffuse gliomas (grade II and III, LGG) and its potential to predict IDH mutation status. METHODS: One hundred and two patients with diffuse cerebral glioma (56 males; median age, 43.5 years) underwent iVASO and dynamic susceptibility contrast (DSC) MR imaging. The iVASO-derived arteriolar cerebral blood volume (CBVa), relative CBVa (rCBVa), and the DSC-derived relative cerebral blood volume (rCBV) were obtained, and these measurements were compared between the GBM group (n = 43) and the LGG group (n = 59) and between the IDH-mutation group (n = 54) and the IDH-wild group (n = 48). RESULTS: Significant correlation was observed between rCBV and CBVa (P < 0.001) or rCBVa (P < 0.001). Both CBVa (P < 0.001) and rCBVa (P < 0.001) were higher in the GBM group. Both CBVa (P < 0.001) and rCBVa (P < 0.001) were lower in the IDH-mutation group compared to the IDH-wild group. Receiver operating characteristic analyses showed the area under curve (AUC) of 0.95 with CBVa and 0.97 with rCBVa in differentiating GBM from LGG. The AUCs were 0.82 and 0.85 for CBVa and rCBVa in predicting IDH gene status, respectively, which were lower than that of rCBV (AUC = 0.90). Combined rCBV and rCBVa significantly improved the diagnostic performance (AUC = 0.95). CONCLUSIONS: iVASO MR imaging has the potential to predict IDH mutation and grade in glioma.

Noninvasive Assessment of O(6)-Methylguanine-DNA Methyltransferase Promoter Methylation Status in World Health Organization Grade II-IV Glioma Using Histogram Analysis of Inflow-Based Vascular-Space-Occupancy Combined with Structural Magnetic Resonance Imaging.

BACKGROUND: O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation is an important prognostic factor for gliomas and is associated with tumor angiogenesis. Arteriolar cerebral blood volume (CBVa) obtained from inflow-based vascular-space-occupancy (iVASO) magnetic resonance imaging (MRI) is assumed to be an indicator of tumor microvasculature. Its preoperative predictive ability for MGMT promoter methylation remains unclear. PURPOSE: To investigate the role of iVASO-CBVa histogram features in determining MGMT promoter methylation status of grade II-IV gliomas. STUDY TYPE: Retrospective SUBJECTS: Forty-six patients consisting of 20 MGMT methylated and 26 unmethylated gliomas. FIELD STRENGTH/SEQUENCE: 3.0 T magnetic resonance images containing iVASO MRI, T1 -weighted image (T1 WI), T2 -weighted image, T2 -weighted fluid attenuated inversion recovery image images, and enhanced T1 WI. ASSESSMENT: Sixteen structural imaging features were visually evaluated on structural MRI and 14 CBVa histogram features were extracted from iVASO-CBVa maps. STATISTICAL TESTS: Imaging features were screened and ranked using Fisher's exact test, Mann-Whitney U-test, and randomforest algorithm. Features with higher importance were selected to develop logistic regression models to determine MGMT methylation status. Receiver operating characteristics (ROC) curve with the area under the curve (AUC) and leave-one-out cross-validation (LOOCV) were used to assess effectiveness and stability. RESULTS: The top two CBVa histogram features were root mean squared (RMS) and variance. The top two structural imaging features were contrast-enhancing component of the tumor (CET) location and tumor location. Both the CBVa model of RMS and variance (ROC, AUC = 0.867; LOOCV, AUC = 0.819) and the model of structural features (ROC, AUC = 0.882; LOOCV, AUC = 0.802) accurately identified MGMT methylation. The fusion model of CBVa RMS and CET location improved diagnostic performance (ROC, AUC = 0.931; LOOCV, AUC =0.906). DATA CONCLUSION: iVASO-CBVa has potential in evaluating MGMT methylation status in grade II-IV gliomas. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.

BACKGROUND: Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy. PURPOSE: To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma. STUDY TYPE: Retrospective. POPULATION: A total of 164 patients from two medical centers were enrolled in this study. Patients from the first medical center were divided into a training cohort (N = 99) and an internal validation cohort (N = 33). Patients from the second medical center were used as the external independent validation cohort (N = 32). FIELD STRENGTH/SEQUENCE: Axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w) on 3.0 T or 1.5 T magnetic resonance scanners. ASSESSMENT: Pathological subtypes (squamous papillary craniopharyngioma and adamantinomatous craniopharyngioma) were confirmed by surgery and hematoxylin and eosin staining. Optimal radiomic feature selection was performed by SelectKBest, the least absolute shrinkage and selection operator algorithm, and support vector machine (SVM) with a recursive feature elimination algorithm. Models based on each sequence or combinations of sequences were built using a SVM classifier and used to differentiate pathological subtypes of craniopharyngioma in the training cohort, internal validation, and external validation cohorts. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the radiomic models. RESULTS: Seven texture features, three from T1 -w, two from T2 -w, and two from CET1 -w, were selected and used to construct the radiomic model. The AUC values of the radiomic model were 0.899, 0.810, and 0.920 in the training cohort, internal and external validation cohorts, respectively. The AUC values of the clinicoradiological model were 0.677, 0.655, and 0.671 in the training cohort, internal and external validation cohorts, respectively. DATA CONCLUSION: The model based on radiomic features from T1 -w, T2 -w, and CET1 -w has a high discriminatory ability for pathological subtypes of craniopharyngioma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: 2.

Decreased Muscular Perfusion in Dermatomyositis: Initial Results Detected by Inflow-Based Vascular-Space-Occupancy MRI.

OBJECTIVE. This study aimed to determine whether inflow-based vascular-space-occupancy (iVASO) MRI could reproducibly quantify skeletal muscle perfusion and differentiate patients with dermatomyositis (DM) from healthy subjects. MATERIALS AND METHODS. A total of 25 patients with DM and 22 healthy volunteers underwent iVASO MRI in a 3-T MRI scanner. Maximum and mean arteriolar muscle blood volume (MBV) values of four subgroups of muscles (normal muscles, morphologically normal-appearing muscles, edematous muscles, and atrophic or fat-infiltrated muscles) were obtained. Maximum and mean arteriolar MBV values were compared among the different subgroups, and repeat testing was performed in 20 subjects to assess reproducibility. RESULTS. Compared with normal muscles in healthy subjects, morphologically normal-appearing muscles, edematous muscles, and atrophic or fat-infiltrated muscles in patients with DM showed a significant decrease of both maximum and mean arteriolar MBV (p < .001). Both parameters were significantly lower in atrophic or fat-infiltrated muscles than in morphologically normal-appearing and edematous muscles (p < .001). ROC AUCs for discriminating patients with DM from healthy volunteers were 0.842 and 0.812 for maximum and mean arteriolar MBV values, respectively. As a measure of test-retest studies, the intraclass correlation coefficients (ICCs) were 0.990 (95% CI, 0.986-0.993) and 0.990 (95% CI, 0.987-0.993) for maximum and mean arteriolar MBV, respectively. For interobserver reproducibility, the ICCs were 0.989 (95% CI, 0.986-0.991) and 0.980 (95% CI, 0.975-0.983), respectively. CONCLUSION. iVASO MRI can reproducibly quantify arteriolar MBV in the thigh and discriminate between healthy volunteers and patients with DM.

Whole-Brain Functional and Diffusion Tensor MRI in Human Participants with Metallic Orthodontic Braces.

Background MRI performed with echo-planar imaging (EPI) sequences is sensitive to susceptibility artifacts in the presence of metallic objects, which presents a substantial barrier for performing functional MRI and diffusion tensor imaging (DTI) in patients with metallic orthodontic material and other head implants. Purpose To evaluate the ability to reduce susceptibility artifacts in healthy human participants wearing metallic orthodontic braces for two alternative approaches: T2-prepared functional MRI and diffusion-prepared DTI with three-dimensional fast gradient-echo readout. Materials and Methods In this prospective study conducted from February to September 2018, T2-prepared functional MRI and diffusion-prepared DTI were performed in healthy human participants. Removable dental braces with bonding trays were used so that MRI could be performed with braces and without braces in the same participants. Results were evaluated in regions with strong (EPI dropout regions for functional MRI and the inferior fronto-occipital fasciculus for DTI) and minimal (motor cortex for functional MRI and the posterior limb of internal capsule for DTI) susceptibility artifacts. Signal-to-noise ratio (SNR), contrast-to-noise ratio for functional MRI, apparent diffusion coefficient and fractional anisotropy for DTI, and degree of distortion (quantified with the Jaccard index, which measures the similarity of geometric shapes) were compared in regions with strong or minimal susceptibility effects between the current standard EPI sequences and the proposed alternatives by using paired t test. Results Six participants were evaluated (mean age ± standard deviation, 40 years ± 6; three women). In brain regions with strong susceptibility effects from the metallic braces, T2-prepared functional MRI showed significantly higher SNR (37.8 ± 2.4 vs 15.5 ± 5.3; P < .001) and contrast-to-noise ratio (0.83 ± 0.16 vs 0.29 ± 0.10; P < .001), whereas diffusion-prepared DTI showed higher SNR (5.8 ± 1.5 vs 3.8 ± 0.7; P = .03) than did conventional EPI methods. Apparent diffusion coefficient and fractional anisotropy were consistent with the literature. Geometric distortion was substantially reduced throughout the brain with the proposed methods (significantly higher Jaccard index, 0.95 ± 0.12 vs 0.81 ± 0.61; P < .001). Conclusion T2-prepared functional MRI and diffusion-prepared diffusion tensor imaging can acquire functional and diffusion MRI, respectively, in healthy human participants wearing metallic dental braces with less susceptibility artifacts and geometric distortion than with conventional echo-planar imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Dietrich in this issue.

Imaging algorithm and multimodality evaluation of spinal osteoblastoma.

BACKGROUND: To analyze the features of CT, MRI and PET/CT and their diagnostic value for spinal osteoblastomas (OBs). METHODS: The radiological and clinical data of 21 patients with histopathologically-confirmed spinal OBs were analyzed retrospectively. RESULTS: Sixteen of the 21 cases were benign and 5 were aggressive OBs. Tumors were located in the lumbar (n = 11), cervical (n = 4), thoracic (n = 5), and sacral (n = 1) spinal regions. Nineteen cases were centered in the posterior elements of the spine, 13 of which extended into the vertebral body. Punctate or nodular calcifications were found in all cases on CT with a complete sclerotic rim (n = 12) or incomplete sclerotic rim (n = 8). The flare phenomenon (indicative of surrounding tissue inflammation) was found in 17/21 cases on CT, thin in 11 cases and thick in 6 cases, and in 19/19 cases on MRI, thin in 1 case and thick in 18 cases. On 18F-FDG PET/CT, all cases (8/8) were metabolically active with the SUVmax of 12.3-16.0; the flare sign was observed in 8 cases, including 7 cases of hypometabolism and 1 case of coexistence of hypermetabolism and hypometabolism. Based on CT, 3, 12, and 6 cases were classified as Enneking stage 1, 2 and 3, respectively. Of 19 cases with MRI, 1 and 18 cases were classified as Enneking stage 2 and 3, respectively. CONCLUSIONS: Spinal OB has multiple unique characteristic radiological features. Although a larger sample size is needed, combining CT, MRI and PET may be beneficial to optimize preoperative diagnosis and care of patients with OBs.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas.

OBJECTIVES: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas. METHODS: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters. RESULTS: Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10-3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92. CONCLUSION: The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

Association of Glioma Grading With Inflow-Based Vascular-Space-Occupancy MRI: A Preliminary Study at 3T.

BACKGROUND: Inflow-based vascular-space-occupancy (iVASO) MRI is a noninvasive perfusion technique that does not require administration of exogenous contrast agents. Arteriolar cerebral blood volume (CBVa) obtained from iVASO MRI is hypothesized to be an indicator of tumor microvasculature. PURPOSE: To assess the diagnostic performance of iVASO MRI implemented at 3T in predicting histologic grades of cerebral gliomas. STUDY TYPE: Retrospective. SUBJECTS: Forty-five patients (31 males) consisting of 14 WHO grade IV glioblastoma multiformes, 14 grade III, and 17 grade II gliomas. FIELD STRENGTH/SEQUENCE: At 3T we acquired CBVa data using an iVASO sequence. ASSESSMENT: The maximum and mean CBVa (CBVa_max and CBVa_mean) values were calculated in the tumor and normalized to the contralateral thalamus (nCBVa_max and nCBVa_mean). STATISTICAL TESTS: Kruskal-Wallis test, Mann-Whitney test, and receiver operating characteristics (ROC) curve were used for statistical analysis. RESULTS: Both CBVa_max and nCBVa_max increased with tumor grade (P < 0.001). Grade II gliomas showed CBVa_max <0.78 ml / 100 ml in 10/17 cases and nCBVa_max <1.20 in 11/17 cases. Grade III gliomas showed both CBVa_max >0.78 ml / 100 ml and nCBVa_max >1.20 in 13/14 cases, and CBVa_max <2.06 ml / 100 ml in 13/14 cases and nCBVa_max <2.33 in 11/14 cases. Grade IV gliomas showed CBVa_max >2.06 ml / 100 ml in 9/14 cases and nCBVa_max >2.33 in 13/14 cases. The areas under the ROC curve, sensitivity, and specificity were 0.839 (P < 0.001), 92.9% (26/28), and 64.7% (11/17) for CBVa_max, and 0.883 (P < 0.001), 92.9% (26/28), and 70.6% (12/17) for nCBVa_max in the discrimination between grade II and high-grade (grade III and grade IV) tumors, respectively. DATA CONCLUSION: iVASO MRI might be used to help determine and predict glioma grade. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1817-1823.

Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality.

OBJECTIVES: To evaluate the image quality of ultralow-dose computed tomography (ULDCT) reconstructed with knowledge-based iterative model reconstruction (IMR) in patients with pulmonary tuberculosis (TB). METHODS: This IRB-approved prospective study enrolled 59 consecutive patients (mean age, 43.9 ± 16.6 years; F:M 18:41) with known or suspected pulmonary TB. Patients underwent a low-dose CT (LDCT) using automatic tube current modulation followed by an ULDCT using fixed tube current. Raw image data were reconstructed with filtered-back projection (FBP), hybrid iterative reconstruction (iDose), and IMR. Objective measurements including CT attenuation, image noise, and contrast-to-noise ratio (CNR) were assessed and compared using repeated-measures analysis of variance. Overall image quality and visualization of normal and pathological findings were subjectively scored on a five-point scale. Radiation output and subjective scores were compared by the paired Student t test and Wilcoxon signed-rank test, respectively. RESULTS: Compared with FBP and iDose, IMR yielded significantly lower noise and higher CNR values at both dose levels (p < 0.01). Subjective ratings for pathological findings including centrilobular nodules, consolidation, tree-in-bud, and cavity were significantly better with ULDCT IMR images than those with LDCT iDose images (p < 0.01), but blurred edges were observed. With IMR implementation, a 59% reduction of the mean effective dose was achieved with ULDCT (0.28 ± 0.02 mSv) compared with LDCT (0.69 ± 0.15 mSv) without impairing image quality (p < 0.001). CONCLUSIONS: IMR offers considerable noise reduction and improvement in image quality for patients with pulmonary TB undergoing chest ULDCT at an effective dose of 0.28 mSv. KEY POINTS: • Radiation dose is a major concern for tuberculosis patients requiring repeated follow-up CT. • IMR allows substantial radiation dose reduction in chest CT without compromising image quality. • ULDCT reconstructed with IMR allows accurate depiction of CT features of pulmonary tuberculosis.

Clivopalate angle: a new diagnostic method for basilar invagination at magnetic resonance imaging.

OBJECTIVES: To investigate the diagnostic value of clivopalate angle (CPA) for basilar invagination (BI) at magnetic resonance imaging (MRI). METHODS: In this retrospective case-control study, CPA, clivodens angle (CDA), and clivoaxial angle (CXA) were measured on midsagittal MR images from 112 patients with BI (22 men; mean age, 43.9 years ± 13.1 years; range, 21-79 years) and 200 control subjects (57 men; mean age, 47.1 years ± 13.3 years; range, 20-80 years). Intraclass correlation coefficient (ICC), linear regression, Mann-Whitney U test, binary logistic regression, and receiver operating characteristic (ROC) curve were used for statistical analysis. RESULTS: Clivopalate angle showed better inter-observer agreement (ICC = 0.951) than CXA (0.867) or CDA (0.853). CPA significantly correlated with CXA (R = 0.811, p < 0.001) and CDA (R = 0.716, p < 0.001). Patients with BI had a significantly smaller CPA (45.9° ± 9.9°) than control subjects (61.9° ± 6.2°) (p < 0.001). With the optimal cutoff value of 53.5°, CPA had a sensitivity of 0.839 (94/112) and a specificity of 0.915 (183/200). The area under the ROC curve (AUC) was 0.937 (95% CI, 0.911-0.963) for CPA, which was similar to that of CXA (AUC, 0.957; 95% CI, 0.936-0.978) or CDA (AUC, 0.925; 95% CI, 0.892-0.957). The combination of CPA and CDA or CXA showed a higher diagnostic value than CDA or CXA alone. CONCLUSIONS: The diagnostic performance of CPA was similar to that of CXA or CDA, but CPA might be more reliable in evaluation of BI. CPA provided complementary information to CXA and CDA. KEY POINTS: • Clivopalate angle has a high diagnostic value for basilar invagination. • Clivopalate angle demonstrates high inter-reader agreement than does clivoaxial angle or clivodens angle. • Clivopalate angle provides complementary information to clivoaxial angle and clivodens angle.

Radiation Dose Reduction by Using CT with Iterative Model Reconstruction in Patients with Pulmonary Invasive Fungal Infection.

Purpose To compare the diagnostic quality of reduced radiation dose computed tomography (CT) with iterative model reconstruction (IMR) versus that of conventional low-dose CT in patients with pulmonary invasive fungal infection. Materials and Methods This prospective observational study included 48 patients (mean age ± standard deviation, 39.9 years ± 11.3) known to have or suspected of having pulmonary invasive fungal infection between October 2016 and July 2017. Patients underwent CT with IMR (at 80 kV with 20 mA) immediately after low-dose CT (at 80 kV with automatic exposure control). Images were reconstructed by using a hybrid iterative reconstruction (HIR) algorithm and IMR. Two radiologists independently assessed subjective image quality, noise, and visibility of normal and abnormal findings by using a five-point scale. Objective measurements, including image noise, contrast-to-noise ratio (CNR), and corresponding figure of merit (FOM), were compared by using repeated-measures analysis of variance with Bonferroni post hoc tests for multiple comparisons. Results The mean effective dose was 0.3 mSv ± 0.3 for CT with IMR and 0.7 mSv ± 0.2 for low-dose CT (P < .01). When the image noise and CNR were normalized to the effective dose, CT images obtained with IMR had significantly higher FOM than did other image series (P < .0001). Subjectively, visibility of CT features of invasive fungal infection on CT scans reconstructed with IMR was rated as noninferior to that on low-dose CT scans reconstructed with HIR, except for the halo sign. Conclusion CT with IMR had approximately 60% dose reduction compared with conventional low-dose CT, with reduced noise and improved depiction of abnormal findings, in patients with pulmonary invasive fungal infection.

Use of T1 relaxation time in rotating frame (T1 ρ) and apparent diffusion coefficient to estimate cerebral stroke evolution.

BACKGROUND: The major factor for the appropriate treatment strategies for ischemia patients is its onset timing. PURPOSE/HYPOTHESIS: To study to evaluate the diagnostic accuracy of T1 relaxation time in a rotating frame (T1 ρ) and apparent diffusion coefficient (ADC) from MRI to estimate ischemia stages. STUDY TYPE: Prospective. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL: In all, 73 patients (49 males, aged 29-78 years and 24 females, aged 22-94 years) with ischemia were prospectively imaged with T1 ρ and diffusion MRI during the postischemic period. FIELD STRENGTH/SEQUENCE: 3T/T1 ρ and diffusion-weighted imaging (DWI). ASSESSMENT: Ischemic parenchyma included tissue with elevated signal areas on DWI and correlative hypointense areas on ADC maps. STATISTICAL TESTS: The sensitivity of variables to ischemia time was quantified by analyzing the respective correlations of these values with onset time. RESULTS: ΔT1 ρ (ipsilateral-contralateral differences in T1 ρ) (R2 = 0.956) and T1 ρipsi (ipsilateral ischemia T1 ρ values) (R2 = 0.941) were elevated in all ischemic lesions; these values increased linearly as a function of time, unlike ΔADC (ipsilateral-contralateral differences in ADC) (R2 = -0.410) and ADCipsi (ipsilateral ischemia ADC values) (R2 = 0.550). ΔT1 ρ and T1 ρipsi were significantly different between all stages (P < 0.01), except the acute and hyperacute stages (P = 0.589 for ΔT1 ρ, P = 0.290 for T1 ρipsi , respectively), but ΔADC and ADCipsi only between the late subacute and early subacute stages (P < 0.01) and the late subacute and chronic stages (P < 0.01). DATA CONCLUSION: These data suggest that T1 ρ can provide estimates for the ischemic time in patients. T1 ρ has the potential to outperform diffusion for single-timepoint examination because the T1 ρ change during strokes is positive and linear. If patients with suspected stroke are scanned by MRI within the appropriate timeframe, T1 ρ may provide tools for evaluating stroke onset, potentially aiding in treatment strategies. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1247-1254.

Diagnostic value of six MRI features for central neurocytoma.

OBJECTIVES: To increase our understanding of the imaging features of central neurocytoma (CN) and improve the preoperative MRI diagnosis accuracy. METHODS: Preoperative MR images of 30 CNs and another 68 intraventricular non-CN tumours were analysed by one experienced neuroradiologist retrospectively to identify previously reported features and new features of CN. Six blinded radiologists independently reviewed all these MRI images, and scored all characteristic features on a five-point scale. Diagnostic value was assessed by the area under the receiver operating characteristic curve (AUC); sensitivity, specificity and accuracy were also calculated. RESULTS: In addition to the 'scalloping' sign, 'broad-based attachment' sign and 'soap-bubble' sign, three new MRI features of CN were identified, including the 'peripheral cysts' sign, 'fluid-fluid level' sign and the 'gemstone' sign. The scalloping sign showed the highest AUC value (0.82), followed by the peripheral cysts sign (0.75) and broad-based attachment sign (0.75). The scalloping sign exhibited the highest specificity (82%), followed by the fluid-fluid level sign (79%) and gemstone (78%) sign. The broad-based attachment sign (85%) was the most sensitive feature, followed by the soap-bubble sign (84%) and peripheral cysts sign (77%). CONCLUSION: There are six characteristic MRI features that help to improve the preoperative diagnostic accuracy of CN. KEY POINTS: • This study is the largest magnetic resonance imaging (MRI) cohort on central neurocytoma (CN). • Three new features helpful for the diagnosis of CN were reported. • Diagnostic value of six MRI features of CN was preliminarily determined.

Clinical, radiological, pathological and prognostic aspects of intraventricular oligodendroglioma: comparison with central neurocytoma.

Studies comparing intraventricular oligodendroglioma (IVO) and central neurocytoma (CN) in terms of their clinical, radiological and pathological features are scarce. We, therefore, investigated the similarities and differences between these types of tumors to get a better understanding of how they may be more properly diagnosed and treated. The clinical manifestations, CT/MRI findings, pathological characteristics and clinical outcomes of 8 cases of IVOs and 12 cases of CNs were analyzed retrospectively. Both IVO and CN occurred most commonly in young adults and manifested with symptoms of increased intracranial pressure secondary to obstructive hydrocephalus. However, they were radiologically different in location (p = 0.007), diffusion-weighted imaging (p = 0.001), "scalloping" appearance (p = 0.006), flow void sign (p = 0.006) and ventricular wall invasion (p = 0.000). Histologically, significant differences in mitotic count (p = 0.008) and parenchymal infiltration (p = 0.01) were noted. Immunohistochemically, significant differences in the expression of Olig2 (p = 0.000), Syn (p = 0.01) and NeuN (p = 0.000) were observed. In addition, MIB-1 labeling index (p = 0.035) and case fatality rate (p = 0.021) of IVO were much higher than those of CN, while survival rate of IVO was much lower than that of CN (p = 0.028). IVO and CN are similar in onset age and clinical manifestations, but have different imaging and pathological features. Patients with IVOs may have a relatively poorer prognosis compared to those with CNs.

Use of intravoxel incoherent motion diffusion-weighted MR imaging for assessment of treatment response to invasive fungal infection in the lung.

OBJECTIVES: The purpose of this study was to determine whether intravoxel incoherent motion (IVIM) -derived parameters and apparent diffusion coefficient (ADC) could act as imaging biomarkers for predicting antifungal treatment response. METHODS: Forty-six consecutive patients (mean age, 33.9 ± 13.0 y) with newly diagnosed invasive fungal infection (IFI) in the lung according to EORTC/MSG criteria were prospectively enrolled. All patients underwent diffusion-weighted magnetic resonance (MR) imaging at 3.0 T using 11 b values (0-1000 sec/mm2). ADC, pseudodiffusion coffiecient D*, perfusion fraction f, and the diffusion coefficient D were compared between patients with favourable (n=32) and unfavourable response (n=14). RESULTS: f values were significantly lower in the unfavourable response group (12.6%±4.4%) than in the favourable response group (30.2%±8.6%) (Z=4.989, P<0.001). However, the ADC, D, and D* were not significantly different between the two groups (P>0.05). Receiver operating characteristic curve analyses showed f to be a significant predictor for differentiation, with a sensitivity of 93.8% and a specificity of 92.9%. CONCLUSIONS: IVIM-MRI is potentially useful in the prediction of antifungal treatment response to patients with IFI in the lung. Our results indicate that a low perfusion fraction f may be a noninvasive imaging biomarker for unfavourable response. KEY POINTS: • Recognition of IFI indicating clinical outcome is important for treatment decision-making. • IVIM can reflect diffusion and perfusion information of IFI lesions separately. • Perfusion characteristics of IFI lesions could help differentiate treatment response. • An initial low f may predict unfavourable response in IFI.

Measurement of arteriolar blood volume in brain tumors using MRI without exogenous contrast agent administration at 7T.

PURPOSE: Arteriolar cerebral-blood-volume (CBVa) is an important perfusion parameter that can be measured using inflow-based vascular-space-occupancy (iVASO) MRI without exogenous contrast agent administration. The purpose of this study is to assess the potential diagnostic value of CBVa in brain tumor patients by comparing it with total-CBV (including arterial, capillary and venous vessels) measured by dynamic-susceptibility-contrast (DSC) MRI. MATERIALS AND METHODS: Twelve brain tumor patients were scanned using iVASO (on 7T as part of a research project) and DSC (on 3T as part of routine clinical protocols) MRI. Region-of-interest analysis was performed to compare the resulting perfusion measures between tumoral and contralateral regions, and to evaluate their associations with tumor grades. RESULTS: CBVa measured by iVASO MRI significantly correlated with WHO grade (ρ = 0.37, P = 0.04). Total-CBV measured by DSC MRI showed a trend of correlation with WHO grade (ρ = 0.28, P = 0.5). The signal-to-noise ratio was comparable (P > 0.1) between the two methods, while the contrast-to-noise ratio between tumoral and contralateral regions was higher in iVASO-CBVa than DSC-CBV in WHO II/III patients (P < 0.05) but comparable in WHO IV patients (P > 0.1). A trend of positive correlation between DSC-CBV and iVASO-CBVa was observed (R2 = 0.28, P = 0.07). CONCLUSION: In this initial patient study, CBVa demonstrated a stronger correlation with WHO grade than total-CBV. Further investigation with a larger cohort is warranted to validate whether CBVa can be a better classifier than total-CBV for the stratification of brain tumors, and whether iVASO MRI can be a useful alternative method for the assessment of tumor perfusion, especially when exogenous contrast agent administration is difficult in certain patient populations. J. Magn. Reson. Imaging 2016;44:1244-1255.

Lung MRI of invasive fungal infection at 3 Tesla: evaluation of five different pulse sequences and comparison with multidetector computed tomography (MDCT).

OBJECTIVES: To evaluate the diagnostic performance of five MR sequences to detect pulmonary infectious lesions in patients with invasive fungal infection (IFI), using multidetector computed tomography (MDCT) as the reference standard. METHODS: Thirty-four immunocompromised patients with suspected IFI underwent MDCT and MRI. The MR studies were performed using five pulse sequences at 3.0 T: T2-weighted turbo spin echo (TSE), short-tau inversion recovery (STIR), spectrally selective attenuated inversion recovery (SPAIR), T1-weighted high resolution isotropic volume excitation (e-THRIVE) and T1-weighted fast field echo (T1-FFE). The size, lesion-to-lung contrast ratio and the detectability of pulmonary lesions on MR images were assessed. Image quality and artefacts on different sequences were also rated. RESULTS: A total of 84 lesions including nodules (n = 44) and consolidation (n = 40) were present in 75 lobes. SPAIR and e-THRIVE images achieved high overall lesion-related sensitivities for the detection of pulmonary abnormalities (90.5% and 86.9%, respectively). STIR showed the highest lesion-to-lung contrast ratio for nodules (21.8) and consolidation (17.0), whereas TSE had the fewest physiological artefacts. CONCLUSIONS: MRI at 3.0 T can depict clinically significant pulmonary IFI abnormalities with high accuracy compared to MDCT. SPAIR and e-THRIVE are preferred sequences for the detection of infectious lesions of 5 mm and larger. KEY POINTS: • A radiation-free radiological method is desirable for assessing pulmonary infectious lesions • MRI at 3 T can depict lung infiltrates with good concordance to MDCT • SPAIR and e-THRIVE are favourable sequences for the detection of pulmonary lesions • The greatest benefit is for the diagnosis of lesions larger than 5 mm.