Sohi Bae

Gadolinium deposition in the brain: association with various GBCAs using a generalized additive model

AbstractObjectivesTo determine the relationship between the number of administrations of various gadolinium-based contrast agents (GBCAs) and increased T1 signal intensity in the globus pallidus (GP) and dentate nucleus (DN).MethodsThis retrospective study included 122 patients who underwent double-dose GBCA-enhanced magnetic resonance imaging. Two radiologists calculated GP-to-thalamus (TH) signal intensity ratio, DN-to-pons signal intensity ratio and relative change (Rchange) between the baseline and final examinations. Interobserver agreement was evaluated. The relationships between Rchange and several factors, including number of each GBCA administrations, were analysed using a generalized additive model.ResultsSix patients (4.9%) received linear GBCAs (mean 20.8 number of administration; range 15–30), 44 patients (36.1%) received macrocyclic GBCAs (mean 26.1; range 14–51) and 72 patients (59.0%) received both types of GBCAs (mean 31.5; range 12–65). Interobserver agreement was almost perfect (0.99; 95% CI: 0.99–0.99). Rchange (DN:pons) was associated with gadodiamide (p = 0.006) and gadopentetate dimeglumine (p < 0.001), but not with other GBCAs. Rchange (GP:TH) was not associated with GBCA administration.ConclusionsPrevious administration of linear agents gadoiamide and gadopentetate dimeglumine is associated with increased T1 signal intensity in the DN, whereas macrocyclic GBCAs do not show an association.Key points• Certain linear GBCAs are associated with T1 signal change in the dentate nucleus. • The signal change is related to the administration number of certain linear GBCAs. • Difference in signal change may reflect differences in stability of agents.

Uterine Artery Embolization for Adenomyosis: Percentage of Necrosis Predicts Midterm Clinical Recurrence.

PURPOSE To evaluate the effect of degree of necrosis after uterine artery embolization (UAE) on symptom recurrence at midterm clinical follow-up in patients with adenomyosis. MATERIALS AND METHODS Women (N = 50) who underwent UAE for symptomatic adenomyosis were retrospectively analyzed. All patients underwent contrast-enhanced magnetic resonance (MR) imaging at baseline and 3 months after UAE and were followed clinically for at least 18 months. The type of adenomyosis was classified as focal or diffuse. The uterine volume and the percentage of necrosis after embolization were measured three-dimensionally on MR imaging. The percentage of the necrosis cutoff point for predicting recurrence was estimated. Patients were divided into 2 groups according to the cutoff point. The rate of recurrence was compared between groups, and risk factors for recurrence were identified. RESULTS During the follow-up period (range, 18-48 mo), symptom recurrence occurred in 12 of 50 patients. A necrosis cutoff point of 34.3% was calculated to predict recurrence (area under the curve = 0.721; 95% confidence interval [CI] = 0.577-0.839; P = .004). Patients with < 34.3% necrosis (group A, n = 12) were at a significantly higher risk of recurrence than patients with > 34.3% necrosis (group B, n = 38; hazard ratio = 7.0; 95% CI = 2.2, 22.4; P = .001). Initial uterine volume and type of adenomyosis were not associated with recurrence. CONCLUSIONS The percentage of necrosis in patients with adenomyosis after UAE may predict symptom recurrence at midterm follow-up. The cutoff percentage of necrosis required to predict symptom recurrence was 34.3% in this study.

Breast Microcalcifications: Diagnostic Outcomes According to Image-Guided Biopsy Method.

Objective To evaluate the diagnostic outcomes of ultrasonography-guided core needle biopsy (US-CNB), US-guided vacuum-assisted biopsy (US-VAB), and stereotactic-guided vacuum-assisted biopsy (S-VAB) for diagnosing suspicious breast microcalcification. Materials and Methods We retrospectively reviewed 336 cases of suspicious breast microcalcification in patients who subsequently underwent image-guided biopsy. US-CNB was performed for US-visible microcalcifications associated with a mass (n = 28), US-VAB for US-visible microcalcifications without an associated mass (n = 59), and S-VAB for mammogram-only visible lesions (n = 249). Mammographic findings, biopsy failure rate, false-negative rate, and underestimation rate were analyzed. Histological diagnoses and the Breast Imaging Reporting and Data System (BI-RADS) categories were reported. Results Biopsy failure rates for US-CNB, US-VAB, and S-VAB were 7.1% (2/28), 0% (0/59), and 2.8% (7/249), respectively. Three false-negative cases were detected for US-CNB and two for S-VAB. The rates of biopsy-diagnosed ductal carcinoma in situ that were upgraded to invasive cancer at surgery were 41.7% (5/12), 12.9% (4/31), and 8.6% (3/35) for US-CNB, US-VAB, and S-VAB, respectively. Sonographically visible lesions were more likely to be malignant (66.2% [51/77] vs. 23.2% [46/198]; p < 0.001) or of higher BI-RADS category (61.0% [47/77] vs. 22.2% [44/198]; p < 0.001) than sonographically invisible lesions. Conclusion Ultrasonography-guided vacuum-assisted biopsy is more accurate than US-CNB when suspicious microcalcifications are detected on US. Calcifications with malignant pathology are significantly more visible on US than benign lesions.

Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach

ObjectivesTo evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from non-necrotic atypical glioblastoma (GBM).MethodsSeventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes. These features were subjected to recursive feature elimination and random forest (RF) analysis with nested cross-validation. The diagnostic abilities of a radiomics machine-learning classifier, apparent diffusion coefficient (ADC), and three readers, who independently classified the tumours based on conventional MR sequences, were evaluated using receiver operating characteristic (ROC) analysis. Areas under the ROC curves (AUC) of the radiomics classifier, ADC value, and the radiologists were compared.ResultsThe mean AUC of the radiomics classifier was 0.921 (95 % CI 0.825–0.990). The AUCs of the three readers and ADC were 0.707 (95 % CI 0.622–0.793), 0.759 (95 %CI 0.656–0.861), 0.695 (95 % CI 0.590–0.800) and 0.684 (95 % CI0.560–0.809), respectively. The AUC of the radiomics-based classifier was significantly higher than those of the three readers and ADC (p< 0.001 for all).ConclusionsLarge-scale radiomics with a machine-learning algorithm can be useful for differentiating PCNSL from atypical GBM, and yields a better diagnostic performance than human radiologists and ADC values.Key Points• Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM.• This approach yields a higher diagnostic accuracy than visual analysis by radiologists.• Radiomics can strengthen radiologists’ diagnostic decisions whenever conventional MRI sequences are available.

Prediction ofIDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas

BACKGROUND AND PURPOSE: WHO grade II gliomas are divided into three classes: isocitrate dehydrogenase (IDH)-wildtype, IDH-mutant and no 1p/19q codeletion, and IDH-mutant and 1p/19q-codeleted. Different molecular subtypes have been reported to have prognostic differences and different chemosensitivity. Our aim was to evaluate the predictive value of imaging phenotypes assessed with the Visually AcceSAble Rembrandt Images lexicon for molecular classification of lower grade gliomas. MATERIALS AND METHODS: MR imaging scans of 175 patients with lower grade gliomas with known IDH1 mutation and 1p/19q-codeletion status were included (78 grade II and 97 grade III) in the discovery set. MR imaging features were reviewed by using Visually AcceSAble Rembrandt Images (VASARI); their associations with molecular markers were assessed. The predictive power of imaging features for IDH1-wild type tumors was evaluated using the Least Absolute Shrinkage and Selection Operator. We tested the model in a validation set (40 subjects). RESULTS: Various imaging features were significantly different according to IDH1 mutation. Nonlobar location, larger proportion of enhancing tumors, multifocal/multicentric distribution, and poor definition of nonenhancing margins were independent predictors of an IDH1 wild type according to the Least Absolute Shrinkage and Selection Operator. The areas under the curve for the prediction model were 0.859 and 0.778 in the discovery and validation sets, respectively. The IDH1-mutant, 1p/19q-codeleted group frequently had mixed/restricted diffusion characteristics and showed more pial invasion compared with the IDH1-mutant, no codeletion group. CONCLUSIONS: Preoperative MR imaging phenotypes are different according to the molecular markers of lower grade gliomas, and they may be helpful in predicting the IDH1-mutation status.

Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction

Purpose To investigate whether radiomic features at MRI improve survival prediction in patients with glioblastoma multiforme (GBM) when they are integrated with clinical and genetic profiles. Materials and Methods Data in patients with a diagnosis of GBM between December 2009 and January 2017 (217 patients) were retrospectively reviewed up to May 2017 and allocated to training and test sets (3:1 ratio). Radiomic features (n = 796) were extracted from multiparametric MRI. A random survival forest (RSF) model was trained with the radiomic features along with clinical and genetic profiles (O-6-methylguanine-DNA-methyltransferase promoter methylation and isocitrate dehydrogenase 1 mutation statuses) to predict overall survival (OS) and progression-free survival (PFS). The RSF models were validated on the test set. The incremental values of radiomic features were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC). Results The 217 patients had a mean age of 57.9 years, and there were 87 female patients (age range, 22-81 years) and 130 male patients (age range, 17-85 years). The median OS and PFS of patients were 352 days (range, 20-1809 days) and 264 days (range, 21-1809 days), respectively. The RSF radiomics models were successfully validated on the test set (iAUC, 0.652 [95% confidence interval {CI}, 0.524, 0.769] and 0.590 [95% CI: 0.502, 0.689] for OS and PFS, respectively). The addition of a radiomics model to clinical and genetic profiles improved survival prediction when compared with models containing clinical and genetic profiles alone (P = .04 and .03 for OS and PFS, respectively). Conclusion Radiomic MRI phenotyping can improve survival prediction when integrated with clinical and genetic profiles and thus has potential as a practical imaging biomarker.

MR lymphography for sentinel lymph node detection in patients with oral cavity cancer: Preliminary clinical study

The purpose of this study was to evaluate the feasibility of MR lymphography with interstitial injection of a gadolinium‐based contrast agent for identifying sentinel lymph nodes in patients with oral cavity cancer and clinically negative neck.

Detection of clinically occult primary tumours in patients with cervical metastases of unknown primary tumours: comparison of three-dimensional THRIVE MRI, two-dimensional spin-echo MRI, and contrast-enhanced CT

AIM To evaluate and compare the utility of contrast-enhanced three-dimensional (3D) T1-weighted high-resolution isotropic volume examination (THRIVE), spin-echo (SE) T1-weighted magnetic resonance imaging (MRI), and computed tomography (CT) for detecting clinically occult primary tumours in patients with cervical lymph node metastases. MATERIALS AND METHODS Seventy-three consecutive patients with tumours that went undetected during endoscopic or physical examinations underwent preoperative contrast-enhanced CT and MRI (SE and 3D THRIVE) after gadolinium injection. Guided biopsy results served as reference standards. The diagnostic performances of the imaging techniques were compared with McNemars tests. RESULTS Primary tumours were identified in 59 (80.8%) of the 73 patients after surgery. Of these, 36 were found in the palatine tonsil, 11 in the base of the tongue, seven in the nasopharynx, and five in the pyriform sinus. The sensitivity (72.9%) and accuracy (71.2%) of 3D THRIVE for detecting primary tumours were higher than were those of SE T1-weighted MRI (49.2% and 53.4%, p≤0.002) or CT (36.4% and 46.4%, p≤0.001). The specificities of these techniques did not differ. The diagnostic performance of 3D THRIVE (area under the curve [AUC]=0.681) for detecting tumours did not differ from that of SE T1-weighted MRI or CT (AUC=0.671 and 0.608, p>0.05). CONCLUSION 3D THRIVE was more sensitive at detecting primary tumours than was SE T1-weighted MRI or CT in patients with cervical metastases of unknown primary tumours. This sequence may improve biopsy and therapeutic planning in these patients.