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We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.

To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance. In a retrospective and multicenter study, MR images with aneurysms based on radiological reports were extracted. The examinations were randomly divided into two data sets: training set of 468 examinations and internal test set of 120 examinations. Additionally, 50 examinations without aneurysms were randomly selected and added to the internal test set. External test data set consisted of 56 examinations with intracranial aneurysms and 50 examinations without aneurysms, which were extracted based on radiological reports from a different institution. After manual ground truth segmentation of aneurysms, a deep learning algorithm based on 3D ResNet architecture was established with the training set. Its sensitivity, positive predictive value, and specificity were evaluated in the internal and external test sets. MR images included 551 aneurysms (mean diameter, 4.17 ± 2.49 mm) in the training, 147 aneurysms (mean diameter, 3.98 ± 2.11 mm) in the internal test, 63 aneurysms (mean diameter, 3.23 ± 1.69 mm) in the external test sets. The sensitivity, the positive predictive value, and the specificity were 87.1%, 92.8%, and 92.0% for the internal test set and 85.7%, 91.5%, and 98.0% for the external test set, respectively. A deep learning algorithm detected intracranial aneurysms with a high diagnostic performance which was validated using external data set. • A deep learning-based algorithm for the automated diagnosis of intracranial aneurysms demonstrated a high sensitivity, positive predictive value, and specificity. • The high diagnostic performance of the algorithm was validated using external test data set from a different institution with a different scanner. • The algorithm might be robust and effective for general use in real clinical settings.

To determine whether radiological phenotype can improve the predictive performance of the risk model based on molecular subtype and clinical risk factors in anaplastic glioma patients. This retrospective study was approved by our institutional review board with waiver of informed consent. MR images of 86 patients with pathologically diagnosed anaplastic glioma (WHO grade III) between January 2007 and February 2016 were analyzed according to the Visually Accessible Rembrandt Images (VASARI) features set. Significant imaging findings were selected to generate a radiological risk score (RRS) for overall survival (OS) and progression-free survival (PFS) using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The prognostic value of RRS was evaluated with multivariate Cox regression including molecular subtype and clinical risk factors. The C-indices of multivariate models with and without RRS were compared by bootstrapping. Eight VASARI features contributed to RRS for OS and six contributed to PFS. Multifocality or multicentricity was the most influential feature, followed by restricted diffusion. RRS was significantly associated with OS and PFS (P

Background Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics. Methods We reviewed 1166 preoperative MR images of gliomas (grades II–IV) from Severance Hospital (n = 856), Seoul National University Hospital (SNUH; n = 107), and The Cancer Imaging Archive (TCIA; n = 203). The Severance set was subdivided into the development (n = 727) and internal test (n = 129) sets. Based on T1 postcontrast, T2, and fluid-attenuated inversion recovery images, a fully automated model was developed that comprised a CNN for tumor segmentation (Model 1) and CNN-based classifier for IDH status prediction (Model 2) that uses a hybrid approach based on 2D tumor images and radiomic features from 3D tumor shape and loci guided by Model 1. The trained model was tested on internal (a subset of the Severance set) and external (SNUH and TCIA) test sets. Results The CNN for tumor segmentation (Model 1) achieved a dice coefficient of 0.86–0.92 across datasets. Our hybrid model achieved accuracies of 93.8%, 87.9%, and 78.8%, with areas under the receiver operating characteristic curves of 0.96, 0.94, and 0.86 and areas under the precision-recall curves of 0.88, 0.82, and 0.81 in the internal test, SNUH, and TCIA sets, respectively. Conclusions Our fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613-0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467-0.759) and 0.663 (95% CI, 0.531-0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.

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. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Jain and Lui in this issue.

Background 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. Methods Pretreatment MR lymphography with a differential subsampling with cartesian ordering (DISCO) sequence was performed in 26 patients with resectable oral cavity cancer and clinically negative neck, after peritumoral injection of 1-mL diluted gadobutrol. The accuracy of sentinel lymph node identification by MR lymphography was assessed and compared with the final histopathological results. Results The MR lymphography consistently visualized the 44 sentinel lymph nodes in all 26 patients. In all but 1 patient with pathologically positive neck, assumed sentinel lymph nodes revealed metastatic involvement. Conclusion Pretreatment MR lymphography is a safe and feasible imaging technique that can help clinicians identify sentinel lymph nodes with a high risk of occult metastases in patients with oral cavity cancer, enabling focused preoperative biopsy in these high-risk patients.

Intracranial schwannomas are common but intra-suprasellar schwannomas are extremely rare. We represent the case of a 57-year-old woman with an intra-suprasellar tumor that was presurgically diagnosed as an anterior clinoidal meningioma. Intraoperatively it adhered tightly to the internal carotid artery wall and could not easily be dissected, possibly leading to profuse hemorrhage and sacrifice of the carotid artery with coil embolization. Histopathology demonstrated a schwannoma. We reviewed and summarized the clinical, imaging, and intraoperative findings of previously reported intra-suprasellar schwannomas.

Since children are more radio-sensitive than adults, there is a need to minimize radiation exposure during CT exams. To evaluate the effects of adaptive statistical iterative reconstruction (ASIR) on radiation dose reduction, image quality and diagnostic accuracy in pediatric abdominal CT. We retrospectively reviewed the abdominal CT examinations of 41 children (24 boys and 17 girls; mean age: 10 years) with a low-dose radiation protocol and reconstructed with ASIR (the ASIR group). We also reviewed routine-dose abdominal CT examinations of 41 age- and sex-matched controls reconstructed with filtered-back projection (control group). Image quality was assessed objectively as noise measured in the liver, spleen and aorta, as well as subjectively by three pediatric radiologists for diagnostic acceptability using a four-point scale. Radiation dose and objective image qualities of each group were compared with the paired t-test. Diagnostic accuracy was evaluated by reviewing follow-up imaging studies and medical records in 2012 and 2013. There was 46.3% dose reduction of size-specific dose estimates in ASIR group (from 13.4 to 7.2 mGy) compared with the control group. Objective noise was higher in the liver, spleen and aorta of the ASIR group (P

To 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). This 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. Six 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

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 McNemar's 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.

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.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.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 with34.3% necrosis (group A, n = 12) were at a significantly higher risk of recurrence than patients with34.3% 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.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.

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.