BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively using conventional MR images is challenging. Deep learning models have shown promise in performing classification tasks. The diagnostic performance of a deep learning–based model in discriminating glioblastoma from solitary brain metastasis using preoperative conventional MR images was evaluated. MATERIALS AND METHODS: Records of 598 patients with histologically confirmed glioblastoma or solitary brain metastasis at our institution between February 2006 and December 2017 were retrospectively reviewed. Preoperative contrast-enhanced T1WI and T2WI were preprocessed and roughly segmented with rectangular regions of interest. A deep neural network was trained and validated using MR images from 498 patients. The MR images of the remaining 100 were used as an internal test set. An additional 143 patients from another tertiary hospital were used as an external test set. The classifications of ResNet-50 and 2 neuroradiologists were compared for their accuracy, precision, recall, F1 score, and area under the curve. RESULTS: The areas under the curve of ResNet-50 were 0.889 and 0.835 in the internal and external test sets, respectively. The area under the curve of neuroradiologists 1 and 2 were 0.889 and 0.768 in the internal test set and 0.857 and 0.708 in the external test set, respectively. CONCLUSIONS: A deep learning–based model may be a supportive tool for preoperative discrimination between glioblastoma and solitary brain metastasis using conventional MR images.

To measure shear wave velocities (SWVs) by acoustic radiation force impulse (ARFI) ultrasound elastography in normal kidneys and in hydronephrotic kidneys in young children and to compare SWVs between the hydronephrosis grades. This study was approved by an institutional review board, and informed consent was obtained from the parents of all the children included. Children under the age of 24 months were prospectively enrolled. Hydronephrosis grade was evaluated on ultrasonography, and three valid ARFI measurements were attempted using a high-frequency transducer for both kidneys. Hydronephrosis was graded from 0 to 4, and high-grade hydronephrosis was defined as grades 3 and 4. Fifty-one children underwent ARFI measurements, and three valid measurements for both kidneys were obtained in 96% (49/51) of the patients. Nineteen children (38.8%) had no hydronephrosis. Twenty-three children (46.9%) had unilateral hydronephrosis, and seven children (14.3%) had bilateral hydronephrosis. Seven children had ureteropelvic junction obstruction (UPJO). Median SWVs in kidneys with high-grade hydronephrosis (2.02 m/sec) were higher than those in normal kidneys (1.75 m/sec; P=0.027). However, the presence of UPJO did not influence the median SWVs in hydronephrotic kidneys (P=0.362). Obtaining ARFI measurements of the kidney is feasible in young children with median SWVs of 1.75 m/sec in normal kidneys. Median SWVs increased in high-grade hydronephrotic kidneys but were not different between hydronephrotic kidneys with and without UPJO.

BACKGROUND AND PURPOSE: The detection of cerebral aneurysms on MRA is a challenging task. Recent studies have used deep learning–based software for automated detection of aneurysms on MRA and have reported high performance. The purpose of this study was to evaluate the incremental value of using deep learning–based software for the detection of aneurysms on MRA by 2 radiologists, a neurosurgeon, and a neurologist. MATERIALS AND METHODS: TOF-MRA examinations of intracranial aneurysms were retrospectively extracted. Four physicians interpreted the MRA blindly. After a washout period, they interpreted MRA again using the software. Sensitivity and specificity per patient, sensitivity per lesion, and the number of false-positives per case were measured. Diagnostic performances, including subgroup analysis of lesions, were compared. Logistic regression with a generalized estimating equation was used. RESULTS: A total of 332 patients were evaluated; 135 patients had positive findings with 169 lesions. With software assistance, patient-based sensitivity was statistically improved after the washout period (73.5% versus 86.5%, P < .001). The neurosurgeon and neurologist showed a significant increase in patient-based sensitivity with software assistance (74.8% versus 85.2%, P = .03, and 56.3% versus 84.4%, P < .001, respectively), while the number of false-positive cases did not increase significantly (23 versus 30, P = .20, and 22 versus 24, P = .75, respectively). CONCLUSIONS: Software-aided reading showed significant incremental value in the sensitivity of clinicians in the detection of aneurysms on MRA without a significant increase in false-positive findings, especially for the neurosurgeon and neurologist. Software-aided reading showed equivocal value for the radiologist.