Imaging uterine lipoleiomyomas: A case series and review of the literature.

Background: Lipoleiomyomas are uncommon uterine lesions containing adipose and smooth muscle tissue. They have a variable presentation and are usually found incidentally on imaging or post-hysterectomy tissue analysis. Given their low prevalence, there is a dearth of literature describing imaging characteristics for uterine lipoleiomyomas. In this image-rich case series, we summarize an example of an initial presentation as well as present ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) findings for 36 patients. Case presentation: We present the detailed clinical course of a representative patient evaluated for uterine lipoleiomyoma and describe imaging findings seen in another 35 patients. This includes ultrasound findings from 16 patients, CT findings from 25 patients, and MRI findings from 5 patients. Among the 36 total patients, symptoms at the time of diagnosis were variable but often included abdominal or pelvic pain; however, most patients were asymptomatic, and the lipoleiomyomas were incidentally discovered on imaging. Conclusions: Uterine lipoleiomyomas are rare and benign tumors with variable presentations. Ultrasound, CT, and MRI findings can assist in diagnosis. Findings on ultrasound typically include well-circumscribed hyperechoic and septated lesions with minimal to no internal blood flow. CT shows fat-containing either homogeneous or heterogeneous circumscribed lesions depending on their ratio of fat and smooth muscle tissue. Lastly, on MRI, uterine lipoleiomyomas commonly appear heterogenous with loss of signal on fat-suppressed sequences. These imaging findings are highly specific for lipoleiomyomas, and familiarity with these findings may reduce unnecessary and potentially invasive procedures.

Artificial Intelligence for Improved Hepatosplenomegaly Diagnosis.

Hepatosplenomegaly is commonly diagnosed by radiologists based on single dimension measurements and heuristic cut-offs. Volumetric measurements may be more accurate for diagnosing organ enlargement. Artificial intelligence techniques may be able to automatically calculate liver and spleen volume and facilitate more accurate diagnosis. After IRB approval, 2 convolutional neural networks (CNN) were developed to automatically segment the liver and spleen on a training dataset comprised of 500 single-phase, contrast-enhanced CT abdomen and pelvis examinations. A separate dataset of ten thousand sequential examinations at a single institution was segmented with these CNNs. Performance was evaluated on a 1% subset and compared with manual segmentations using Sorensen-Dice coefficients and Pearson correlation coefficients. Radiologist reports were reviewed for diagnosis of hepatomegaly and splenomegaly and compared with calculated volumes. Abnormal enlargement was defined as greater than 2 standard deviations above the mean. Median Dice coefficients for liver and spleen segmentation were 0.988 and 0.981, respectively. Pearson correlation coefficients of CNN-derived estimates of organ volume against the gold-standard manual annotation were 0.999 for the liver and spleen (P < 0.001). Average liver volume was 1556.8 ± 498.7 cc and average spleen volume was 194.6 ± 123.0 cc. There were significant differences in average liver and spleen volumes between male and female patients. Thus, the volume thresholds for ground-truth determination of hepatomegaly and splenomegaly were determined separately for each sex. Radiologist classification of hepatomegaly was 65% sensitive, 91% specific, with a positive predictive value (PPV) of 23% and an negative predictive value (NPV) of 98%. Radiologist classification of splenomegaly was 68% sensitive, 97% specific, with a positive predictive value (PPV) of 50% and a negative predictive value (NPV) of 99%. Convolutional neural networks can accurately segment the liver and spleen and may be helpful to improve radiologist accuracy in the diagnosis of hepatomegaly and splenomegaly.

Impact of easing COVID-19 safety measures on trauma computed tomography imaging volumes.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to substantial disruptions in healthcare staffing and operations. Stay-at-home (SAH) orders and limitations in social gathering implemented in spring 2020 were followed by initial decreases in healthcare and imaging utilization. This study aims to evaluate the impact of subsequent easing of SAH on trauma volumes, demand for, and turnaround times for trauma computed tomography (CT) exams, hypothesizing that after initial decreases, trauma volumes have increased as COVID safety measures have been reduced. METHODS: Patient characteristics, CT imaging volumes, and turnaround time were analyzed for all adult activated emergency department trauma patients requiring CT imaging at a single Level-I trauma center (1/2018-2/2022) located in the sixth most populous county in the USA. Based on COVID safety measures in place in the state of California, three time periods were compared: baseline (PRE, 1/1/2018-3/19/2020), COVID safety measures (COVID, 3/20/2020-1/25/2021), and POST (1/26/2021-2/28/2022). RESULTS: There were 16,984 trauma patients across the study (PRE = 8289, COVID = 3139, POST = 5556). The average daily trauma patient volumes increased significantly in the POST period compared to the PRE and COVID periods (13.9 vs. 10.3 vs. 10.1, p < 0.001), with increases in both blunt (p < 0.001) and penetrating (p = 0.002) trauma. The average daily number of trauma CT examinations performed increased significantly in the POST period compared to the PRE and COVID periods (56.7 vs. 48.3 vs. 47.6, p < 0.001), with significant increases in average turnaround time (47 min vs. 31 and 37, p < 0.001). CONCLUSION: After initial decreases in trauma radiology volumes following stay-at-home orders, subsequent easing of safety measures has coincided with increases in trauma imaging volumes above pre-pandemic levels and longer exam turnaround times.

Hemimegalencephaly: Evolution From an Atypical Focal Early Appearance on Fetal MRI to More Conventional MR Findings.

Hemimegalencephaly, or unilateral megalencephaly, is a sporadic congenital brain malformation characterized by enlargement of a cerebral hemisphere due to an abnormal proliferation of neurons or glial cells. Hemimegalencephaly is part of a spectrum of disorders, increasingly referred to as mTORopathies, which arise as a result of dysregulation or hyperactivation of the mammalian target of rapamycin (mTOR)-signaling cascade resulting in less restricted cell growth and survival. The resultant cortical disorganization and enhanced neuronal excitability often manifest clinically in the form of seizures. Ultrasound and magnetic resonance imaging (MRI) are often used to characterize hemimegalencephaly. Typical imaging findings seen include diffuse unilateral enlargement of a cerebral hemisphere with overlying cortical malformation and ipsilateral dilation of the lateral ventricle. This paper will review an unusual case of focal hemimegalencephaly diagnosed on prenatal imaging. Initial in utero MRI revealed a mass-like lesion in the frontal lobe without associated perilesional cerebral edema. Keying in on abnormalities within the overlying cortex was crucial in suggesting focal hemimegalencephaly as a leading diagnosis and distinguishing it from alternative diagnoses such as a neoplasm. Follow-up fetal MRI demonstrated the evolution of the cerebral abnormality and confirmed the diagnosis. Early diagnosis facilitated appropriate counseling of the parents and guided postnatal imaging and management.