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.

Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning.

Purpose To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. Materials and Methods This retrospective study was composed of patients who underwent a multiparametric prostate MRI and an MRI/transrectal US fusion biopsy between January 2013 and May 2016. A board-certified abdominal radiologist manually segmented the prostate, TZ, and PZ on the entire data set. Included accessions were split into 60% training, 20% validation, and 20% test data sets for model development. Three convolutional neural networks with a U-Net architecture were trained for automatic recognition of the prostate organ, TZ, and PZ. Model performance for segmentation was assessed using Dice scores and Pearson correlation coefficients. Results A total of 242 patients were included (242 MR images; 6292 total images). Models for prostate organ segmentation, TZ segmentation, and PZ segmentation were trained and validated. Using the test data set, for prostate organ segmentation, the mean Dice score was 0.940 (interquartile range, 0.930-0.961), and the Pearson correlation coefficient for volume was 0.981 (95% CI: 0.966, 0.989). For TZ segmentation, the mean Dice score was 0.910 (interquartile range, 0.894-0.938), and the Pearson correlation coefficient for volume was 0.992 (95% CI: 0.985, 0.995). For PZ segmentation, the mean Dice score was 0.774 (interquartile range, 0.727-0.832), and the Pearson correlation coefficient for volume was 0.927 (95% CI: 0.870, 0.957). Conclusion Deep learning with an architecture composed of three U-Nets can accurately segment the prostate, TZ, and PZ. Keywords: MRI, Genital/Reproductive, Prostate, Neural Networks Supplemental material is available for this article. © RSNA, 2021.

A 61-year-old woman with jejunal lymphatic malformation visualized on computed tomography: a case report.

BACKGROUND: Jejunal lymphatic malformations are congenital lesions that are seldom diagnosed in adults and rarely seen on imaging. CASE PRESENTATION: A 61-year-old Caucasian woman was initially diagnosed and treated for mucinous ovarian carcinoma. After an exploratory laparotomy with left salpingo-oophorectomy, a computed tomography scan of the abdomen and pelvis demonstrated suspicious fluid-containing lesions involving a segment of jejunum and adjacent mesentery. Resection of the lesion during subsequent debulking surgery revealed that the lesion seen on imaging was a jejunal lymphatic malformation and not a cancerous implant. CONCLUSIONS: Abdominal lymphatic malformations are difficult to diagnose solely on imaging but should remain on the differential in adult cancer patients with persistent cystic abdominal lesions despite chemotherapy and must be differentiated from metastatic implants.

Prostate minimally invasive procedures: complications and normal vs. abnormal findings on multiparametric magnetic resonance imaging (mpMRI).

Minimally invasive alternatives to traditional prostate surgery are increasingly utilized to treat benign prostatic hyperplasia and localized prostate cancer in select patients. Advantages of these treatments over prostatectomy include lower risk of complication, shorter length of hospital stay, and a more favorable safety profile. Multiparametric magnetic resonance imaging (mpMRI) has become a widely accepted imaging modality for evaluation of the prostate gland and provides both anatomical and functional information. As prostate mpMRI and minimally invasive prostate procedure volumes increase, it is important for radiologists to be familiar with normal post-procedure imaging findings and potential complications. This paper reviews the indications, procedural concepts, common post-procedure imaging findings, and potential complications of prostatic artery embolization, prostatic urethral lift, irreversible electroporation, photodynamic therapy, high-intensity focused ultrasound, focal cryotherapy, and focal laser ablation.

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.