Psoriasis Patients Treated With Methotrexate Have an Increased Risk of Nonmelanoma Skin Cancer: A Systematic Review and Meta-Analysis.

Both psoriasis and methotrexate are associated with an increased risk of nonmelanoma skin cancer. The effect of methotrexate on the development of nonmelanoma skin cancer in patients with psoriasis is currently unknown. To evaluate this relationship, a systematic review of the literature was conducted using databases including Ovid Medline (from 1946), Scopus (from 1970), and Embase (from 1974) through June 2019. Observational comparative and case-control studies comparing psoriasis patients treated with methotrexate to those not treated with methotrexate with data on the subsequent development of nonmelanoma skin cancer in both cohorts were included based on prespecified criteria. Two reviewers analyzed all studies for relevant data, which were analyzed using OpenMeta-Analyst statistical software. Quality was assessed with the Newcastle-Ottawa method. Nine cohort and case-control comparative studies of 1,486 screened abstracts met the inclusion criteria. Of 11,875 reported patients with psoriasis, 2,192 were taking methotrexate. A meta-analysis demonstrated an odds ratio of 2.8 (95% confidence interval = 1.47-5.39; p = 0.002) for nonmelanoma skin cancer development in patients with psoriasis taking methotrexate compared with those not taking methotrexate. Based on these findings, psoriasis patients treated with methotrexate are at a significantly increased (2.8 times higher) risk of developing nonmelanoma skin cancer. Risk counseling can improve healthcare outcomes in patients with psoriasis.

Current and Future Directions of Breast MRI.

Magnetic resonance imaging (MRI) is the most sensitive exam for detecting breast cancer. The American College of Radiology recommends women with 20% or greater lifetime risk of developing breast cancer be screened annually with MRI. However, other high-risk populations would also benefit. Hartmann et al. reported women with atypical hyperplasia have nearly a 30% incidence of breast cancer at 25-year follow-up. Women with dense breast tissue have up to a 4-fold increased risk of breast cancer when compared to average-risk women; their cancers are more likely to be mammographically occult. Because multiple cohorts of women are at high risk for developing breast cancer, there has been a movement to develop an abbreviated MRI (abMRI) protocol to expand the availability of MRI screening. Studies on abMRI effectiveness have been promising, with Weinstein et al. demonstrating a cancer detection rate of 27.4/1000 in women with dense breasts after a negative digital breast tomosynthesis. Breast MRI is also used to evaluate the extent of disease as part of preoperative assessment in women with newly diagnosed breast cancer, and to assess a patient's response to neoadjuvant chemotherapy. This paper aims to explore the current uses of MRI and propose future indications and directions.

The Rare, the Odd, and the Atypical: A Pictorial Essay of Testicular and Paratesticular Diseases.

ABSTRACT: Understanding a variety of scrotal diseases is essential to developing an accurate differential diagnosis and is critical in providing optimal patient care. Ultrasound is the imaging modality of choice when evaluating for scrotal pathology, with the major purpose of locating such pathology to either the testis, or epididymis, or other intrascrotal structures, as well as characterizing lesions as solid or cystic. It is generally assumed that most solid intratesticular masses are more likely malignant, whereas most extratesticular ones are benign, although some exceptions to that rule exist. This pictorial essay will focus on rare and less commonly encountered benign and malignant testicular and paratesticular pathologies, which may pose a diagnostic dilemma for interpreting radiologists and treating physicians. Knowledge of their imaging characteristics will help narrow the differential diagnosis and assist in proper patient management and care.

Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study.

OBJECTIVE: To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms. METHODS: This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs. RESULTS: Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases. CONCLUSIONS: The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.