Deep Learning-Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs

To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist

miR-19, miR-345, miR-519c-5p Serum Levels Predict Adverse Pathology in Prostate Cancer Patients Eligible for Active Surveillance

<div><p>Serum microRNAs hold great promise as easily accessible and measurable biomarkers of disease. In prostate cancer, serum miRNA signatures have been associated with the presence of disease as well as correlated with previously validated risk models. However, it is unclear whether miRNAs can provide independent prognostic information beyond current risk models. Here, we focus on a group of low-risk prostate cancer patients who were eligible for active surveillance, but chose surgery. A major criteria for the low risk category is a Gleason score of 6 or lower based on pre-surgical biopsy. However, a third of these patients are upgraded to Gleason 7 on post surgical pathological analysis. Both in a discovery and a validation cohort, we find that pre-surgical serum levels of miR-19, miR-345 and miR-519c-5p can help identify these patients independent of their pre-surgical age, PSA, stage, and percent biopsy involvement. A combination of the three miRNAs increased the area under a receiver operator characteristics curve from 0.77 to 0.94 (<i>p</i><0.01). Also, when combined with the CAPRA risk model the miRNA signature significantly enhanced prediction of patients with Gleason 7 disease. In-situ hybridizations of matching tumors showed miR-19 upregulation in transformed versus normal-appearing tumor epithelial, but independent of tumor grade suggesting an alternative source for the increase in serum miR-19a/b levels or the release of pre-existing intracellular miR-19a/b upon progression. Together, these data show that serum miRNAs can predict relatively small steps in tumor progression improving the capacity to predict disease risk and, therefore, potentially drive clinical decisions in prostate cancer patients. It will be important to validate these findings in a larger multi-institutional study as well as with independent methodologies.</p></div

Summaries of logistic regression models for different combinations of miRNAs in validation cohort accounting for age, PSA, stage and biopsy characteristics.

<p>Summaries of logistic regression models for different combinations of miRNAs in validation cohort accounting for age, PSA, stage and biopsy characteristics.</p

Prediction models for miRNAs.

<p>A) ROC curves for age, PSA, and stage plus/minus individual miRNAs or combination of all 3 miRNAs. B) Percentage of patients with either Gleason 6 (light grey) or Gleason 7 (dark grey) post-surgery relative to pre-surgery CAPRA score. C) Same as B, but with addition of miRNAs. A value of 1 is given for each positive miRNA and added to CAPRA score. CAPRA nomogram combines the following variables: Age at diagnosis, PSA at diagnosis, Gleason score of pre-surgical biopsy, Clinical stage, and Percent of involved biopsies.</p

Schematic of the study design.

<p>Schematic of the study design.</p