Iliopsoas plane (IP) is a fascial plane deep to the iliopsoas complex that can serve as a potential space for the injection of local anesthetics to selectively block the articular branches of femoral nerve and accessory obturator nerve to the anterior hip capsule. Two highly similar ultrasound-guided interfascial plane blocks that target the IP, pericapsular nerve group (PENG) block and iliopsoas plane block (IPB), were both designed to achieve motor-sparing sensory block to the anterior hip capsule. However, the most recent evidence shows that PENG block can cause 25% or more of quadriceps weakness, while IPB remains the hip block that can preserve quadriceps strength. In this scoping review of quadriceps weakness after PENG block and IPB, we first performed a focused review on the complicated anatomy surrounding the anterior hip capsule. Then, we systematically searched for all currently available cadaveric and clinical studies utilizing PENG block and IPB, with a focus on quadriceps weakness and its potential mechanism from the perspectives of fascial plane spread along and outside of the IP. We conclude that quadriceps weakness after PENG block, which places its needle tip directly deep to iliopsoas tendon (IT), may be the result of iliopectineal bursal injection. The incidental bursal injection, which can be observed on ultrasound as a medial fascial plane spread, can cause bursal rupture/puncture and an anteromedial extra-IP spread to involve the femoral nerve proper within fascia iliaca compartment (FIC). In comparison, IPB places its needle tip lateral to IT and injects just one-fourth of the volume of PENG block. The current evidence, albeit still limited, supports IPB as the true motor-sparing hip block. To avoid quadriceps weakness after PENG block, a more laterally placed needle tip, away from the undersurface of IT, and a reduction in injection volume should be considered. Future studies should focus on comparing the analgesic effects and quadriceps function impairment between PENG block and IPB.

Background: The performance of chest radiography-based age and sex prediction has not been well validated. We used a deep learning model to predict the age and sex of healthy adults based on chest radiographs (CXRs). Methods: In this retrospective study, 66,643 CXRs of 47,060 healthy adults were used for model training and testing. In total, 47,060 individuals (mean age ± standard deviation, 38.7 ± 11.9 years; 22,144 males) were included. By using chronological ages as references, mean absolute error (MAE), root mean square error (RMSE), and Pearson’s correlation coefficient were used to assess the model performance. Summarized class activation maps were used to highlight the activated anatomical regions. The area under the curve (AUC) was used to examine the validity for sex prediction. Results: When model predictions were compared with the chronological ages, the MAE was 2.1 years, RMSE was 2.8 years, and Pearson’s correlation coefficient was 0.97 (p < 0.001). Cervical, thoracic spines, first ribs, aortic arch, heart, rib cage, and soft tissue of thorax and flank seemed to be the most crucial activated regions in the age prediction model. The sex prediction model demonstrated an AUC of >0.99. Conclusion: Deep learning can accurately estimate age and sex based on CXRs.

Purpose: To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Solid Tumors (RECIST) categories. Materials and Methods: Target lesions in lung, liver, and lymph nodes were randomly chosen from patients in 10 historical clinical trials for various cancers, ensuring a balanced representation of lesion types, diameter ranges described in the QIBA Profile, and variations in change magnitudes. Three radiologists independently segmented these lesions at baseline and follow-up scans using 2 software tools. Two types of predefined disagreements were assessed: Type I: substantive disagreement, where the disagreement between QIBA CTvol classifications and RECIST categories could not be attributed to the improved sensitivity of volumetry in detecting changes; and Type II: disagreement potentially arising from the improved sensitivity of volumetry in detecting changes. The proportion of lesions with disagreements between QIBA CTvol and RECIST, as well as the type of disagreements, was reported along with 95% CIs, both overall and within subgroups representing various factors. Results: A total of 2390 measurements from 478 lesions (158 lungs, 170 livers, 150 lymph nodes) in 281 patients were included. QIBA CTvol agreed with RECIST in 66.6% of interpretations. Of the 33.4% of interpretations with discrepancies, substantive disagreement (Type I) occurred in only 1.5% (95% CI: [0.8%, 2.1%]). Factors such as scanner vendor (P = .584), segmentation tool (P = .331), and lesion type (P = .492) were not significant predictors of disagreement. Significantly more disagreements were observed for larger lesions (≥50 mm, as defined in the QIBA Profile). Conclusion: We conclude that QIBA CTvol classifications agree with RECIST categories.

The iliopsoas plane (IP) is a fascial plane deep to the iliopsoas complex and is the target of several novel ultrasound-guided analgesic interventions for hip. Currently, limited information is known about its parameters. From the pelvic magnetic resonance (MR) images of an adult Eastern Asian population (n = 49), the IP width, depth, and needle-beam angle in the axial plane immediately caudal to the level of indirect tendon of rectus femoris (RF) were found to be 10.7 ± 1.6 mm, 48.5 ± 15.5 mm, and 84.2 ± 8.2 degrees, respectively. There was a statistically significant difference in the age categories for IP width, and older patients seemed to have wider IP. Our data may provide applications for the technical modification of ultrasound-guided iliopsoas plane block (IPB) in acute hip pain management and the future development of ultrasound-guided single-needle-entry radiofrequency neuroablation in chronic hip pain management.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.