Automated Morphometric Analysis of the Hip Joint on MRI from the German National Cohort Study

Purpose

To develop and validate an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study.

Materials and Methods

A secondary analysis on 40 participants (mean age, 51 years; age range, 30-67 years; 25 women) from the prospective GNC MRI study (2015-2016) was performed. Based on a proton density-weighted three-dimensional fast spin-echo sequence, a morphometric analysis approach was developed, including deep learning-based landmark localization, bone segmentation of the femora and pelvis, and a shape model for annotation transfer. The centrum-collum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion were derived. Quantitative validation was provided by comparison with average manual assessments of radiologists in a cross-validation format. Paired-sample t tests with a Bonferroni-corrected significance level of .005 were employed alongside mean differences and 10th/90th percentiles, median absolute deviations (MADs), and intraclass correlation coefficients (ICCs).

Results

High agreement in mean Dice similarity coefficients was achieved (average of 97.52% ± 0.46 [standard deviation]). The subsequent morphometric analysis produced results with low mean MAD values, with the highest values of 3.34° (alpha 03:00 o'clock position) and 0.87 mm (HNO) and ICC values ranging between 0.288 (HNO ratio) and 0.858 (CE) compared with manual assessments. These values were in line with interreader agreements, which at most had MAD values of 4.02° (alpha 12:00 o'clock position) and 1.07 mm (HNO) and ICC values ranging between 0.218 (HNO ratio) and 0.777 (CE).

Conclusion

Automatic extraction of geometric hip parameters from MRI is feasible using a morphometric analysis approach with deep learning.Keywords: Computer-Aided Diagnosis (CAD), Interventional-MSK, MR-Imaging, Neural Networks, Skeletal-Appendicular, Hip, Anatomy, Computer Applications-3D, Segmentation, Vision, Application Domain, Quantification Supplemental material is available for this article. © RSNA, 2021.

Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA

The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generation sequencing of circulating tumor DNA (ctDNA) encode tumor-specific radiogenomic expression patterns that can be probed to study this problem. Preliminary findings are reported from a radiogenomic analysis of CT imaging, cfDNA, and ctDNA in 24 patients (median age, 64 years; range, 49-74 years) with stage III lung cancer undergoing chemoradiation on a prospective pilot study (NCT00921739) between September 2009 and September 2014. Unsupervised clustering of radiomic signatures resulted in two clusters that were associated with ctDNA TP53 mutations (P = .03) and changes in cfDNA concentration after 2 weeks of chemoradiation (P = .02). The radiomic features dissimilarity (hazard ratio [HR] = 0.56; P = .05), joint entropy (HR = 0.56; P = .04), sum entropy (HR = 0.53; P = .02), and normalized inverse difference (HR = 1.77; P = .05) were associated with overall survival. These results suggest heterogeneous and low-attenuating disease without a detectable ctDNA TP53 mutation was associated with early surges of cfDNA concentration in response to therapy and a generally better prognosis. Keywords: CT-Quantitative, Radiation Therapy, Lung, Computer Applications-3D, Oncology, Tumor Response, Outcomes Analysis Clinical trial registration no. NCT00921739 Supplemental material is available for this article. © RSNA, 2021.

Multiplanar reformations and minimum intensity projections using multi-detector row CT for assessing anomalies and disorders of the pancreaticobiliary tree

CT scan is regarded as the imaging modality of choice in patients with pancreaticobiliary ductal abnormalities. However, the axial orientation of the CT images provides only limited anatomical view of pancreaticobiliary ductal abnormalities. The technological advances of multi-detector row CT and three-dimensional image processing in workstations allows rapid image acquisition and a short postprocessing time. In particular, multiplanar reformations (MPR) and minimum intensity projections (MinIP) offer rapid and accurate images of the anatomy and abnormalities of the pancreaticobiliary tree. Moreover, MPR and MinIP help determine the relationship between the pancreaticobiliary ductal anatomy and the surrounding structures. This pictorial review illustrates the wide spectrum of images obtained by the MPR and MinIP of the anomalies and disorders of the pancreaticobiliary tree.