CT and MR for bone mineral density and trabecular bone score assessment in osteoporosis evaluation

The influence of proliferative tissue on Hounsfield unit and its correlation with BMD in middle-aged and elderly patients with lumbar degenerative diseases

PURPOSE

The difference of Hounsfield Unit (HU) value in different regions of L3 vertebra in middle-aged and elderly patients with lumbar degeneration diseases (LDD) was analyzed. To investigate the influence of proliferative tissue on HU value of cancellous bone and its correlation with bone mineral density (BMD).

METHODS

The medical records of middle-aged and elderly patients with LDD in our hospital from December 2020 to December 2023 were retrospectively analyzed. The patients were divided into osteophyte group and no-osteophyte group according to the presence or absence of osteophyte formation on lumbar spine X-ray. In osteophyte group, cancellous bone HU value, containing cortical bone overall HU value and containing osteophyte overall HU value in L3 vertebra were measured on the lumbar CT cross-section. In no-osteophyte group, only the cancellous bone HU value and the containing cortical bone overall HU value were measured. Differences in HU value in different regions of the L3 vertebral body were compared within and between groups of middle-aged and elderly patients with LDD, respectively. To investigate its effect on cancellous bone HU measurements and to do a correlation analysis with patients' BMD.

RESULTS

A total of 115 patients with LDD were included in this study, including 65 males and 50 females, with an average age of 67.83 ± 6.59 years. The results of the study showed no statistical differences in age (P = 0.15), gender (P = 0.57), smoking (P = 0.88), drinking history (P = 0.76), medical history (P > 0.05) and BMI(P = 0.29) between the two groups. In osteophyte group, the mean cancellous bone HU value was 98.00 ± 25.50 HU, the containing cortical bone overall HU value was 189.02 ± 46.18 HU, and the containing osteophyte overall HU value was 232.69 ± 56.01 HU. The overall HU values containing cortical bone and containing osteophyte were significantly higher than cancellous bone HU value (P < 0.001). In no-osteophyte group, the mean cancellous bone HU value was 102.04 ± 19.64 HU, and the containing cortical bone overall HU value was 175.00 ± 28.97 HU, which was statistically significantly different (P < 0.001). There was no significant difference in cancellous bone HU value and the containing cortical bone overall HU value between the two groups (P > 0.05). The results of the Pearson correlation analysis showed a significant correlation between the cancellous bone HU value of the L3 vertebrae and the QCT BMD value of the patients (r = 0.95, P < 0.001). However, there was no significant correlation between containing cortical bone overall HU value and containing osteophyte overall HU value and the patient's QCT BMD value (P > 0.05).

CONCLUSIONS

Vertebral HU value is an alternative measurement that effectively reflects the patient's BMD. In middle-aged and elderly LDD patients, HU values in different areas of L3 vertebra are significantly different, and hyperplastic tissues such as cortical bone and osteophytes may exponentially lead to higher HU value in patients. Compared with other measurement areas, vertebral cancellous bone HU value have the advantage of accurately assessing patients' BMD.

Deep learning-based pseudo-CT synthesis from zero echo time MR sequences of the pelvis

OBJECTIVES

To generate pseudo-CT (pCT) images of the pelvis from zero echo time (ZTE) MR sequences and compare them to conventional CT.

METHODS

Ninety-one patients were prospectively scanned with CT and MRI including ZTE sequences of the pelvis. Eleven ZTE image volumes were excluded due to implants and severe B1 field inhomogeneity. Out of the 80 data sets, 60 were used to train and update a deep learning (DL) model for pCT image synthesis from ZTE sequences while the remaining 20 cases were selected as an evaluation cohort. CT and pCT images were assessed qualitatively and quantitatively by two readers.

RESULTS

Mean pCT ratings of qualitative parameters were good to perfect (2-3 on a 4-point scale). Overall intermodality agreement between CT and pCT was good (ICC = 0.88 (95% CI: 0.85-0.90); p < 0.001) with excellent interreader agreements for pCT (ICC = 0.91 (95% CI: 0.88-0.93); p < 0.001). Most geometrical measurements did not show any significant difference between CT and pCT measurements (p > 0.05) with the exception of transverse pelvic diameter measurements and lateral center-edge angle measurements (p = 0.001 and p = 0.002, respectively). Image quality and tissue differentiation in CT and pCT were similar without significant differences between CT and pCT CNRs (all p > 0.05).

CONCLUSIONS

Using a DL-based algorithm, it is possible to synthesize pCT images of the pelvis from ZTE sequences. The pCT images showed high bone depiction quality and accurate geometrical measurements compared to conventional CT. CRITICAL RELEVANCE STATEMENT: pCT images generated from MR sequences allow for high accuracy in evaluating bone without the need for radiation exposure. Radiological applications are broad and include assessment of inflammatory and degenerative bone disease or preoperative planning studies.

KEY POINTS

pCT, based on DL-reconstructed ZTE MR images, may be comparable with true CT images. Overall, the intermodality agreement between CT and pCT was good with excellent interreader agreements for pCT. Geometrical measurements and tissue differentiation were similar in CT and pCT images.

Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint

OBJECTIVE

This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral density (BMD) measurements and clinical information based on machine learning (ML) models.

METHODS

A retrospective cohort of 89 patients was analyzed, including 63 with both OP and RCT (OPRCT) and 26 with OP only. The study analyzed a series of shoulder radiographs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were developed and compared based on their performance in predicting the occurrence and severity of RCT from ROIs' greyscale values and clinical information (age, gender, advantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model predictions.

RESULTS

R1-6 had a positive correlation with BMD respectively. The nine variables, including greyscale R1-6, age, BMD, and AM, were used in the prediction models. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, validation, and testing sets, respectively. SHAP values revealed that the most influential factors on the diagnostic outcomes were the grayscale values of all cancellous bones in ROIs. A column-line graph prediction model based on nine variables was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most superior in predicting the types of RCT within the OPRCT group, with an accuracy of 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the greater tubercle, among others.

CONCLUSIONS

ML models, particularly the RF algorithm, show significant promise in diagnosing RCT occurrence and severity in OP patients using conventional shoulder X-rays based on the nine variables. This method presents a cost-effective, accessible, and non-invasive diagnostic strategy that has the potential to substantially enhance the early detection and management of RCT in OP patient population.

Chemical Shift-Encoded MRI of the Lumbar Vertebral Bone Marrow for Detecting Osteoporosis With Low Trabecular Bone Quality in Patients With Breast Cancer Receiving Aromatase Inhibitors

BACKGROUND

Osteoporosis with low trabecular bone quality (OLB) in patients with breast cancer receiving aromatase inhibitor (AI) therapy is associated with an increased risk of vertebral fractures. The capability of chemical shift-encoded MRI (CSE-MRI) in detecting OLB needs to be investigated.

PURPOSE

To assess the diagnostic performance of proton density fat fraction (PDFF) and R2* measurements from CSE-MRI for detecting OLB in postmenopausal women with breast cancer undergoing AI therapy.

STUDY TYPE

Prospective.

POPULATION

126 postmenopausal females (mean age: 69.5 ± 8.8 years) receiving AIs (average period: 41.6 ± 26.5 months) after breast cancer surgery.

FIELD STRENGTH/SEQUENCE

1.5-T, three-dimensional CSE-MRI (six echoes), T1-weighted Dixon, short tau inversion recovery, and diffusion-weighted images.

ASSESSMENT

Both CSE-MRI and dual-energy x-ray absorptiometry were performed on the same day. Measurements included averaged PDFF, R2*, bone mineral density (BMD), and trabecular bone score (TBS) from L1 to L4 vertebrae. A T-score ≤ -2.5 from BMD measurements indicated osteoporosis, whereas T-scores of ≤ - 2.5 plus TBS ≤-3.7 indicated OLB. The diagnostic performance of PDFF, R2*, and the combination of PDFF and R2* for identifying osteoporosis or OLB was assessed.

STATISTICAL TESTS

Student's t-test; Mann-Whitney U test; χ2 or Fisher exact tests; Pearson correlation; multivariate analysis; Receiver operating characteristic (ROC) analysis with the area under the curve (AUC); logistic regression model; intraclass correlation coefficient. A P-value <0.05 was considered statistically significant.

RESULTS

For detecting osteoporosis, AUC values were 0.59 (PDFF), 0.66 (R2*), and 0.65 (combined PDFF and R2*). Significant mean differences were noted between patients with and without OLB for PDFF (66.11 ± 5.36 vs. 57.49 ± 6.43) and R2* (46.62 ± 9.24 vs. 63.36 ± 12.44). AUC values for detecting OLB were 0.75 (PDFF), 0.82 (R2*), and 0.84 (combined PDFF and R2*).

DATA CONCLUSION

R2* may perform better than PDFF for identifying OLB in patients with breast cancer receiving AIs.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 4.

Factors influencing vertebral collapse in osteoporotic vertebral fractures: a case-control study of symptomatic patients attended in the emergency department

This study aimed to identify risk factors for the collapse of osteoporotic vertebral fractures (OVFs). We analyzed data from conventional radiography and computed tomography in patients with OVFs and found that older age and two radiological measurements were predictive for vertebral collapse. These factors can be useful for clinical practice.

PURPOSE

To identify risk factors for collapse of osteoporotic vertebral fractures (OVF) on computed tomography (CT) and conventional radiography (CR).

METHODS

This is a retrospective case-control study including a series of patients with OVF diagnosed at the emergency department of our institution from January to September 2019. Inclusion criteria were to have standing CR and supine CT within 2 weeks after the diagnosis of OVF and a follow-up CR at 6 months or later. We evaluated different imaging measurements at the initial diagnostic examinations, including vertebral height loss, local kyphosis, vertebral density, and fracture type according to the grading systems of Genant, Sugita, Association of Osteosynthesis (AO) Spine, and the German Society for Orthopaedics and Trauma. Vertebral collapse was defined as loss of ≥ 50% of vertebral area or height. Cases and controls were defined as OVFs which collapse and do not collapse, respectively, on follow-up.

RESULTS

Fifty-six patients were included in the study, with a mean age of 72.6 ± 1.2 years, including 48 women. Twenty-five (44.6%) OVFs developed collapse on follow-up. None of the fracture classification systems were found to be predictive of collapse. Multivariate analysis showed that older age, increased density ratio (≥ 2) between the fractured and non-fractured vertebral bodies, and a ≥ 6% difference in posterior vertebral height (PVH) loss between standing CR and supine CT exhibited 88% discriminative power in predicting vertebral collapse.

CONCLUSIONS

Age over 72.5 years, a density ratio ≥ 2 between the fractured and non-fractured vertebral bodies, and a difference equal to or higher than 6% in PVH loss between standing CR and supine CT, are risk factors for developing vertebral collapse after OVF.

Association between patellofemoral osteoarthritis with demographic features and anatomical variants of the knee in non-traumatic patients

BACKGROUND

Patellofemoral osteoarthritis (PFOA) is a common cause of knee discomfort and impairment, particularly among athletes. The development of PFOA has been associated with anatomical knee variations, such as trochlear dysplasia and patella alta. However, the relationship between these anatomical variants and the development of PFOA remains poorly understood. This study aimed to investigate the association between PFOA and knee anatomical variants in a cohort of patients.

METHODS

The study included 200 patients with PFOA and 200 healthy controls. In this study, we investigate the relationship of osteoarthritis with both anatomical variants and demographic characteristics. The participants underwent Magnetic resonance imaging (MRI) evaluation of the knee, and anatomical variants including trochlear dysplasia and patella alta were assessed. The severity of PFOA was also graded based on cartilage area and depth, as well as the bone marrow involvement and presence of osteophytes.

RESULTS

Statistically significant differences were observed between the two groups in terms of Tibial tuberosity-trochlear groove (TT-TG) distance, patella position, trochlear dysplasia, and Insall-Salvati ratio. The mean TT-TG distance, prevalence of alta patella position, and Insall-Salvati ratio were significantly higher in cases (P<0.001 for all), and cases had a higher incidence of trochlear dysplasia (P<0.001). There were no significant differences between cases and controls regarding patella baja.

CONCLUSION

Anatomical knee variants, including the TT-TG distance, trochlear dysplasia, and Insall-Salvati ratio, are significant risk factors for PFOA progression. The results also indicate that higher BMI and older age are significantly associated with more measures of MRI Osteoarthritis Knee Score (MOAKS) than demographic information. Among anatomical variants, a higher TT-TG distance and an increased grade of trochlear dysplasia show a significant relationship with more measures of MOAKS. Understanding the relationship between these factors has important clinical and research implications and can help inform the development of new treatments.