Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20 000 Adults

Opportunistic use of chest low-dose computed tomography (LDCT) imaging for low bone mineral density and osteoporosis screening: cutoff thresholds for the attenuation values of the lower thoracic and upper lumbar vertebrae

Background

Osteoporosis remains substantially underdiagnosed and undertreated worldwide. Chest low-dose computed tomography (LDCT) may provide a valuable and popular opportunity for osteoporosis screening. This study sought to evaluate the feasibility of the screening of low bone mineral density (BMD) and osteoporosis with mean attenuation values of the lower thoracic compared to upper lumbar vertebrae. The cutoff thresholds of the mean attenuation values in Hounsfield units (HU) were derived to facilitate implementation of opportunistic screening using chest LDCT.

Methods

The participants aged 30 years or older who underwent chest LDCT and quantitative computed tomography (QCT) examinations from August 2018 to October 2020 in our hospital were consecutively included in this retrospective study. A region of interest (ROI) was placed in the trabecular bone of each vertebral body to measure the HU values. The correlations of mean HU values of lower thoracic (T11-T12) and upper lumbar (L1-L2) vertebrae with age and lumbar BMD obtained with QCT were performed using the Pearson correlation coefficient, respectively. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was generated to determine the cutoff thresholds for distinguishing low BMD from normal and osteoporosis from non-osteoporosis.

Results

A total of 1,112 participants were included in the final study cohort (743 men and 369 women, mean age 58.2±8.9 years; range, 32-88 years). The mean HU values of T11-T12 and L1-L2 were significantly different among 3 QCT-defined BMD categories of osteoporosis, osteopenia, and normal (P<0.001). The differences in HU values between T11-T12 and L1-L2 in each category of bone status were statistically significant (P<0.001). The mean HU values of T11-T12 (r=-0.453, P<0.001) and L1-L2 (r=-0.498, P<0.001) had negative correlations with age. Positive correlations were observed between the mean HU values of T11-T12 (r=0.872, P<0.001) and L1-L2 (r=0.899, P<0.001) with BMD. The optimal cutoff thresholds for distinguishing low BMD from normal were average T11-T12 ≤157 HU [AUC =0.941, 95% confidence interval (CI): 0.925-0.954, P<0.001] and L1-L2 ≤138 HU (AUC =0.950, 95% CI: 0.935-0.962, P<0.001), as well as distinguishing osteoporosis from non-osteoporosis were average T11-T12 ≤125 HU (AUC =0.960, 95% CI: 0.947-0.971, P<0.001) and L1-L2 ≤107 HU (AUC =0.961, 95% CI: 0.948-0.972, P<0.001). There was no significant difference between the AUC values of T11-T12 and L1-L2 for low BMD (P=0.07) and osteoporosis (P=0.92) screening.

Conclusions

We have conducted a study on low BMD and osteoporosis screening using mean attenuation values of lower thoracic and upper lumbar vertebrae. Assessment of mean attenuation values of T11-T12 and L1-L2 can be used interchangeably for low BMD and osteoporosis screening using chest LDCT, and their cutoff thresholds were established.

Osteosarcopenia and Mortality in Older Adults Undergoing Transcatheter Aortic Valve Replacement

Importance

Osteosarcopenia is an emerging geriatric syndrome characterized by age-related deterioration in muscle and bone. Despite the established relevance of frailty and sarcopenia among older adults undergoing transcatheter aortic valve replacement (TAVR), osteosarcopenia has yet to be investigated in this setting.

Objective

To determine the association between osteosarcopenia and adverse outcomes following TAVR.

Design, Setting, and Participants

This is a post hoc analysis of the Frailty in Aortic Valve Replacement (FRAILTY-AVR) prospective multicenter cohort study and McGill extension that enrolled patients aged 70 years or older undergoing TAVR from 2012 through 2022. FRAILTY-AVR was conducted at 14 centers in Canada, the United States, and France between 2012 and 2016, and patients at the McGill University-affiliated center in Montreal, Québec, Canada, were enrolled on an ongoing basis up to 2022.

Exposure

Osteosarcopenia as measured on computed tomography (CT) scans prior to TAVR.

Main Outcomes and Measures

Clinically indicated CT scans acquired prior to TAVR were analyzed to quantify psoas muscle area (PMA) and vertebral bone density (VBD). Osteosarcopenia was defined as a combination of low PMA and low VBD according to published cutoffs. The primary outcome was 1-year all-cause mortality. Secondary outcomes were 30-day mortality, hospital length of stay, disposition, and worsening disability. Multivariable logistic regression was used to adjust for potential confounders.

Results

Of the 605 patients (271 [45%] female) in this study, 437 (72%) were octogenarian; the mean (SD) age was 82.6 (6.2) years. Mean (SD) PMA was 22.1 (4.5) cm2 in men and 15.4 (3.5) cm2 in women. Mean (SD) VBD was 104.8 (35.5) Hounsfield units (HU) in men and 98.8 (34.1) HU in women. Ninety-one patients (15%) met the criteria for osteosarcopenia and had higher rates of frailty, fractures, and malnutrition at baseline. One-year mortality was highest in patients with osteosarcopenia (29 patients [32%]) followed by those with low PMA alone (18 patients [14%]), low VBD alone (16 patients [11%]), and normal bone and muscle status (21 patients [9%]) (P < .001). Osteosarcopenia, but not low VBD or PMA alone, was independently associated with 1-year mortality (odds ratio [OR], 3.18; 95% CI, 1.54-6.57) and 1-year worsening disability (OR, 2.11; 95% CI, 1.19-3.74). The association persisted in sensitivity analyses adjusting for the Essential Frailty Toolset, Clinical Frailty Scale, and geriatric conditions such as malnutrition and disability.

Conclusions and Relevance

The findings suggest that osteosarcopenia detected using clinical CT scans could be used to identify frail patients with a 3-fold increase in 1-year mortality following TAVR. This opportunistic method for osteosarcopenia assessment could be used to improve risk prediction, support decision-making, and trigger rehabilitation interventions in older adults.

Performance of calcium quantifications on low-dose photon-counting detector CT with high-pitch: A phantom study

Purpose

To evaluate the performance of calcium quantification on photon-counting detector CT (PCD-CT) with high-pitch at low radiation doses compared to third-generation dual-source energy-integrating detector CT (EID-CT).

Materials and methods

The phantom with three calcium inserts (50, 100, and 300 mg of calcium per milliliter), with and without the elliptical outer layer, was evaluated using high-pitch (3.2) and standard pitch (0.8) on PCD-CT, and standard pitch on EID-CT. Scans were performed with different tube voltages (PCD-CT: 120 and 140 kilo-voltage peak [kVp]; EID-CT: 70/Sn150 and 100/Sn150 kVp) and four radiation doses (1, 3, 5, and, 10 milli-Gray [mGy]). Utilizing the true calcium concentrations (CCtrue) of the phantom as the gold standard references, regression equations for each kVp setting were formulated to convert CT attenuations (CaCT) into measured calcium concentrations (CCm). The correlation analysis between CaCT and CCtrue was performed. The percentage absolute bias (PAB) was calculated from the differences between CCm and CCtrue and used to analyze the effects of scanning parameters on calcium quantification accuracy.

Results

A strong correlation was found between CaCT and CCtrue on PCD-CT (r > 0.99) and EID-CT (r > 0.98). For high- and standard-pitch scans on PCD-CT, the accuracy of calcium quantification is comparable (p = 0.615): the median (interquartile range [IQR]) of PAB was 5.59% (2.79%-8.31%) and 4.87 % (2.62%-8.01%), respectively. The PAB median (IQR) was 7.43% (3.77%-11.75%) for EID-CT. The calcium quantification accuracy of PCD-CT is superior to EID-CT at the large phantom (5.46% [2.68%-9.55%] versus 9.01% [6.22%-12.74%]), and at the radiation dose of 1 mGy (4.43% [2.08%-8.59%] versus 13.89% [8.93%-23.09%]) and 3 mGy (4.61% [2.75%-6.51%] versus 9.97% [5.17%-14.41%]), all p < 0.001.

Conclusions

Calcium quantification using low-dose PCD-CT with high-pitch scanning is feasible and accurate, and superior to EID-CT.

Accelerated fusion dynamics by recombinant human bone morphogenetic protein-2 following transforaminal lumbar interbody fusion, particularly in osteoporotic conditions

BACKGROUND CONTEXT

Early fusion is crucial in interbody procedures to minimize mechanical complications resulting from delayed union, especially for patients with osteoporosis. Bone morphogenetic proteins (BMPs) are used in spinal fusion procedures; however, limited evaluation exists regarding time-to-fusion for BMP use, particularly in patients with osteoporosis.

PURPOSE

To evaluate the difference in time-to-fusion after single-level transforaminal lumbar interbody fusion (TLIF) surgery between recombinant human bone morphogenetic protein-2 (rhBMP-2) usage and nonusage groups according to bone density.

STUDY DESIGN

Retrospective single-center cohort study.

PATIENT SAMPLE

This study enrolled 132 patients (mean age, 65.25±8.66; male patients, 40.9%) who underwent single-level TLIF for degenerative disorders between February 2012 and December 2021, with pre and postoperative computed tomography (CT).

OUTCOME MEASURE

The interbody fusion mass and bone graft status on postoperative CT scans was obtained annually, and time-to-fusion was recorded for each patient.

METHODS

The patients were divided into 2 groups based on rhBMP-2 use during the interbody fusion procedure. Patients were further divided into osteoporosis, osteopenia, and normal groups based on preoperative L1 vertebral body attenuation values, using cutoffs of 90 and 120 Hounsfield units. It was strictly defined that fusion is considered complete when a trabecular bone bridge was formed, and therefore, the time-to-fusion was measured in years. Time-to-fusion was statistically compared between BMP group and non-BMP groups, followed by further comparison according to bone density.

RESULTS

The time-to-fusion differed significantly between BMP and non-BMP groups, with half of the patients achieving fusion within 2.5 years in the BMP group compared with 4 years in the non-BMP group (p<.001). The fusion rate varied based on bone density, with the maximum difference observed in the osteoporosis group, when half of the patients achieved fusion within 3 years in the BMP group compared to 5 years in the non-BMP group (p<.001). Subgroup analysis was conducted, revealing no significant associations between time-to-fusion and factors known to influence the fusion process, including age, gender, medical history, smoking and alcohol use, and medication history, except for rh-BMP2 use and bone density.

CONCLUSIONS

RhBMP-2 usage significantly reduced time-to-fusion in single-level TLIF, especially in patients with osteoporosis.

LEVEL OF EVIDENCE

Level III.

The association between computed tomography-based osteosarcopenia and osteoporotic vertebral fractures: a longitudinal study

PURPOSE

Osteoporosis and sarcopenia usually coexist in older population. The concept of osteosarcopenia has been proposed in recent years. However, studies on the relationship between osteosarcopenia and the risk of fracture are rare, and the association is unclear at present. This study aimed to investigate the association between osteosarcopenia evaluated based on chest computed tomography (CT) and osteoporotic vertebral fracture (OVF).

METHODS

This study recruited 7906 individuals aged 50 years and older who did not have OVFs and underwent chest CT for physical examination between July 2016 and September 2019. Subjects were followed up annually until June 2023. Osteosarcopenia was defined by a low muscle area of the erector spinae (< 25.4 cm2) and the bone attenuation (Hounsfield unit, HU < 135). Genant's grades were used to define OVFs. Control subjects were selected by Propensity Score Matching at a ratio 20:1. Cox proportional hazards models were used to assess the associations between osteosarcopenia and OVFs.

RESULTS

Of the 7906 participants included, 95 had a new OVF within a median follow-up of 3 years. A total of 1900 control subjects were matched. Individuals in the osteosarcopenia group had a higher prevalence of spinal fractures than those in normal group (16.4% vs. 0.4%, P < 0.001). Osteosarcopenia was independently associated with OVF (adjusted hazard ratio (aHR): 12.67, 95% confidence interval (CI) 3.79-42.40) and severe OVF (aHR = 14.07, 95% CI 1.84-107.66). Similar trends were observed in males, females and those subjects aged older than 60 years. Osteosarcopenia had good predictive efficacy for OVF (area under the curve = 0.836). A nomogram was also developed for clinical application.

CONCLUSION

Osteosarcopenia assessed based on chest CT was associated with OVF, and osteosarcopenia has good performance for vertebral fracture prediction.

Vertebral HU value and the pectoral muscle index based on chest CT can be used to opportunistically screen for osteoporosis

BACKGROUND

Existing studies have shown that computed tomography (CT) attenuation and skeletal muscle tissue are strongly associated with osteoporosis; however, few studies have examined whether vertebral HU values and the pectoral muscle index (PMI) measured at the level of the 4th thoracic vertebra (T4) are strongly associated with bone mineral density (BMD). In this study, we demonstrate that vertebral HU values and the PMI based on chest CT can be used to opportunistically screen for osteoporosis and reduce fracture risk through prompt treatment.

METHODS

We retrospectively evaluated 1000 patients who underwent chest CT and DXA scans from August 2020-2022. The T4 HU value and PMI were obtained using manual chest CT measurements. The participants were classified into normal, osteopenia, and osteoporosis groups based on the results of dual-energy X-ray (DXA) absorptiometry. We compared the clinical baseline data, T4 HU value, and PMI between the three groups of patients and analyzed the correlation between the T4 HU value, PMI, and BMD to further evaluate the diagnostic efficacy of the T4 HU value and PMI for patients with low BMD and osteoporosis.

RESULTS

The study ultimately enrolled 469 participants. The T4 HU value and PMI had a high screening capacity for both low BMD and osteoporosis. The combined diagnostic model-incorporating sex, age, BMI, T4 HU value, and PMI-demonstrated the best diagnostic efficacy, with areas under the receiver operating characteristic curve (AUC) of 0.887 and 0.892 for identifying low BMD and osteoporosis, respectively.

CONCLUSIONS

The measurement of T4 HU value and PMI on chest CT can be used as an opportunistic screening tool for osteoporosis with excellent diagnostic efficacy. This approach allows the early prevention of osteoporotic fractures via the timely screening of individuals at high risk of osteoporosis without requiring additional radiation.

End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography

BACKGRUOUND

Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA).

METHODS

The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae.

RESULTS

Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R2 of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R2 of 0.781.

CONCLUSION

CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.

The influence of osteoporosis on mechanical complications in lumbar fusion surgery: a systematic review

Background

Adults undergoing spine surgery often have underlying osteoporosis, which may be a risk factor for postoperative complications. Although these associations have been described, osteoporosis remains profoundly underdiagnosed and undertreated in the spine surgery population. A thorough, comprehensive systematic review summarizing the relationships between bone mineral density (BMD) and specific complications of lumbar fusion surgery could be a valuable resource for raising awareness and supporting clinical practice changes.

Methods

PubMed, Embase, and Web of Science databases were searched for original clinical research articles reporting on BMD, or surrogate measure, as a predictor of complications in adults undergoing elective lumbar fusion for degenerative disease or deformity. Endpoints included cage subsidence, screw loosening, pseudarthrosis, vertebral fracture, junctional complications, and reoperation.

Results

A total of 71 studies comprising 12,278 patients were included. Overall, considerable heterogeneity in study populations, methods of bone health assessment, and definition and evaluation of clinical endpoints precluded meta-analysis. Nevertheless, low BMD was associated with higher rates of implant failures like cage subsidence and screw loosening, which were often diagnosed with concomitant pseudarthrosis. Osteoporosis was also a significant risk factor for proximal junctional kyphosis, particularly due to fracture. Many studies found surgical site-specific BMD to best predict focal complications. Functional outcomes were inconsistently addressed.

Conclusions

Our findings suggest osteoporosis is a significant risk factor for mechanical complications of lumbar fusion. These results emphasize the importance of preoperative osteoporosis screening, which allows for medical and surgical optimization of high-risk patients. This review also highlights current practical challenges facing bone health evaluation in patients undergoing elective surgery. Future prospective studies using standardized methods are necessary to strengthen existing evidence, identify optimal predictive thresholds, and establish specialty-specific practice guidelines. In the meantime, an awareness of the surgical implications of osteoporosis and utility of preoperative screening can provide for more informed, effective patient care.

Femoral osteoporosis prediction model using autosegmentation and machine learning analysis with PyRadiomics on abdomen-pelvic computed tomography (CT)

Background

With the advancement of artificial intelligence technology and radiomics analysis, opportunistic prediction of osteoporosis with computed tomography (CT) is a new paradigm in osteoporosis screening. This study aimed to assess the diagnostic performance of osteoporosis prediction by the combination of autosegmentation of the proximal femur and machine learning analysis with a reference standard of dual-energy X-ray absorptiometry (DXA).

Methods

Abdomen-pelvic CT scans were retrospectively analyzed from 1,122 patients who received both DXA and abdomen-pelvic computed tomography (APCT) scan from January 2018 to December 2020. The study cohort consisted of a training cohort and a temporal validation cohort. The left proximal femur was automatically segmented, and a prediction model was built by machine-learning analysis using a random forest (RF) analysis and 854 PyRadiomics features. The technical success rate of autosegmentation, diagnostic test, area under the receiver operator characteristics curve (AUC), and precision recall curve (AUC-PR) analysis were used to analyze the training and validation cohorts.

Results

The osteoporosis prevalence of the training and validation cohorts was 24.5%, and 10.3%, respectively. The technical success rate of autosegmentation of the proximal femur was 99.7%. In the diagnostic test, the training and validation cohorts showed 78.4% vs. 63.3% sensitivity, 89.4% vs. 98.1% specificity. The prediction performance to identify osteoporosis within the groups used for training and validation cohort was high and the AUC and AUC-PR to forecast the occurrence of osteoporosis within the training and validation cohorts were 90.8% [95% confidence interval (CI), 88.4-93.2%] vs. 78.0% (95% CI, 76.0-79.9%) and 94.6% (95% CI, 89.3-99.8%) vs. 88.8% (95% CI, 86.2-91.5%), respectively.

Conclusions

The osteoporosis prediction model using autosegmentation of proximal femur and machine-learning analysis with PyRadiomics features on APCT showed excellent diagnostic feasibility and technical success.

Older Tissue Age Derived From Abdominal Computed Tomography Biomarkers of Muscle, Fat, and Bone Is Associated With Chronic Conditions and Higher Mortality

OBJECTIVE

To determine whether body composition derived from medical imaging may be useful for assessing biologic age at the tissue level because people of the same chronologic age may vary with respect to their biologic age.

METHODS

We identified an age- and sex-stratified cohort of 4900 persons with an abdominal computed tomography scan from January 1, 2010, to December 31, 2020, who were 20 to 89 years old and representative of the general population in Southeast Minnesota and West Central Wisconsin. We constructed a model for estimating tissue age that included 6 body composition biomarkers calculated from abdominal computed tomography using a previously validated deep learning model.

RESULTS

Older tissue age associated with intermediate subcutaneous fat area, higher visceral fat area, lower muscle area, lower muscle density, higher bone area, and lower bone density. A tissue age older than chronologic age was associated with chronic conditions that result in reduced physical fitness (including chronic obstructive pulmonary disease, arthritis, cardiovascular disease, and behavioral disorders). Furthermore, a tissue age older than chronologic age was associated with an increased risk of death (hazard ratio, 1.56; 95% CI, 1.33 to 1.84) that was independent of demographic characteristics, county of residency, education, body mass index, and baseline chronic conditions.

CONCLUSION

Imaging-based body composition measures may be useful in understanding the biologic processes underlying accelerated aging.

Bone loss over time and risk of osteoporosis in advanced pancreatic cancer

BACKGROUND

Pancreatic cancer has a high risk of developing osteoporosis. However, the impact of osteoporosis has not been well-studied. This study aimed to evaluate bone loss over time and risk of osteoporosis in patients with advanced pancreatic cancer.

METHODS

We retrospectively examined consecutive patients with unresectable pancreatic cancer who had evaluable computed tomography before treatment and at 1-year follow-up. Bone mineral density at the first lumbar vertebra was measured on computed tomography, and osteoporosis was defined as bone mineral density < 135 Hounsfield units. The prevalence and risk factors for osteoporosis, changes in bone mineral density over time and incidence of bone fractures were analyzed.

RESULTS

Three hundred eighty patients were included. Osteoporosis was associated with older age, female sex, low body mass index and poor performance status at baseline. A consistent decrease in bone mineral density was observed over time regardless of age, sex or disease status, resulting in an increase in the prevalence of osteoporosis over time (47% at baseline, 79% at 1 year, 88% at 2 years, 89% at 3 years, 95% at 4 years and 100% at 5 years). Changes in bone mineral density from baseline were greater in patients with locally-advanced pancreatic cancer, in those who received modified FOLFIRINOX or S-IROX for more than 3 months, and in those who received radiation therapy. Incident fractures developed in 45 patients (12%) during follow-up.

CONCLUSIONS

Osteoporosis and osteoporotic fractures were highly prevalent in patients with advanced pancreatic cancer. This study highlights the importance of screening for osteoporosis in such patients.

Clinical use of quantitative computed tomography to evaluate the effect of less paraspinal muscle damage on bone mineral density changes after lumbar interbody fusion

STUDY DESIGN

A retrospective cohort study.

PURPOSE

This study aimed to assess the reliability of quantitative computed tomography (QCT) in measuring bone mineral density (BMD) of instrumented vertebrae and investigate the effect of less paraspinal muscle damage on BMD changes after lumbar interbody fusion.

OVERVIEW OF LITERATURE

Patients always experience a decrease in vertebral BMD after lumbar interbody fusion. However, to the best of our knowledge, no study has analyzed the effect of paraspinal muscles on BMD changes.

METHODS

This retrospective analysis included a total of 155 patients who underwent single-level lumbar fusion, with 81 patients in the traditional group and 74 patients in the Wiltse group (less paraspinal muscle damage). QCT was used to measure the volumetric BMD (vBMD), Hounsfield unit value, and cross-sectional area of the paraspinal muscles at the upper instrumented vertebrae (UIV), vertebrae one segment above the UIV (UIV+1), and the vertebrae one segment above the UIV+1 (UIV+2). Statistical analyses were performed.

RESULTS

No significant differences in general data were observed between the two groups (p>0.05). Strong correlations were noted between the preoperative and 1-week postoperative vBMD of each segment (p<0.01), with no significant difference between the two time points in both groups (p>0.05). Vertebral BMD loss was significantly higher in UIV+1 and UIV+2 in the traditional group than in the Wiltse group (-13.6%±19.1% vs. -4.2%±16.5%, -10.8%±20.3% vs. -0.9%±37.0%; p<0.05). However, no statistically significant difference was observed in the percent vBMD changes in the UIV segment between the two groups (37.7%±70.1% vs. 36.1%±78.7%, p>0.05).

CONCLUSIONS

QCT can reliably determine BMD in the instrumented spine after lumbar interbody fusion. With QCT, we found that reducing paraspinal muscle destruction through the Wiltse approach during surgery can help preserve the adjacent vertebral BMD; however, it does not help increase the BMD in the instrumented vertebrae.

Interpreting Lung Cancer Screening CTs: Practical Approach to Lung Cancer Screening and Application of Lung-RADS

Lung cancer screening via low-dose computed tomography (CT) reduces mortality from lung cancer, and eligibility criteria have recently been expanded to include patients aged 50 to 80 with at least 20 pack-years of smoking history. Lung cancer screening CTs should be interepreted with use of Lung Imaging Reporting and Data System (Lung-RADS), a reporting guideline system that accounts for nodule size, density, and growth. The revised version of Lung-RADS includes several important changes, such as expansion of the definition of juxtapleural nodules, discussion of atypical pulmonary cysts, and stepped management for suspicious nodules. By using Lung-RADS, radiologists and clinicians can adopt a uniform approach to nodules detected during CT lung cancer screening and reduce false positives.

Effect of fully automatic classification model from different tube voltage images on bone density screening: A self-controlled study

PURPOSE

To develop two bone status prediction models combining deep learning and radiomics based on standard-dose chest computed tomography (SDCT) and low-dose chest computed tomography (LDCT), and to evaluate the effect of tube voltage on reproducibility of radiomics features and predictive efficacy of these models.

METHODS

A total of 1508 patients were enrolled in this retrospective study. LDCT was conducted using 80 kVp, tube current ranging from 100 to 475 mA. On the other hand, SDCT was performed using 120 kVp, tube current ranging from 100 to 520 mA. We developed an automatic thoracic vertebral cancellous bone (TVCB) segmentation model. Subsequently, 1184 features were extracted and two classifiers were developed based on LDCT and SDCT images. Based on the diagnostic results of quantitative computed tomography examination, the first-level classifier was initially developed to distinguish normal or abnormal BMD (including osteoporosis and osteopenia), while the second-level classifier was employed to identify osteoporosis or osteopenia. The Dice coefficient was used to evaluate the performance of the automated segmentation model. The Concordance Correlation Coefficients (CCC) of radiomics features were calculated between LDCT and SDCT, and the performance of these models was evaluated.

RESULTS

Our automated segmentation model achieved a Dice coefficient of 0.98 ± 0.01 and 0.97 ± 0.02 in LDCT and SDCT, respectively. Alterations in tube voltage decreased the reproducibility of the extracted radiomic features, with 85.05 % of the radiomic features exhibiting low reproducibility (CCC < 0.75). The area under the curve (AUC) using LDCT-based and SDCT-based models was 0.97 ± 0.01 and 0.94 ± 0.02, respectively. Nonetheless, cross-validation with independent test sets of different tube voltage scans suggests that variations in tube voltage can impair the diagnostic efficacy of the model. Consequently, radiomics models are not universally applicable to images of varying tube voltages. In clinical settings, ensuring consistency between the tube voltage of the image used for model development and that of the acquired patient image is critical.

CONCLUSIONS

Automatic bone status prediction models, utilizing either LDCT or SDCT images, enable accurate assessment of bone status. Tube voltage impacts reproducibility of features and predictive efficacy of models. It is necessary to account for tube voltage variation during the image acquisition.

The Suitable Population for Opportunistic Low Bone Mineral Density Screening Using Computed Tomography

Objective

To explore the suitable population of CT value for predicting low bone mineral density (low-BMD).

Methods

A total of 1268 patients who underwent chest CT examination and DXA within one-month period retrospectively analyzed. The CT attenuation values of trabecular bone were measured in mid-sagittal plane from thoracic vertebra 7 (T7). Receiver operating characteristic (ROC) curves were used to evaluate the ability to diagnose low-BMD.

Results

The AUC for diagnosing low BMD was larger in women than in men (0.894 vs 0.744, p < 0.05). The AUC increased gradually with the increase of age but decreased gradually with the increase in height and weight (p < 0.05). In females, when specificity was adjusted to approximately 90%, a threshold of 140.25 HU has a sensitivity of 69.3%, which is higher than the sensitivity of 36.5% in males for distinguishing low-BMD from normal. At the age of 70 or more, when specificity was adjusted to approximately 90%, a threshold of 126.31 HU has a sensitivity of 76.1%, which was higher than that of other age groups.

Conclusion

For patients who had completed chest CTs, the CT values were more effective in predicting low-BMD in female, elderly, lower height, and lower weight patients.

Do Hounsfield Units From Intraoperative CT Scans Correlate With Preoperative Values?

BACKGROUND

There is increasing interest in forecasting postoperative complications using bone density metrics. Vertebral Hounsfield unit measurements obtained from CT scans performed for surgical planning or other purposes, known as opportunistic CTs, have shown promise for their ease of measurement and the ability to target density measurement to a particular region of interest. Concomitant with the rising interest in prognostic bone density measurement use has been the increasing adoption of intraoperative advanced imaging techniques. Despite the interest in both outcome prognostication and intraoperative advanced imaging, there is little information regarding the use of CT-based intraoperative imaging as a means to measure bone density.

QUESTIONS/PURPOSES

(1) Can vertebral Hounsfield units be reliably measured by physician reviewers from CT scans obtained intraoperatively? (2) Do Hounsfield units measured from intraoperative studies correlate with values measured from preoperative CT scans?

METHODS

To be eligible for this retrospective study, patients had to have been treated with the use of an intraoperative CT scan for instrumented spinal fusion for either degenerative conditions or traumatic injuries between January 2015 and December 2022. Importantly, patients without a preoperative CT scan of the fused levels within 180 days before surgery or who were indicated for surgery because of infection, metastatic disease, or who were having revision surgery after prior instrumentation were excluded from the query. Of the 285 patients meeting these inclusion criteria, 53% (151) were initially excluded for the following reasons: 36% (102) had intraoperative CT scans obtained after placement of instrumentation, 16% (47) had undergone intraoperative CT scans but the studies were not accessible for Hounsfield unit measurement, and 0.7% (2) had prior kyphoplasty wherein the cement prevented Hounsfield unit measurement. Finally, an additional 19% (53) of patients were excluded because the preoperative CT and intraoperative CT were obtained at different peak voltages, which can influence Hounsfield unit measurement. This yielded a final population of 81 patients from whom 276 preoperative and 276 intraoperative vertebral Hounsfield unit measurements were taken. Hounsfield unit data were abstracted from the same vertebra(e) from both preoperative and intraoperative studies by two physician reviewers (one PGY3 and one PGY5 orthopaedic surgery resident, both pursuing spine surgery fellowships). For a small, representative subset of patients, measurements were taken by both reviewers. The feasibility and reliability of Hounsfield unit measurement were then assessed with interrater reliability of values measured from the same vertebra by the two different reviewers. To compare Hounsfield unit values from intraoperative CT scans with preoperative CT studies, an intraclass correlation using a two-way random effects, absolute agreement testing technique was employed. Because the data were formatted as multiple measurements from the same vertebra at different times, a repeated measures correlation was used to assess the relationship between preoperative and intraoperative Hounsfield unit values. Finally, a linear mixed model with patients handled as a random effect was used to control for different patient and clinical factors (age, BMI, use of bone density modifying agents, American Society of Anesthesiologists [ASA] classification, smoking status, and total Charlson comorbidity index [CCI] score).

RESULTS

We found that Hounsfield units can be reliably measured from intraoperative CT scans by human raters with good concordance. Hounsfield unit measurements of 31 vertebrae from a representative sample of 10 patients, measured by both reviewers, demonstrated a correlation value of 0.82 (95% CI 0.66 to 0.91), indicating good correlation. With regard to the relationship between preoperative and intraoperative measurements of the same vertebra, repeated measures correlation testing demonstrated no correlation between preoperative and intraoperative measurements (r = 0.01 [95% CI -0.13 to 0.15]; p = 0.84). When controlling for patient and clinical factors, we continued to observe no relationship between preoperative and intraoperative Hounsfield unit measurements.

CONCLUSION

As intraoperative CT and measurement of vertebral Hounsfield units both become increasingly popular, it would be a natural extension for spine surgeons to try to extract Hounsfield unit data from intraoperative CTs. However, we found that although it is feasible to measure Hounsfield data from intraoperative CT scans, the obtained values do not have any predictable relationship with values obtained from preoperative studies, and thus, these values should not be used interchangeably. With this knowledge, future studies should explore the prognostic value of intraoperative Hounsfield unit measurements as a distinct entity from preoperative measurements.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Risk factors for treatment-related bone loss and osteoporosis in patients with follicular lymphoma

The survival rate of non-Hodgkin lymphoma (NHL) has steadily improved. However, osteoporosis introduced by treatment is prevalent and associated with increased mortality and disability for patients with NHL. We aimed to investigate factors impacting bone mineral density (BMD) reduction and osteoporosis, and the trend of BMD after chemotherapy. Overall, 97 newly diagnosed patients with follicular lymphoma (FL) were retrospectively enrolled. CT attenuation values were measured to assess BMD levels. Although 73.2% of patients received calcium and vitamin D supplements, 44.3% showed significant BMD reduction, and baseline BMD and hemoglobin levels were the risk factors. 26.6% of patients newly developed osteoporosis post-chemotherapy where age and cumulative dose of glucocorticoid were risk factors. The results of 20 patients with consecutive follow-up showed that BMD continued to decline for 6 months post-chemotherapy and did not return to baseline values. Therefore, BMD evaluation and more positive anti-resorption treatments should be administered for high-risk patients.

State of the Art Imaging of Osteoporosis

Osteoporosis is a common disease, particularly prevalent in geriatric populations, which causes significant worldwide morbidity due to increased bone fragility and fracture risk. Currently, the gold-standard modality for diagnosis and evaluation of osteoporosis progression and treatment relies on dual-energy x-ray absorptiometry (DXA), which measures bone mineral density (BMD) and calculates a score based upon standard deviation of measured BMD from the mean. However, other imaging modalities can also be used to evaluate osteoporosis. Here, we review historical as well as current research into development of new imaging modalities that can provide more nuanced or opportunistic analyses of bone quality, turnover, and density that can be helpful in triaging severity and determining treatment success in osteoporosis. We discuss the use of opportunistic computed tomography (CT) scans, as well as the use of quantitative CT to help determine fracture risk and perform more detailed bone quality analysis than would be allowed by DXA . Within magnetic resonance imaging (MRI), new developments include the use of advanced MRI techniques such as quantitative susceptibility mapping (QSM), magnetic resonance spectroscopy, and chemical shift encoding-based water-fat MRI (CSE-MRI) to enable clinicians improved assessment of nonmineralized bone compartments as well as a way to longitudinally assess bone quality without the repeated exposure to ionizing radiation. Within ultrasound, development of quantitative ultrasound shows promise particularly in future low-cost, broadly available screening tools. We focus primarily on historical and recent developments within radiotracer use as applicable to osteoporosis, particularly in the use of hybrid methods such as NaF-PET/CT, wherein patients with osteoporosis show reduced uptake of radiotracers such as NaF. Use of radiotracers may provide clinicians with even earlier detection windows for osteoporosis than would traditional biomarkers. Given the metabolic nature of this disease, current investigation into the role molecular imaging can play in the prediction of this disease as well as in replacing invasive diagnostic procedures shows particular promise.

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm

OBJECTIVES

To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans.

MATERIALS AND METHODS

Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT".

RESULTS

The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification.

CONCLUSION

An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability.

CLINICAL RELEVANCE STATEMENT

Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging.

KEY POINTS

Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults

BACKGROUND

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality.

METHODS

The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141).

RESULTS

The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.

CONCLUSIONS

A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

The impact of CHOP versus bendamustine on bone mineral density in patients with indolent lymphoma enrolled in the GALLIUM study

Standard CHOP treatment includes a high cumulative dose of prednisone, and studies have shown increased fracture risk following CHOP. It is unclear whether reductions in bone mineral density (BMD) are caused by glucocorticoids or by the combination with chemotherapy. Our objective was to determine the effect of obinutuzumab (G)/rituximab (R)-bendamustine versus G/R-CHOP on BMD in follicular lymphoma patients. Patients in this GALLIUM post hoc study were ≥60 years old and in complete remission at induction treatment completion (ITC), following treatment with G or R in combination with bendamustine or CHOP. To assess BMD, Hounsfield units (HU) were measured in lumbar vertebra L1 on annual computed tomography. Furthermore, vertebral compression fractures were recorded. Of 173 patients included, 59 (34%) received CHOP and 114 (66%) received bendamustine. At baseline, there was no difference in HU between groups. The mean HU decrease from baseline to ITC was 27.8 after CHOP and 17.3 after bendamustine, corresponding to a difference of 10.4 (95% CI: 3.2-17.6). Vertebral fractures were recorded in 5/59 patients receiving CHOP and in 2/114 receiving bendamustine. CHOP was associated with a significant greater decrease in BMD and more frequent fractures. These results suggest that prophylaxis against BMD loss should be considered.

Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures

OBJECTIVE

Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls.

METHODS

In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived.

RESULTS

Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686.

CONCLUSION

Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging.

ADVANCES IN KNOWLEDGE

CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Does the presence of macroscopic intralesional fat exclude malignancy? An analysis of 613 histologically proven malignant bone lesions

OBJECTIVE

To determine if macroscopic intralesional fat detected in bone lesions on CT by Hounsfield unit (HU) measurement and on MRI by macroscopic assessment excludes malignancy.

MATERIALS AND METHODS

All consecutive CT-guided core needle biopsies (CNB) of non-spinal bone lesions performed at a tertiary center between December 2005 and September 2021 were reviewed. Demographic and histopathology data were recorded. All cases with malignant histopathology were selected, and imaging studies were reviewed. Two independent readers performed CT HU measurements on all bone lesions using a circular region of interest (ROI) to quantitate intralesional fat density (mean HU < -30). MRI images were reviewed to qualitatively assess for macroscopic intralesional fat signal in a subset of patients. Inter-reader agreement was assessed with Cronbach's alpha and intraclass correlation coefficient.

RESULTS

In 613 patients (mean age 62.9 years (range 19-95 years), 47.6% female), CT scans from the CNB of 613 malignant bone lesions were reviewed, and 212 cases had additional MRI images. Only 3 cases (0.5%) demonstrated macroscopic intralesional fat on either CT or MRI. One case demonstrated macroscopic intralesional fat density on CT in a case of metastatic prostate cancer. Two cases demonstrated macroscopic intralesional fat signal on MRI in cases of chondrosarcoma and osteosarcoma. Inter-reader agreement was excellent (Cronbach's alpha, 0.95-0.98; intraclass correlation coefficient, 0.90-0.97).

CONCLUSION

Malignant lesions rarely contain macroscopic intralesional fat on CT or MRI. While CT is effective in detecting macroscopic intralesional fat in primarily lytic lesions, MRI may be better for the assessment of heterogenous and infiltrative lesions with mixed lytic and sclerotic components.

CLINICAL RELEVANCE STATEMENT

Macroscopic intralesional fat is rarely seen in malignant bone tumors and its presence can help to guide the diagnostic workup of bone lesions.

KEY POINTS

• Presence of macroscopic intralesional fat in bone lesions has been widely theorized as a sign of benignity, but there is limited supporting evidence in the literature. • CT and MRI are effective in evaluating for macroscopic intralesional fat in malignant bone lesions with excellent inter-reader agreement. • Macroscopic intralesional fat is rarely seen in malignant bone lesions.

Bone mineral density in lower thoracic vertebra for osteoporosis diagnosis in older adults during CT lung cancer screening

BACKGROUND

Quantitative computed tomography (QCT)-based lumbar bone mineral density (LBMD) has been used to diagnose osteoporosis. This study explored the value of lower thoracic BMD (TBMD) in diagnosing osteoporosis in older adults during CT lung cancer screening.

METHODS

This study included 751 subjects who underwent QCT scans with both LBMD and TBMD. 141 of them was selected for a validation. Osteoporosis was diagnosed based on LBMD using the ACR criteria (gold standard). TBMD thresholds were obtained using receiver operating characteristic curve. TBMD was also translated into LBMD (TTBMD) and osteoporosis was defined based on TTBMD using ACR criteria. The performance of TBMD and TTBMD in identifying osteoporosis was determined by Kappa test. The associations between TBMD- and TTBMD-based osteoporosis and fracture were tested in 227 subjects with followed up status of spine fracture.

RESULTS

The performance of TBMD in identifying osteoporosis was low (kappa = 0.66) if using the ACR criteria. Two thresholds of TBMD for identifying osteopenia (128 mg/cm3) and osteoporosis (91 mg/cm3) were obtained with areas under the curve of 0.97 and 0.99, respectively. The performance of the identification of osteoporosis/osteopenia using the two thresholds or TTBMD both had good agreement with the gold standard (kappa = 0.78, 0.86). Similar results were observed in validation population. Osteoporosis identified using the thresholds (adjusted hazard ratio (HR) = 18.72, 95% confidence interval (CI): 5.13-68.36) or TTBMD (adjusted HR = 10.28, 95% CI: 4.22-25.08) were also associated with fractures.

CONCLUSION

Calculating the threshold of TBMD or normalizing TBMD to LBMD are both useful in identifying osteoporosis in older adults during CT lung cancer screening.

Opportunistic screening with multiphase contrast-enhanced dual-layer spectral CT for osteoblastic lesions in prostate cancer compared with bone scintigraphy

Our study aimed to compare bone scintigraphy and dual-layer detector spectral CT (DLCT) with multiphase contrast enhancement for the diagnosis of osteoblastic bone lesions in patients with prostate cancer. The patients with prostate cancer and osteoblastic bone lesions detected on DLCT were divided into positive bone scintigraphy group (pBS) and negative bone scintigraphy group (nBS) based on bone scintigraphy. A total of 106 patients (57 nBS and 49 pBS) was included. The parameters of each lesion were measured from DLCT including Hounsfield unit (HU), 40-140 keV monochromatic HU, effective nuclear numbers (Zeff), and Iodine no water (InW) value in non-contrast phase (N), the arterial phase (A), and venous phase (V). The slope of the spectral curve at 40 and 100 keV, the different values of the parameters between A and N phase (A-N), V and N phase (V-N), and hybrid prediction model with multiparameters were used to differentiate pBS from nBS. Receiver operating characteristic analysis was performed to compare the area under the curve (AUC) for differentiating the pBS group from the nBS group. The value of conventional HU values, slope, and InW in A-N and V-N, and hybrid model were significantly higher in the pBS group than in the nBS group. The hybrid model of all significant parameters had the highest AUC of 0.988, with 95.5% sensitivity and 94.6% specificity. DLCT with arterial contrast enhancement phase has the potential to serve as an opportunistic screening tool for detecting positive osteoblastic bone lesions, corresponding to those identified in bone scintigraphy.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data

PURPOSE

To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.

METHODS

In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis.

RESULTS

A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400.

CONCLUSION

Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.

A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment

Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1-L4 vertebrae derived from DXA (r = 0.91, p < 0.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R2 value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.

Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis

BACKGROUND

Osteoporosis is a serious global public health issue. Currently, there are few studies that explore the use of multiparametric MRI radiomics for osteoporosis detection. The purpose of this study was to compare the performance of radiomics features from multiple MRI sequences (T1WI, T2WI and T1WI combined with T2WI) for detecting osteoporosis in patients.

METHODS

A retrospective analysis was performed on 160 patients who had undergone dual-energy X-ray absorptiometry(DXA) and lumbar magnetic resonance imaging (MRI) at our hospital. Among them, 86 patients were diagnosed with abnormal bone mass (osteoporosis or low bone mass), and 74 patients were diagnosed with normal bone mass based on the DXA results. Sagittal T1-and T2-weighted images of all patients were imported into the uAI Research Portal (United Imaging Intelligence) for image delineation and radiomics analysis, where a series of radiomic features were obtained. A radiomic model that included T1WI, T2WI, and T1WI+T2WI was established using features selected by LASSO regression. We used ROC curve analysis to evaluate the predictive efficacy of each model for identifying bone abnormalities and conducted decision curve analysis (DCA) to evaluate the net benefit of each model. Finally, we validated the model in a sample of 35 patients from different health care institution.

RESULTS

The T1WI + T2WI radiomics model showed better screening performance for patients with abnormal bone mass. In the training group, the sensitivity was 0.758, the specificity was 0.78, and the accuracy was 0.768 (AUC =0.839, 95% CI=0.757-0.901). In the validation group, the sensitivity was 0.792, the specificity was 0.875, and the accuracy was 0.833 (AUC =0.86, 95% CI=0.73-0.943).The DCA also showed that the combined model had better net benefits. In the external validation group, the sensitivity was 0.764, the specificity was 0.833, and the accuracy was 0.8 (AUC =0.824, 95% CI 0.678-0.969).

CONCLUSIONS

Radiomics-based multiparametric MRI can be used for the quantitative analysis of lumbar MRI and for accurately screening patients with abnormal bone mass.

Sex differences and age-related changes in vertebral body volume and volumetric bone mineral density at the thoracolumbar spine using opportunistic QCT

Objectives

To quantitatively investigate the age- and sex-related longitudinal changes in trabecular volumetric bone mineral density (vBMD) and vertebral body volume at the thoracolumbar spine in adults.

Methods

We retrospectively included 168 adults (mean age 58.7 ± 9.8 years, 51 women) who received ≥7 MDCT scans over a period of ≥6.5 years (mean follow-up 9.0 ± 2.1 years) for clinical reasons. Level-wise vBMD and vertebral body volume were extracted from 22720 thoracolumbar vertebrae using a convolutional neural network (CNN)-based framework with asynchronous calibration and correction of the contrast media phase. Human readers conducted semiquantitative assessment of fracture status and bony degenerations.

Results

In the 40-60 years age group, women had a significantly higher trabecular vBMD than men at all thoracolumbar levels (p<0.05 to p<0.001). Conversely, men, on average, had larger vertebrae with lower vBMD. This sex difference in vBMD did not persist in the 60-80 years age group. While the lumbar (T12-L5) vBMD slopes in women only showed a non-significant trend of accelerated decline with age, vertebrae T1-11 displayed a distinct pattern, with women demonstrating a significantly accelerated decline compared to men (p<0.01 to p<0.0001). Between baseline and last follow-up examinations, the vertebral body volume slightly increased in women (T1-12: 1.1 ± 1.0 cm3; L1-5: 1.0 ± 1.4 cm3) and men (T1-12: 1.2 ± 1.3 cm3; L1-5: 1.5 ± 1.6 cm3). After excluding vertebrae with bony degenerations, the residual increase was only small in women (T1-12: 0.6 ± 0.6 cm3; L1-5: 0.7 ± 0.7 cm3) and men (T1-12: 0.7 ± 0.6 cm3; L1-5: 1.2 ± 0.8 cm3). In non-degenerated vertebrae, the mean change in volume was <5% of the respective vertebral body volumes.

Conclusion

Sex differences in thoracolumbar vBMD were apparent before menopause, and disappeared after menopause, likely attributable to an accelerated and more profound vBMD decline in women at the thoracic spine. In patients without advanced spine degeneration, the overall volumetric changes in the vertebral body appeared subtle.

Biomarkers of Body Composition

The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.

Opportunistic Muscle Evaluation During Chest CT Is Associated With Vertebral Compression Fractures in Old Adults: A Longitudinal Study

BACKGROUND

Sarcopenia and bone loss are both common in older individuals. However, the association between sarcopenia and bone fractures has not been evaluated longitudinally. In this study, we evaluated the association between computed tomography (CT)-based erector spinae muscle area and attenuation and vertebral compression fracture (VCF) in elderly individuals in a longitudinal study.

METHODS

This study recruited individuals 50 years of age and older, who did not have VCF and underwent CT imaging for lung cancer screening during January 2016 to December 2019. Participants were followed up annually until January 2021. Muscle CT value and muscle area of the erector spinae were determined for muscle assessment. Genant score was used to define new-onset VCF. Cox proportional hazards models were used to assess the association between muscle area/attenuation and VCF.

RESULTS

Of the 7 906 included participants, 72 developed new VCF over a median follow-up of 2 years. Large area of the erector spinae (adjusted hazard ratio [HR] = 0.2, 95% confidence interval [CI]: 0.1-0.7) and high bone attenuation (adjusted HR = 0.2, 95% CI: 0.1-0.5) were independently associated with VCF. High muscle attenuation was associated with severe VCF (adjusted HR = 0.46, 95% CI: 0.24-0.86). The addition of muscle area improved the area under the curve of bone attenuation from 0.79 (95% CI: 0.74-0.86) to 0.86 (95% CI: 0.82-0.91; p = .001).

CONCLUSIONS

CT-based muscle area/attenuation of the erector spinae was associated with VCF in elderly individuals, independently of bone attenuation. The addition of muscle area improved the performance of bone attenuation in predicting VCF.

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in CT use has caused improved diagnostic certainty in conditions that were not previously routinely evaluated using CT, such as headaches, back pain, and chest pain. Unused data, unrelated to the primary diagnosis, embedded within these scans have the potential to provide organ-specific measurements that can be used to prognosticate or risk-profile patients for a wide variety of conditions. The recent increased availability of computing power, expertise and software for automated segmentation and measurements, assisted by artificial intelligence, provides a conducive environment for the deployment of these analyses into routine use. Data gathering from CT has the potential to add value to examinations and help offset the public perception of harm from radiation exposure. We review the potential for the collection of these data and propose the incorporation of this strategy into routine clinical practice.

Opportunistic screening for osteopathy with routine abdominal computed tomography scan in chronic pancreatitis

BACKGROUND AND AIMS

Lumbar vertebral bone attenuation, measured in Hounsfield units (HU) can indirectly indicate the bone mineral density (BMD). The aim of this study is to determine the optimal HU threshold on abdominal computed tomography (CT) scans to detect osteopathy in patients with chronic pancreatitis (CP).

METHODS

This cross-sectional study included patients with CP who underwent CT scans to measure HU at L1 to L4 vertebrae. The mean lumbar vertebral attenuation of female renal transplant donors, aged 20-30 years was utilized to calculate the T-scoreHU of all patients at each vertebral level. Receiver operator characteristic analysis was used to determine the HU and T-scoreHU for diagnosis of osteopathy in patients with CP. Dual-energy X-ray absorptiometry value was used to categorize osteopenia and osteoporosis.

RESULTS

A total of 175 patients (mean age, 34.5 ± 10.9 years; 72 % males) and 33 female renal transplant donors (mean age, 28 ± 2.4 years) were included. A threshold HU value 212 or T scoreHU of -1.80 at L1 vertebra was found to have a 78 % sensitivity and 70 % specificity for differentiating between osteoporosis and non-osteoporosis (osteopenia and normal BMD). Similarly, a threshold HU value of 254 or a T-scoreHU of -0.46 at L1 vertebra had 78 % sensitivity and 71 % specificity for distinguishing between normal and low BMD (osteoporosis and osteopenia).

CONCLUSION

Abdominal CT images, which are routinely performed in chronic pancreatitis, can be used for opportunistic screening of osteoporosis and osteopenia without additional cost or radiation exposure.

Correlation of body composition metrics with bone mineral density and computed tomography-based trabecular attenuation

PURPOSE

To investigate the relationship of body composition metrics with bone mineral density (BMD) and trabecular attenuation in a cohort of healthy individuals.

METHODS

We retrospectively analyzed data of consecutively evaluated individuals who underwent dual-energy X-ray absorptiometry (DXA) and abdominopelvic computed tomography (CT) on the same day during routine medical check-ups between January 2021 and December 2021. Trabecular attenuation was measured at L1 level, while body composition metrics, including skeletal muscle index (SMI), skeletal muscle attenuation (SMA), visceral fat index (VFI), and subcutaneous fat index (SFI), were measured at L3 level. The association of body composition metrics with BMD and trabecular attenuation was analyzed using partial correlation analysis.

RESULTS

A total of 634 patients (median age, 56 years; range 50-62 years; 392 men) were included. In men, the SMI and SMA were positively correlated with BMD and trabecular attenuation, both before (r, 0.157-0.344; p < 0.05) and after (r, 0.103-0.246; p < 0.05) adjusting for age and body mass index. The VFI showed negative correlations with trabecular attenuation in both men (r, -0.170; p = 0.001) and women (r, -0.394; p < 0.001), which remained significant after adjusting for age and body mass index (r, -0.181 to -0.122; p < 0.05).

CONCLUSION

Low skeletal muscle mass and attenuation were significantly correlated with low BMD and trabecular attenuation in men. Visceral adiposity was associated with reduced BMD and trabecular attenuation in both men and women, demonstrating a stronger correlation with trabecular attenuation.

Osteoporosis and Fragility Fractures in Patients With Cirrhosis Evaluated for Liver Transplantation: Identification of High-Risk Patients Based on Computed Tomography at Evaluation

INTRODUCTION

Osteoporosis in candidates for liver transplantation (LT) is often underdiagnosed despite the important consequences of morbidity.

METHODS

We included 376 patients with cirrhosis evaluated for LT with available computed tomography (CT) scans. Prevalent vertebral fractures (VFs) were identified on CT reconstructions, and bone density was assessed by measuring CT attenuation of the L1 vertebra (L1-CT).

RESULTS

We identified 139 VFs in 55 patients (14.6%). Logistic regression models showed that low L1-CT was the only independent determinant of VF.

DISCUSSION

In patients with cirrhosis evaluated for LT, CT scans identified persons with severe osteoporosis without additional costs.

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson's correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening

This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong correlation with QCT results, showing promise for expanding osteoporosis screening and reducing unnecessary radiation and costs.

PURPOSE

To explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set.

METHODS

A total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated.

RESULTS

The ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship.

CONCLUSION

Using deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.

Quantitative CT Evaluation of Bone Mineral Density in the Thoracic Spine on 18F-Fluorocholine PET/CT Imaging in Patients With Primary Hyperparathyroidism

INTRODUCTION

Measurement of bone mineral density (BMD) with quantitative CT (QCT) carries several advantages over other densitometric techniques, including superior assessment of the spine. As most QCT studies evaluated the lumbar spine, measurements of the thoracic spine are limited. We performed QCT analysis of the thoracic spine in a cohort of patients with primary hyperparathyroidism.

MATERIALS AND METHODS

This study was a retrospective QCT analysis of the thoracic spine on 18F-fluorocholine PET/CT scans in patients with primary hyperparathyroidism patients between March 2018 and December 2022. Correlations between QCT-derived BMD or Hounsfield units (HU) and demographic data, laboratory parameters, results from histopathological examination after parathyroidectomy and results of DXA imaging were analyzed, when available.

RESULTS

In 189 patients, mean QCT-derived BMD at the thoracic spine was 85.6 mg/cm3. Results from recent DXA were available in 122 patients. Mean thoracic QCT-derived BMD and HU were significantly correlated with DXA-derived BMD in lumbar spine, total hip and femoral neck and with the lowest T-score at DXA imaging. Only weak correlations were found with BMI or 18F-fluorocholine uptake, while no significant correlations were found with adenoma weight, PTH or calcium levels.

CONCLUSION

Our study confirms correlation between QCT-derived BMD in the thoracic spine with age and DXA-derived BMD measurements within a population of patients with primary hyperparathyroidism. Establishment of reference BMD values for individual thoracic vertebrae, may allow direct osteoporosis classification on thoracic CT imaging.

Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images

Background

The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and their patterns.

Methods

The retrospective cohort comprised patients who visited our hospital from 2017-2018; the prospective cohort (control group) was consecutively recruited from the same hospital from February-June 2023. All patients had blunt chest trauma and underwent CT. Volumetric BMD of L1 vertebra was measured by using an AI software. Analyses were done by using BMD categorized as osteoporosis (<80 mg/cm3), osteopenia (80-120 mg/cm3), or normal (>120 mg/cm3). Pearson's χ2, Fisher's exact, or Kruskal-Wallis tests and Bonferroni correction were used for comparisons. Negative binomial, and logistic regression analyses were used to assess the associations and impacts of BMD status. Sensitivity analyses were also performed.

Findings

The retrospective cohort included 2,076 eligible patients, of whom 954 (46%) had normal BMD, 806 (38.8%) had osteopenia, and 316 (15.2%) had osteoporosis. After sex- and age-adjustment, osteoporosis was significantly associated with higher rib fracture rates, and a higher likelihood of fractures in ribs 4-7. Furthermore, both the osteopenia and osteoporosis groups demonstrated a significantly higher number of fractured ribs and fracture sites on ribs, with a higher likelihood of fractures in ribs 1-3, as well as flail chest. The prospective cohort included 205 eligible patients, of whom 92 (44.9%) had normal BMD, 74 (36.1%) had osteopenia, and 39 (19.0%) had osteoporosis. The findings observed within this cohort were in concurrence with those in the retrospective cohort.

Interpretation

Traumatic rib fractures are associated with decreased BMD. CT-AI can help to identify individuals who have decreased BMD and a greater rib fracture rate, along with their fracture patterns.

Radiology reporting of incidental osteoporotic vertebral fragility fractures present on CT studies: results of UK national re-audit

AIM

To describe a UK-wide re-audit of the 2019 Royal College of Radiologists (RCR) audit evaluating patient-related data and organisational infrastructure in the radiological reporting of vertebral fragility fractures (VFFs) on computed tomography (CT) studies and to assess the impact of a series of RCR interventions, initiated to raise VFF awareness, on reporting practice and outcomes.

MATERIALS AND METHODS

Patient specific and organisational questionnaires largely replicated those utilised in 2019. The patient questionnaire involved retrospective analysis of between 50 and 100 consecutive, non-traumatic CT studies which included the thoracolumbar spine. All RCR radiology audit leads were invited to participate. Data collection commenced from 1 April 2022.

RESULTS

Data were supplied by 129/194 (67%) departments. One thousand five hundred and eighty-six of 7,316 patients (21.7%) had a VFF on auditor review. Overall improvements were demonstrated in key initial/provisional reporting results; comment on spine/bone (93.2%, 14.4% improvement, p<0.0002); fracture severity assessment (34.7%, 8.5% improvement, p=0.0007); use of recommended terminology (67.8%, 7.5% improvement, p=0.0034); recommendations for further management (11.7%, 9.1% improvement, p<0.0002).

CONCLUSIONS

The 2022 national re-audit confirms improvements in diagnostic performance and practice in VFF reporting. Continuing work is required to build on this improvement and to further embed best practice.

MRI-based vertebral bone quality score: relationship with age and reproducibility

Vertebral bone quality (VBQ) score is an opportunistic measure of bone mineral density using routine preoperative MRI in spine surgery. VBQ score positively correlates with age and is reproducible across serial scans. However, extrinsic factors, including MRI machine and protocol, affect the VBQ score and must be standardized.

PURPOSE

The purposes of this study were to determine whether VBQ score increased with age and whether VBQ remained consistent across serial MRI studies obtained within 3 months.

METHODS

This retrospective study evaluated 136 patients, age 20-69, who received two T1-weighted lumbar MRI within 3 months of each other between January 2011 and December 2021. VBQ(L1-4) score was calculated as the quotient of L1-L4 signal intensity (SI) and L3 cerebral spinal fluid (CSF) SI. VBQ(L1) score was calculated as the quotient of L1 SI and L1 CSF SI. Regression analysis was performed to determine correlation of VBQ(L1-4) score with age. Coefficient of variation (CV) was used to determine reproducibility between VBQ(L1-4) scores from serial MRI scans.

RESULTS

One hundred thirty-six patients (mean ± SD age 44.9 ± 12.5 years; 53.7% female) were included in this study. Extrinsic factors affecting the VBQ score included patient age, MRI relaxation time, and specific MRI machine. When controlling for MRI relaxation/echo time, the VBQ(L1-4) score was positively correlated with age and had excellent reproducibility in serial MRI with CV of 0.169. There was excellent agreement (ICC > 0.9) of VBQ scores derived from the two formulas, VBQ(L1) and VBQ(L1-4).

CONCLUSION

Extrinsic factors, including MRI technical factors and age, can impact the VBQ(L1-4) score and must be considered when using this tool to estimate bone mineral density (BMD). VBQ(L1-4) score was positively correlated with age. Reproducibility of the VBQ(L1-4) score across serial MRI is excellent especially when controlling for technical factors, supporting use of the VBQ score in estimating BMD. The VBQ(L1) score was a reliable alternative to the VBQ(L1-4) score.

Detecting whether L1 or other lumbar levels would be excluded from DXA bone mineral density analysis during opportunistic CT screening for osteoporosis using machine learning

PURPOSE

One or more vertebrae are sometimes excluded from dual-energy X-ray absorptiometry (DXA) analysis if the bone mineral density (BMD) T-score estimates are not consistent with the other lumbar vertebrae BMD T-score estimates. The goal of this study was to build a machine learning framework to identify which vertebrae would be excluded from DXA analysis based on the computed tomography (CT) attenuation of the vertebrae.

METHODS

Retrospective review of 995 patients (69.0% female) aged 50 years or greater with CT scans of the abdomen/pelvis and DXA within 1 year of each other. Volumetric semi-automated segmentation of each vertebral body was performed using 3D-Slicer to obtain the CT attenuation of each vertebra. Radiomic features based on the CT attenuation of the lumbar vertebrae were created. The data were randomly split into training/validation (90%) and test datasets (10%). We used two multivariate machine learning models: a support vector machine (SVM) and a neural net (NN) to predict which vertebra(e) were excluded from DXA analysis.

RESULTS

L1, L2, L3, and L4 were excluded from DXA in 8.7% (87/995), 9.9% (99/995), 32.3% (321/995), and 42.6% (424/995) patients, respectively. The SVM had a higher area under the curve (AUC = 0.803) than the NN (AUC = 0.589) for predicting whether L1 would be excluded from DXA analysis (P = 0.015) in the test dataset. The SVM was better than the NN for predicting whether L2 (AUC = 0.757 compared to AUC = 0.478), L3 (AUC = 0.699 compared to AUC = 0.555), or L4 (AUC = 0.751 compared to AUC = 0.639) were excluded from DXA analysis.

CONCLUSIONS

Machine learning algorithms could be used to identify which lumbar vertebrae would be excluded from DXA analysis and should not be used for opportunistic CT screening analysis. The SVM was better than the NN for identifying which lumbar vertebra should not be used for opportunistic CT screening analysis.

CT-based body composition in diffuse large B cell lymphoma patients: changes after treatment and association with survival

PURPOSE

Primary purpose was to assess changes of bone mineral density (BMD) in diffuse large B cell lymphoma (DLBCL) patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone R-CHOP (like) chemotherapy regimen. Secondary purposes were to assess other body composition features changes and to assess the association of pre-therapy values and their changes over time with survival.

MATERIAL AND METHODS

Patients selected underwent R-CHOP(like) regimen for DLBCL, and underwent PET-CT before and after treatment. Main clinical data collected included body mass index, date of last follow-up, date of progression, and date of death. From the low-dose CT images, BMD was assessed at the L1 level; the other body composition values, including muscle and fat distribution, were assessed at the L3 level by using a dedicated software. Descriptive statistics were reported as median and interquartile range, or frequencies and percentages. Statistical comparisons of body composition variables between pre- and post-treatment assessments were performed using the Wilcoxon matched pairs signed rank test. Non-normal distribution of variables was tested with the Shapiro-Wilk test. For qualitative variables, the Fisher exact test was used. Log rank test was used to compare survival between different subgroups of the study population defined by specific body composition cutoffs. The significance level was set at p < 0.05.

RESULTS

Eighty-two patients were included. The mean follow-up was 37.5 ± 21.4 months. A significant difference was found in mean BMD before and after R-CHOP(like) treatment (p < 0.0001). The same trend was observed for mean skeletal muscle area (SMA) (p = 0.004) and mean skeletal muscle index (SMI) (p = 0.006). No significant association was demonstrated between body composition variables, PFS and OS.

CONCLUSION

R-CHOP(like) treatment in DLBCL patients was associated with significant reduction of BMD, SMA and SMI.

Hounsfield Unit for Evaluating Bone Mineral Density and Strength: Variations in Measurement Methods

OBJECTIVE

To assess the consistency and accuracy of various measurements of the Hounsfield unit (HU) in lumbar vertebrae.

METHODS

The study reviewed lumbar spine computed tomography images of 60 postmenopausal women aged >50 years. A total of 240 vertebrae were measured and analyzed for the variations of HU values in different sections and regions. Investigated the relationship between HU values of the lumbar spine under different measurements and dual-energy X-ray absorptiometry results and the ability to identify patients with osteoporosis.

RESULTS

HU values measured in midsagittal (r = 0.763), midcoronal (r = 0.768), and midaxial (r = 0.786) sections exhibited a strong positive correlation with dual-energy X-ray absorptiometry T-scores. HU values measured in midsagittal and midaxial sections of the vertebral body were in good agreement (P > 0.1), but decreased in the midcoronal (P < 0.001). HU values in the middle of the vertebral body were significantly higher than in the near end plate (P < 0.001). HU values varied between L1 and L4 vertebrae, but all had a good ability to identify osteoporosis and did not differ significantly in screening ability (P > 0.05).

CONCLUSIONS

An averaged HU value in axial multilevel is a comprehensive assessment of vertebral bone density. Using the HU value of the lumbar spine can help identify patients with osteoporosis, and the screening ability does not differ significantly across vertebral segments.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study

OBJECTIVE

To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.

METHODS

In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.

RESULTS

The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).

CONCLUSION

Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.

CLINICAL RELEVANCE STATEMENT

In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.

KEY POINTS

• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.