UNASSIGNED: Novel methods of bone density assessment using computed tomography (CT) and magnetic resonance imaging (MRI) have been increasingly reported in the spine surgery literature. Correlations between these newer measurements and traditional Dual-Energy X-ray Absorptiometry (DEXA) is not well known. The purpose of this study is to perform an updated systematic review of correlations between bone mineral density (BMD) from CT or MRI and DEXA.
UNASSIGNED: Articles published between 2011 and 2021 that reported correlations between the CT-HU or MRI measurements to DEXA t-scores or BMD of lumbar spine or hip were included in this systematic review.
UNASSIGNED: A total of 25 studies (15 CT, 10 MRI) met the inclusion criteria with a total number of 2,745 patients. The pooled correlation coefficient of spine CT-HU versus spine DEXA, spine CT-HU versus hip DEXA and spine CT-HU versus lowest t-score were 0.60, 0.50 and 0.60 respectively. Regarding spine DEXA parameters, the pooled r2 for spine CT-HU versus spine t-score was 0.684 and spine CT-HU versus spine BMD was 0.598. Furthermore, in patients undergoing spine surgery in four studies, the pooled correlation between spine CT and spine DEXA was (r2: 0.64). In MRI studies, the pooled r2 of spine MRI versus spine DEXA and spine MRI versus hip DEXA were -0.41 and -0.44 respectively.
UNASSIGNED: CT-HU has stronger correlations with DEXA than MRI measurements. Lumbar CT-HU has the highest pooled correlation (r2 = 0.6) with both spine DEXA and lowest skeletal t-score followed by lumbar CT-HU with hip DEXA (r2 = 0.5) and lumbar MRI with hip (r2 = 0.44) and spine (r2 = 0.41) DEXA. Both imaging modalities achieved only a moderate correlation with DEXA. Few studies in both modalities have investigated the correlation in spine surgery populations and the available data shows that the correlations are worse in the degenerative spine population. A careful interruption of CT HU and MRI measurement when evaluation of BMD as they only moderately correlated with DEXA scores. At this time, it is unclear which modality is a better predictor of mechanical complications and clinical outcomes in spine surgery patients.

OBJECTIVE: Extracorporeal shock wave lithotripsy success rates depend on several stone and patient-related factors, one of which is stone density which is calculated on computed tomography scan in Hounsfield Units. Studies have shown inverse correlation between SWL success and HU; however, there remains considerable variation between studies. We performed a systematic review regarding the use of HU in SWL for renal calculi to consolidate the current evidence and address current knowledge gaps.
RESULTS: Database including MEDLINE, EMBASE, and Scopus were searched from inception through August 2022. Studies in English language analysing stone density/attenuation in adult patients undergoing SWL for renal calculi were included for assessment of Shockwave lithotripsy outcomes, use of stone attenuation to predict success, use of mean and peak stone density and Hounsfield unit density, determination of optimum cut-off values, nomograms/scoring systems, and assessment of stone heterogeneity. 28 studies with a total of 4,206 patients were included in this systematic review with sample size ranging from 30 to 385 patients. Male to female ratio was 1.8, with an average age of 46.3 years. Mean overall ESWL success was 66.5%. Stone size ranged from 4 to 30 mm in diameter. Mean stone density was used by two-third of the studies to predict the appropriate cut-off for SWL success, ranging from 750 to 1000 HU. Additional factors such as peak HU and stone heterogeneity index were also evaluated with variable results. Stone heterogeneity index was considered a better indicator for success in larger stones (cut-off value of 213) and predicting SWL stone clearance in one session. Prediction scores had been attempted, with researchers looking into combining stone density with other factors such as skin to stone distance, stone volume, and differing heterogeneity indices with variable results. Numerous studies demonstrate a link between shockwave lithotripsy outcomes and stone density. Hounsfield unit < 750 has been found to be associated with shockwave lithotripsy success, with likelihood of failure strongly associated with values over 1000. Prospective standardisation of Hounsfield unit measurement and predictive algorithm for shockwave lithotripsy outcome should be considered to strengthen future evidence and help clinicians in the decision making.
BACKGROUND: International Prospective Register of Systematic Reviews (PROSPERO) database: CRD42020224647.

OBJECTIVE: The aim was to systematically assess the literature on possible effect of administration of iodinated contrast media on CT-estimated bone mineral density (BMD).
METHODS: The Web of Science and PubMed databases were searched. Studies that used both CT principles of BMD measurement (volumetric quantitative BMD and CT attenuation in Hounsfield Units) were included. The baseline patient data, skeletal site, contrast medium data (if reported), and change in BMD on contrast-enhanced CT scans were collected.
RESULTS: Sixteen studies met our review criteria, the majority of which was performed on lumbar spine, and the others on proximal femur. Almost all studies reported a significant increase in BMD values on the contrast-enhanced CT scans, ranging from 0.8 to 30.3%. The increase was most frequently reported to be about 10 to 15% for the spine and 5 to 10% for the femur. In addition to the difference in skeletal site, some authors found the contrast effect was age-, sex-, and contrast dose-dependent. BMD values in arterial phase were generally somewhat lower than in venous phase, and the effect of contrast in venous phase was more predictable.
CONCLUSIONS: The review revealed significant changes in BMD values between unenhanced and contrast-enhanced CT. The change was more pronounced in lumbar spine than in proximal femur and appeared to depend on age, sex, contrast dose, and postcontrast imaging protocol. The review suggests the understanding of all mentioned factors during the interpretation of BMD measured on contrast-enhanced CT.

OBJECTIVE: We assessed the efficacy of some predictive factors that can be measured with non-contrast computed tomography and may affect ESWL success with a systematic review and meta-analysis.
METHODS: All data sources were broadly investigated up to April 2022. Data were extracted from the relevant studies and analyzed with RevMan software. In a random effects model, standard mean difference (SMD) and risk ratio (RR) values were given with 95% confidence intervals.
RESULTS: In total, pooled analysis included 7148 patients in 43 studies. The combined effect estimate showed significant differences between the ESWL success and ESWL failure groups in terms of Hounsfield unit (HU), Hounsfield density (HD), skin to stone distance (SSD), ureteral wall thickness (UWT), stone volume, stone area, abdominal fat parameters, diameter of proximal ureter, and hydronephrosis. However, perinephric stranding and renal cortical thickness were not found to be statistically significant between the study groups.
CONCLUSIONS: HU, HD, SSD, UWT, stone volume, stone area, abdominal fat parameters, diameter of proximal ureter and hydronephrosis are effective factors for prediction of ESWL success. It is important to decide on treatment before the procedure for stones with appropriate diameter for ESWL.

BACKGROUND: Thread rhinoplasty can trigger a reaction to thread material, which is a foreign body. We compared clinical features induced by absorbable and non-absorbable threads following thread rhinoplasty.
METHODS: Two patients who underwent different thread materials showed different clinical courses and different Hounsfield unit (HU) values in computed tomography. Patients with absorbable thread showed high HU values similar to a metallic material, and the HU value of inflammation was similar to vascular tissues with a lot of water (250). In the intraoperative field, absorbable thread materials and micro-abscesses were observed. In contrast, in the case of a non-absorbable thread, an object presumed to be thread was seen on the computed tomography (CT), and the HU value of inflammatory tissues was less than 100. In both patients, post-operative HU decreased to less than 100 and the clinical course improved. In both cases, histopathologic findings revealed foreign body granuloma associated with inflammation.
CONCLUSIONS: Absorbable threads were more aggressive and are more easily detected on CT.

OBJECTIVE: The purpose of this systematic review was to answer the focus question: \"Could the gray values (GVs) from CBCT (cone beam computed tomography) be converted to Hounsfield units (HUs) in multidetector computed tomography (MDCT)?\"
METHODS: The included studies try to answer the research question according to the PICO strategy. Studies were gathered by searching several electronic databases and partial grey literature up to January 2021 without language or time restrictions. The methodological assessment of the studies was performed using The Oral Health Assessment Tool (OHAT) for in vitro studies and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) for in vivo studies. The Grading of Recommendations Assessment, Development and Evaluation (GRADE system) instrument was applied to assess the level of evidence across the studies.
RESULTS: 2710 articles were obtained in Phase 1, and 623 citations remained after removing duplicates. Only three studies were included in this review using a two-phase selection process and after applying the eligibility criteria. All studies were methodologically acceptable, although in general terms with low risks of bias. There are some included studies with quite low and limited evidence estimations and recommendation forces; evidencing the need for clinical studies with diagnostic capacity to support its use.
CONCLUSIONS: This systematic review demonstrated that the GVs from CBCT cannot be converted to HUs due to the lack of clinical studies with diagnostic capacity to support its use. However, it is evidenced that three conversion steps (equipment calibration, prediction equation models, and a standard formula (converting GVs to HUs)) are needed to obtain pseudo Hounsfield values instead of only obtaining them from a regression or directly from the software.