3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures

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.

Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging

RATIONALE AND OBJECTIVES

To develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (CT) virtual monochromatic imaging.

METHODS

A total of 606 lumbar vertebrae with dual-energy CT imaging and quantitative CT (QCT) evaluation were included in the retrospective study and randomly divided into the training (n = 424) and validation (n = 182) cohorts. Radiomics features and DTL features were extracted from 70-keV monochromatic CT images, followed by feature selection and model construction, radiomics and DTL features models were established. Then, we integrated the selected two types of features into a features fusion model. We developed a two-level classifier for the hierarchical pairwise classification of each vertebra. All the vertebrae were first classified into osteoporosis and non-osteoporosis groups, then non-osteoporosis group was classified into osteopenia and normal groups. QCT was used as reference. The predictive performance and clinical usefulness of three models were evaluated and compared.

RESULTS

The area under the curve (AUC) of the features fusion, radiomics and DTL models for the classification between osteoporosis and non-osteoporosis were 0.981, 0.999, 0.997 in the training cohort and 0.979, 0.943, 0.848 in the validation cohort. Furthermore, the AUCs of the previously mentioned models for the differentiation between osteopenia and normal were 0.994, 0.971, 0.996 in the training cohort and 0.990, 0.968, 0.908 in the validation cohort. The overall accuracy of the previously mentioned models for two-level classifications was 0.979, 0.955, 0.908 in the training cohort and 0.918, 0.885, 0.841 in the validation cohort. Decision curve analysis showed that all models had high clinical value.

CONCLUSION

The feature fusion model can be used for osteoporosis and osteopenia prediction with improved predictive ability over a radiomics model or a DTL model alone.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features

PURPOSE

To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).

METHODS

A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs.

RESULTS

Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs.

CONCLUSION

Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography

Background

To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia.

Methods

A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value.

Results

The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful.

Conclusions

The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00868-5.

Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery

Simple Summary

The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery.

Abstract

We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density

OBJECTIVE

This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images.

METHODS

A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31-94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1-L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix.

RESULTS

The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979-1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779-0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891-0.989) for normal BMD versus osteopenia.

CONCLUSIONS

The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.

MRI Radiomics in Prostate Cancer: A Reliability Study

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols, as well as by the segmentation of the region of interest (ROI). Prostate cancer (PCa) represents a difficult playground for this technique, due to discrepancies in the identification of the cancer lesion and the heterogeneity of the acquisition protocols. The aim of this study was to investigate the reliability of radiomics in PCa magnetic resonance imaging (MRI).

METHODS

A homogeneous cohort of patients with a PSA rise that underwent multiparametric MRI imaging of the prostate before biopsy was tested in this study. All the patients were acquired with the same MRI scanner, with a standardized protocol. The identification and the contouring of the region of interest (ROI) of an MRI suspicious cancer lesion were done by two radiologists with great experience in prostate cancer (>10 years). After the segmentation, the texture features were extracted with LIFEx. Texture features were then tested with intraclass coefficient correlation (ICC) analysis to analyze the reliability of the segmentation.

RESULTS

Forty-four consecutive patients were included in the present analysis. In 26 patients (59.1%), the prostate biopsy confirmed the presence of prostate cancer, which was scored as Gleason 6 in 6 patients (13.6%), Gleason 3 + 4 in 8 patients (18.2%), and Gleason 4 + 3 in 12 patients (27.3%). The reliability analysis conversely showed poor reliability in the majority of the MRI acquisition (61% in T2, 89% in DWI50, 44% in DWI400, and 83% in DWI1,500), with ADC acquisition only showing better reliability (poor reliability in only 33% of the texture features).

CONCLUSIONS

The low ratio of reliability in a monoinstitutional homogeneous cohort represents a significant alarm bell for the application of MRI radiomics in the field of prostate cancer. More work is needed in a clinical setting to further study the potential of MRI radiomics in prostate cancer.

Delta radiomics: a systematic review

BACKGROUND

Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.

METHODS

Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool.

RESULTS

Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.

CONCLUSIONS

Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Simple Summary

This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy.

Abstract

Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer)

Background

To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC).

Methods

Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups: 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed.

Results

In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R²: 0.546 for the internal cohort and P=0.014, Nagelkerke R²: 0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763.

Conclusions

Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.

Delta-radiomics increases multicentre reproducibility: a phantom study

Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.

Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software^©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.

Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion

Considering the poor medical conditions in some regions of China, this paper attempts to develop a simple and easy way to extract and process the bone features of blurry medical images and improve the diagnosis accuracy of osteoporosis as much as possible. After reviewing the previous studies on osteoporosis, especially those focusing on texture analysis, a convexity optimization model was proposed based on intra-class dispersion, which combines texture features and shape features. Experimental results show that the proposed model boasts a larger application scope than Lasso, a popular feature selection method that only supports generalized linear models. The research findings ensure the accuracy of osteoporosis diagnosis and enjoy good potentials for clinical application.

Discordance in diagnosis of osteoporosis by quantitative computed tomography and dual-energy X-ray absorptiometry in Chinese elderly men

Objective

The objective of this study was to investigate the diagnostic discordance of osteoporosis by quantitative computed tomography (QCT) and dual-energy X-ray absorptiometry (DXA) in Chinese elderly men.

Methods

A total of 313 males older than 60 years, who underwent both spinal QCT and lumbar spine and hip DXA in our department, were included. The diagnostic criteria established by the World Health Organisation in 1994 were used for DXA to diagnose osteoporosis, and the criteria recommended by the International Society of Clinical Densitometry were used for QCT. The osteoporosis detection rate by the two techniques was calculated, and the difference was compared. The minor discordance was considered present when the different diagnostic classes between the two techniques were adjacent. Major discordance was present when the diagnosis by one technique was osteoporosis and the other was normal. The computed tomography images were reviewed by radiologists to assess whether vertebral fracture, aorta calcification or degeneration was present.

Results

In the 313 participants (mean age, 79.6 ± 7.2 years), the osteoporosis detection rate was 10.9% for DXA (lumbar spine and hip) and 45.1% for QCT, a significant difference (p < 0.001). The major discordance, minor discordance and concordance of diagnosis between the two techniques were seen in 8.3%, 50.8% and 40.9%, respectively. QCT detected osteoporosis better than DXA. The causes of this discordance were degeneration of spine, abdominal aorta calcification and vertebral fractures.

Conclusion

Our study demonstrated that discordance was common when using QCT and DXA to diagnose osteoporosis and that spinal degeneration, aorta calcification and fracture obscure the bone mineral density measurement of spine by DXA. QCT is a more sensitive method of choice to identify osteoporosis in elderly Chinese men.

The translational potential of this article

This study investigated the diagnostic discordance of osteoporosis by quantitative computed tomography (QCT) and dual-energy X-ray absorptiometry (DXA) in Chinese elderly men. The results demonstrated that QCT is a more sensitive method of choice to identify osteoporosis in elderly Chinese men. This work may help clinicians make an appropriate choice of technique for the accurate diagnosis of osteoporosis and identify the patients at high risk of osteoporosis who should be treated early to prevent fractures. This may influence the therapeutic plan and the overall prognosis of patients.