Quantitative Ultrasound Data Research Articles

Development and validation of multivariable quantitative ultrasound for diagnosing hepatic steatosis

This study developed and validated multivariable quantitative ultrasound (QUS) model for diagnosing hepatic steatosis. Retrospective secondary analysis of prospectively collected QUS data was performed. Participants underwent QUS examinations and magnetic resonance imaging proton density fat fraction (MRI-PDFF; reference standard). A multivariable regression model for estimating hepatic fat fraction was determined using two QUS parameters from one tertiary hospital (development set). Correlation between QUS-derived estimated fat fraction(USFF) and MRI-PDFF and diagnostic performance of USFF for hepatic steatosis (MRI-PDFF ≥ 5%) were assessed, and validated in an independent data set from the other health screening center(validation set). Development set included 173 participants with suspected NAFLD with 126 (72.8%) having hepatic steatosis; and validation set included 452 health screening participants with 237 (52.4%) having hepatic steatosis. USFF was correlated with MRI-PDFF (Pearson r = 0.799 and 0.824; development and validation set). The model demonstrated high diagnostic performance, with areas under the receiver operating characteristic curves of 0.943 and 0.924 for development and validation set, respectively. Using cutoff of 6.0% from development set, USFF showed sensitivity, specificity, positive predictive value, and negative predictive value of 87.8%, 78.6%, 81.9%, and 85.4% for diagnosing hepatic steatosis in validation set. In conclusion, multivariable QUS parameters-derived estimated fat fraction showed high diagnostic performance for detecting hepatic steatosis.

Predicting Spontaneous Pre-term Birth Risk Is Improved When Quantitative Ultrasound Data Are Included With Historical Clinical Data.

Predicting women at risk for spontaneous pre-term birth (sPTB) has been medically challenging because of the lack of signs and symptoms of pre-term birth until interventions are too late. We hypothesized that prediction of the sPTB risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared with using only HC data. HC data defined herein included birth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment and physical examination data. The study population included 248 full-term births (FTBs) and 26 sPTBs. QUS scans (Siemens S2000 and MC9-4) were performed by registered diagnostic medical sonographers using a standard cervical length approach. Two cervical QUS scans were conducted at 20 ± 2 and 24 ± 2 wk of gestation. Multiple QUS features were evaluated from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: (i) HC data alone and (ii) combined HC and QUS data. Model comparisons included a likelihood ratio test, cross-validated receiver operating characteristic area under the curve, sensitivity and specificity. The study's birth outcomes were only FTBs and sPTBs; medically induced pre-term births were not included. Combined HC and QUS data identified women at risk of sPTB with better AUC (0.68, 95% confidence interval [CI]: 0.57-0.78) compared with HC data alone (0.53, 95% CI: 0.40-0.66) and HC data+cervical length at 18-20 wk of gestation (average AUC=0.51, 95% CI: 0.38-0.64). A likelihood ratio test for significance of QUS features in the classification model was highly statistically significant (p < 0.01). Even with only 26 sPTBs among 274 births, value was added in predicting sPTB when QUS data were included with HC data.

Testicular Dysfunction in 47,XXY Boys: When It All Begins. A Semilongitudinal Study.

Klinefelter syndrome is the most common chromosomal disorder in males and the most common cause of hypergonadotropic hypogonadism. We describe the natural history of testicular dysfunction in patients with Klinefelter syndrome through the integration of clinical, hormonal, and quantitative ultrasound data in a life-course perspective. Prospective semilongitudinal study. We included 155 subjects with 47,XXY karyotype (age range: 7 months-55 years) naïve to testosterone replacement therapy. Subjects were divided according to pubertal stage and age group (transition age and adults). Serial clinical, hormonal, and testicular ultrasound (US) assessments were performed. Testicular development progresses until Tanner stage 4, with subsequent regression, whereas Sertoli and germ cell impairment is not hormonally detected before Tanner stages 3-4, as reflected by normal inhibin B values until stage 4 and the fall in the inhibin B/follicle-stimulating hormone ratio thereafter. The testosterone/luteinizing hormone ratio peaks during Tanner stages 2-3 and declines from Tanner stage 4 onward, preceding the development of overt hypogonadism. US echotexture progressively worsens until transition age, reflecting ongoing gonadal compromise, whereas quantitative US echotexture measures and the presence of both hypoechoic lesions and microlithiasis independently and significantly predict a lower circulating testosterone level. The findings from this large prospective study contribute to our understanding of the natural history of testicular dysfunction in Klinefelter syndrome, underlining the importance of quantitative testicular US in infancy and childhood, as well as during pubertal development and transition age, for the optimal care of Klinefelter syndrome patients.

Quantitative ultrasound for preterm birth risk prediction—Part 1: Statistical evaluation

Hypothesis: Predicting the spontaneous preterm birth (sPTB) risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data. HC data defined herein includebirth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and physical examination data. Study population included 248 full-term births (FTBs) and 26 sPTBs. Advanced statistical analyses were performed for supervised classification containing 53 scaled candidate features (48 QUS, 5 HC) using nested fivefold cross-validation of L1-penalized linear logistic regression with 1000 repetitions to identify potential predictors. Statistical models for HC data alone and HC + QUS data were compared with likelihood-ratio test, cross-validated receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, and specificity. To assess performance, the ROC-AUC was estimated with 10-fold cross-validation logistic regression and 1000 repetitions. Averaged ROC curves plus AUCs were computed using threshold averaging. AUC confidence intervals and test statistics to test the two ROC curves’ differences were constructed via DeLong method. Combined HC and QUS data identified women at sPTB risk with better AUC (0.68; 95% CI, 0.57–0.78) than those of HC data alone (0.53; 95% CI, 0.40–0.66). [Work supported by NIHR01HD089935.]

Predicting spontaneous PTB risk is improved when quantitative ultrasound data are included with clinical data

Predicting women at risk for spontaneous preterm birth (sPTB) has been medically challenging due to the lack of signs and symptoms of preterm labor until intervention is too late. Hypothesis: Predicting the sPTB risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data, with HC data being defined as birth history prior current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and data from a physical exam. Study population included 250 full-term births (FTBs) and 25 sPTBs. QUS scans (Siemens S2000 & MC9-4) were performed using a standard cervical length approach by RDMS sonographers. QUS is a measure of cervical remodeling at the microstructure level. Two cervical QUS scans were conducted at 20±2 and 24±2 weeks’ gestation. Multiple QUS features were evaluated from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: 1) HC data alone and 2) combined HC and QUS data. Test ROC AUC compared both models. The study’s birth outcomes were only FTBs or sPTBs; medically induced preterm births were not included. Combined HC and QUS data identified women at sPTB risk with better AUC (0.68; 95% CI, 0.58-0.78) than that of HC data alone (0.52; 95% CI, 0.38-0.65). A likelihood ratio test for significance of QUS features in the classification model was highly statistically significant (p < 0.01). Traditional risk factors for sPTB (age, parity, previous history of PTB, cervical length < 25 mm, smoking, BMI and alcohol use) were not predictive of sPTB in our sample of participants. Cervical length measurements and HC have been the mainstay of risk assessment for sPTB for years because no other credible biomarkers were available. Even with only 25 sPTBs, there was value-added for predicting sPTB when QUS data were included with HC data.

Ambient air pollution, bone mineral density and osteoporosis: Results from a national population-based cohort study

Evidence concerning the associations of ambient air pollution exposure with bone mineral density and osteoporosis has been mixed. We conducted cross-sectional and prospective analysis of the associations between air pollution exposure and osteoporosis using data from UK Biobank study. Estimated bone mineral density (eBMD) of each participant at baseline survey was calculated using quantitative ultrasound data, and incident osteoporosis cases were identified during the follow-up period according to health-related records. Air pollution concentrations were assessed using land use regression models. We fitted multivariable linear and logistic regression models to estimate the associations of air pollution with eBMD and osteoporosis prevalence at baseline. We applied cox proportional hazard regression models to assess the relationships between air pollution and osteoporosis incidence. Among the 341,311 participants at baseline, higher air pollution exposure was associated with lower eBMD levels and increased odds of osteoporosis prevalence. For example, an IQR increase in PM2.5, PM2.5 absorbance, PM10, NO2 and NOx levels were associated with 0.0018 (95% CI: 0.0012, 0.0023) to 0.0052 (95% CI: 0.0046, 0.0058) g/cm2 decrease in eBMD. A total of 330,988 participants without osteoporosis were followed up for an average of 12.0 years. We identified 8105 incident osteoporosis cases (456 cases with pathological fracture and 7634 cases without pathological fracture) during the follow-up. The hazard ratios for an interquartile range increase in PM2.5, PM2.5 absorbance, PM10, NO2 and NOx were 1.09 (95% CI: 1.06, 1.12), 1.04 (95% CI: 1.02, 1.07), 1.04 (95% CI: 1.01, 1.07), 1.07 (95% CI: 1.04, 1.10), and 1.06 (95% CI: 1.03, 1.09), respectively. Our study suggests that ambient air pollution might be a risk factor of decreased bone mineral density and osteoporosis incidence.

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer.

Simple SummaryPatients diagnosed with breast cancer treated with chemotherapy before surgery were included in this study. The tumor was imaged using ultrasound before the chemotherapy was started and in the middle of chemotherapy treatment (one month after starting). After treatment completion, patients were followed up according to standard clinical practice and categorized into two groups based on disease recurrence until the last follow-up. The ultrasound imaging was analyzed using advanced computational techniques, and artificial intelligence was used to develop models to differentiate between the two outcomes. We demonstrated that using the ultrasound image data, the final outcomes can be predicted as early as within one month of the start of chemotherapy.Background: This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC). Methods: Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC (week 0) and during treatment (week 4). Spectral parametric maps were generated corresponding to tumor volume. Twenty-four textural features (QUS-Tex1) were determined from parametric maps acquired using grey-level co-occurrence matrices (GLCM) for each patient, which were further processed to generate 64 texture derivatives (QUS-Tex1-Tex2), leading to a total of 95 features from each time point. Analysis was carried out on week 4 data and compared to baseline (week 0) data. ∆Week 4 data was obtained from the difference in QUS parameters, texture features (QUS-Tex1), and texture derivatives (QUS-Tex1-Tex2) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using k-nearest neighbor (k-NN) and support vector machines (SVMs) were used to generate radiomic models. Internal validation was undertaken using leave-one patient out cross-validation method. Results: With a median follow up of 69 months (range 7–118 months), 28 patients had disease recurrence. The k-NN classifier was the best performing algorithm at week 4 with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 75%, 81%, and 0.83, respectively. The inclusion of texture derivatives (QUS-Tex1-Tex2) in week 4 QUS data analysis led to the improvement of the classifier performances. The AUC increased from 0.70 (0.59 to 0.79, 95% confidence interval) without texture derivatives to 0.83 (0.73 to 0.92) with texture derivatives. The most relevant features separating the two groups were higher-order texture derivatives obtained from scatterer diameter and acoustic concentration-related parametric images. Conclusions: This is the first study highlighting the utility of QUS radiomics in the prediction of recurrence during the treatment of LABC. It reflects that the ongoing treatment-related changes can predict clinical outcomes with higher accuracy as compared to pretreatment features alone.

Predicting spontaneous preterm birth risk is improved when quantitative ultrasound data are included with prior clinical data

Predicting women at risk for spontaneous preterm birth (sPTB) has been medically challenging due to lack of signs and symptoms of preterm labor until intervention is too late. Hypothesis: advanced statistical modeling predicts sPTB risk from quantitative ultrasound (QUS) plus prior data (prior birth history through first clinical cervical length) better than that of prior data alone. Study population included 250 full-term births (FTBs) and 25sPTBs. QUS scans (Siemens S2000 & MC9-4) were performed using a standard cervical length approach by registered diagnostic medical sonographers. Two cervical QUS scans were conducted at 20 ± 2 and 24 ± 2 wk gestation. Multiple QUS features were processed from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: (1) QUS plus prior data and (2) prior data alone. Test ROC AUC compared both models. Using statistical methods, QUS plus prior data identified women at risk for sPTB with better AUC (0.68; std error 0.01; 95% CI, 0.66–0.70) than that of prior data alone (0.63; std error 0.01; 95% CI, 0.61–0.65). Even with only 25 sPTBs, data suggest that there is value added for predicting sPTB when QUS data are included with prior data. [R01HD089935.]

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

PurposeThis study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC). MethodsFifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation. ResultsThe median follow up for the entire group was 38 months (range 7–64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment. ConclusionQUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Challenges of conducting quantitative ultrasound with a multimodal optical imaging system

High-frequency quantitative ultrasound is a potential non-invasive source of imaging cell-tissue scale biomarkers for major diseases such as heart disease, cancer, and preterm birth. However, one of the barriers to developing such biomarkers is that it is labor-intensive to compare quantitative ultrasound images to optical images of the tissue structure. We have previously developed a multiscale imaging system that can obtain registered qualitative ultrasound and optical images, but there are further technical challenges to obtaining quantitative data: System-specific details of obtaining and processing data with Verasonics high-frequency transducers; the need for high-frequency reference phantoms; and off-axis clutter from imaging above a glass coverslip. This paper provides a characterization of the Verasonics ultrasound system with the 18.5 MHz L22-14v and 28.5 MHz L38-22v transducers, describes the construction of high-frequency reference phantoms, and details methods for reducing off-axis clutter. The paper features a demonstration multiscale image of a wild type mouse mammary gland that incorporates quantitative ultrasound with both transducers and second harmonic generation microscopy. These advances demonstrate a way to obtain, on a single system with a cohesive and integrated pipeline, quantitative ultrasound data that is correlated with optical imaging without the need for extensive sample preparation.

Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results.

BackgroundNeoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting.MethodsFifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher’s linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models.ResultsThirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively.ConclusionQUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.

Predicting Recurrence for Patients with Head-Neck Squamous Cell Carcinoma Using Quantitative Ultrasound Based Radiomic Signatures Integrated with Machine Learning

We have demonstrated the ability of quantitative ultrasound (QUS) in characterizing tumor biology in previous in vitro and in vivo studies. This aim of this project was to investigate the clinical utility of QUS as a biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). Fifty patients with HNSCC were treated with RT (70 Gy/33 #) (+/- concomitant chemotherapy) were included in the current analysis. QUS data acquisition involved scanning nodes with a conventional-frequency device (7 MHz). Data were collected at the following time intervals relative to the start of treatment: week 0 (baseline), after week 1, and week 4. Acoustic backscatter features were quantified using spectral analysis of the radiofrequency data within a region of interest. Second order features included a set of 41 QUS radiomic features from a grey-level co-occurrence matrix. Imaging was acquired as part of the usual standard-of-care at 3 months after completing RT. Subsequently, patients were followed every 3 months for the initial two years and then every 6 months with clinical examination supplemented with imaging as indicated. Patients were categorized into two groups based on clinical outcomes (recurrence vs. no recurrence). Results were compared from 3 classification algorithms-Fisher linear discriminant, k-nearest-neighbors, and support vector machine (SVM). Three features used in each model were selected using a forward sequential selection method and validated using leave-one-out cross-validation. The median follow up for the entire group was 18 months. The most common primary site was oropharynx in 34 patients, followed by hypopharynx (7), larynx (4), unknown primary (4), and oral cavity (1). There were 16 complete responders (CR) and 34 partial responders (PR) at 3 months following completion of RT. Fourteen patients had recurrences: isolated regional (2), regional-distant (3), isolated distant (7), local-regional-distant (2), all belonging to the cohort of PR. The SVM classifier resulted in the best predictive performance (sensitivity, specificity, accuracy, and area under the curve values of 92%, 71%, 86%, and 71% respectively, for week 0). There was an improvement in classification accuracy at week 1 and week 4 using SVM classifier (Table 1). Most of the features selected for the recurrence classification were second-order QUS-texture features. Our preliminary results demonstrate encouraging results for QUS radiomic features as a potential biomarker for predicting patients at higher risk of disease recurrence. This is the first clinical report showing the ability of ultrasound to predict the outcomes even before the initiation of treatment in HNSCC.Abstract 2996; Table 1TimeModelSensitivitySpecificityAccuracyAUCWeek 0k-NN89%57%80%71%SVM92%71%86%71%Week 0 + ΔWeek 1k-NN89%57%80%71%SVM97%71%90%76%Week 0 + ΔWeek 4k-NN97%57%86%84%SVM100%71%92%87% Open table in a new tab

P81-F Neuromuscular ultrasound as diagnostic tool and marker of disease progression in amyotrophic lateral sclerosis

Background Several methods have been proposed to diagnose and score disease severity in Amyotrophic Lateral Sclerosis (ALS) patients (pt.). Traditionally neurophysiological tests have been employed. The development of neuromuscular ultrasound (US) have provided a further useful tool. Our aim was to evaluate sonographic changes of muscles and nerve trunks to define diagnosis and assess ALS severity. Materials and methods 20 ALS pt. (15 m e 5 f, mean age: 63.9 + 9.5 yrs) were enrolled. All investigations were performed using an Esaote MyLabGamma device in conventional B-Mode. We evaluated the following muscles: sternocleidomastoid, diaphragm, biceps brachii/brachialis, forearm flexors, rectus femoris and anterior tibial; among the nerve, vagus, median, ulnar and sural. Results We found in all cases increased echogenicity with decreased muscle thickness and fasciculations; mean diaphragm rest thickness was reduced; changes in thickness during inspiration and expiration were also reduced and loss in the most severe cases. Median and Ulnar nerve cross-sectional area (CSA) resulted smaller; sural and vagal nerve resulted spared. ALS disease severity, measured using Functional Rating Scale (ALSFRS), correlated with quantitative ultrasound data. Conclusions neuromuscular ultrasound resulted a simple, fast and easy method, painless and risk-free, able to provide useful functional and structural information in ALS pt. Furthermore, diaphragm US may allow to point out concomitant respiratory failure. Moreover is desirable that US become an indispensable tool of the diagnostic armamentarium of the neuromuscular physician in pt. with ALS.

Performance of Sri Lankan FRAX algorithm without bone mineral density and with Quantitative Ultrasound data input

Fracture risk assessment algorithm (FRAX) is the most validated method available to predict fracture risk. Its use is restricted due to limited availability of Dual Energy X-ray Absorptiometry (DXA). FRAX has the option of assessing facture risk without BMD data. To assess the ability of Sri Lankan FRAX algorithm without BMD input in evaluating fracture risk. The possibility of replacing the BMD input with Quantitative Ultrasound (QUS) data of radius in calculating fracture risk also assessed. Data of clinical risk factors associated with fractures were collected from community dwelling postmenopausal women (n=339). DXA scans were performed in all subjects and QUS scans (in radius) were performed in a randomly selected sample (n=207). Ten-year risks of major osteoporotic fracture (MOFR) and hip fracture (HFR) were calculated with BMD, without BMD (FRAX-FN0) and with US T score instead of BMD (FRAX-UST). Nearly 35.7% had high risk of fractures. FRAX-FN0 had 79.2% sensitivity, 80.1% specificity, 68.8% positive predictive value (PPV) and 87.4% negative predictive value (NPV). FRAX-UST showed 78.4% sensitivity, 70% specificity, 59.8% PPV and 85% NPV. ROC AUCs were above 0.80 in both FRAX-FN0 and FRAX-UST. The standard errors of estimate (SEE) were less in FRAX-FN0 (3.96 and 2.76 for MOFR-FN0 and HFR-FN0 respectively) compared to FRAX-UST (6.13 and 4.83 for MOFR-UST and HFR-UST, respectively). In conclusion, Sri Lankan FRAX without BMD is an acceptable alternative in areas with restricted DXA facility. Radial QUS data cannot be used as a substitute to FN-BMD in Sri Lankan FRAX.

S54. Neuromuscular ultrasound as diagnostic tool and marker of disease severity in amyotrophic lateral sclerosis

Introduction Several methods have been proposed to diagnose and quantify disease severity in patients (pt.) with Amyotrophic Lateral Sclerosis (ALS). Traditionally neurophysiological tests have been employed. The development of neuromuscular ultrasound (US) have provided a further useful tool to supplement clinical and neurophysiological assessment of ALS. Our aim was to evaluate sonographic changes of muscles and nerve trunks in an attempt to define diagnosis and assess severity of the disease. Methods 35 consecutive ALS pt. (20 males e 15 females, mean age: 64.6 ± 10.5 yrs) were enrolled; in four cases the diagnosis was not previously established. All investigations were performed using an Esaote MyLabGamma or a Telemed Echo-wave II device in conventional B-Mode. We evaluated the following muscles: tongue, sternocleidomastoid, diaphragm, biceps brachii/brachialis, forearm flexors, rectus femoris and anterior tibial; among the nerve, brachial plexus, vagus, median, ulnar and sural. Results We found in all cases increased echogenicity with decreased muscle thickness and fasciculations; these findings allowed us to diagnose an ALS in the 4 cases not previously identified and later confirmed. Mean diaphragm rest thickness was reduced; changes in thickness during inspiration and expiration were also reduced and loss in the most severe cases (3 pt.). Brachial plexus, Median and Ulnar nerve cross-sectional area (CSA) resulted smaller in ALS; vagus and sural nerve resulted spared. ALS disease severity, measured using Functional Rating Scale (ALSFRS), correlated with quantitative ultrasound data. Conclusion Neuromuscular ultrasound resulted a simple, fast and easy method, painless and risk-free, able to provide useful functional and structural informations in ALS pt. Furthermore diaphragm US may allow to point out concomitant respiratory failure. Moreover is desirable that US become an indispensable tool of the diagnostic armamentarium of the neuromuscular physician in pt. with ALS.

In‐vitro study of human proximal femur microstructure: analysis of the relationship between micro‐computed tomography data and quantitative ultrasound parameters

The aim of this study was to investigate the relationships between selected quantitative ultrasound (QUS) parameters and human femur microstructure properties, as quantified by micro-computed tomography (micro-CT). The authors employed an innovative custom-designed experimental set-up, which allowed the insonification of each portion of an excised femoral head sample, simultaneously including trabecular region, cortical layer and cartilage in their physiologic morphological configuration. Thirty different, uniformly distributed, regions of interest were analysed for the calculation of apparent integrated backscatter (AIB), integrated reflection coefficient (IRC) and several micro-CT parameters. QUS data acquisitions were performed through both single-element ultrasound transducers at two different frequencies (2.25 and 3.5 MHz) and a clinically available 128-element echographic probe. Obtained results showed that AIB was strongly correlated with trabecular network properties (|r| up to 0.80) and IRC had appreciable linear correlations with cortical bone density (|r| up to 0.57). The agreement between single-element transducers and echographic probe, combined with the innovative approach of considering the entire femoral head in its physiological shape with all its components (cartilage, cortical layer, trabecular region), encourages the clinical translation of the proposed approach as a possible new method for early osteoporosis diagnosis.

Longitudinal assessment of bone loss using quantitative ultrasound in a blood-induced arthritis rabbit model.

Osteoporosis is common in haemophilic arthropathy. Quantitative ultrasound (QUS) can be a suitable alternative for dual-energy x-ray absorptiometry for diagnosing osteoporosis in haemophiliacs due to its lack of ionizing radiation, and ease to use. We investigated the intra- and inter-operator reliability of QUS, its responsiveness to bone growth, its ability to differentiate bone adjacent to blood-injected vs. control joints, and the effect of soft tissues on the speed of sound (SOS) QUS values in a juvenile white New Zealand rabbit model of blood-induced arthritis. Eight of 16 rabbits were injected with autologous blood (0.1 mL kg(-1) ) 8 times over a 17-week period, the remaining eight rabbits served as controls. SOS was measured at baseline, weeks 8 and 17 in vivo and after the bones were excised on week 17. Intra- and inter-operator coefficients of variation for QUS data were <5% and intraclass correlation coefficients were >60% for 22/27 (81.5%) of bones assessed. The level of interval increase in SOS values from baseline to week 17 was significantly different in tibiae of injected, contralateral to injected and non-injected knee groups by anova (P = 0.01). In vivo (mean ± SD, 4147.17 ± 96.27 m s(-1) ) and postmortem (4457.85 ± 104.00 m s(-1) ) measurements on week 17 differed (P < 0.01) indicating an effect of soft tissues on SOS. In conclusion, QUS' acceptable reliability, its responsiveness to growth-related changes and its ability to discriminate injected and non-injected joints make this technique a plausible candidate as a diagnostic tool for osteoporosis in the paediatric haemophilic population if these results are confirmed upon animal-human translation.

AB0995 Peculiarities of relationship between state of bone tissue and knee osteoarthritis in postmenopausal women

BackgroundThe aim was to study the age-related particularity of relationship between structural-functional state of bone tissue and knee osteoarthritis (KO) in postmenopausal women.Objectives175 postmenopausal women aged 50-79 years old with...

SU-D-134-07: Bayesian Classifier for Differentiating Fibroadenoma and Carcinoma in Breast

Purpose: Quantitative ultrasound (QUS) provides acoustic parameters that are closely related to tissue microstructure and sound wave propagation. This study evaluated a Bayesian classifier applied to breast QUS data to aid in differentiating benign from malignant masses. This presentation outlines methodology for deriving these quantitative parameters and reports preliminary results using the classifier on a small data set of human tumors. Methods: This IRB-approved, HIPAA compliant study recruited 35 subjects who were scheduled for core biopsy of a suspicious breast mass. A Siemens S2000 scanner equipped with linear array transducers was used to acquire radiofrequency echo data at beam-steered angles from −10 to 10 degrees from the normal to the transducer surface in both longitudinal and transverse planes. Echo signals also were obtained from a reference phantom using the same settings. Parameters included acoustic attenuation (ATT), backscatter coefficients (BSC), effective scatterer diameter (ESD), and a scatterer size heterogeneity index(HI). ATT and BSC within the tumor were estimated using a reference phantom method, while theESD was estimated from the BSC using a Gaussian form factor. HI was taken to be the standard deviation among the ESD estimates. A Bayesian classifier was designed to differentiate between Fibroadenoma and carcinoma. The data were divided into training (18 patients) and testing (17 patients) sets. Classification was performed using the minimum distance to the centroid of the training class and the results were compared with the biopsy results. Results: The best performance with a parameter pair (ATT, ESD) was about 80% which increased to about 100% with three parameters (ATT, ESD, HI). However, more verification studies are needed to include a larger data set and more specific tumor categories. Conclusion: QUS with a Bayesian classifier is a promising noninvasive tool to characterize breast tumors. This work was supported, in part, by NIH (grants R01CA111289, R21HD061896, R21HD063031, and R01HD072077) and the Consejo Nacional de Ciencia y Tecnologia of Mexico (Reg. 206414).

Nonlinear optical microscopy and ultrasound imaging of human cervical structure

The cervix softens and shortens as its collagen microstructure rearranges in preparation for birth, but premature change may lead to premature birth. The global preterm birth rate has not decreased despite decades of research, likely because cervical microstructure is poorly understood. Our group has developed a multilevel approach to evaluating the human cervix. We are developing quantitative ultrasound (QUS) techniques for noninvasive interrogation of cervical microstructure and corroborating those results with high-resolution images of microstructure from second harmonic generation imaging (SHG) microscopy. We obtain ultrasound measurements from hysterectomy specimens, prepare the tissue for SHG, and stitch together several hundred images to create a comprehensive view of large areas of cervix. The images are analyzed for collagen orientation and alignment with curvelet transform, and registered with QUS data, facilitating multiscale analysis in which the micron-scale SHG images and millimeter-scale ultrasound data interpretation inform each other. This novel combination of modalities allows comprehensive characterization of cervical microstructure in high resolution. Through a detailed comparative study, we demonstrate that SHG imaging both corroborates the quantitative ultrasound measurements and provides further insight. Ultimately, a comprehensive understanding of specific microstructural cervical change in pregnancy should lead to novel approaches to the prevention of preterm birth.