Ultrasound Shear Wave Elastography of the Normal Prostate: Interobserver Reproducibility and Comparison with Functional Magnetic Resonance Tissue Characteristics

Influence of transducer pressure and examiner experience on muscle active shear modulus measured by shear wave elastography

INTRODUCTION

This study examined the effects of ultrasound transducer pressure and examiner experience on the biceps femoris long head and semitendinosus muscle active shear modulus in healthy individuals (n = 28).

METHODS

Active shear modulus was assessed using shear wave elastography at 20% of knee flexor maximal voluntary isometric contraction. Examiners with different experience levels measured the muscles' shear modulus with three pressure levels: mild, moderate, and hard.

RESULTS

A main effect of transducer pressure was found for both biceps femoris long head (p < 0.001; η2p = 0.314) and semitendinosus muscles (p < 0.001; η2p = 0.280), whereas differences were found between mild-moderate (biceps femoris long head: p = 0.013, d = 0.23; semitendinosus: p = 0.024, d = 0.25), and mild-hard pressures (biceps femoris long head: p = 0.001, d = 0.47; semitendinosus: p = 0.002, d = 0.47). Examiners performed similar shear modulus measurements in the biceps femoris long head (p = 0.299; η2p = 0.041) and semitendinosus (p = 0.177; η2p = 0.066), although the experienced examiner showed a higher measurement repeatability (biceps femoris long head: ICC = 0.86-0.95, semitendinosus: ICC = 0.89-0.96; vs. biceps femoris long head: ICC = 0.78-0.87, semitendinosus: ICC = 0.66-0.87).

CONCLUSION

Transducer pressure influences the active shear modulus measurement between mild and moderate or hard pressures. Additionally, examiner experience seems to have no influence on muscle active shear modulus measurement when assessed at the same site (using casts).

IMPLICATIONS FOR PRACTICE

Future studies assessing active muscle shear modulus should use mild transducer pressure and having experienced examiners in order to improve measurement reliability.

Application of Shear Wave Elastography and Contrast-Enhanced Ultrasound in Transrectal Prostate Biopsy

OBJECTIVE

To explore the clinical value of ultrasound shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) in transrectal prostate biopsy.

METHODS

A total of 54 patients (average age: 67.79±12.01 years) in the experimental group underwent transrectal prostate biopsy under the guidance of SWE, while 46 patients (average age: 69.22±11.54 years) in the control group underwent transrectal prostate biopsy guided by CEUS.

RESULTS

There were a total of 451 needles, with an average of 8.35±1.67 needles per patient in the experimental group, and a total of 462 needles, with an average of 10.04±1.33 needles per patient in the control group. The difference in puncture times between the two groups was statistically significant (P<0.05). There was no significant difference in the positive detection rate, sensitivity or specificity between the two groups (P>0.05), but there was a significant difference in the diagnostic accuracy between the two groups (P<0.05). The E_mean and E_max of prostate cancer were significantly higher in the experimental group than in benign prostatic hyperplasia (P<0.05). The receiver operating characteristic curve (ROC) analysis showed that the area under the ROC curve (AUC) of E_mean was 0.752 (S.E. =0.072, 95% CI=0.611-0.894, P=0.007), and the best cutoff value was 47.005 kPa.

CONCLUSION

In summary, both SWE- and CEUS-guided transrectal prostate biopsy can help find the focus and guide the puncture, and improve the positive detection rate.

Prostate Cancer Gleason Score From Biopsy to Radical Surgery: Can Ultrasound Shear Wave Elastography and Multiparametric Magnetic Resonance Imaging Narrow the Gap?

Objectives

To investigate the impact of ultrasound shear wave elastography (USWE) and multiparametric magnetic resonance imaging (mpMRI) in predicting a change in biopsy-assigned Gleason Score (GS) after radical surgery for localised prostate cancer (PCa).

Method

A total of 212 men opting for laparoscopic radical prostatectomy (LRP) between September 2013 and June 2017 were recruited into this study. All the participants had 12-core transrectal ultrasound (TRUS) biopsies and imaging using USWE and mpMRI before radical surgery. The predictive accuracy for imaging modalities was assessed in relation to upgrading and downgrading of PCa GS between the biopsies and radical prostatectomy using Student's t-test and multivariable logistic regression analyses. A decision analysis curve was constructed assessing the impact of nomogram on clinical situations using different thresholds of upgrading probabilities.

Results

Most GS 6 diseases on biopsies were upgraded on radical surgery (37/42, 88.1%). Major downgrading was seen in GS 8 category of disease (14/35; 37.1%), whereas no alteration was observed in GS 7 on biopsies in most men (55/75; 73.3%). In univariate analysis, higher preoperative prostate-specific antigen (PSA) (p = 0.001), higher prostate-specific antigen density (PSAD) (p = 0.002), stiffer USWE lesions (p = 0.009), and higher prostate imaging-reporting and data system (PIRADS) (p = 0.002) on mpMRI were significant predictors of upgrading. In multivariate logistic regression analyses, only PSA (p = 0.016) and USWE-measured tissue stiffness (p = 0.029) showed statistical significance in predicting upgrading.

Conclusions

Measurement of tissue stiffness using USWE in clinically localised PCa can predict upgrading of GS and has the potential to improve patient management options.

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions

Objectives

To evaluate the potential of a clinical-based model, a multiparametric ultrasound-based radiomics model, and a clinical-radiomics combined model for predicting prostate cancer (PCa).

Methods

A total of 112 patients with prostate lesions were included in this retrospective study. Among them, 58 patients had no prostate cancer detected by biopsy and 54 patients had prostate cancer. Clinical risk factors related to PCa (age, prostate volume, serum PSA, etc.) were collected in all patients. Prior to surgery, patients received transrectal ultrasound (TRUS), shear-wave elastography (SWE) and TRUS-guided prostate biopsy. We used the five-fold cross-validation method to verify the results of training and validation sets of different models. The images were manually delineated and registered. All modes of ultrasound radiomics were retrieved. Machine learning used the pathology of “12+X” biopsy as a reference to draw the benign and malignant regions of interest (ROI) through the application of LASSO regression. Three models were developed to predict the PCa: a clinical model, a multiparametric ultrasound-based radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared by receiver operating characteristic curve (ROC) analysis and decision curve.

Results

The multiparametric ultrasound radiomics reached area under the curve (AUC) of 0.85 for predicting PCa, meanwhile, AUC of B-mode radiomics and SWE radiomics were 0.74 and 0.80, respectively. Additionally, the clinical-radiomics combined model (AUC: 0.90) achieved greater predictive efficacy than the radiomics model (AUC: 0.85) and clinical model (AUC: 0.84). The decision curve analysis also showed that the combined model had higher net benefits in a wide range of high risk threshold than either the radiomics model or the clinical model.

Conclusions

Clinical-radiomics combined model can improve the accuracy of PCa predictions both in terms of diagnostic performance and clinical net benefit, compared with evaluating only clinical risk factors or radiomics score associated with PCa.

Characterisation of Prostate Lesions Using Transrectal Shear Wave Elastography (SWE) Ultrasound Imaging: A Systematic Review

BACKGROUND

ultrasound-based shear wave elastography (SWE) can non-invasively assess prostate tissue stiffness. This systematic review aims to evaluate SWE for the detection of prostate cancer (PCa) and compare diagnostic estimates between studies reporting the detection of all PCa and clinically significant PCa (csPCa).

METHODS

a literature search was performed using the MEDLINE, EMBASE, Cochrane Library, ClinicalTrials.gov, and CINAHL databases. Studies evaluating SWE for the detection of PCa using histopathology as reference standard were included.

RESULTS

16 studies including 2277 patients were included for review. Nine studies evaluated SWE for the detection of PCa using systematic biopsy as a reference standard at the per-sample level, with a pooled sensitivity and specificity of 0.85 (95% CI = 0.74-0.92) and 0.85 (95% CI = 0.75-0.91), respectively. Five studies evaluated SWE for the detection of PCa using histopathology of radical prostatectomy (RP) specimens as the reference standard, with a pooled sensitivity and specificity of 0.71 (95% CI = 0.55-0.83) and 0.74 (95% CI = 0.42-0.92), respectively. Sub-group analysis revealed a higher pooled sensitivity (0.77 vs. 0.62) and specificity (0.84 vs. 0.53) for detection of csPCa compared to all PCa among studies using RP specimens as the reference standard.

CONCLUSION

SWE is an attractive imaging modality for the detection of PCa.

Diagnostic performance of conventional ultrasound and quantitative and qualitative real-time shear wave elastography in musculoskeletal soft tissue tumors

BACKGROUND

To explore the feasibility to identify malignant musculoskeletal soft tissue tumors using real-time shear wave elastography (rtSWE).

METHODS

One hundred fifteen musculoskeletal soft tissue tumors in 92 consecutive patients were examined using both conventional ultrasonography (US) and rtSWE. For each patient, the rtSWE parameters including maximum elasticity (E_max), mean elasticity (E_mean), minimum elasticity (E_min), standard deviation of the elasticity (E_sd), and rtSWE image pattern were obtained. Eighty-one histopathologically confirmed tumors from 73 patients were subjected to analysis.

RESULTS

The 81 lesions included in the study were histopathologically classified as malignant (n = 21) or benign (n = 60). The statistically significant differences between benign and malignant lesions were found in conventional US characters including size, depth, margin, echogenicity, mass texture, and power Doppler signal. Meanwhile, the significant differences were also found in quantitative rtSWE findings including E_max, E_mean, E_min, and E_sd values and in qualitative rtSWE parameter named rtSWE image pattern. Multivariate analysis showed that infiltrative margin (OR, 4.470), and size (OR, 1.046) were independent predictors for malignancy in US findings, while E_sd value (OR, 9.047) was independent predictors for malignancy in quantitative rtSWE parameters. Areas under the ROC curve (Azs) for US features, E_sd value, and rtSWE image pattern were 0.851, 0.795, and 0.792, respectively.

CONCLUSIONS

Conventional US and quantitative and qualitative rtSWE parameters are useful for malignancy prediction of musculoskeletal soft tissue tumors. rtSWE can be used to supplement conventional US to diagnose musculoskeletal soft tissue tumors.

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

OBJECTIVES

The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.

METHODS

This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.

RESULTS

The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.

CONCLUSIONS

In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.

KEY POINTS

• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

Quantitative Evaluation of Shear Wave Elastography on Radiation-Induced Neck Fibrosis in Patients With Nasopharyngeal Carcinoma

ABSTRACT

The value of shear wave elastography (SWE) for quantitatively assessing neck fibrosis induced by radiotherapy (RT) in patients with nasopharyngeal carcinoma was evaluated over time. We prospectively observed 56 patients with nasopharyngeal carcinoma before and after therapeutic neck irradiation. The elasticity parameters including Emax and Emean were used to measure the stiffness of the bilateral sternocleidomastoid muscles. Twenty-seven patients completed a 1.5-year follow-up, with examinations beginning at 3, 6, 12, and 18 months after RT. Forty controls were recruited for reliability tests (along with the patients) and measurement comparisons. The consistency of SWE measurements with the Late Effects Normal Tissue Task Force-Subjective, Objective, Management and Analytic (LENT-SOMA) scale was tested. The intraclass correlation coefficients of elasticity indices for both patients and controls were higher than 0.75. The Emax and Emean of bilateral sternocleidomastoid muscles in the pre-RT patient group were comparable with those of the controls, and increased with increasing postirradiation duration (r = 0.514-0.555; P < 0.01). Significant increases in the Emax and Emean were observed 18 months after RT. The SWE correlated well with the LENT-SOMA score when assessing radiation-induced neck fibrosis 1.5 years after RT (r = 0.557-0.649; P < 0.01). Furthermore, both the Emax and Emean in the LENT-SOMA grade 0 subtype were higher 18 months after RT than before RT (P < 0.01). Because of its high reliability and good consistency with the LENT-SOMA score and better stiffness reflection at grade 0, SWE may be used to objectively and quantitatively evaluate the variation trend of radiation-induced neck fibrosis.

Spatiotemporal switching signals for cancer stem cell activation in pediatric origins of adulthood cancer: Towards a watch-and-wait lifetime strategy for cancer treatment

Pediatric origin of cancer stem cell hypothesis holds great promise and potential in adult cancer treatment, however; the road to innovation is full of obstacles as there are plenty of questions left unanswered. First, the key question is to characterize the nature of such stem cells (concept). Second, the quantitative imaging of pediatric stem cells should be implemented (technology). Conceptually, pediatric stem cell origins of adult cancer are based on the notion that plasticity in early life developmental programming evolves local environments to cancer. Technologically, such imaging in children is lacking as all imaging is designed for adult patients. We postulate that the need for quantitative imaging to measure space-time changes of plasticity in early life developmental programming in children may trigger research and development of the imaging technology. Such quantitative imaging of pediatric origin of adulthood cancer will help develop a spatiotemporal monitoring system to determine cancer initiation and progression. Clinical validation of such speculative hypothesis-that cancer originates in a pediatric environment-will help implement a wait-and-watch strategy for cancer treatment.