Diagnostic Performance of Elastography in Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis

Free-hand ultrasound strain elastography in evaluation of soft tissue tumors

OBJECTIVE

The purpose of this study is to evaluate elastography in a wide spectrum of soft tissue superficial lesions by correlating the elastographic characteristics of these lesions with the elastographic score (ES) system established by Asteria.

METHODS

Forty patients with different superficial lesions of the soft tissues were studied, including lipomas, schwannomas, neuromas, epidermal inclusion cysts, "in transit" melanoma metastasis, arterio-venous malformation, and giant-cell tumor. An ultrasound examination was performed combined with color-Doppler and elastographic module. The B-mode criteria were echogenicity, margins, and structural homogeneity of the lesion. The color-Doppler criterion was irregular and mainly intra-nodular vascularization. ES 1-4 was attributed, in relation with the increasing tissue stiffness, according to the classification of Asteria adapted for soft tissues. Subsequently, we added to each single B-mode and color-Doppler criterion the ES 3 and 4, thus crossing two parameters of malignancy. All the presumptive diagnoses formulated were confirmed with the clinical data or with the histopathological result.

RESULTS

The hypoechoic appearance had the best diagnostic performance. Sensitivity was 87%, specificity 71%, positive predictive value (PPV) 80%, negative predictive value (NPV) 80%, and diagnostic accuracy 80%. There was a good correlation with the clinical and biopsy data, the irregularity of margins the worst performance, the inhomogeneity an intermediate. Color-Doppler had sensitivity 74%, specificity 82%, PPV 85%, NPV 70% and diagnostic accuracy 77.5%. Elastography had sensitivity 87%, specificity 94%, PPV 95%, NPV 84%, and diagnostic accuracy 90%. The combination hypoechoic appearance + ES3/ES4 showed sensitivity 83%, specificity 100%, PPV 100%, NPV 81%,and diagnostic accuracy of 90%. The combination of irregularity of margins + ES3/ES4 showed sensitivity 43%, specificity 100%, PPV 100%, NPV 59%, and diagnostic accuracy of 67.5%. The combination of inhomogeneity of the lesion + ES3/ES4 showed sensitivity 65%, specificity 94%, PPV 94%, NPV 68%, and diagnostic accuracy of 78%. The combination of the color-Doppler with the ES3/ES4 showed sensitivity 69.5%, specificity 100%, PPV 100%, NPV 71%, and diagnostic accuracy of 82.5%.In the combined evaluation, there was a significant increase in specificity, allowing healthy subjects to be categorized as correctly negative, with a reduction in false positives which also translates into an increase in PPV.

CONCLUSIONS

Elastography alone is not sufficient for a correct diagnostic classification and must be considered as an additional parameter in the study of soft-tissue lesions. Although there was a good agreement between B-mode malignancy criteria and ES3/ES4, there is no significant improvement in sensitivity. Ultrasound assessment, especially of superficial lesions, cannot be separated from an integrated approach that foresees the additional and routine use of the elastographic examination.

Mechanical regulation of mitochondrial morphodynamics in cancer cells by extracellular microenvironment

Recently, it has been recognized that physical abnormalities (e.g. elevated solid stress, elevated interstitial fluid pressure, increased stiffness) are associated with tumor progression and development. Additionally, these mechanical forces originating from tumor cell environment through mechanotransduction pathways can affect metabolism. On the other hand, mitochondria are well-known as bioenergetic, biosynthetic, and signaling organelles crucial for sensing stress and facilitating cellular adaptation to the environment and physical stimuli. Disruptions in mitochondrial dynamics and function have been found to play a role in the initiation and advancement of cancer. Consequently, it is logical to hypothesize that mitochondria dynamics subjected to physical cues may play a pivotal role in mediating tumorigenesis. Recently mitochondrial biogenesis and turnover, fission and fusion dynamics was linked to mechanotransduction in cancer. However, how cancer cell mechanics and mitochondria functions are connected, still remain poorly understood. Here, we discuss recent studies that link mechanical stimuli exerted by the tumor cell environment and mitochondria dynamics and functions. This interplay between mechanics and mitochondria functions may shed light on how mitochondria regulate tumorigenesis.

Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes

Background

Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving.

Objectives

The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients.

Methods

We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study.

Results

The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01).

Conclusion

The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.

Understanding a mass in the paraspinal region: an anatomical approach

The paraspinal region encompasses all tissues around the spine. The regional anatomy is complex and includes the paraspinal muscles, spinal nerves, sympathetic chains, Batson's venous plexus and a rich arterial network. A wide variety of pathologies can occur in the paraspinal region, originating either from paraspinal soft tissues or the vertebral column. The most common paraspinal benign neoplasms include lipomas, fibroblastic tumours and benign peripheral nerve sheath tumours. Tumour-like masses such as haematomas, extramedullary haematopoiesis or abscesses should be considered in patients with suggestive medical histories. Malignant neoplasms are less frequent than benign processes and include liposarcomas and undifferentiated sarcomas. Secondary and primary spinal tumours may present as midline expansile soft tissue masses invading the adjacent paraspinal region. Knowledge of the anatomy of the paraspinal region is of major importance since it allows understanding of the complex locoregional tumour spread that can occur via many adipose corridors, haematogenous pathways and direct contact. Paraspinal tumours can extend into other anatomical regions, such as the retroperitoneum, pleura, posterior mediastinum, intercostal space or extradural neural axis compartment. Imaging plays a crucial role in formulating a hypothesis regarding the aetiology of the mass and tumour staging, which informs preoperative planning. Understanding the complex relationship between the different elements and the imaging features of common paraspinal masses is fundamental to achieving a correct diagnosis and adequate patient management. This review gives an overview of the anatomy of the paraspinal region and describes imaging features of the main tumours and tumour-like lesions that occur in the region.

The Effect of Hand Massage on Anxiety Levels in Preoperative Patients: A Case Study

Surgery can trigger high levels of anxiety in preoperative patients. If this anxiety is not managed effectively, it can disrupt the surgical plan. Preoperative nurses can help prepare patients for their surgical experience by implementing interventions that reduce the stress that causes preoperative anxiety. One intervention that can be used to manage preoperative anxiety is hand massage. We report our experience with Mr. S, a 34-year-old man scheduled for surgery to remove a lump in his left upper back. The lump appeared approximately 3 years ago. It was initially small, but enlarged over time. The patient sought medical treatment and was diagnosed with a soft tissue tumor (STT) of his left scapula. His surgeons recommended surgical excision of the tumor. Our study aimed to determine the effect of hand massage on reducing anxiety in a preoperative patient with a diagnosis of STT of the scapula.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study

OBJECTIVE

We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs).

MATERIALS AND METHODS

This combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital.

RESULTS

The multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model.

CONCLUSION

The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.

Clinical studies of magnetic resonance elastography from 1995 to 2021: Scientometric and visualization analysis based on CiteSpace

BACKGROUND

To assess the knowledge framework around magnetic resonance elastography (MRE) and to explore MRE research hotspots and emerging trends.

METHODS

The Science Citation Index Expanded of the Web of Science Core Collection was searched on 22 October 2021 for MRE-related studies published between 1995 and 2021. Excel 2016 and CiteSpace V (version 5.8.R3) were used to analyze the downloaded data.

RESULTS

In all, 1,236 articles published by 726 authors from 540 institutions in 40 countries were included in this study. The top 10 authors published 57.6% of all included articles. The 3 most productive countries were the USA (n=631), Germany (n=202), and France (n=134), and the 3 most productive institutions were the Mayo Clinic (n=240), Charité (n=131), and the University of Illinois (n=56). The USA and the Mayo Clinic had the highest betweenness centrality among countries and institutions, respectively, and played an important role in the field of MRE. In this study, the 24,347 distinct references were clustered into 48 categories via reasonable clustering using specific keywords, forming the knowledge framework. Among the 294 co-occurring keywords, "hepatic fibrosis", "stiffness", "skeletal muscle", "acoustic strain wave", "in vivo", and "non-invasive assessment" were research hotspots. "Diagnostic performance", "diagnostic accuracy", "hepatic steatosis", "chronic hepatitis B", "radiation force impulse", "children", and "echo" were frontier topics.

CONCLUSIONS

Scientometric and visualized analysis of MRE can provide information regarding the knowledge framework, research hotspots, frontier areas, and emerging trends in this field.

Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence

Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in musculoskeletal imaging of the extremities. Methods: Twenty-three patients who underwent MRI of the extremities were prospectively included. Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging was compared to a deep learning-accelerated T2w TSE (TSEDL) sequence. Image analysis of 23 patients with a mean age of 60 years (range 30−86) was performed regarding image quality, noise, sharpness, contrast, artifacts, lesion detectability and diagnostic confidence. Pathological findings were documented measuring the maximum diameter. Results: The analysis showed a significant improvement for the T2 TSEDL with regard to image quality, noise, contrast, sharpness, lesion detectability, and diagnostic confidence, as compared to T2 TIRMStd (each p < 0.001). There were no differences in the number of detected lesions. The time of acquisition (TA) could be reduced by 52−59%. Interrater agreement was almost perfect (κ = 0.886). Conclusion: Accelerated T2 TSEDL was technically feasible and superior to conventionally applied T2 TIRMStd. Concurrently, TA could be reduced by 52−59%. Therefore, deep learning-accelerated MR imaging is a promising and applicable method in musculoskeletal imaging.

Ultrasonography of Superficial Soft-Tissue Masses: Society of Radiologists in Ultrasound Consensus Conference Statement

The Society of Radiologists in Ultrasound convened a panel of specialists from radiology, orthopedic surgery, and pathology to arrive at a consensus regarding the management of superficial soft-tissue masses imaged with US. The recommendations in this statement are based on analysis of current literature and common practice strategies. This statement reviews and illustrates the US features of common superficial soft-tissue lesions that may manifest as a soft-tissue mass and suggests guidelines for subsequent management.

Qualitative and Quantitative Contrast-Enhanced Ultrasound Combined with Conventional Ultrasound for Predicting the Malignancy of Soft Tissue Tumors

This study was aimed at evaluating the performance of perfusion patterns and the quantitative parameters of contrast-enhanced ultrasound (CEUS) in the detection of soft tissue tumors (STTs) and establishing a US workflow for STTs to improve patient management. Conventional ultrasound (US) and CEUS data were retrospectively collected from 156 soft tissue masses. Six perfusion patterns (P1-P6) were applied for CEUS qualitative analysis. Multivariate logistic regression was used to evaluate the performance of conventional US and qualitative and quantitative CEUS in distinguishing benign and malignant STTs. The malignancy rates of P1-P6 in STTs were 0%, 50.0%, 9.1%, 33.3%, 73.4% and 61.0%, respectively. For "non-P1" STTs, the predictive model combining quantitative CEUS parameters with conventional US features, including margin (odds ratio [OR] = 4.490, p = 0.000), vascular density (OR = 2.307, p = 0.013), 50% wash-out intensity (OR = 1.904, p = 0.032) and 50% wash-out time (OR = 1.031, p = 0.019), performed favorably in predicting malignancy, with an accuracy of 81.0% and an area under the receiver operating characteristic curve of 0.868. Furthermore, a US workflow for the detection of STTs based on conventional US and CEUS was established. CEUS with qualitative and quantitative analyses could be an effective tool for STT diagnosis. The US workflow in this study may improve the management of STT patients.