Characterization of limb lymphedema using the statistical analysis of ultrasound backscattering

Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Ultrasonography as an instrument to evaluate lymphedema secondary to breast cancer: systematic review

Lymphedema is a chronic and progressive disease characterized by fluid accumulation, causing tissue edema as a result of a compromised lymphatic system. Diagnostic ultrasound (DUS) is a method capable of assessing soft tissue characteristics that can be used reliably to diagnose lymphedema as well as for measuring tissue compliance in a clinical setting. This is a systematic review, aiming to evaluate articles that made use of DUS in management of lymphedema secondary to breast cancer. A total of 570 articles were selected, exported to the Rayyan QCRI review program, and then screened by two researchers. From this search, 25 articles were selected after the authors reached consensus and were catalogued as to their main results. Diagnostic ultrasound was identified as an advantageous method that is safe, minimally invasive, low cost, and radiation free and is useful for evaluating the efficacy of therapies used in lymphedema treatment.

Imaging Modalities for Evaluating Lymphedema

Lymphedema is a progressive condition. Its therapy aims to reduce edema, prevent its progression, and provide psychosocial aid. Nonsurgical treatment in advanced stages is mostly insufficient. Therefore-in many cases-surgical procedures, such as to restore lymph flow or excise lymphedema tissues, are the only ways to improve patients' quality of life. Imaging modalities: Lymphoscintigraphy (LS), near-infrared fluorescent (NIRF) imaging-also termed indocyanine green (ICG) lymphography (ICG-L)-ultrasonography (US), magnetic resonance lymphangiography (MRL), computed tomography (CT), photoacoustic imaging (PAI), and optical coherence tomography (OCT) are standardized techniques, which can be utilized in lymphedema diagnosis, staging, treatment, and follow-up. Conclusions: The combined use of these imaging modalities and self-assessment questionnaires deliver objective parameters for choosing the most suitable surgical therapy and achieving the best possible postoperative outcome.

Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm

OBJECTIVE

Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms look similar in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of tissue layers.

METHODS

For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects and manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of 0.94±0.08 and 0.92±0.06, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy.

RESULTS

We got an average DSC of 0.87±0.11 on the test set, which confirms the high performance of the method.

CONCLUSION

Automatic segmentation can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods.

SIGNIFICANCE

Timely diagnosis and treatment of BCRL have crucial importance in preventing irreversible damage.

Acquired lymphedema: Molecular contributors and future directions for developing intervention strategies

Lymphedema is a debilitating chronic disease that mostly develops as an adverse reaction to cancer treatment modalities such as chemotherapy, surgery, and radiotherapy. Lymphedema also appears to be a deteriorating consequence of roundworm infections, as best represented by filariasis. According to its origin, lymphedema is classified as primary lymphedema and acquired lymphedema. The latter is an acquired condition that, hitherto, received a considerably low attention owing to the less number of fatal cases been reported. Notably, despite the low mortality rate in lymphedema, it has been widely reported to reduce the disease-free survival and thus the quality of life of affected patients. Hence, in this review, we focused on acquired lymphedema and orchestration of molecular interplays associated with either stimulation or inhibition of lymphedema development that were, in vast majority, clearly depicted in animal models with their specific and distinct technical approaches. We also discussed some recent progress made in phytochemical-based anti-lymphedema intervention strategies and the specific mechanisms underlying their anti-lymphedema properties. This review is crucial to understand not only the comprehensive aspects of the disease but also the future directions of the intervention strategies that can address the quality of life of affected patients rather than alleviating apparent symptoms only.

Utility of quantitative ultrasound in community screening for hepatic steatosis

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Quantitative ultrasound facilitates clinical grading of hepatic steatosis (the early stage of NAFLD). However, the utility of quantitative ultrasound as a first-line method for community screening of hepatic steatosis remains unclear. Therefore, this study aimed to investigate the utility of quantitative ultrasound to screen for hepatic steatosis and for metabolic evaluation at the community level. In total, 278 participants enrolled from a community satisfied the study criteria. Each subject underwent anthropometric and biochemical examinations, and abdominal ultrasound imaging was performed to measure the controlled attenuation (CAP), integrated backscatter (IB), and information Shannon entropy (ISE). The assessment outcomes were compared with the fatty liver index (FLI), hepatic steatosis index (HSI), metabolic syndrome (MetS), and insulin resistance to evaluate the screening performance through the area under the receiver operating characteristic curve (AUROC) and Delong's test. Ultrasound ISE, CAP, and IB were effective in screening hepatic steatosis, MetS, and insulin resistance. In screening for hepatic steatosis, the AUROCs of ISE, CAP, and IB were 0.85, 0.83, and 0.80 (the cutoff FLI = 60), respectively, and 0.84, 0.75, 0.77 (the cutoff HSI = 36), respectively, and those for the evaluation of MetS and insulin resistance were 0.79, 0.75, 0.79, respectively, and 0.83, 0.76, 0.78, respectively. Delong's test revealed that ISE outperformed CAP and IB for the detection of hepatic steatosis and insulin resistance (P < .05). Based on the present results, ultrasound ISE is a potential imaging biomarker during first-line community screening of hepatic steatosis and insulin resistance.