Noninvasive Young's modulus visualization of fibrosis progression and delineation of pancreatic ductal adenocarcinoma (PDAC) tumors using Harmonic Motion Elastography (HME) <i>in vivo</i>

Nabavizadeh, Alireza; Payen, Thomas; Iuga, Alina; Sagalovskiy, Irina; Desrouilleres, Deborah; Saharkhiz, Niloufar; Palermo, Carmine F.; Sastra, Stephen A.; Oberstein, Paul E.; Rosario, Vilma; Kluger, Michael D.; Schrope, Beth; Chabot, John A.; Olive, Kenneth P.; Konofagou, Elisa E.

doi:10.7150/thno.37965

Cited by 43 publications

(34 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Table 1 , the ratio was found to be higher than 0.4 for all the benign tumors (except for subject 8). In order to have more quantitative assessment of the viscoelastic properties and compare the results among other techniques, the Young’s modulus can be estimated based on the HMI displacements by using directional filters to extract the shear waves 58 , 59 . However, estimation of the Young’s modulus from HMI displacements is out of the scope of this study due to the computationally costly procedures.…”

Section: Discussionmentioning

confidence: 99%

“…The effect of neoadjuvant therapy on the softening of the tumors was not investigated herein. The change in the HMI displacement can also be used to assess the response of the solid tumors to neoadjuvant therapy, as shown in pre-clinical studies 44 , 49 , 59 . According to the histological reviews of all the ex vivo specimens in this study and our previous studies 44 , 48 , 49 , no adverse pathological effects, structural nor thermal damage, were observed from exposure to HMI.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Harmonic motion imaging of human breast masses: an in vivo clinical feasibility

Saharkhiz

Taback

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Non-invasive diagnosis of breast cancer is still challenging due to the low specificity of the imaging modalities that calls for unnecessary biopsies. The diagnostic accuracy can be improved by assessing the breast tissue mechanical properties associated with pathological changes. Harmonic motion imaging (HMI) is an elasticity imaging technique that uses acoustic radiation force to evaluate the localized mechanical properties of the underlying tissue. Herein, we studied the in vivo feasibility of a clinical HMI system to differentiate breast tumors based on their relative HMI displacements, in human subjects. We performed HMI scans in 10 female subjects with breast masses: five benign and five malignant masses. Results revealed that both benign and malignant masses were stiffer than the surrounding tissues. However, malignant tumors underwent lower mean HMI displacement (1.1 ± 0.5 µm) compared to benign tumors (3.6 ± 1.5 µm) and the adjacent non-cancerous tissue (6.4 ± 2.5 µm), which allowed to differentiate between tumor types. Additionally, the excised breast specimens of the same patients (n = 5) were imaged post-surgically, where there was an excellent agreement between the in vivo and ex vivo findings, confirmed with histology. Higher displacement contrast between cancerous and non-cancerous tissue was found ex vivo, potentially due to the lower nonlinearity in the elastic properties of ex vivo tissue. This preliminary study lays the foundation for the potential complementary application of HMI in clinical practice in conjunction with the B-mode to classify suspicious breast masses.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Harmonic motion imaging of human breast masses: an in vivo clinical feasibility

Saharkhiz

Taback

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…This architecture slightly outperforms Inception-V3 on the ImageNet dataset. NASNet learns the model architectures directly on the dataset of interest by searching the best convolutional layer (or “cell”) on a small dataset and then applying it to a larger dataset by stacking together more copies of this cell [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging

Bhuiyan

Govindaiah

Rjh

2021

Journal of Ophthalmology

View full text Add to dashboard Cite

Background and Objective. Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial-intelligence-based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting, and Participants. 1546 disc-centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age-Related Eye Disease Study and Singapore Malay Eye Study to develop the system. First, a proprietary semiautomated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep-learning architectures were used to train and test a binary classifier system. Measurements. The binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results. The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). Conclusions. Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.

show abstract

“…Alvarez et al demonstrated a reduction in tumour stiffness when measured in vivo using endoscopic ultrasound elastography (EUSE) post NAT with two cycles of nab-paclitaxel and gemcitabine [ 90 ]. However, ex vivo analysis using harmonic motion elastography did not find a significant change in PDAC stiffness post NAT, although 11 different NAT regimes involving chemotherapeutics and radiotherapy were used within the 14 NAT samples [ 91 ]. In breast cancer, it has been shown that reductions in the stiffness of the tumour are positively correlated to response [ 92 , 93 , 94 ].…”

Section: The Biomechanical Pdac Microenvironmentmentioning

confidence: 99%

Pancreatic Ductal Adenocarcinoma: Relating Biomechanics and Prognosis

MacCurtain

Quirke

Thorpe

et al. 2021

JCM

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and carries a dismal prognosis. Resectable patients are treated predominantly with surgery while borderline resectable patients may receive neoadjuvant treatment (NAT) to downstage their disease prior to possible resection. PDAC tissue is stiffer than healthy pancreas, and tissue stiffness is associated with cancer progression. Another feature of PDAC is increased tissue heterogeneity. We postulate that tumour stiffness and heterogeneity may be used alongside currently employed diagnostics to better predict prognosis and response to treatment. In this review we summarise the biomechanical changes observed in PDAC, explore the factors behind these changes and describe the clinical consequences. We identify methods available for assessing PDAC biomechanics ex vivo and in vivo, outlining the relative merits of each. Finally, we discuss the potential use of radiological imaging for prognostic use.

show abstract

Noninvasive Young's modulus visualization of fibrosis progression and delineation of pancreatic ductal adenocarcinoma (PDAC) tumors using Harmonic Motion Elastography (HME) in vivo

Cited by 43 publications

References 56 publications

Harmonic motion imaging of human breast masses: an in vivo clinical feasibility

Harmonic motion imaging of human breast masses: an in vivo clinical feasibility

An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging

Pancreatic Ductal Adenocarcinoma: Relating Biomechanics and Prognosis

Contact Info

Product

Resources

About