Compression optical coherence elastography versus strain ultrasound elastography for breast cancer detection and differentiation: pilot study

Downgrading Breast Imaging Reporting and Data System categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study

OBJECTIVE

To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a-c categories and avoid unnecessary biopsies.

METHODS

This prospective, multicentre study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 and 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures.

RESULTS

Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off <2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group.

CONCLUSION

Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%.

ADVANCES IN KNOWLEDGE

Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort.

A novel stress sensor enables accurate estimation of micro-scale tissue mechanics in quantitative micro-elastography

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique enabling the estimation of tissue mechanical properties on the micro-scale. QME utilizes a compliant layer as an optical stress sensor, placed between an imaging window and tissue, providing quantitative estimation of elasticity. However, the implementation of the layer is challenging and introduces unpredictable friction conditions at the contact boundaries, deteriorating the accuracy and reliability of elasticity estimation. This has largely limited the use of QME to ex vivo studies and is a barrier to clinical translation. In this work, we present a novel implementation by affixing the stress sensing layer to the imaging window and optimizing the layer thickness, enhancing the practical use of QME for in vivo applications by eliminating the requirement for manual placement of the layer, and significantly reducing variations in the friction conditions, leading to substantial improvement in the accuracy and repeatability of elasticity estimation. We performed a systematic validation of the integrated layer, demonstrating >30% improvement in sensitivity and the ability to provide mechanical contrast in a mechanically heterogeneous phantom. In addition, we demonstrate the ability to obtain accurate estimation of elasticity (<6% error compared to <14% achieved using existing QME) in homogeneous phantoms with mechanical properties ranging from 40 to 130 kPa. Furthermore, we show the integrated layer to be more robust, exhibiting increased temporal stability, as well as improved conformity to variations in sample surface topography, allowing for accurate estimation of elasticity over acquisition times 3× longer than current methods. Finally, when applied to ex vivo human breast tissue, we demonstrate the ability to distinguish between healthy and diseased tissue features, such as stroma and cancer, confirmed by co-registered histology, showcasing the potential for routine use in biomedical applications.

The Application Value of Using Semiquantitative and Quantitative Parameters in Multimodal Ultrasound to Distinguish Between Benign and Malignant BI-RADS 4 Lesions

ABSTRACT

This study aims to explore the value of real-time strain elastography (RTE) and contrast-enhanced ultrasonography (CEUS) in the diagnosis of breast BI-RADS 4 lesions. It collected 85 cases (totaling 85 lesions) diagnosed with breast BI-RADS 4 through routine ultrasound from October 2020 to December 2022 in Huangshan City People's Hospital. All lesions underwent RTE and CEUS examination before surgery, and the ImageJ software was used to measure the periphery of lesion images in the enhancement peak mode and grayscale mode to calculate the contrast-enhanced ultrasound area ratio. The diagnostic capabilities of single-modal and multimodal ultrasound examination for the malignancy of breast BI-RADS 4 lesions were compared using the receiver operating characteristic curve; the Spearman correlation analysis was adopted to evaluate the correlation between multimodal ultrasound and CEUS area ratio. As a result, among the 85 lesions, 51 were benign, and 34 were malignant. The areas under the curve (AUCs) of routine ultrasound (US), US + RTE, US + CEUS, and US + RTE + CEUS were 0.816, 0.928, 0.953, and 0.967, respectively, with the combined method showing a higher AUC than the single application. The AUC of the CEUS area ratio diagnosing breast lesions was 0.888. There was a strong positive correlation (r = 0.819, P < 0.001) between the diagnostic performance of US + RTE + CEUS and the CEUS area ratio. In conclusion, based on routine ultrasound, the combination of RTE and CEUS can further improve the differential diagnosis of benign and malignant lesions in breast BI-RADS 4.

In vivo endoscopic optical coherence elastography based on a miniature probe

Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). It offers high-resolution elasticity assessment with nanoscale tissue displacement sensitivity and high quantification accuracy, promising to enhance diagnostic precision. However, in vivo endoscopic OCE imaging has not been demonstrated yet, which needs to overcome key challenges related to probe miniaturization, high excitation efficiency and speed. This study presents a novel endoscopic OCE system, achieving the first endoscopic OCE imaging in vivo. The system features the smallest integrated OCE probe with an outer diameter of only 0.9 mm (with a 1.2-mm protective tube during imaging). Utilizing a single 38-MHz high-frequency ultrasound transducer, the system induced rapid deformation in tissues with enhanced excitation efficiency. In phantom studies, the OCE quantification results match well with compression testing results, showing the system's high accuracy. The in vivo imaging of the rat vagina demonstrated the system's capability to detect changes in tissue elasticity continually and distinguish between normal tissue, hematomas, and tissue with increased collagen fibers precisely. This research narrows the gap for the clinical implementation of the endoscopic OCE system, offering the potential for the early diagnosis of intraluminal diseases.

In-vivo characterization of scleral rigidity in myopic eyes using fundus-pulsation optical coherence elastography

The sclera plays an important role in the structural integrity of the eye. However, as myopia progresses, the elongation of the eyeball exerts stretching forces on the posterior sclera, which typically happens in conjunction with scleral remodeling that causes rigidity loss. These biomechanical alterations can cause localized eyeball deformation and vision impairment. Therefore, monitoring scleral rigidity is clinically important for the management and risk assessment of myopia. In this study, we propose fundus pulsation optical coherence elastography (FP-OCE) to characterize posterior scleral rigidity in living humans. This methodology is based on a choroidal pulsation model, where the scleral rigidity is inversely associated with the choroidal max strain obtained through phase-sensitive optical coherence tomography (PhS-OCT) measurement of choroidal deformation and thickness. Using FP-OCE, we conducted a pilot clinical study to explore the relationship between choroidal strain and myopia severity. The results revealed a significant increase in choroidal max strain in pathologic myopia, indicating a critical threshold beyond which scleral rigidity decreases significantly. Our findings offer a potential new method for monitoring myopia progression and evaluating therapies that alter scleral mechanical properties.

Viscoelasticity quantification of cancerous tongue using intraoral optical coherence elastography: a preliminary study

Quantifying the biomechanical properties of the tongue is significant for early diagnosis of tongue carcinoma. Therefore, an intraoral optical coherence elastography system based on a miniature probe was proposed here to evaluate the viscoelasticity of in vivo tongue for the first time. Results of experiments with Sprague-Dawley rats indicate that considerable elasticity diversity occurred between cancerous and normal tongues, and the corresponding ratio of their Young's modulus was evaluated to be 3.74. It is also found that, viscosity in diseased tissue is smaller than that in normal tissue. Additionally, healthy, transitional and cancerous regions in the cancerous tongue can be distinguished easily by calculating viscoelasticity characteristics. Based on this preliminary attempt, our method with advantages of noninvasive, high-resolution, high-sensitivity and real-time detection and convenient operation may have good potential to become a useful tool for tongue carcinoma assessment after further optimization.

Analysis of friction in quantitative micro-elastography

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique capable of measuring the mechanical properties of tissue on the micro-scale. As QME requires contact between the imaging window and the sample, the presence of friction affects the accuracy of the estimated elasticity. In previous implementations, a lubricant was applied at the contact surfaces, which was assumed to result in negligible friction. However, recently, errors in the estimation of elasticity caused by friction have been reported. This effect has yet to be characterized and is, therefore, not well understood. In this work, we present a systematic analysis of friction in QME using silicone phantoms. We demonstrate that friction, and, therefore, the elasticity accuracy, is influenced by several experimental factors, including the viscosity of the lubricant, the mechanical contrast between the compliant layer and the sample, and the time after the application of a compressive strain. Elasticity errors over an order of magnitude were observed in the absence of appropriate lubrication when compared to uniaxial compression testing. Using an optimized lubrication protocol, we demonstrate accurate elasticity estimation (<10% error) for nonlinear elastic samples with Young's moduli ranging from 3 kPa to 130 kPa. Finally, using a structured phantom, we demonstrate that friction can significantly reduce mechanical contrast in QME. We believe that the framework established in this study will facilitate more robust elasticity estimations in QME, as well as being readily adapted to understand the effects of friction in other contact elastography techniques.

Real-Time Nondestructive Viscosity Measurement of Soft Tissue Based on Viscoelastic Response Optical Coherence Elastography

Viscoelasticity of the soft tissue is an important mechanical factor for disease diagnosis, biomaterials testing and fabrication. Here, we present a real-time and high-resolution viscoelastic response-optical coherence elastography (VisR-OCE) method based on acoustic radiation force (ARF) excitation and optical coherence tomography (OCT) imaging. The relationship between displacements induced by two sequential ARF loading-unloading and the relaxation time constant of the soft tissue-is established for the Kelvin-Voigt material. Through numerical simulation, the optimal experimental parameters are determined, and the influences of material parameters are evaluated. Virtual experimental results show that there is less than 4% fluctuation in the relaxation time constant values obtained when various Young's modulus and Poisson's ratios were given for simulation. The accuracy of the VisR-OCE method was validated by comparing with the tensile test. The relaxation time constant of phantoms measured by VisR-OCE differs from the tensile test result by about 3%. The proposed VisR-OCE method may provide an effective tool for quick and nondestructive viscosity testing of biological tissues.

Towards targeted colorectal cancer biopsy based on tissue morphology assessment by compression optical coherence elastography

Identifying the precise topography of cancer for targeted biopsy in colonoscopic examination is a challenge in current diagnostic practice. For the first time we demonstrate the use of compression optical coherence elastography (C-OCE) technology as a new functional OCT modality for differentiating between cancerous and non-cancerous tissues in colon and detecting their morphological features on the basis of measurement of tissue elastic properties. The method uses pre-determined stiffness values (Young’s modulus) to distinguish between different morphological structures of normal (mucosa and submucosa), benign tumor (adenoma) and malignant tumor tissue (including cancer cells, gland-like structures, cribriform gland-like structures, stromal fibers, extracellular mucin). After analyzing in excess of fifty tissue samples, a threshold stiffness value of 520 kPa was suggested above which areas of colorectal cancer were detected invariably. A high Pearson correlation (r =0.98; p &lt;0.05), and a negligible bias (0.22) by good agreement of the segmentation results of C-OCE and histological (reference standard) images was demonstrated, indicating the efficiency of C-OCE to identify the precise localization of colorectal cancer and the possibility to perform targeted biopsy. Furthermore, we demonstrated the ability of C-OCE to differentiate morphological subtypes of colorectal cancer – low-grade and high-grade colorectal adenocarcinomas, mucinous adenocarcinoma, and cribriform patterns. The obtained ex vivo results highlight prospects of C-OCE for high-level colon malignancy detection. The future endoscopic use of C-OCE will allow targeted biopsy sampling and simultaneous rapid analysis of the heterogeneous morphology of colon tumors.

Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods

Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithm-based method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.

Nonlinear Elasticity Assessment with Optical Coherence Elastography for High-Selectivity Differentiation of Breast Cancer Tissues

Soft biological tissues, breast cancer tissues in particular, often manifest pronounced nonlinear elasticity, i.e., strong dependence of their Young’s modulus on the applied stress. We showed that compression optical coherence elastography (C-OCE) is a promising tool enabling the evaluation of nonlinear properties in addition to the conventionally discussed Young’s modulus in order to improve diagnostic accuracy of elastographic examination of tumorous tissues. The aim of this study was to reveal and quantify variations in stiffness for various breast tissue components depending on the applied pressure. We discussed nonlinear elastic properties of different breast cancer samples excised from 50 patients during breast-conserving surgery. Significant differences were found among various subtypes of tumorous and nontumorous breast tissues in terms of the initial Young’s modulus (estimated for stress < 1 kPa) and the nonlinearity parameter determining the rate of stiffness increase with increasing stress. However, Young’s modulus alone or the nonlinearity parameter alone may be insufficient to differentiate some malignant breast tissue subtypes from benign. For instance, benign fibrous stroma and fibrous stroma with isolated individual cancer cells or small agglomerates of cancer cells do not yet exhibit significant difference in the Young’s modulus. Nevertheless, they can be clearly singled out by their nonlinearity parameter, which is the main novelty of the proposed OCE-based discrimination of various breast tissue subtypes. This ability of OCE is very important for finding a clean resection boundary. Overall, morphological segmentation of OCE images accounting for both linear and nonlinear elastic parameters strongly enhances the correspondence with the histological slices and radically improves the diagnostic possibilities of C-OCE for a reliable clinical outcome.