Purpose Changes in microcirculation of axillary lymph nodes (ALNs) may indicate metastasis. Reliable noninvasive imaging technique to quantify such variations is lacking. We aim to develop and investigate a contrast-free ultrasound quantitative microvasculature imaging technique for detection of metastatic ALN in vivo. Experimental design The proposed ultrasound-based technique, high-definition microvasculature imaging (HDMI) provides superb images of tumor microvasculature at sub-millimeter size scales and enables quantitative analysis of microvessels structures. We evaluated the new HDMI technique on 68 breast cancer patients with ultrasound-identified suspicious ipsilateral axillary lymph nodes recommended for fine needle aspiration biopsy (FNAB). HDMI was conducted before the FNAB and vessel morphological features were extracted, analyzed, and the results were correlated with the histopathology. Results Out of 15 evaluated quantitative HDMI biomarkers, 11 were significantly different in metastatic and reactive ALNs (10 with P << 0.01 and one with 0.01 < P < 0.05). We further showed that through analysis of these biomarkers, a predictive model trained on HDMI biomarkers combined with clinical information (i.e., age, node size, cortical thickness, and BI-RADS score) could identify metastatic lymph nodes with an area under the curve of 0.9 (95% CI [0.82,0.98]), sensitivity of 90%, and specificity of 88%. Conclusions The promising results of our morphometric analysis of HDMI on ALNs offer a new means of detecting lymph node metastasis when used as a complementary imaging tool to conventional ultrasound. The fact that it does not require injection of contrast agents simplifies its use in routine clinical practice.

In this work, a phantom study is performed to investigate the feasibility of quantitative tissue stiffness assessment of breast cancer masses using transfer learning ultrasound elastography. A transfer learning ultrasound elastography model is developed to classify the breast masses into quantifiable Young’s modulus (kilopascals, kP) values. The transfer learning model combines features of B-mode images and elastograms from Google’s deep learning model AlexNet. The B-mode images and elastograms from a calibrated phantom with elastic inclusions are used to train and validate the model. Thereafter, the model is used to quantify Young’s modulus of inclusions from an uncalibrated breast phantom. The accuracy of the transfer learning results with and without the inclusion of the B-mode is discussed.

Nucleic acid probes are used for diverse applications in vitro, in situ, and in vivo. In any setting, their power is limited by imperfect selectivity (binding of undesired targets) and incomplete affinity (binding is reversible, and not all desired targets are bound). These difficulties are fundamental, stemming from reliance on base pairing alone to provide both selectivity and affinity. Shielded covalent (SC) probes eliminate the longstanding trade-off between selectivity and durable target capture, achieving selectivity via programmable molecular conformation change and durable target capture via activatable covalent cross-linking (Vieregg et al, J. Am. Chem. Soc. 2013). In pure and mixed samples, SC probes covalently capture complementary DNA or RNA oligonucleotide targets and reject two-nucleotide mismatched targets with near-quantitative yields at room temperature, achieving discrimination ratios of 2À3 orders of magnitude. Semi-quantitative studies with full-length mRNA targets demonstrate selective covalent capture comparable to that for RNA oligo targets. Single-nucleotide DNA or RNA mismatches, including nearly isoenergetic RNA wobble pairs, can be efficiently rejected with discrimination ratios of 1À2 orders of magnitude. Covalent capture yields appear consistent with the thermodynamics of probe/target hybridization, facilitating rational probe design. If desired, cross-links can be reversed to release the target after capture. In contrast to existing probe chemistries, SC probes achieve the high sequence selectivity of a structured probe, yet durably retain their targets even under denaturing conditions. This previously incompatible combination of properties suggests diverse applications in vitro and in vivo; this talk will present our latest results on SC probe applications.

In this work, ultrasound elastography is employed to evaluate age-related changes of eye tissues. Of particular interest is the eye lens nucleus stiffness. Age-related changes in the eye lens nucleus stiffness are one of the most important causes of cataract. Ultrasound elastogram studies are performed by mechanical scanning porcine eyes using a handheld ultrasound system. Employing deep learning techniques, tissue stiffness assessments are made to differentiate porcine eyes with and without cataracts. Results and limitations of the elastogram assessment will be presented and discussed.

In this work, a phantom study is performed to investigate the feasibility of tissue stiffness assessment of breast cancer masses using transfer learning ultrasound elastography. A transfer learning ultrasound elastography model is developed to classify the breast masses into quantifiable Young’s modulus (kilopascals, kP) values regimes. The transfer learning model combines features of ultrasound elastography elastograms and images from Google’s deep learning models. The elastogram features used in the training and validation of the transfer learning model were obtained from a calibrated elastography phantom with inclusions having various Young’s moduli. The model was tested on a breast phantom with spherical inclusions. Test results show that the transfer learning model yields greater than 88% validation accuracy. Further results and limitations of the transfer learning techniques will be presented and discussed.