Advancing precision medicine in cutaneous oncology through 3D high-frequency ultrasound imaging.

Abstract

e13625 Background: Clinical assessment of tumor depth and shape often requires biopsy, especially when evaluating skin cancers. High Frequency Ultrasound (HFUS) is a non-invasive clinical tool that can quickly produce high resolution images ideally suited for evaluating topical tumors. In comparison to other imaging modalities, HFUS do not always provide the spatial data required to model 3-dimensional (3D) tumors. Currently, 3D-images are an amalgamation of many segmented image slices. However, in outpatient clinics it is unrealistic for HFUS devices and clinicians to gather large sets of image data. In our present original research, we aimed to overcome limited number of HFUS and dermatoscopic images by constructing a simple framework to process sparse and non-spatial data into working 3D skin tumor models. Methods: We analyzed two different skin tumors, the dermal duct tumor (DDT) and basal cell carcinoma (BCC), each with different number of HFUS images at 30Hz and 50Hz respectively. 1 set of 6 parallel images were obtained for the BCC, while 2 orthogonal sets of 6 parallel images were obtained for the DDT. For each tumor, a single dermatoscopic photo and sets of bitmap HFUS images with a resolution of 1024 x 512 were taken. Our study was IRB-approved. Mathematica and MATLAB, both with accessible documentation, were used to process images. Results: We developed a novel algorithmic approach to produce 3D tumor models with limited medical data. Both tumors appear corporally hypoechoic under HFUS. Mathematica was used to first reduce HFUS image noise and binarize the tumor region with a bilateral Gaussian filter. The user-friendly MATLAB Image Segmenter application manually filled the region of interest. Next, Mathematica was used to orient and plot tumor borders in 3D. Finally, we encompassed the borders with a 3D convex hull to conservatively predict tight margins around the tumor. Conclusions: Our simplified approach allows clinicians and scientists to produce 3D renderings of tumor margins. Margin control must always be balanced with a positive surgical outcome. By approaching our 3D-model with a convex hull around sampled border cross-sections, we chose a conservative approach that focuses skin tumor margin evaluation. Our results promote precision medicine and planning into the current standard of skin cancer treatment. Our findings can allow tracking tumor progression or treatment possible via non-invasive evaluation of tumor depth and contour. An aim of our present research is to lower the barrier of 3D construction in clinical settings. Simple algorithms, such as shown in our work, transform orthogonal and sparse data into usable data that can be implemented to train machine learning and artificial intelligent programs. Future research tailoring this approach may allow instant programming of HFUS images to non-invasively render cutaneous tumor borders and depth in real time during clinical examination.