The Reflectance Confocal Microscopy – Optical Coherence Tomography (RCM-OCT) device has demonstrated its effectiveness in the in vivo detection and depth assessment of basal cell carcinoma (BCC), though its interpretation can be challenging for novices. Artificial intelligence (AI) has the potential to assist in identifying BCC and measuring its depth in these images. Our goal was to develop an AI model capable of generating 3D volumetric representations of BCC to enhance its detection and depth measurement. We developed AI models trained on OCT images of biopsy-confirmed BCC to detect BCC, generate 3D volumetric representations, and automatically assess tumor depth. These models were then tested on a separate dataset containing images of BCC, benign lesions, and normal skin. The effectiveness of the AI models was evaluated through a blinded reader study and by comparing tumor depth measurements with those obtained from histopathology. The addition of AI-generated 3D renders of BCC improved BCC detection rates, with sensitivity increasing from 73.3% to 86.7% and specificity from 45.5% to 48.5%. A Pearson Correlation coefficient r2 = 0.59 (p=0.02) was achieved in comparing tumor depth measurements between AI -generated renders and histopathology slides. Incorporating AI-generated 3D renders has the potential to improve the diagnosis of BCC and the automated measurement of tumor depth in OCT images, reducing reader dependent variability and standardizing diagnostic accuracy.
Ex vivo confocal microscopy (EVCM) images tissues rapidly at near histological resolution without the need for histological processing. Other health institutes can benefit by sending their tissues to confocal experts in formalin, a readily available media that preserves tissue integrity. Fresh tissues imaged with an EVCM device at various timepoints after being put in formalin exhibited easier tissue flattening, improved visualization of the epidermis, reduced tissue movement (due to fat fixation), improved image contrast, and lack of photobleaching (due to dye fixation). Normal skin structures and tumors were readily identified at all TPs by an expert in real-time.
Melanoma is the most aggressive skin cancer with the highest associated mortality, early diagnosis ensures high survival rates. Currently, in vivo morphological imaging such as reflectance confocal microscopy (RCM) is associated with high sensitivity but moderate specificity. Addition of molecular imaging using PARPi-FL (PARP1-targeted fluorophore) can improve distinction between malignant/potentially malignant lesions. Towards multimodal imaging in vivo, we first investigated differential PARP1 expression in the spectrum of melanocytic lesions. Higher PARP area positivity and intensity were found in melanoma as compared to benign nevi. Thus, PARPi-FL in association with RCM can potentially improve melanoma diagnosis non-invasively in patients.
Cutaneous metastases are relatively rare and often require an invasive biopsy for diagnosis. A novel, non-invasive RCM-OCT device combines the advantage of reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) by providing RCM high-resolution images in the horizontal plane and OCT low-resolution images in the transverse plane. We describe RCM and OCT characteristics of cutaneous metastases using this device to elucidate its utility for diagnosis and management. Seven patients with clinically suspicious cutaneous metastases from breast cancer were imaged using the RCM-OCT device. We found that the RCM-OCT device can detect cutaneous metastases and aid in non-invasive diagnosis.
In this paper, we demonstrate deep learning-based denoising of high-speed (180 fps) confocal images obtained with our low-cost SECM device. The CARE network was trained with 3090 high- and low-SNR image pairs on the Google Colab platform and tested with 45 unseen image pairs. The CARE prediction showed significant increase of SSIM and PSNR, and reduction of the banding noise while maintaining the cellular details. The preliminary results show the potential of using a deep learning-based denoising approach to enable high-speed SECM imaging.
Basal cell carcinoma (BCC) is the most common skin cancer worldwide. In the diagnosis process million benign biopsies are performed annually, increasing morbidity and healthcare costs. Noninvasive in vivo technologies such as multiphoton microscopy (MPM) can reduce biopsies. We explored the potential of MPM to differentiate collagen changes associated with BCC and surrounding normal skin structures using quantitative analysis (Fast Fourier transformation and Integrated optical density using ImageJ software, and its CurveAlign and CT-FIRE fiber analysis plugins) on second harmonic generation images. Our results showed that collagen distribution is more aligned surrounding BCCs when compared to the skin normal structures, showing the feasibility of detecting BCC in a quantitative way. Our initial results are limited to a small number of samples therefore, large-scale studies are needed to validate these collagen analysis methods.
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