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Kidney cancer is a kind of high mortality cancer because of the difficulty in early diagnosis and the high metastatic dissemination in treatments. The surgical resection of tumors is the most effective treatment for renal cancer patients. However, precise assessment of tumor margins is a challenge during surgical resection. The objective of this study is to demonstrate an optical imaging tool in precisely distinguishing kidney tumor borders and identifying tumor zones from normal tissues to assist surgeons in accurately resecting tumors from kidneys during the surgery. 30 samples from six human kidneys were imaged using polarization-sensitive optical coherence tomography (PS-OCT). Cross-sectional, enface, and spatial information of kidney samples were obtained for microenvironment reconstruction. Polarization parameters (phase retardation, optic axis direction, and degree of polarization uniformity (DOPU) and Stokes parameters (Q, U, and V) were utilized for multiparameter analysis. To verify the detection accuracy of PS-OCT, H&E histology staining and dice-coefficient were utilized to quantify the performance of PS-OCT in identifying tumor borders and regions. In this study, tumor borders were clearly identified by PS-OCT imaging, which outperformed the conventional intensity-based OCT. With H&E histological staining as golden standard, PS-OCT precisely identified the tumor regions and tissue distributions at different locations and different depths based on polarization and Stokes parameters. Compared to the traditional attenuation coefficient quantification method, PS-OCT demonstrated enhanced contrast of tissue characteristics between normal and cancerous tissues due to the birefringence effects. Our results demonstrated that PS-OCT was promising to provide imaging guidance for the surgical resection of kidney tumors and had the potential to be used for other human kidney surgeries in clinics such as renal biopsy. 

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

 

Epidural anesthesia helps manage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization‐sensitive optical coherence tomography (PS‐OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS‐OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross‐testing accuracy of 91.53%. These results showed the improved precision by PS‐OCT in guiding epidural anesthesia needle placement.