Objective: Multicellular tumor spheroids (MCTs) are indispensable models for evaluating drug efficacy for precision cancer therapeutic strategies as well as for repurposing FDA-approved drugs for ovarian cancer. However, current imaging techniques cannot provide effective monitoring of pathological responses due to shallow penetration and experimentally operative destruction. We plan to utilize a noninvasive optical imaging tool to achieve in vivo longitudinal monitoring of the growth of MCTs and therapeutic responses to repurpose three FDA-approved drugs for ovarian cancer therapy. Methods: A swept-source optical coherence tomography (SS-OCT) system was used to monitor the volume growth of MCTs over 11 days. Three inhibitors of 2-Methoxyestradiol (2-ME), AZD1208, and R-Ketorolac (R-keto) with concentrations of 1, 10, and 25 µM were employed to treat ovarian MCTs on day 5. Three-dimensional (3D), intrinsic optical attenuation contrast, and degree of uniformity were applied to analyze the therapeutic effect of these inhibitors on ovarian MCTs. Results: We found that 2-ME, AZD1208, and R-keto with concentration of 10 and 25 µM significantly inhibited the volume growth of ovarian MCTs. There was no effect to necrotic tissues from all concentrations of 2-ME, AZD1208, and R-keto inhibitors from our OCT results. 2-ME and AZD1208 inhibited the growth of high uniformity tissues within MCTs and higher concentrations provided more significant inhibitory effects. Conclusion: Our results indicated that OCT was capable and reliable to monitor the therapeutic effect of inhibitors to ovarian MCTs and it can be used for the rapid characterization of novel therapeutics for ovarian cancers in the future.

Background: At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. Objective: This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. Methods: A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. Results: Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. Conclusions: This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a label-free, non-invasive approach to monitor the viability of ex vivo mouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDS _e ), and late OCT correlation decay speed (OCDS _l ) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary information relevant to tissue viability, in addition to the morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making.

Despite its widespread adoption, the success of radiofrequency (RF) ablation is often limited by challenges in achieving durable, transmural lesions while avoiding complications such as incomplete ablation or over-ablation. Optical coherence tomography (OCT) offers high-resolution visualization of cardiac tissue, enabling real-time assessment of lesion depth. Therefore, we investigated the impact of RF ablation on donated human left atrial tissue using an OCT imaging system to assess lesion formation in a controlled laboratory setting. RF ablation lesions were created at durations of 20, 40, 60, 80, and 100 seconds (s), and their effects were quantified by calculating the attenuation coefficient from OCT images. To identify tissue regions in the image, a U-Net model was employed for segmentation, achieving a mean intersection over union (IoU) of 0.9748 with an inference time of 82 milliseconds. Our results demonstrated clear differences in tissue response before and after ablation, with distinct lesion characteristics across the tested durations. One-way ANOVA analysis yielded a p-value of 1.09 × 10⁻⁸, confirming statistically significant differences among the ablation durations. These findings highlight the potential of SD-OCT for precise, real-time monitoring of ablation effects. Future work aims to integrate OCT with ablation catheters for clinical applications, enhancing the precision of catheter-based treatments for conditions like atrial fibrillation.

Human liver transplantation is constrained by a critical shortage of viable donor livers. In response to this shortage, marginal livers from extended criteria donors are increasingly used to expand the donor pool. Donor liver viability assessment remains limited by invasive, biopsy-based pathological sampling, and there is a need for more comprehensive and noninvasive evaluation techniques to meet the increasing demand for liver transplants. In this study, we propose the use of polarization-sensitive optical coherence tomography (PS-OCT) to perform a multiparameter viability evaluation across the entire surface of donor livers. PS-OCT imaging was conducted on multiple regions of human donor livers, and the findings were cross-validated with histopathological evaluations. Machine learning and texture analysis were used to evaluate hepatic steatosis, fibrosis, inflammation, and necrosis from PS-OCT measurements in comparison with traditional pathological assessments, finding a correlation (>80%) between PS-OCT quantifications and pathology. PS-OCT imaging findings demonstrated strong correlations with donor liver functional performance during normothermic machine perfusion and with clinical liver transplant outcomes. PS-OCT offers a noninvasive assessment of liver viability by quantifying hepatic parameters across the entire donor liver, effectively complementing current pathological analysis. These results suggest that PS-OCT provides a robust approach to assessing donor liver viability, which could potentially decrease the discard rate of high-risk livers, thereby expanding the donor pool and reducing the inadvertent use of unsuitable livers for transplantation.