Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks.

Recent years have ushered in a transformative era in in vitro modeling with the advent of organoids, three-dimensional structures derived from stem cells or patient tumor cells. Still, fully harnessing the potential of organoids requires advanced imaging technologies and analytical tools to quantitatively monitor organoid growth. Optical coherence tomography (OCT) is a promising imaging modality for organoid analysis due to its high-resolution, label-free, non-destructive, and real-time 3D imaging capabilities, but accurately identifying and quantifying organoids in OCT images remain challenging due to various factors. Here, we propose an automatic deep learning-based pipeline with convolutional neural networks that synergistically includes optimized preprocessing steps, the implementation of a state-of-the-art deep learning model, and ad-hoc postprocessing methods, showcasing good generalizability and tracking capabilities over an extended period of 13 days. The proposed tracking algorithm thoroughly documents organoid evolution, utilizing reference volumes, a dual branch analysis, key attribute evaluation, and probability scoring for match identification. The proposed comprehensive approach enables the accurate tracking of organoid growth and morphological changes over time, advancing organoid analysis and serving as a solid foundation for future studies for drug screening and tumor drug sensitivity detection based on organoids.

The incidence and relative risk of major adverse cardiovascular events and hypertension in patients treated with immune checkpoint inhibitors plus tyrosine-kinase inhibitors for solid tumors: a systemic review and meta-analysis.

INTRODUCTION: Combinations of immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs) can be responsible for major adverse cardiovascular events (MACEs). We performed a meta-analysis to assess the relative risk (RR) of MACEs and hypertension in cancer patients treated with ICI+TKI combinations. RESEARCH DESIGN AND METHODS: We selected prospective trials through MEDLINE/PubMed, Cochrane Library, and ASCOMeeting abstracts. We calculated combined ORs, RRs, and 95% CIs using RevMansoftware for meta-analysis (v.5.2.3). RESULTS: Seven studies were selected for the analysis of MACEs (3849 patients). The incidence MACEs were 0.8% with ICI+TKI combinations, compared to 0.2% in the control arms for both any- and high-grade. ICI+TKI combinations significantly increased the risk of any- (OR = 3.21; p = 0.01) and high-grade MACEs (OR = 2.72; p = 0.05). Ten studies were selected for the analysis of hypertension (5744 patients). The incidence of treatment-related hypertension of any-grade and high-grade was41.3% (vs. 20.8%) and 26.1% (vs. 12.3%) with ICI+TKI combinations, respectively. ICI+TKI combinations significantly increased the risk of treatment-related hypertension of any-grade (RR = 2.10; p = 0.001), but not of high-grade (p = 0.11). CONCLUSIONS: ICI+TKI combinations increase the risk of MACEs compared to controls, although the absolute incidence is eventually low. Routine cardiovascular monitoring in asymptomatic patients is therefore not recommended.