Multi-Dimensional Morphological Characterization and Drug Effects of Tumor Organoids Based on OCT and Machine Learning
Objective Three-dimensional(3D)tumor organoids,serving as in vitro models that replicate the critical structural and functional features of organs and tumor tissues,have demonstrated their unique value in disease modeling,personalized medicine,and drug screening.Patient-derived organoids(PDOs)not only recapitulate the morphological characteristics and physiological functions of their original tissues but also maintain the genetic and heterogeneity of tumors,rendering them invaluable resources for cancer research and treatment.However,current methods for analyzing organoid growth and drug effects have limitations,particularly in the absence of 3D high-throughput and label-free monitoring tools,hampering the more effective assessment of organoid growth and drug actions.To address this challenge,this study is dedicated to developing a comprehensive evaluation method based on optical coherence tomography(OCT)and machine learning algorithms.The aim is to establish a novel,non-invasive,label-free tool for the morphological characterization of organoids,enabling longitudinal evaluation of their responses to drug treatments.This approach holds significant potential for the application of PDOs in personalized cancer therapy,particularly for intrahepatic cholangiocarcinoma(iCCA),for which treatment options are limited.Methods In this study,we propose a method that combines OCT imaging with machine learning to perform longitudinal,accurate,label-free,and parallel morphological characterization of a large number of individual organoids within organoid clusters.Through 3D OCT imaging and organoid segmentation technology,we achieved 3D imaging and morphological analysis of individual organoids,including parameters such as organoid volume,organoid surface area,and organoid cavity volume.Subsequently,based on undersampling,we conducted a cluster analysis on multiple organoids within the organoid clusters to obtain statistical information on multi-dimensional morphological parameters for different categories.Feature selection and principal component analysis(PCA)were then applied to construct a comprehensive evaluation scoring function that combines the factor scores of each principal component and weights according to their variance contribution rates.Furthermore,we characterized the relative growth value of organoid clusters by calculating the difference in the comprehensive evaluation scores of the growth levels between two time points.Alternatively,the growth rate of the organoid clusters was represented by the slope of linear fitting based on the comprehensive evaluation scores from multiple time points.Ultimately,we validated the effectiveness of the comprehensive evaluation model of the growth levels based on the organoid clusters and PCA using adenosine triphosphate(ATP)testing results.Results and Discussions Our study results highlight the significant advantages of OCT imaging and machine learning in characterizing organoid growth and drug responses.A notable correlation is observed between organoid morphological changes and drug treatments,such as the transition of cystic organoids to solid organoids under the influence of medication(Fig.3).The comprehensive evaluation model that we constructed shows an 82.9％consistency with traditional ATP biochemistry testing,which is a widely recognized indicator of cellular activity and proliferation(Table 5).More importantly,the correlation between the relative growth values derived from our comprehensive evaluation model and ATP measurements reaches an impressive 90.4％.This high degree of consistency confirms that our model can serve as a reliable proxy for assessing organoid growth and drug sensitivity.Additionally,the study results underscore the potential of our method to reveal morphological changes in organoids,which may be significant indicators of drug response and may provide new insights into the complexity of tumor-drug interactions.Conclusions This study marks significant progress in the field of organoid research and its implications for cancer treatment.By integrating OCT with machine learning,we have developed a robust and comprehensive evaluation model that is capable of accurately assessing organoid growth levels and responses to drugs.This method stands poised to revolutionize traditional approaches to drug efficacy screening and sensitivity testing,particularly for PDOs.The high consistency observed between our evaluation model and traditional ATP testing underscores its potential as a reliable and non-invasive tool in cancer research.As we transition into the era of personalized medicine,the precise measurement and prediction of individual organoid drug responses are becoming increasingly crucial.The methodology outlined in this study not only reveals the morphological changes of organoids under the influence of drugs but also lays the groundwork for a new technological platform for cancer drug screening and clinical drug sensitivity testing based on PDOs.Its aim is twofold:to deepen our understanding of tumor biology and to advance the development of more precise and effective cancer treatment strategies.
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