Identification of serous ovarian tumors based on polarization imaging and correlation analysis with clinicopathological features  

作　　者：Yulu Huang Anli Hou Jing Wang Yue Yao Wenbin Miao Xuewu Tian Jiawen Yu Cheng Li Hui Ma Yujuan Fan 

机构地区：[1]Department of Gynaecology,Wuzhou Red Cross Hospital,Wuzhou Guangxi 543002,P.R.China [2]Shenzhen Key Laboratory for Minimal Invasive Medical Technologies Guangdong Engineering Center of Polarization Imaging and Sensing Technology,Tsinghua Shenzhen International Graduate School Tsinghua University,Shenzhen,Guangdong 518055,P.R.China [3]Department of Gynaecology,University of Chinese Academy of Sciences Shenzhen Hospital,Shenzhen Guangdong 518106,P.R.China [4]Tsinghua-Berkeley Shenzhen Institute Tsinghua University,Shenzhen,Guangdong 518071,P.R.China [5]|Department of Physics,Tsinghua University Beijing 100084,P.R.China [6]Department of Pathology,University of Chinese Academy of Sciences Shenzhen Hospital,Shenzhen Guangdong 518106,P.R.China [7]Department of Pathology Wuzhou Red Cross Hospital,Wuzhou Guangxi 543002,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2023年第5期33-46,共14页创新光学健康科学杂志（英文）

基　　金：supported by the Guangming District Economic Development Special Fund(2020R01043).

摘　　要：Ovarian cancer is one of the most aggressive and heterogeneous female tumors in the world,and serous ovarian cancer(SOC)is of particular concern for being the leading cause of ovarian cancer death.Due to its clinical and biological complexities,ovarian cancer is still considered one of the most di±cult tumors to diagnose and manage.In this study,three datasets were assembled,including 30 cases of serous cystadenoma(SCA),30 cases of serous borderline tumor(SBT),and 45 cases of serous adenocarcinoma(SAC).Mueller matrix microscopy is used to obtain the polarimetry basis parameters(PBPs)of each case,combined with a machine learning(ML)model to derive the polarimetry feature parameters(PFPs)for distinguishing serous ovarian tumor(SOT).The correlation between the mean values of PBPs and the clinicopathological features of serous ovarian cancer was analyzed.The accuracies of PFPs obtained from three types of SOT for identifying dichotomous groups(SCA versus SAC,SCA versus SBT,and SBT versus SAC)were 0.91,0.92,and 0.8,respectively.The accuracy of PFP for identifying triadic groups(SCA versus SBT versus SAC)was 0.75.Correlation analysis between PBPs and the clinicopathological features of SOC was performed.There were correlations between some PBPs(δ,β,q_(L),E_(2),rqcross,P_(2),P_(3),P_(4),and P_(5))and clinicopathological features,including the International Federation of Gynecology and Obstetrics(FIGO)stage,pathological grading,preoperative ascites,malignant ascites,and peritoneal implantation.The research showed that PFPs extracted from polarization images have potential applications in quantitatively differentiating the SOTs.These polarimetry basis parameters related to the clinicopathological features of SOC can be used as prognostic factors.

关 键 词：Serous ovarian tumor(SOT) polarimetry basis parameter(PBP) polarimetry feature parameter(PFP) polarization imaging machine learning(ML). 

分 类 号：R737.31[医药卫生—肿瘤]