Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma
Abstract Pancreatic ductal adenocarcinoma (PDAC) presents a clinical challenge due to its poor prognosis and high mortality rate. Here, we aimed to enhance the prognostic prediction of patients with PDAC by studying collagen features in tumor microenvironment using multiphoton microscopy (MPM) combi...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-88984-4 |
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| author | Xiwen Chen Jikui Miao Xingxin Huang Xiahui Han Liqin Zheng Jianxin Chen Linying Chen Lianhuang Li |
| author_facet | Xiwen Chen Jikui Miao Xingxin Huang Xiahui Han Liqin Zheng Jianxin Chen Linying Chen Lianhuang Li |
| author_sort | Xiwen Chen |
| collection | DOAJ |
| description | Abstract Pancreatic ductal adenocarcinoma (PDAC) presents a clinical challenge due to its poor prognosis and high mortality rate. Here, we aimed to enhance the prognostic prediction of patients with PDAC by studying collagen features in tumor microenvironment using multiphoton microscopy (MPM) combining with image processing technique. We identified eight distinct tumor-associated collagen signatures (TACS1-8) from multiphoton images of PDAC tissues and developed an optical biomarker, TACS-score, based on the TACS1-8 using ridge regression analysis. Additionally, we also extracted 142 microscopic TACS (M-TACS) from second-harmonic generation (SHG) images and constructed a new robust biomarker, M-TACS-score, using the least absolute shrinkage and selection operator (LASSO) regression analysis. Our statistical results demonstrate that as two new optical biomarkers, TACS- and M-TACS-score, are independent prognostic factors and have good discriminatory ability (high AUC) as well as risk stratification (high HR) comparing with traditional clinical model (combining seven clinical risk factors, age, sex, TNM stage, tumor location and differentiation, perineural and lymph-vascular invasion) in predicting overall survival (OS) of patients with PDAC, highlighting their potential prognostic and predictive value. A combination of label-free multiphoton imaging technique and computer-aided image processing method may offer a novel and promising approach for finding new biomarkers to improve prognosis prediction and thereby tailor treatment strategies more effectively. |
| format | Article |
| id | doaj-art-23efb0815932425eaeec0616358417a8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-23efb0815932425eaeec0616358417a82025-02-09T12:37:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-88984-4Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinomaXiwen Chen0Jikui Miao1Xingxin Huang2Xiahui Han3Liqin Zheng4Jianxin Chen5Linying Chen6Lianhuang Li7Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityDepartment of Pathology, The First Affiliated Hospital of Fujian Medical UniversityKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal UniversityAbstract Pancreatic ductal adenocarcinoma (PDAC) presents a clinical challenge due to its poor prognosis and high mortality rate. Here, we aimed to enhance the prognostic prediction of patients with PDAC by studying collagen features in tumor microenvironment using multiphoton microscopy (MPM) combining with image processing technique. We identified eight distinct tumor-associated collagen signatures (TACS1-8) from multiphoton images of PDAC tissues and developed an optical biomarker, TACS-score, based on the TACS1-8 using ridge regression analysis. Additionally, we also extracted 142 microscopic TACS (M-TACS) from second-harmonic generation (SHG) images and constructed a new robust biomarker, M-TACS-score, using the least absolute shrinkage and selection operator (LASSO) regression analysis. Our statistical results demonstrate that as two new optical biomarkers, TACS- and M-TACS-score, are independent prognostic factors and have good discriminatory ability (high AUC) as well as risk stratification (high HR) comparing with traditional clinical model (combining seven clinical risk factors, age, sex, TNM stage, tumor location and differentiation, perineural and lymph-vascular invasion) in predicting overall survival (OS) of patients with PDAC, highlighting their potential prognostic and predictive value. A combination of label-free multiphoton imaging technique and computer-aided image processing method may offer a novel and promising approach for finding new biomarkers to improve prognosis prediction and thereby tailor treatment strategies more effectively.https://doi.org/10.1038/s41598-025-88984-4Multiphoton imagingTwo-photon excited fluorescenceSecond-harmonic generationPancreatic ductal adenocarcinoma |
| spellingShingle | Xiwen Chen Jikui Miao Xingxin Huang Xiahui Han Liqin Zheng Jianxin Chen Linying Chen Lianhuang Li Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma Scientific Reports Multiphoton imaging Two-photon excited fluorescence Second-harmonic generation Pancreatic ductal adenocarcinoma |
| title | Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma |
| title_full | Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma |
| title_fullStr | Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma |
| title_full_unstemmed | Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma |
| title_short | Multiphoton imaging-based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma |
| title_sort | multiphoton imaging based quantifiable collagen signatures for predicting outcomes in patients with pancreatic ductal adenocarcinoma |
| topic | Multiphoton imaging Two-photon excited fluorescence Second-harmonic generation Pancreatic ductal adenocarcinoma |
| url | https://doi.org/10.1038/s41598-025-88984-4 |
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