Prognostic significance of collagen signatures in pancreatic ductal adenocarcinoma obtained from second-harmonic generation imaging.
Humans
Carcinoma, Pancreatic Ductal
/ diagnostic imaging
Prognosis
Female
Male
Collagen
/ metabolism
Pancreatic Neoplasms
/ diagnostic imaging
Middle Aged
Aged
Second Harmonic Generation Microscopy
/ methods
Biomarkers, Tumor
/ metabolism
Kaplan-Meier Estimate
Microscopy, Fluorescence, Multiphoton
/ methods
Adult
Tumor Microenvironment
Multiphoton microscopy
Pancreatic ductal adenocarcinoma
Second-harmonic generation
Two-photon excited fluorescence
Journal
BMC cancer
ISSN: 1471-2407
Titre abrégé: BMC Cancer
Pays: England
ID NLM: 100967800
Informations de publication
Date de publication:
29 May 2024
29 May 2024
Historique:
received:
22
12
2023
accepted:
22
05
2024
medline:
30
5
2024
pubmed:
30
5
2024
entrez:
29
5
2024
Statut:
epublish
Résumé
Pancreatic ductal adenocarcinoma (PDAC) ranks among the deadliest types of cancer, and it will be meaningful to search for new biomarkers with prognostic value to help clinicians tailor therapeutic strategies. Here we tried to use an advanced optical imaging technique, multiphoton microscopy (MPM) combining second-harmonic generation (SHG) and two-photon excited fluorescence (TPEF) imaging, for the label-free detection of PDAC tissues from a cohort of 149 patients. An automated image processing method was used to extract collagen features from SHG images and the Kaplan-Meier survival analysis and Cox proportional hazards regression were used to assess the prognostic value of collagen signatures. SHG images clearly show the different characteristics of collagen fibers in tumor microenvironment. We gained eight collagen morphological features, and a Feature-score was derived for each patient by the combination of these features using ridge regression. Statistical analyses reveal that Feature-score is an independent factor, and can predict the overall survival of PDAC patients as well as provide well risk stratification. SHG imaging technique can potentially be a tool for the accurate diagnosis of PDAC, and this optical biomarker (Feature-score) may help clinicians make more approximate treatment decisions.
Sections du résumé
BACKGROUND
BACKGROUND
Pancreatic ductal adenocarcinoma (PDAC) ranks among the deadliest types of cancer, and it will be meaningful to search for new biomarkers with prognostic value to help clinicians tailor therapeutic strategies.
METHODS
METHODS
Here we tried to use an advanced optical imaging technique, multiphoton microscopy (MPM) combining second-harmonic generation (SHG) and two-photon excited fluorescence (TPEF) imaging, for the label-free detection of PDAC tissues from a cohort of 149 patients. An automated image processing method was used to extract collagen features from SHG images and the Kaplan-Meier survival analysis and Cox proportional hazards regression were used to assess the prognostic value of collagen signatures.
RESULTS
RESULTS
SHG images clearly show the different characteristics of collagen fibers in tumor microenvironment. We gained eight collagen morphological features, and a Feature-score was derived for each patient by the combination of these features using ridge regression. Statistical analyses reveal that Feature-score is an independent factor, and can predict the overall survival of PDAC patients as well as provide well risk stratification.
CONCLUSIONS
CONCLUSIONS
SHG imaging technique can potentially be a tool for the accurate diagnosis of PDAC, and this optical biomarker (Feature-score) may help clinicians make more approximate treatment decisions.
Identifiants
pubmed: 38811917
doi: 10.1186/s12885-024-12412-5
pii: 10.1186/s12885-024-12412-5
doi:
Substances chimiques
Collagen
9007-34-5
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
652Subventions
Organisme : National Natural Science Foundation of China
ID : 82171991, 82172800, 81700576
Organisme : Natural Science Foundation of Fujian Province
ID : 2023J01082
Organisme : Joint Funds for the Innovation of Science and Technology of Fujian Province
ID : 2023Y9337
Informations de copyright
© 2024. The Author(s).
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