Risk assessment of thyroid cancer recurrence based on quantitative multiphoton imaging

Abstract

Thyroid cancer is a common endocrine malignancy with rising incidence, and although its overall prognosis is relatively favorable, postoperative recurrence remains a clinical challenge due to limited insight into tumor microstructure and stromal remodeling from current risk assessment methods. To address this issue, this study investigated the association between collagen fiber morphological and textural characteristics and tumor recurrence using multiphoton microscopy (MPM), a high-resolution, label-free imaging technique capable of visualizing tissue microstructure and stromal architecture in detail, providing detailed quantitative information on both cellular and extracellular components. To identify key features linked to recurrence, a multi-stage pipeline-univariate testing, correlation-based feature reduction, and least absolute shrinkage and selection operator regression were employed. The distributions of the selected features were visualized using box plots and kernel density curves. Finally, based on the selected features, a logistic regression model was developed and its ability to discriminate between high- and low-risk patients was evaluated using stratified five-fold cross-validation, with performance quantified by the receiver operating characteristic curve and the area under the curve (AUC). Qualitatively MPM imaging revealed pronounced collagen linearization and aligned fiber remodeling in high-risk tissues, contrasting with the wavy, interwoven architecture of low-risk tissues. Through the feature selection process, ten key features were identified, all showing significant distributional differences between risk groups (P < 0.0001). The resulting logistic regression model achieved strong discrimination, with an AUC of 0.959 in cross-validation. These findings demonstrate that the developed model effectively stratifies recurrence risk in thyroid cancer and may serve as an objective, quantitative tool to support individualized postoperative monitoring and prognostic assessment.