Radiographic signature in apical periodontitis improves prediction of apical lesion healing through survival prediction model

Yuebo Liu, Ge Kong, Fantai Meng, Chunlan Guo and Kuo Wan

PLOS ONE, 2025, vol. 20, issue 7, 1-14

Abstract: This retrospective study aimed to evaluate the effectiveness of radiographic signatures of apical periodontitis (AP), particularly lesion boundary features, in predicting lesion healing periods using survival analysis. A total of 254 AP cases with apical lesions were included. Canny edge detection and fragment analysis (FA) were used to define the regions of interest (ROI) S1-S4 on radiographs. Radiographic signatures were extracted, and a radiomics score (rad-score) was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression. Preliminary validation was performed using Kaplan-Meier survival analysis. Survival models were fitted, and model performance was evaluated. Clinical benefit was assessed through decision curve analysis. The results showed that radiographic signatures of the lesion boundary identified via the FA method significantly improved the performance of the survival model (Delong test; p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0327970

DOI: 10.1371/journal.pone.0327970

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