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. 2023 Feb 24:13:1074445.
doi: 10.3389/fonc.2023.1074445. eCollection 2023.

Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma

Xinsen Xu  1 , Jiaqi Qu  2 , Yijue Zhang  3 , Xiaohua Qian  2 , Tao Chen  1 , Yingbin Liu  1   4   5   6
Affiliations

Development and validation of an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma

Xinsen Xu et al. Front Oncol. .

Abstract

Objective: To develop and validate an MRI-radiomics nomogram for the prognosis of pancreatic ductal adenocarcinoma (PDAC).

Background: "Radiomics" enables the investigation of huge amounts of radiological features in parallel by extracting high-throughput imaging data. MRI provides better tissue contrast with no ionizing radiation for PDAC.

Methods: There were 78 PDAC patients enrolled in this study. In total, there were 386 radiomics features extracted from MRI scan, which were screened by the least absolute shrinkage and selection operator algorithm to develop a risk score. Cox multivariate regression analysis was applied to develop the radiomics-based nomogram. The performance was assessed by discrimination and calibration.

Results: The radiomics-based risk-score was significantly associated with PDAC overall survival (OS) (P < 0.05). With respect to survival prediction, integrating the risk score, clinical data and TNM information into the nomogram exhibited better performance than the TNM staging system, radiomics model and clinical model. In addition, the nomogram showed fine discrimination and calibration.

Conclusions: The radiomics nomogram incorporating the radiomics data, clinical data and TNM information exhibited precise survival prediction for PDAC, which may help accelerate personalized precision treatment.

Clinical trial registration: clinicaltrials.gov, identifier NCT05313854.

Keywords: MRI; nomogram; pancreatic cancer; prognosis; radiomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Feature selection of the radiomics data. (A) Selection of the tuning parameter lamda in the LASSO model. (B) LASSO coefficient profiles of the selected texture features. (C) C-index of the of the 11 features cox regression and validation by the randomization of training and testing cohorts.
Figure 2
Figure 2
Development of the risk score and Kaplan-Meier survival analysis based on the risk score. (A) Forest plot showing hazard ratios for selected features. (B) Development of the risk score. (C) Kaplan-Meier survival curves for the patients in high risk group and low risk group.
Figure 3
Figure 3
Development of the nomogram incorporating the radiomics data, clinical data and TNM information. (A) Development of the nomogram. (B) Calibration curves of the nomogram.
Figure 4
Figure 4
Comparison of the prediction ability of the nomogram and different cox-multivariate regression models. C-index of the different models and validation by the randomization of training and testing cohorts.
See this image and copyright information in PMC

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