Synopsis

Keywords: Tumors, Cancer, Iower-grade glioma

When radiological signature was combined with new molecular signature, higher performance was achieved in predicting the prognosis of IDH wild-type lower-grade glioma patients. The combined model showed great risk stratification ability, which improving survival prediction in patients with IDH wild-type lower-grade glioma.

INTRODUCTION

According to the 2021 World Health Organization (WHO) classification of central nervous system tumors, molecular diagnosis plays an important role in the diagnosis of isocitrate dehydrogenase-wildtype (IDHwt) diffuse gliomas. IDHwt lower-grade gliomas (LGGs) with one or more of the molecular signature, which include: epidermal growth factor receptor (EGFR) amplification, telomerase reverse transcriptase promoter (TERTp) mutations and chromosome 10 (whole chromosome, 10p or 10q) deletion and gain of chromosome 7 (whole chromosome, 7p or 7q), are expected to be more aggressive and have a worse prognosis.
As a non-invasive examination method, neuroimaging not only plays an important role in the diagnosis and differential diagnosis of tumors, but also provides imaging information of tumors, which is also useful in molecular diagnosis and prognosis prediction of tumors. However, previous studies were mostly based on the 2016 WHO tumor classification criteria, and there were few studies on combining the radiological signature and molecular signature of IDHwt LGGs to predict the prognosis of IDHwt LGGs. Therefore, if radiological signature can be combined with molecular signature, the survival prediction ability of IDH wild-type LGGs may be improved.
The purpose of this study was to combine new molecular signature with radiological signature to construct an IDHwt LGGs model with better predictive ability.

METHODS

A total of 93 patients were collected and divided into training cohorts and validation cohorts using computer-generated random numbers in a 7:3 ratio stratified sampling. Random survival forest model was used to establish clinical gene model, radiological model and combined model. Evaluate the three models using the concordance index (C-index), the integrated Brier score (IBS) and the integrated area under the receiver operating characteristic curve (iAUC). All patients were divided into high-risk or low-risk groups according to the best model. Survival was calculated using the Kaplan-Meier method, and differences in overall survival between groups were compared using the log-rank test.

RESULTS

Combined model had the best performance than the other two models (C-index: 0.799 vs 0.775 and 0.699; IBSl:0.156 vs 0.152 and 0.174). The combined model had a higher iAUC value, the difference was statistically significant (difference to clinical gene model: 0.021[95%CI:0.005,0.059], P=0.035 and difference to radiological model:0.091[95%CI:0.031,0.157], P=0.023). Risk stratification of patients can be accurately performed in train cohort and validation cohort (both P<0.0001)

DISCUSSION

When radiological signature was combined with new molecular signature, higher performance was achieved in predicting the prognosis of IDHwt LGGs patients. The combined model showed good risk stratification ability in both training and validation cohorts, which improving survival prediction in patients with IDHwt LGGs.
Several previous studies have applied radiological features to predict survival in LGG patients. However, these studies lack the information of EGFR amplification and TERTp mutation status, which were key molecular markers for stratification of LGG prognosis with IDHwt according to the 2021 WHO classification. In this study, the combined model combining radiological and new molecular features exhibited higher diagnostic performance, suggesting that radiological features play an important role in improving the prognostic predictive power of IDHwt LGGs.

CONCLUSION

We build a combined model incorporating molecular and radiological signature that could well predict the prognosis of patients with IDHwt LGGs. Integrating molecular and radiological signature can improve survival prediction in patients with IDHwt LGGs.

Acknowledgements

Acknowledgment

The authors want to thank the Shanxi Medical University, participants who took part in the study and data collection.

Ethical approval

The study was approved by the Ethics and Human Committees of Shanxi Medical University.

Consent to participate

All participants voluntarily participated and signed an informed consent form.

Consent for publication

All authors provide consent for publication.

Conflict of Interest

The authors report no conflict of interest.

Availability of data and material

The data for this study are not publicly available because the First Clinical Medical Hospital of Shanxi Medical University, the center from which the data were collected, does not agree to make the data publicly accessible. Further inquiry about data sharing maybe directed to Prof. Yan Tan, tanyan123456@sina.com.

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