# 18F-Fluorodeoxyglucose Positron Emission Tomography-Based Risk Score Model for Prediction of Five-Year Survival Outcome after Curative Resection of Non-Small-Cell Lung Cancer

## TL;DR

This study creates a model using PET scans to predict five-year survival in lung cancer patients after surgery, improving risk assessment for personalized treatment.

## Contribution

A novel PET-based risk score model was developed and validated for predicting five-year survival in NSCLC patients after curative resection.

## Key findings

- The PET-based risk score outperformed individual PET parameters in predicting five-year survival.
- Combining the PET-based risk score with clinical variables improved predictive accuracy significantly.
- The hybrid model achieved an AUC of 0.771, showing strong predictive performance in the test set.

## Abstract

The 18F-FDG PET parameters reflecting the intensity and distribution of glucose uptake by the tumor are associated with prognosis in non-small-cell lung cancer (NSCLC) patients. We developed and evaluated an imaging-based model utilizing these 18F-FDG PET-derived features for predicting the five-year survival in NSCLC patients after curative surgery. The PET-based risk score constructed using the LASSO logistic method outperformed the predictive performances of individual 18F-FDG PET parameters. The PET-based risk score was an independent prognostic factor for clinical variables. Additionally, it demonstrated better predictive performance when combined with clinical variables. The FDG PET-based imaging model could aid in risk stratification for personalized adjuvant treatment and follow-up management of NSCLC patients after surgery.

The aim of our retrospective study is to develop and assess an imaging-based model utilizing 18F-FDG PET parameters for predicting the five-year survival in non-small-cell lung cancer (NSCLC) patients after curative surgery. A total of 361 NSCLC patients who underwent curative surgery were assigned to the training set (n = 253) and the test set (n = 108). The LASSO regression model was used to construct a PET-based risk score for predicting five-year survival. A hybrid model that combined the PET-based risk score and clinical variables was developed using multivariate logistic regression analysis. The predictive performance was determined by the area under the curve (AUC). The individual features with the best predictive performances were co-occurrence_contrast (AUC = 0.675) and SUL peak (AUC = 0.671). The PET-based risk score was identified as an independent predictor after adjusting for clinical variables (OR 5.231, 95% CI 1.987–6.932; p = 0.009). The hybrid model, which integrated clinical variables, significantly outperformed the PET-based risk score alone in predictive accuracy (AUC = 0.771 vs. 0.696, p = 0.022), a finding that was consistent in the test set. The PET-based risk score, especially when integrated with clinical variables, demonstrates good predictive ability for five-year survival in NSCLC patients following curative surgery.