NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine
Qianyi Huang1, Junjian Mo2, Xuanbang Deng1
1Department of Nuclear Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhu Hai, Guangdong Province, 519000, China.
Purpose:
NHOC and NHOP, defined as the normalized distances from peak uptake to tumour centroid and perimeter, are novel PET/CT metrics of tumour aggressiveness. This two-centre study assessed the baseline NHOC/NHOP for predicting lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC), then developed and validated an interpretable machine learning model combining clinical data, NHOC/NHOP and PET radiomics for LNM and occult nodal metastasis (ONM) prediction.
Methods:
342 patients from two centres underwent 18F-FDG PET/CT scans, and data were divided into training (n = 188), internal (n = 63), and external (n = 91) sets. NHOC/NHOP and 284 radiomics features were initially extracted using LIFEx software. These features were normalized using Z-score and harmonized via ComBat. To avoid single algorithmic bias, eight machine-learning models were trained on the optimal radiomics features. The best-performing algorithm was employed to develop four predictive models including clinical, NHOC/NHOP, radiomics, and their combination. Shapley Additive Explanations (SHAP) values were used to interpret model contributions.
Results:
Key independent predictors were PD-L1 value, lesion size and the novel biomarker NHOC, establishing the clinical model (PD-L1 and size) and the NHOC model. The multi-layer perceptron classifier (MLP) model achieved the highest Area Under the Curve (AUC) (0.82, 95% CI: 0.69-0.92). For LNM prediction, the combined model demonstrated superior performance across training (AUC 0.852), internal test (AUC 0.822), and external test (AUC 0.885) sets. It significantly outperformed clinical and NHOC models (p < 0.05). For ONM prediction, the combined model achieved the AUC (0.85) on the full datasets. SHAP analysis highlighted key features like GLCM_InverseVariance-PET and NGTDM_Strength-CT. A nomogram and online calculator were developed, with decision-curve analysis confirming superior net clinical benefit.
Conclusion:
This study established an accurate, interpretable machine learning model for preoperative prediction of LNM and ONM in NSCLC. NHOC emerged as a novel independent predictor with respect to classical PET parameters.
