Integrated primary tumor and all metastatic lymph nodes radiomics from 18 F-FDG PET/CT for precise M1 subclassification in nasopharyngeal carcinoma

The false-negative rate of approximately 18% in fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) can lead to understaging by misclassifying M1b disease as M1a of nasopharyngeal carcinoma (NPC), which may result in the inappropriate administration of intensified therapy. This study aims to develop a 18F-FDG PET/CT-based prediction tool to assist clinicians in diagnosing TNM-9 M1 staging and guiding treatment decisions for patients with synchronous NPC (smNPC). Ninety-seven patients with squamous cell carcinoma smNPC underwent pretreatment 18F-FDG PET/C. Radiomic features were extracted from primary tumors and all metastatic lymph nodes using CT and PET data. Ten metabolic parameters of the primary tumors and most FDG-avid metastatic lymph node, measured through three methods, were also assessed. After feature selection, nine logistic regression-based radiomics models and one random forest model were developed. Model Performance was evaluated via 10 repeats×5-fold cross-validation mean the Area Under the Receiver Operating Characteristic Curve (AUC). The optimal prediction model was presented using a nomogram developed on the entire cohort and was validated with a calibration curve, decision curve analysis (DCA), and by determining its optimal cut-off value. The M1a and M1b patient groups showed no significant differences in baseline characteristics or metabolic parameters from the primary tumors and metastatic lymph nodes (p > 0.05). The combined primary tumors and metastatic lymph nodes radiomics model achieved the highest AUC of 0.907 in validation set. The final nomogram demonstrated excellent discrimination (AUC 0.963), with sensitivity of 98.1% and specificity of 86.0% at the optimal cutoff (0.439). Calibration was reliable (slope 0.853, intercept 0.132). DCA confirmed clinical utility, providing net benefit across a wide threshold range (0.135–0.700) with a maximum net benefit of 0.533 at a threshold probability of 0.050. This study offers a complementary diagnostic tool for M1 staging of smNPC that may inform treatment decisions. The findings also provide metabolic evidence supporting the robustness of the TNM-9 stage IV classification system of NPC. This retrospective study was in accordance with the Declaration of Helsinki and approved by the XXX.

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Journal
BMC Cancer
Published
2026-01-21
DOI
https://doi.org/10.1186/s12885-026-15589-z
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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article

Integrated primary tumor and all metastatic lymph nodes radiomics from 18 F-FDG PET/CT for precise M1 subclassification in nasopharyngeal carcinoma

Yuxiao Hu, Shanshan Xu, Yun Zhang
BMC Cancer
Radiomics and Machine Learning in Medical Imaging
article

Integrated primary tumor and all metastatic lymph nodes radiomics from 18 F-FDG PET/CT for precise M1 subclassification in nasopharyngeal carcinoma

Yuxiao Hu, Shanshan Xu, Yun Zhang
article en

Abstract

The false-negative rate of approximately 18% in fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) can lead to understaging by misclassifying M1b disease as M1a of nasopharyngeal carcinoma (NPC), which may result in the inappropriate administration of intensified therapy. This study aims to develop a 18F-FDG PET/CT-based prediction tool to assist clinicians in diagnosing TNM-9 M1 staging and guiding treatment decisions for patients with synchronous NPC (smNPC). Ninety-seven patients with squamous cell carcinoma smNPC underwent pretreatment 18F-FDG PET/C. Radiomic features were extracted from primary tumors and all metastatic lymph nodes using CT and PET data. Ten metabolic parameters of the primary tumors and most FDG-avid metastatic lymph node, measured through three methods, were also assessed. After feature selection, nine logistic regression-based radiomics models and one random forest model were developed. Model Performance was evaluated via 10 repeats×5-fold cross-validation mean the Area Under the Receiver Operating Characteristic Curve (AUC). The optimal prediction model was presented using a nomogram developed on the entire cohort and was validated with a calibration curve, decision curve analysis (DCA), and by determining its optimal cut-off value. The M1a and M1b patient groups showed no significant differences in baseline characteristics or metabolic parameters from the primary tumors and metastatic lymph nodes (p > 0.05). The combined primary tumors and metastatic lymph nodes radiomics model achieved the highest AUC of 0.907 in validation set. The final nomogram demonstrated excellent discrimination (AUC 0.963), with sensitivity of 98.1% and specificity of 86.0% at the optimal cutoff (0.439). Calibration was reliable (slope 0.853, intercept 0.132). DCA confirmed clinical utility, providing net benefit across a wide threshold range (0.135–0.700) with a maximum net benefit of 0.533 at a threshold probability of 0.050. This study offers a complementary diagnostic tool for M1 staging of smNPC that may inform treatment decisions. The findings also provide metabolic evidence supporting the robustness of the TNM-9 stage IV classification system of NPC. This retrospective study was in accordance with the Declaration of Helsinki and approved by the XXX.

BMC CancerVol. 26(1)
Jiangsu Cancer Hospital (CN)
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 83%
Radiomics and Machine Learning in Medical Imaging
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