From imaging heterogeneity to clinical decision: a novel imaging biomarker based on bone marrow kinetics for advanced NSCLC patients in partial response

Abstract Background Risk of disease progression varies substantially among advanced non-small cell lung cancer (NSCLC) patients who achieve a partial response to initial therapy, and this subgroup currently lacks practical prognostic tools. This study aimed to develop a novel imaging biomarker by assessing bone marrow kinetic heterogeneity via pretreatment dynamic 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT). We retrospectively analyzed 51 patients with advanced NSCLC, focusing on the 32 who attained a partial response following first-line treatment. Time-activity curves (TACs) from the tumor and bone marrow were metabolically decomposed. Through integrated analysis of clinical characteristics, conventional PET parameters, and 28 TAC-derived features, coupled with a rigorous bootstrap-LASSO-Cox procedure (1000 resamples) for robust feature selection, we developed a multivariate Cox model and a simplified risk-stratification model. Results The bootstrap-LASSO-Cox analysis identified the slope of the bone marrow free FDG TAC between 10 and 30 min post-injection (BM_C f _Slope 10−30 ) as the most significant independent predictor. In the multivariate Cox model, this feature was strongly associated with a reduced risk of progression (Hazard Ratio = 0.10, 95% CI: 0.03–0.35, P < 0.001) and demonstrated good discriminative ability (C-index = 0.77, 95% CI: 0.68–0.86). Using an optimal cutoff value of -0.01119, this single parameter significantly stratified patients into high- and low-risk groups with distinct progression-free survival (Log-rank P < 0.001). The simplified model showed robust predictive performance, with average time-dependent AUCs of 0.792, 0.774, and 0.731 at 12, 18, and 24 months, respectively, and provided clinical net benefit. Neither conventional metabolic parameters nor clinical characteristics showed significant predictive value. Conclusion For advanced NSCLC patients achieving an initial partial response, pretreatment assessment of bone marrow kinetics via dynamic 18 F-FDG PET/CT emerges as a potential prognostic biomarker. The TAC slope feature, which reflects early interstitial clearance, can identify patients at high risk of early progression, offering potential for personalizing surveillance strategies and guiding timely therapeutic interventions.

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Journal
EJNMMI Research
Published
2026-05-21
DOI
https://doi.org/10.1186/s13550-026-01440-w
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

From imaging heterogeneity to clinical decision: a novel imaging biomarker based on bone marrow kinetics for advanced NSCLC patients in partial response

Jingjing Wan, Rongliang Wu, Zhiheng Yao, Yuan Cai et al.
EJNMMI Research
Radiomics and Machine Learning in Medical Imaging
article

From imaging heterogeneity to clinical decision: a novel imaging biomarker based on bone marrow kinetics for advanced NSCLC patients in partial response

Jingjing Wan, Rongliang Wu, Zhiheng Yao, Yuan Cai, Tao Sun, Ying Liang, Yubo Wang, Xinyu Yang, Kebir Sied
article en

Abstract

Abstract Background Risk of disease progression varies substantially among advanced non-small cell lung cancer (NSCLC) patients who achieve a partial response to initial therapy, and this subgroup currently lacks practical prognostic tools. This study aimed to develop a novel imaging biomarker by assessing bone marrow kinetic heterogeneity via pretreatment dynamic 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT). We retrospectively analyzed 51 patients with advanced NSCLC, focusing on the 32 who attained a partial response following first-line treatment. Time-activity curves (TACs) from the tumor and bone marrow were metabolically decomposed. Through integrated analysis of clinical characteristics, conventional PET parameters, and 28 TAC-derived features, coupled with a rigorous bootstrap-LASSO-Cox procedure (1000 resamples) for robust feature selection, we developed a multivariate Cox model and a simplified risk-stratification model. Results The bootstrap-LASSO-Cox analysis identified the slope of the bone marrow free FDG TAC between 10 and 30 min post-injection (BM_C f _Slope 10−30 ) as the most significant independent predictor. In the multivariate Cox model, this feature was strongly associated with a reduced risk of progression (Hazard Ratio = 0.10, 95% CI: 0.03–0.35, P < 0.001) and demonstrated good discriminative ability (C-index = 0.77, 95% CI: 0.68–0.86). Using an optimal cutoff value of -0.01119, this single parameter significantly stratified patients into high- and low-risk groups with distinct progression-free survival (Log-rank P < 0.001). The simplified model showed robust predictive performance, with average time-dependent AUCs of 0.792, 0.774, and 0.731 at 12, 18, and 24 months, respectively, and provided clinical net benefit. Neither conventional metabolic parameters nor clinical characteristics showed significant predictive value. Conclusion For advanced NSCLC patients achieving an initial partial response, pretreatment assessment of bone marrow kinetics via dynamic 18 F-FDG PET/CT emerges as a potential prognostic biomarker. The TAC slope feature, which reflects early interstitial clearance, can identify patients at high risk of early progression, offering potential for personalizing surveillance strategies and guiding timely therapeutic interventions.

EJNMMI ResearchVol. 16(1)
Sun Yat-sen University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Southern University of Science and Technology (CN), National Cancer Center (US), Fifth Affiliated Hospital of Sun Yat-sen University (CN), Essen University Hospital (DE), Shenzhen Institutes of Advanced Technology (CN), Shenzhen Third People’s Hospital (CN), University of Duisburg-Essen (DE)
Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province
Reduced inequalities
Openalex Percentile: Top 5%
Radiomics and Machine Learning in Medical Imaging
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