Segmentation influence on the generalizability of the overall survival models in CT-based oropharyngeal carcinoma patients

Yongqiang Wang; Xuhua Xiao; Jiang Zhang; Xinzhi Teng; Bing Li; Kangdong Liu; Jing Cai; Hong Ge

doi:10.1002/pro6.70056

Precision Radiation Oncology ›› 2026, Vol. 10 ›› Issue (1) :7 -18. DOI: 10.1002/pro6.70056

ORIGINAL ARTICLE

Segmentation influence on the generalizability of the overall survival models in CT-based oropharyngeal carcinoma patients

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Abstract

Objectives: To evaluate the impact of tumor volume segmentation variability on the repeatability of radiomic features (RFs) and to determine how RF repeatability influences the generalizability of radiomic models for predicting overall survival (OS) in patients with oropharyngeal carcinoma (OPC).

Methods: We retrospectively analyzed CT images from 1017 patients with oropharyngeal carcinoma across three institutions. Perturbation methods were applied to simulate variations in gross tumor volume segmentation. RFs were extracted from both the original images and Laplacian of Gaussian-filtered images using different perturbation masks. RF repeatability was quantified using intra-class correlation coefficients (ICC). Repeatable RFs were progressively incorporated into the modeling process according to different ICC thresholds to assess the influence of feature repeatability on model generalizability.

Results: Incorporation of RFs with ICC values between 0.7 and 0.8 improved the AUC index of the two-year and three-year OS models in external validation cohorts. Using an ICC threshold of 0.7, RFs were classified into high- and low-repeatability groups, and OS models were trained and validated using the training, internal testing, and external validation cohorts. Across all cohorts, the OS model trained with high-repeatability RFs demonstrated significantly superior performance compared to the model trained with low-repeatability RFs.

Conclusion: The findings demonstrate that selecting RFs with ICC values greater than 0.7 substantially enhances both the generalizability and predictive performance of CT-based radiomic models for patients with OPC. This study further underscores the importance of considering RF repeatability, particularly in the presence of tumor volume segmentation variability, to improve the robustness and clinical reliability of radiomic models.

Keywords

generalizability / overall survival / oropharyngeal carcinoma / radiomic feature

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Yongqiang Wang, Xuhua Xiao, Jiang Zhang, Xinzhi Teng, Bing Li, Kangdong Liu, Jing Cai, Hong Ge. Segmentation influence on the generalizability of the overall survival models in CT-based oropharyngeal carcinoma patients. Precision Radiation Oncology, 2026, 10 (1) : 7-18 DOI:10.1002/pro6.70056

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References

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[1]	Liu Z, Wang S, Dong D, et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019; 9(5): 1303-1322.

[2]	Gul M, Bonjoc KC, Gorlin D, et al. Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers. Front Oncol. 2021; 11:639326.

[3]	Feng C, Zhu L, Mao W, et al. Expression and prognosis of cellular senescence genes in head and neck squamous cell carcinoma. Holist Integr Oncol. 2025; 4: 2.

[4]	Huang Y, Liu Z, He L, et al. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology. 2016; 281(3): 947-957.

[5]	HajiEsmailPoor Z, Kargar Z, Baradaran M, et al. Prognostic value of CT scan-based radiomics in intracerebral hemorrhage patients: A systematic review and meta-analysis. Eur J Radiol. 2024; 178:111652.

[6]	Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol. 2021; 31(2): 1049-1058.

[7]	Patel M, Zhan J, Natarajan K, et al. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma. Clin Radiol. 2021; 76(8): 628.e17-628.e27.

[8]	Zhang Y, Lam S, Yu T, et al. Integration of an imbalance framework with novel high-generalizable classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal carcinoma. Knowl-Based Syst, 2022, 235: 107649.

[9]	Teng X, Zhang J, Zwanenburg A, et al. Building reliable radiomic models using image perturbation. Sci Rep. 2022; 12(1):10035.

[10]	Teng X, Zhang J, Ma Z, et al. Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma. Front Oncol. 2022; 12:974467.

[11]	Zhang J, Lam SK, Teng X, et al. Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients. Radiother Oncol. 2023; 183:109578.

[12]	Zwanenburg A, Vallières M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020; 295(2): 328-338.

[13]	Whybra P, Zwanenburg A, Andrearczyk V, et al. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology. 2024; 310(2):e231319.

[14]	Kocak B, Durmaz ES, Kaya OK, Ates E, Kilickesmez O. Reliability of Single-Slice-Based 2D CT Texture Analysis of Renal Masses: Influence of Intra- and Interobserver Manual Segmentation Variability on Radiomic Feature Reproducibility. AJR Am J Roentgenol. 2019; 213(2): 377-383.

[15]	Jha AK, Mithun S, Jaiswar V, et al. Repeatability and reproducibility study of radiomic features on a phantom and human cohort. Sci Rep. 2021; 11(1): 2055.

[16]	Pandey U, Saini J, Kumar M, Gupta R, Ingalhalikar M. Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images. J Magn Reson Imaging. 2021; 53(2): 394-407.

[17]	Zhang H, Lu T, Wang L, et al. Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors. Eur Radiol. 2025; 35(8): 4661-4673.

[18]	Zwanenburg A, Leger S, Agolli L, et al. Assessing robustness of radiomic features by image perturbation. Sci Rep. 2019; 9(1): 614.

[19]	Primakov SP, Ibrahim A, van Timmeren JE, et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat Commun. 2022; 13(1): 3423.

[20]	Sahlsten J, Jaskari J, Wahid KA, et al. Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning. Commun Med (Lond). 2024; 4(1): 110.

[21]	Müller-Franzes G, Nebelung S, Schock J, et al. Reliability as a Precondition for Trust-Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction. Diagnostics (Basel). 2022; 12(2):247.

[22]	Louis T, Lucia F, Cousin F, et al. Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence. Sci Rep. 2024; 14(1): 9028.

[23]	Haniff NSM, Abdul Karim MK, Osman NH, Saripan MI, Che Isa IN, Ibahim MJ. Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC). Diagnostics (Basel). 2021; 11(9):1573.

[24]	Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013; 26(6): 1045-1057.