Abstract
Purpose
Clinical feasibility nomograms were developed to facilitate the differentiation between thymic epithelial tumors (TETs) and mediastinal lymphomas (MLs), aiming to minimize the occurrence of non-therapeutic thymectomy.
Methods
A total of 255 patients diagnosed with TETs or MLs underwent pre-treatment 18F-FDG PET/CT. Comprehensive clinical and imaging data were collected, including age, gender, lactate dehydrogenase (LDH) level, pathological results, presence of myasthenia gravis symptoms, B symptoms, mass size, location, morphology, margins, density, and metabolic parameters derived from PET/CT. Radiomic features were extracted from the region of interest (ROI) of the primary lesion. Feature selection techniques were employed to identify the most discriminative subset of features. Machine learning methods were utilized to build candidate models, which were subsequently evaluated based on their area under the curve (AUC). Finally, nomograms were constructed using the optimal model to provide a clinical tool for improved diagnostic accuracy. The performance of the radiomic models was evaluated by their calibration, discrimination, and clinical utility.
Results
Several independent risk factors were identified for distinguishing TETs from MLs, including average standardized uptake value (SUVavg), LDH, age, mass size, and radiomic score (rad-score). Significance was observed in differentiating the two types of tumors based on these factors. The best clinical efficacy was demonstrated by the combined model, with an impressive AUC of 0.954. Decision curve analysis and calibration curves indicated that the combined model was clinically advantageous for discriminating TETs from MLs. Besides, the results of external validation showed a sensitivity of 0.8 and a specificity of 0.78.
Conclusion
Preoperatively, the differentiation of TETs from MLs can be facilitated by the utilization of the combined clinical information and radiomics model. This approach holds promise in minimizing the occurrence of unnecessary anterior mediastinal surgeries.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Acknowledgements
We express our gratitude to the thoracic surgery department at Zhongshan Hospital (Fudan University) for providing the external validation data. We also extend our appreciation to GE Healthcare Company for their valuable technical support in the development of our manuscript.
Funding
This study was supported by the National Natural Science Foundation of China General Projects (Grant No. 81571740) (KW), the Provincial Key Research and Development Program of Heilongjiang Province (Grant No. GA21C001) (KW), the Postdoctoral Special Scientific Research Grant of Heilongjiang Provincial Government (Grant No. LBH-Q17104) (KW), the Distinguished Young Scientist Funding of Harbin Medical University Affiliated Tumor Hospital (Grant No. JCQN2019-02) (KW), the Key Project of the Climbing Funding of the National Cancer Center (Grant No. NCC201808B019) (KW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JL and NC. The first draft of the manuscript was written by JL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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The Institutional Review Board of Cancer Hospital Affiliated to Harbin Medical University approved this retrospective study, and no written informed consent was required.
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Li, J., Cui, N., Jiang, Z. et al. Differentiating thymic epithelial tumors from mediastinal lymphomas: preoperative nomograms based on PET/CT radiomic features to minimize unnecessary anterior mediastinal surgery. J Cancer Res Clin Oncol 149, 14101–14112 (2023). https://doi.org/10.1007/s00432-023-05054-w
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DOI: https://doi.org/10.1007/s00432-023-05054-w