Mahdi Mohammed A, Ahamad Shahanawaj, Saad Sawsan A, Dafhalla Alaa, Alqushaibi Alawi, Qureshi Rizwan
Information and Computer Science Department, College of Computer Science and Engineering, University of Ha'il, Ha'il 55476, Saudi Arabia.
Software Engineering Department, College of Computer Science and Engineering, University of Ha'il, Ha'il 55476, Saudi Arabia.
Diagnostics (Basel). 2024 Sep 14;14(18):2038. doi: 10.3390/diagnostics14182038.
Despite advancements in oncology, predicting recurrence-free survival (RFS) in head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology and treatment responses. This study aims to address the research gap in the prognostic efficacy of traditional clinical predictors versus advanced radiomics features and to explore the potential of weighted fusion techniques for enhancing RFS prediction. We utilized clinical data, radiomic features from CT and PET scans, and various weighted fusion algorithms to stratify patients into low- and high-risk groups for RFS. The predictive performance of each model was evaluated using Kaplan-Meier survival analysis, and the significance of differences in RFS rates was assessed using confidence interval (CI) tests. The weighted fusion model with a 90% emphasis on PET features significantly outperformed individual modalities, yielding the highest C-index. Additionally, the incorporation of contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While the clinical model and the radiomics model, individually, did not achieve statistical significance in survival differentiation, the combined feature set showed improved performance. The integration of radiomic features with clinical data through weighted fusion algorithms enhances the predictive accuracy of RFS outcomes in head and neck cancer. Our findings suggest that the utilization of multi-modal data helps in developing more reliable predictive models and underscore the potential of PET imaging in refining prognostic assessments. This study propels the discussion forward, indicating a pivotal step toward the adoption of precision medicine in cancer care.
尽管肿瘤学取得了进展，但由于肿瘤生物学和治疗反应的异质性，预测头颈癌的无复发生存期（RFS）仍然具有挑战性。本研究旨在解决传统临床预测指标与先进的放射组学特征在预后疗效方面的研究差距，并探索加权融合技术在增强RFS预测方面的潜力。我们利用临床数据、CT和PET扫描的放射组学特征以及各种加权融合算法，将患者分为RFS的低风险和高风险组。使用Kaplan-Meier生存分析评估每个模型的预测性能，并使用置信区间（CI）检验评估RFS率差异的显著性。对PET特征强调90%的加权融合模型显著优于单一模态，产生了最高的C指数。此外，通过改变肿瘤周围半径纳入背景信息并没有显著提高预测准确性。虽然临床模型和放射组学模型单独在生存差异方面没有达到统计学显著性，但组合特征集显示出更好的性能。通过加权融合算法将放射组学特征与临床数据相结合，提高了头颈癌RFS结果的预测准确性。我们的研究结果表明，多模态数据的利用有助于开发更可靠的预测模型，并强调了PET成像在完善预后评估方面的潜力。这项研究推动了讨论的进展，表明在癌症治疗中采用精准医学迈出了关键一步。
