Laryngeal and hypopharyngeal squamous cell carcinoma: association between quantitative parameters derived from dual-energy CT and histopathological prognostic factors

Acta radiologica (Stockholm, Sweden : 1987)(2023)

引用 0|浏览6
暂无评分
摘要
Background Dual-energy computed tomography (DECT) can provide objective evaluation of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC). Purpose To investigate the relationship between quantitative parameters acquired from DECT and histopathological prognostic factors in LHSCC. Material and Methods A total of 65 patients with LHSCC who underwent arterial phase and venous phase DECT scans were retrospectively enrolled. Iodine concentration (IC) and normalized IC (NIC) of the tumor were calculated in both the arterial (ICA and NICA) and venous (ICV and NICV) phases, and compared among different pathological grades, T stages, and lymph node stages. Receiver operating characteristic (ROC) curves were generated to evaluate their diagnostic performance. Results There were significantly differences on ICA and NICA among three pathological grades (ICA, P = 0.001; NICA, P = 0.002). For differentiating moderately and poorly differentiated from well-differentiated LHSCC using ICA and NICA, the areas under curve (AUCs) were 0.753 and 0.726, respectively. High T stage (T3/4) LHSCC showed significantly higher ICA (P = 0.012) and NICA (P = 0.005) than low T stage (T1/2) LHSCC. The AUCs of the ICA and NICA were 0.674 and 0.703, respectively, in discriminating high from low T stage LHSCC. Lymph node metastasis (LNM)-positive (N1/2/3) LHSCC showed significantly higher ICA (P = 0.008) and NICA (P = 0.003) than LNM-negative (N0) LHSCC. For discriminating the LNM-positive from the LNM-negative group using ICA and NICA, the AUCs were 0.697 and 0.744, respectively. Conclusion ICA and NICA might be helpful in assessing histopathological prognostic factors in patients with LHSCC.
更多
查看译文
关键词
Laryngeal and hypopharyngeal squamous cell carcinoma,dual-energy computed tomography,iodine concentration,prognostic factor
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要