NDLI: Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer

Content Provider	Springer Nature : BioMed Central
Author	Wu, Yuting Li, Jingxu Ding, Li Huang, Jianbin Chen, Mingwang Li, Xiaomei Qin, Xiang Huang, Lisheng Chen, Zhao Xu, Yikai Yan, Chenggong
Abstract	Background To explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC). Methods Between July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test. Results This study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively. Conclusions The combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.
Related Links	https://cancerimagingjournal.biomedcentral.com/counter/pdf/10.1186/s40644-024-00793-6.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	14707330
DOI	10.1186/s40644-024-00793-6
Journal	Cancer Imaging
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-10-25
Access Restriction	Open
Subject Keyword	Oncology Cancer Research Imaging Radiology Nuclear Medicine Dual-energy CT Non-small cell lung cancer Thyroid transcription factor 1 Quantitative analysis
Content Type	Text
Resource Type	Article
Subject	Radiological and Ultrasound Technology Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.3/2023

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