Research progress of the clinical application of dual-layer spectral computed tomography in gastrointestinal malignancies

Table 2 Summary of characteristics of representative research of the clinical application of dual-layer spectral computed tomography in gastric cancer

Ref.	Sample size	Cancer type	Main DLCT parameters	Diagnostic performance
Wu et al[14]	568 patients	GC	CT 40keV, NIC-VP, NZeff-VP	Clinical-DLCT model scoring system enables noninvasively, cost-effectively and rapidly predict TP53 expression
Mao et al[15]	108 patients	GC	Zeff-VP and NIC-VP	The Zeff-VP and NIC-VP (AUC: 0.835 and 0.805) showed better performance in discriminating the Ki-67 status
Du et al[16]	72 patients	GC	NIC, IC and iodine-no-water concentration	A positive correlation between Ki-67 expression levels and IC, NIC, and iodine-no-water concentration in the VP
Zhu et al[17]	264 patients	GC	NIC-VP, Zeff-VP, λHU-VP	The model including DLCT parameters, tumor location and N-CT stage in predicting the MSI status of GC achieved a high prediction efficacy in the validation set, with AUC of 0.879
Zhang et al[18]	72 patients	GC	NIC-AP and λHU-DP	The nomogram based on these indicators (Gender, NIC-AP and λHU-DP) for Lauren classification produced the best performance with an AUC of 0.841
Li et al[19]	58 patients	GC	IC-VP, NIC-VP, and attenuation in the VP	The combination of these factors (IC, NIC, and attenuation in the VP) and gastric wall thickness for differentiation of benign and malignant gastric wall thickening had an AUC of 0.884
Luo et al[21]	55 patients	Lymph nodes of GC	CT attenuation on 70 keV-AP images, ED-VP, and clustered features	These combination predictors (CT attenuation on 70keV-AP images, ED-VP, and clustered features) in diagnosing of metastatic lymph nodes of GC had AUC of 0.855 and 0.907 in the training and validation sets
Zhang et al[22]	70 patients	Lymph nodes of GC	IC-DP, NIC-AP, and ECV	The diagnostic efficacy of ECV% for predicting Lymph nodes metastases of GC was higher than that of other parameters in training and test sets (AUC = 0.823 and 0.803). Model 3 (spectral CT parameters and ECV%) for predicting lymph nodes metastases of GC demonstrated significantly higher diagnostic efficacy than other models in training and test sets (AUC = 0.858 and 0.881)
Tan et al[23]	101 patients	GC	λHU-VP and HU values from 40 keV-VP VMIs	The nomogram based on two tumor spectral parameters (λHU-VP, 40 keV-VP VMIs) and VFA yielded an AUC of 0.89 predicting the POCs of GC patients
Li et al[24]	65 patients	GC	NIC-DP	A model incorporating NIC-DP and ADC significantly improved the AUC value to 0.770

DLCT: Dual-layer spectral computed tomography; GC: Gastric cancer; keV: Kiloelectron volt; AUC: Area under curve; Zeff: Effective atomic number; Nzeff: Normalized effective atomic number; AP: Arterial phase; VP: Venous phase; DP: Delayed phases; ED: Electron density; IC: Iodine concentration; NIC: Normalized iodine concentration; MSI: Microsatellite instability; λHU: Slope of spectral HU curves; VMIs: Virtual monoenergetic images; VFA: Visceral fat area; POCs: Postoperative complications; ECV: Extracellular volume.