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

Table 1 Summary of characteristics of representative research of the clinical application of dual-layer spectral computed tomography in esophageal cancer

Ref.	Sample size	Cancer type	Main DLCT parameters	Diagnostic performance
Li et al[11]	53 patients	EC	AEF, ECV	The AUC of the combined clinical and DLCT model was 0.893
Wang et al[12]	172 patients	EC	Zeff-VP and NIC-VP	The spectral CT and clinical model yielded the highest AUC of 0.825
Graf et al[13]	62 patients	EC	NIC, normalized ΔIC	The normalized ΔIC yielded the highest AUC of 0.95; ICafter NAC achieved an AUC of 0.88

DLCT: Dual-layer spectral computed tomography; CT: Computed tomography; EC: Esophageal cancer; AEF: Arterial enhancement fraction; ECV: Extracellular volume; AUC: Area under curve; Zeff: Effective atomic number; VP: Venous phase; IC: Iodine concentration; NIC: Normalized iodine concentration; NAC: Neoadjuvant chemotherapy; ΔIC: Iodine concentration at diagnosis-iodine concentration after normalized iodine concentration.

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.

Table 3 Summary of characteristics of representative research of the clinical application of dual-layer spectral computed tomography in colorectal cancer

Ref.	Sample size	Cancer type	Main DLCT parameters	Diagnostic performance
Wang et al[28]	80 patients	CRC	IC and NIC	The AUC of IC for diagnosing colon tumors was 0.837, with a sensitivity of 91.5% and a specificity of 75.8%. The AUC of NIC for diagnosing colon tumors was 0.899, with a sensitivity and specificity of 85.1% and 84.8%
Sun et al[29]	165 patients	CRC	ECV	ECV had diagnostic efficacy for CRC pT staging in both the training and external validation sets (AUC = 0.919 and 0.892)
Chen et al[30]	131 patients	CRC	Zeff, NIC-AP, NIC-VP, and λHU-AP	The AUCs of Zeff, NIC-AP, NIC-VP, and λHU-AP for distinguishing different stages of CRC were 0.83, 0.80, 0.79, 0.86, and 0.68, respectively. The AUCs of Zeff, NIC-AP, NIC-VP, and λHU-AP for distinguishing different grades of CRC were 0.83, 0.80, 0.79, 0.86, and 0.68, respectively
Lu et al[31]	62 patients	CRC	40 keV attenuation	The nomogram incorporating these two predictors (N-CT stage and 40 keV attenuation) exhibited the best efficacy in the preoperative assessment of PNI status in RC, with an AUC of 0.885
Chen et al[32]	106 patients	CRC	40 keV-VP, 100 keV-VP, Zeff-VP, IC-VP and λHU-VP	The AUCs of 40 keV-VP, 100 keV-VP, Zeff-VP, IC-VP, and λHU-VP in distinguishing LVI status of CRC were 0.688, 0.644, 0.688, 0.703, 0.677, respectively. Spectral CT derived parameters did not significantly vary with the PNI status
Chen et al[33]	255 patients	CRC	IC-AP, NIC-VP	A clinical–radiomic model constructed based on iodine maps showed higher efficacy in predicting the preoperative MSI status
Liu et al[35]	42 patients	Lymph nodes of CRC	NZeff-VP	After combining NZeff and the short-axis diameter, the AUC (0.966) in diagnosing metastatic Lymph nodes in CRC patients was the highest with sensitivity of 100% and specificity of 87.7%
Sauter et al[38]	11 patients	CRC	Absolute IC difference	IC values decreased significantly after RCT. The absolute IC difference and the absolute ADC (both before and after RCT) is high and significant
Peng et al[39]	222 patients	CRC	VEF, λHU-VP and 1/NIC-VP	The clinical-spectral model in predicting VEDM in CRC following surgery achieved further improved AUC of 0.887
Yang et al[40]	100 patients	CRC	IC-VP, λHU and CT attenuation 40 keV	The nomogram based on spectral CT parameters, CEA, and CA199 demonstrated high discriminative ability, with AUC of 0.9078 in the training set and 0.9502 in the internal validation set
Liu et al[41]	134 patients	CRC	NIC-VP, λHU-VP	The combined indicator integrating NIC-VP, λHU-VP and CEA achieved the best diagnostic performance (AUC = 0.900) in predicting prognosis in RC
Tan et al[42]	85 patients	CRC	40 keV-VP VMIs and VFA	The combined model based on predictors (40keV-VP VMIs and VFA) in predicting POCs in colon cancer produced an AUC of 0.84, with a sensitivity of 77.8% and specificity of 87.9%

DLCT: Dual-layer spectral computed tomography; CRC: Colorectal cancer; ECV: Extracellular volume; AUC: Area under curve; Zeff: Effective atomic number; NZeff: Normalized Zeff; IC: Iodine concentration; NIC: Normalized iodine concentration; keV: Kiloelectron volt; AP: Arterial phase; VP: Venous phase; DP: Delayed phases; λHU: Slope of spectral HU curves; PNI: Perineural invasion; LVI: Lymph vascular invasion; RCT: Radiochemotherapy; MSI: Microsatellite instability; VEF: Venous enhancement fraction; VEDM: Very early distant metastasis; VMIs:Virtual monoenergetic images; VFA: Visceral fat area; CEA: Carcinoembryonic antigen; POCs: Postoperative complications; RC: Rectum cancer.