Construction of a postoperative liver metastasis prediction model for colorectal cancer based on spectral CT imaging, CEA, and CA19-9

Introduction

Colorectal cancer (CRC) ranks among the most prevalent cancers worldwide (1). In recent years, the prevalence of CRC in China has risen annually due to alterations in dietary patterns and an aging population. As per the 2022 data from the National Cancer Center of China, there are approximately 4.08 million new cases of CRC and 196,000 fatalities per year, making it the second most prevalent cancer and the fourth major cause of cancer-related mortality in the country (2,3). At present, radical surgery is the most effective treatment for CRC; however, patient prognosis remains suboptimal largely due to the high recurrence rates. The average 5-year overall survival rate is only 11% for patients with metastatic CRC, with distant metastasis being one of the leading contributors to poor prognoses among patients with CRC (4,5). The liver is the most often targeted organ in distant metastases. According to a report, almost 50% of individuals with CRC develop liver metastases shortly after surgery (6). Therefore, the preoperative identification of patients at high risk of liver metastasis is crucial for developing individualized treatment plans, reducing liver metastasis rates, improving treatment outcomes, and enhancing patient prognosis (7,8).

Existing prediction models for liver metastasis following CRC surgery often utilize clinical and histopathological factors; however, there are limitations in these models in terms of predictive accuracy and generalizability. The existing models also mostly rely predominantly on traditional imaging and specific tumor markers, which do not comprehensively account for the complex biological behavior of CRC metastasis. These limitations underline the clinical need for enhanced models that integrate more precise and varied data types. A common preoperative examination technique for patients with CRC is computed tomography (CT), which is used to evaluate the involvement of other tissues and organs, the size of lesions, and the status of regional lymph nodes. Contrast-enhanced CT further helps evaluate the blood supply characteristics of lesions, providing essential information for assessing the nature (9). Traditional CT primarily depends on visual observation, which restricts the beneficial effect garnered by the information acquired and limits the accuracy of the prediction of liver metastasis risk among patients with CRC. Spectral CT, a dual-energy CT technique, collects spectral data using both high-energy and low-energy X-rays, enabling the reconstruction of various spectral images, including virtual monoenergetic images, iodine density images, water-free iodine images, and virtual non-contrast images (10). Compared to conventional CT, spectral images provide detailed morphological information and allow for quantitative analysis, revealing tissue composition and blood supply characteristics of the lesion, thereby significantly improving disease diagnosis and risk prediction (11). Spectral CT has demonstrated considerable clinical value in tumor imaging. Integrating virtual native images with low-energy images enhances the detection and characterization of tumor lesions. In recent years, studies have applied spectral CT for the preoperative prediction of lymph node metastasis in CRC and have achieved a degree of success (12,13). The integration of spectral CT features with biological markers such as carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA19-9) is proposed as a method to potentially improve the accuracy of liver metastasis prediction. This combination leverages the detailed imaging capabilities of spectral CT with the proven tumor-associated biochemical markers, providing a multi-dimensional approach to risk assessment (14). This approach addresses the limitations of previous models by incorporating a broader range of data types and offering a more comprehensive analysis of tumor behavior before surgery.

The objective of this study was to construct a spectral CT-based predictive model to predict liver metastases among patients undergoing surgery for CRC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-570/rc).

Methods

Study population

This study enrolled patients with CRC who underwent resection at Jiangsu Cancer Hospital between June 2020 and June 2022. The inclusion criteria were the following: (I) a pathologically confirmed diagnosis of CRC; (II) no history of chemotherapy, radiation therapy, nor other anticancer treatments before admission; (III) a follow-up period of at least 3 months; and (IV) informed consent to participate in the study. Meanwhile, the exclusion criteria were as follows: presence of hepatic and extrahepatic metastases at the time of initial diagnosis; a history of other malignancies; a pregnant or nursing state; with missing clinical data; an inability to cooperate with follow-up; and the emergence of local recurrence or distant metastasis to organs other than the liver during the follow-up period.

Based on the sample size estimation rule for logistic regression models, which recommends at least 10 cases per predictor variable to ensure sufficient model stability and power, we calculated the required sample size for our study. The model includes five predictor variables: CEA, CA19-9, and three spectral CT parameters. Therefore, following this rule, we initially estimated that a minimum of 50 cases (10 × 5 predictor variables) were required. A total of 100 patients were included. All patients underwent preoperative spectral CT enhanced scanning and radical CRC surgery within 3 weeks after the spectral CT examination. Postoperatively, all patients were followed up for 2 years, with imaging examinations [enhanced CT/magnetic resonance imaging (MRI)] conducted every 3 months during the follow-up period. The endpoint event was the occurrence of liver metastasis or the follow-up cutoff date. Data from lost-to-follow-up patients were excluded from the analysis. Patients were separated into two groups based on whether liver metastases occurred during the follow-up period: those with liver metastases (n=60) and non-metastasis group (n=40). The Chinese CRC Liver Metastasis Diagnosis and Comprehensive Treatment Guidelines (2023 Edition) criteria were used to diagnose liver metastases (15). The criteria include: (I) Imaging diagnosis: Enhanced CT: Venous phase scan reveals single or multiple low-density nodules in the liver with ring enhancement. MRI: High signal on T2-weighted imaging (T2WI), high signal on diffusion-weighted imaging (DWI), and arterial phase marginal enhancement on dynamic contrast-enhanced imaging. (II) Pathological confirmation (if necessary): Ultrasound-guided liver biopsy of atypical lesions for histological confirmation as metastatic adenocarcinoma.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics committee of the Jiangsu Cancer Hospital (No. KY-2024-054), and informed consent was taken from all the patients.

Scanning protocol and parameters

All patients underwent plain routine abdominal and enhanced scans with a dual-layer detector spectral CT scanner. The scan range included the diaphragm to the pubic symphysis. Before the scan, participants were required to fast for at least 4 to 6 hours and consume 800 mL of warm water 10 minutes prior to the scan. The scanning parameters were as follows: pitch, 0.985; collimator width, 64×0.625 mm; tube rotation speed, 0.5 s/r; tube voltage, 120 kVp; and an automatically adjusted tube current (mA) based on patient size and anatomy. Ioversol (350 mg I/mL) was the contrast agent applied, and it was administered at a dose of 1.2 mL/kg at a flow rate of 3 mL/s. The region of interest (ROI) on the abdominal aorta was selected to initiate the scan, and imaging was carried out 30 seconds after the venous-phase threshold of 120 Hounsfield units (HU) was reached.

Image processing and analysis

The accompanying virtual monoenergetic pictures, effective atomic number (Zeff), and iodine concentration (IC) maps were created by transferring the spectral CT venous phase images to a spectral post-processing workstation. ROIs were drawn on the most significant visible layer of the tumor and the same plane as the abdominal aorta or iliac artery, with the blood vessels and necrotic areas inside the tumor being avoided. The tumor ROI diameter was required to be more than half its thickness. Each lesion was measured three times, and the average value was taken. The parameters measured included the following: (I) the CT values of the tumor lesions on the 40-keV (CT_{40 keV}), 70-keV (CT_{70 keV}), and-100 keV (CT_{70 keV}) monoenergetic images of the venous phase; (II) the spectral slope (λ) of the lesion in the venous phase [λ_HU = (CT_{40 keV} – CT_{100 keV})/(100 – 40) HU]; (III) the IC of the lesion and the same-plane abdominal aorta or iliac artery; (IV) the normalized iodine concentration (NIC) of the lesion [NIC = IC_lesion/IC abdominal aorta (or external iliac artery)]; and (V) the Zeff of the lesion in the venous phase.

Statistical analysis

Normality of the data was evaluated using the Kolmogorov-Smirnov test. Group comparisons were performed using independent or paired t-tests. Normally distributed data were expressed as the mean ± standard deviation (χ±s) and non-normally distributed data as the median and interquartile range. The Mann-Whitney test or the Chi-squared test was used for group comparisons, and categorical data were expressed as counts and percentages. The prediction accuracy of quantitative spectral CT parameters for liver metastases was examined using the receiver operating characteristic (ROC) curve. A multivariable logistic regression analysis was conducted to identify independent risk variables for liver metastases following CRC surgery. The “rms“ package in R software (The R Foundation for Statistical Computing) was used to construct a nomogram prediction model for liver metastases following CRC surgery and the dataset was randomly divided into a training set (n=70) and a validation set (n=30) in a 7:3 ratio. The model’s performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). All hypothesis tests were two-sided, with a P value of <0.05 considered statistically significant. SPSS version 26.0 (IBM Corp., Armonk, NY, USA) was used to conduct the statistical analysis. Sensitivity = TP/(TP + FN). [TP (true positive) is the number of positive samples correctly identified. FN (false negative) is the number of positive samples incorrectly identified as negative]. Specificity = TN/(TN + FP). [TN (true negative) is the number of negative samples correctly identified by the model. FP (false positive) is the number of negative samples incorrectly identified as positive by the model].

Results

Comparison of preoperative quantitative spectral CT parameters between the two groups

The liver metastasis group had significantly higher venous-phase IC_lesion, λ_HU, and CT_40keV values than the non-metastasis group (P<0.05 for all values). The venous-phase parameters of NIC, Zeff, CT_{70 keV}, and CT_100keV did not differ between the two groups (P>0.05; Table 1).

Preoperative spectral CT quantitative parameters for predicting CRC liver metastasis: ROC curve analysis

ROC curve analysis was used to evaluate the performance of venous-phase IC_lesion, λ_HU, and CT_{40 keV} values in predicting liver metastasis among patients with CRC. The area under the curve (AUC) for IC_lesion, λ_HU, and CT_{40 keV} was 0.7629, 0.7798, and 0.8008, respectively; the sensitivity was 73.33%, 60.00%, and 75.00%; and the specificity was 70.00%, 87.50%, and 77.50%, respectively (all P values <0.05). All three parameters demonstrated good clinical predictive value (Table 2 and Figure 1).

Figure 1 Analysis of predictive performance. CT, computed tomography; HU, Hounsfield units; IC, iodine concentration.

Comparison of preoperative clinical and pathological characteristics between the two groups

The liver metastasis group, compared to the non-liver metastasis group, had a significantly higher percentage of patients with tumor-node-metastasis (TNM) stage III–IV, CEA levels ≥5 ng/mL, CA19-9 levels ≥37 U/mL, extramural vascular invasion, and vascular cancer thrombi (P<0.05). The two groups showed no significant differences relative to the other clinical and pathological features (P>0.05; Table 3).

Construction of a predictive nomogram model for liver metastasis among patients with CRC following surgery

Examination of independent risk variables for liver metastasis among individuals with CRC following surgery was performed. The development of liver metastases within 2 years after surgery was the dependent variable (no =0 and yes =1), while the aforementioned significant covariates were regarded as independent variables. The multivariate logistic regression analysis identified venous-phase parameters (IC_lesion, λ_HU, and CT_{40 keV}), CEA, and CA19-9 as independent risk factors for liver metastases following surgery among patients with CRC (Table 4).

Nomogram development

The R package “rms” was used to randomly allocate the 100 included patients into a training and a validation set in a 7:3 ratio based on the findings of the logistic multivariate analysis. A predictive nomogram model was subsequently developed. This model predicted the risk of developing liver metastases among individuals with CRC following surgery (Figure 2).

Figure 2 Nomogram for predicting the risk of liver metastasis following surgery in patients with CRC. In the nomogram, patients with CEA <5 ng/mL are represented by “0” on the “CEA” axis, whereas those with CEA ≥5 ng/mL are represented by “1”. In the “CA19-9” axis, “0” represents patients with CA19-9 <37 U/mL, and “1” represents patients with CA19-9 ≥37 U/mL. The “IC_lesion value” axis represents the patients’ preoperative venous-phase IC parameter level. The “λ_HU” axis represents the patients’ preoperative venous-phase λ_HU parameter level. The “CT_{40 keV} value” axis represents the patients’ preoperative venous-phase CT_{40 keV} parameter level. The “Risk” axis indicates the likelihood that liver metastases will occur in patients with CRC following surgery. CA19-9, cancer antigen 19-9; CEA, carcinoembryonic antigen; CRC, colorectal cancer; CT, computed tomography; HU, Hounsfield units; IC, iodine concentration.

The ROC curve for this nomogram model was plotted (Figure 3). The AUC in the training dataset was 0.9078 [95% confidence interval (CI): 0.8419–0.9738], the sensitivity was 88.89% (95% CI: 77.03–100.00%), and the specificity was 83.72% (95% CI: 72.69–94.76%). Internal validation of the nomogram was conducted using 1000-bootstrap analyses, yielding an AUC of 0.9502 (95% CI: 0.8792–1.0000), a sensitivity of 84.62% (95% CI: 65.00–100.00%), and a specificity of 82.35% (95% CI: 64.23–100.00%), indicating that the model demonstrated excellent accuracy. The detailed results are provided in Table 5.

Figure 3 ROC curves for the validation and training sets. Data are presented as AUC (95% CI). AUC, area under the curve; CI, confidence interval; ROC, receiver operating characteristic.

This model’s cutoff value was set at 0.6460. A risk score of ≥0.6460 indicates a high chance of liver metastases following surgery, whereas a score of <0.6460 indicates a low risk. The following is an example of the nomogram’s application: A CRC patient had a preoperative CEA level of 32.54 ng/mL (greater than 5 ng/mL), a CA19-9 level of 121.05 U/mL (greater than 37 U/mL), an IC_lesion value of 0.88 mg/mL, a λ_HU value of 1.21, and a CT_{40 keV} value of 120.33 HU; the corresponding “Points” values for these parameters in the nomogram were as follows: CEA, 22.5; CA19-9, 22.0; IC_lesion, 16.5; λ_HU, 3.1; and CT_{40 keV}, 17.1. These values were summed to yield a total score of 81.2 and then located on the “Total Points” scale. Finally, the corresponding value on the “Risk” axis was approximately 0.28. The model indicated that this patient had a low chance of liver metastases within 2 years following surgery since 0.28<0.6460.

Decision curve and calibration curve

The model-fitting indicator was the Hosmer-Lemeshow test (H-L test). There was no discernible difference between the expected and actual numbers when the P value exceeded 0.05. The findings of the H-L test result for the model (χ²=5.572, P=0.70) suggested that the model had good goodness of fit. Furthermore, the calibration curve demonstrated that, on average, the projected and actual probability matched (Figure 4A).

Figure 4 Calibration and decision curve analysis for the predictive model. (A) Calibration curve; (B) decision curve.

Decision curve analysis (DCA) is a method for evaluating the clinical effectiveness of predictive models. The DCA of the nomogram for liver metastasis risk demonstrated that within the threshold range of 0 to 1, the nomogram model offers a good clinical net benefit for identifying individuals at risk of liver metastasis following CRC surgery in comparison to the situations where all patients have liver metastases (red diagonal line in Figure 4B) or none have liver metastases (green horizontal line in Figure 4B).

Discussion

An emerging imaging technology known as spectral CT expands on the single-parameter scanning mode of conventional CT by providing multiple quantitative parameters, including Zeff images, material decomposition images (such as iodine- or water-based), and monochromatic images at 40–140 keV (16,17). Consequently, spectral CT has been widely used in the diagnosis and treatment of clinical practice in CRC, including prognostic assessment and malignant lymph node identification (18-20). In addition, spectral CT has shown that certain specific parameters in the early assessment of patients with CRC are correlated with the risk of metastasis after treatment and possess certain predictive value. According to Cao et al. (21), in preoperative spectral CT parameters, ICAP [iodine concentration (IC) values in the arterial phase], ICVP (IC value in the venous phase), and Eff ZVP (effective atomic number in the venous phase) are significantly correlated with CRC lymph node metastasis and are independent predictors. The results of our study indicated that the levels of IC_lesion, λ_HU, and CT_{40 keV} parameters in the venous phase of the preoperative liver metastasis group of patients with CRC were independently associated with the development of postoperative liver metastasis following CRC resection. It has been noted that tumor cells with metastatic potential often show stronger proliferative ability and metabolic activity. This hypermetabolic state increases the tumor’s demand for oxygen and nutrients, which induces neovascularization and an increase in microvascular density, resulting in a significant enhancement of local blood supply. This characteristic can be reflected by spectral CT as an increase in IC_lesion, λ_HU, and CT_{40 keV} parameters (22), which is consistent with the results of this study. ROC curve analysis also showed that preoperative IC_lesion, λ_HU, and CT_{40 keV} values in the venous stage had good predictive efficacy for postoperative liver metastasis among patients with CRC. These results provide strong evidence supporting the use of preoperative spectral CT for predicting postoperative liver metastasis among patients with CRC following resection.

A statistical model that visualizes the correlation between several risk variables, tumor occurrence, and/or prognosis is referred to as a nomogram. Converting complex regression equations into an intuitive graphical representation can aid clinicians in assessing the probability of a given outcome event and improve the readability of prediction model findings (23). Cao et al. (21) developed a nomogram that combines clinical risk factors (CEA, CA19-9, and rectal perivisceral fat invasion) and spectral CT parameters (arterial phase IC, venous phase IC, and Zeff) for preoperative prediction of lymph node metastasis among patients with CRC. Moreover, Feng et al. (24) devised an effective noninvasive predictive nomogram based on spectral CT parameters (IC, NIC, and Zeff) to detect occult peritoneal metastasis among patients with advanced gastric cancer, increasing the preoperative detection rate. In our study, we constructed a nomogram to predict the risk of liver metastasis after CRC surgery based on five risk factors: CEA, CA19-9, and the preoperative venous-phase parameters IC_lesion, λ_HU, and CT_{40 keV}. Excellent prediction performance was indicated by the training and validation sets’ respective AUC values (both >0.9). The H-L test and calibration curve indicated good consistency between the actual and predicted risk, supporting the reliability and reproducibility of the model. The nomogram prediction model’s therapeutic value was further validated via DCA. This model offers several benefits: CEA and CA19-9 are both commonly used tumor markers in clinical practice, and high levels of CEA and CA19-9 suggest an increased risk of liver metastasis (25). Among the spectral CT parameters, IC reflects the density and blood flow distribution of tumor neovascularity, while λ_HU and CT_{40 keV} reflect the metabolic activity and hemodynamic characteristics of tumor tissues (26,27). The nomogram prediction model composed of these five indices could comprehensively account for the factors affecting liver metastasis in multiple dimensions, more accurately reflect the biological characteristics and metastasis risk of tumors, and thus improve the prediction accuracy.

However, certain limitations to this model should be noted. For instance, the lack of external validation limits the assessment of its generalizability. In the future, external validation using datasets from sources different from the training set will be performed to further optimize the model. Additionally, our study, being a single-center retrospective design, is subject to inherent biases such as selection bias and information bias, which could influence the results. The limited statistical power resulting from the small sample size also poses a challenge. Another consideration is the variability in the production of cancer antigen markers such as CA19-9. Not all patients produce CA19-9, which might affect the predictive accuracy of our model in a broader population. Future studies could explore alternative markers or a combination of markers to accommodate such variability.

Conclusions

The nomogram model established based on preoperative spectral CT parameters, CEA and CA19-9 has the potential to predict postoperative liver metastasis among patients with CRC following resection. It can accurately assess the risk of postoperative metastasis; provide a scientific basis for early screening, individualized diagnosis and treatment, and postoperative management; and further promote the development of precision medicine.

Acknowledgments

None

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-570/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-570/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-570/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-570/coif). D.I.T. serves as an unpaid editorial board member of Translational Cancer Research from May 2024 to December 2026. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the ethics committee of the Jiangsu Cancer Hospital (No. KY-2024-054), and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)