Dual-Energy Computed Tomography Parameters Combined With Inflammatory Indicators Predict Cervical Lymph Node Metastasis in Papillary Thyroid Cancer

Abstract

Background and Objective

Cervical lymph node metastasis (CLNM) is considered a marker of papillar Fethicy thyroid cancer (PTC) progression and has a potential impact on the prognosis of PTC. The purpose of this study was to screen for predictors of CLNM in PTC and to construct a predictive model to guide the surgical approach in patients with PTC.

Methods

This is a retrospective study. Preoperative dual-energy computed tomography images of 114 patients with pathologically confirmed PTC between July 2019 and April 2023 were retrospectively analyzed. The dual-energy computed tomography parameters [iodine concentration (IC), normalized iodine concentration (NIC), the slope of energy spectrum curve (λ_HU)] of the venous stage cancer foci were measured and calculated. The independent influencing factors for predicting CLNM were determined by univariate and multivariate logistic regression analysis, and the prediction models were constructed. The clinical benefits of the model were evaluated using decision curves, calibration curves, and receiver operating characteristic curves.

Results

The statistical results show that NIC, derived neutrophil-to-lymphocyte ratio (dNLR), prognostic nutritional index (PNI), gender, and tumor diameter were independent predictors of CLNM in PTC. The AUC of the nomogram was .898 (95% CI: .829-.966), and the calibration curve and decision curve showed that the prediction model had good predictive effect and clinical benefit, respectively.

Conclusion

The nomogram constructed based on dual-energy CT parameters and inflammatory prognostic indicators has high clinical value in predicting CLNM in PTC patients.

Keywords

papillary thyroid cancer cervical lymph node metastasis double energy computed tomography inflammatory prognostic indicators nomogram

Introduction

Thyroid cancer is the most common endocrine tumor in the human body, with papillary carcinoma accounting for around 84% of occurrences. The prevalence of thyroid cancer is rising worldwide as screening technologies and diagnosis have improved in recent years.^1,2 According to cancer statistics, there are around 580,000 thyroid cancer cases globally, with women having a 3-fold greater prevalence than men.³ Although the overall prognosis of papillary thyroid cancer (PTC) is the best among all pathological types of thyroid cancer, it is prone to lymph node metastasis (LNM), particularly central lymph node metastasis and lateral cervical lymph node metastasis (CLNM).¹ CLNM is closely related to the prognosis of PTC and the method and scope of surgery. Although prophylactic lymph node dissection can reduce the risk of postoperative CLNM in patients with PTC, it may also lead to hypoparathyroidism and recurrent laryngeal nerve injury.⁴ Therefore, it is necessary to evaluate the presence of enlarged lymph nodes in the neck through imaging methods before undergoing lymph node dissection. Imaging is an important tool for preoperative examination of CLNM, but these methods have limitations. For example, ultrasonography is the preferred method for screening superficial lymph nodes, but it is difficult to scan deep cervical lymph nodes, and the operator’s expertise and technique affect the accuracy and reliability of the examination results.⁵ MRI is not sensitive to calcification and is easily affected by breathing and swallowing movements. Meanwhile, the results of the above imaging methods for identifying benign or malignant enlarged lymph nodes sometimes do not match the actual situation. Dual-energy CT (DECT) is a new imaging device that uses less radiation than traditional CT and can gather image data at various energy levels (40 KeV-140 KeV). Its optimum single-energy images not only optimise image quality but also give qualitative and quantitative information about tissues within the scan area by applying post-processing techniques,⁶ which help to display micro lesions and identify benign and malignant lymph nodes.

The inflammatory microenvironment is a critical component of most tumors, and inflammatory responses mediated by inflammatory factors such as tumor necrosis factor, interleukin-1, and chemokines promote tumor angiogenesis and reduce anticancer effects, playing a key role in tumor invasion, progression, and metastasis.^7,8 Derived neutrophil-to-lymphocyte ratio (dNLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), and other inflammatory biomarkers have been widely used in the study of the relationship between inflammation and cancer interactions, and many studies have demonstrated that some inflammatory indicators are closely associated with the prognosis of cancer patients.

Although some researchers have investigated the relationship between DECT parameters and CLNM, no researchers have yet combined energy spectrum parameters and inflammatory indicators to predict CLNM in PTC patients. This study aimed to develop and validate a nomogram constructed by combining DECT parameters and inflammatory factors and to investigate the feasibility and clinical value of this model in predicting CLNM in PTC patients in order to improve the accuracy of clinicians’ judgement of CLNM, to implement precise and personalized treatment for patients, and to reduce unnecessary intraoperative injuries.

Material and Method

Patients

This is a retrospective study. We retrospectively analyzed the imaging and clinical data of 150 patients with PTC who received preoperative DECT scans of the neck at our institution between July 2019 and April 2023. According to the surgical pathological findings, the patients were divided into a lymph node metastasis group and a non-metastasis group. Inclusion criteria: (1) all patients underwent cervical lymph node dissection, and postoperative pathology confirmed the patient as papillary thyroid cancer; (2) a preoperative cervical DECT scan and a venous phase enhancement scan. Exclusion criteria: (1) a history of other malignant tumors; (2) previous therapy for thyroid cancer; (3) poor quality of CT images; (4) incomplete clinical data of patients. 114 cases were eventually included in the study based on the inclusion and exclusion criteria, and patients were randomly assigned to training and validation groups in a 2:1 ratio. Figure 1 depicts the flow chart of the study population. The study followed relevant Equator guidelines. The reporting of this study conforms to TRIPOD guidelines.⁹ Individual consent for this retrospective analysis was waived, and all patient data were de-identified.

Figure 1.

Flow chart for patient inclusion and data analysis.

Dual-Energy CT Scanning Method

All patients underwent CT on a 64-MDCT scanner (Discovery CT750HD [fifth-generation spectral CT], GE Healthcare) with the gemstone spectral imaging (GSI) mode. The scan ranged from the skull base to the level of the aortic arch. Scanning parameters: 80-140 kVp fast tube voltage switching (.25 ms); 260 mA tube current; 40 mm collimation width; 0.7 s rotation speed; .984:1 pitch. A contrast dose of 1.2 mL/kg (Omnapaque 350 mg I/ml) was injected through the right elbow vein at a flow rate of 3.1 mL/s in the single-phase contrast-enhanced scan, and images of the venous phase were acquired 50 s after the start of contrast injection. After scanning, single-energy images with a reconstituted layer thickness of 1.25 mm in the venous phase were transferred to a workstation (AW4.7, GE Healthcare).

Dual-Energy CT Quantitative Parametric Analysis

The intravenous DECT images were examined by 2 radiologists using a double-blind procedure. GSI software (GSI Viewer 4.7, GE Healthcare) was used to evaluate iodograms and energy spectrum curves (Figure 2). The ROI was plotted on the solid section of the lesion on a 70 keV enhanced image of 1.25 mm while avoiding areas of hemorrhage, necrosis, cystic lesions, and calcification as much as possible. When a primary lesion has multiple cancer foci, the largest cancer foci are selected for measurement, and at least 2 levels are chosen for each lesion, with each level being measured once, the iodine concentration (IC) of the cancer foci and the common carotid artery was obtained. Normalized iodine concentration (NIC) = IC lesion/IC common carotid artery, the slope of energy spectrum curve (λHU) = (CT 40 keV − CT 100 keV)/(100-40).

Figure 2.

Measurement of dual-energy CT parameters in venous phase. Draw the region of interest for iodine concentration measurement (A). L1: Measurement of iodine concentration in thyroid papillary cancer; L2: Measurement of iodine concentration in the common carotid artery (B). Diagram of spectrum curve measurement (C).

Clinical Data Analysis

All of the patients in this study had complete clinical data. The clinical data collected included gender (male or female), age (≥45 years or <45 years), tumor diameter (≥10 mm or <10 mm), number of lesions (single or multiple), calcification (present or absent), TSH (thyroid stimulating hormone), TGAb (thyroglobulin antibody), body mass index (BMI), neutrophil count, lymphocyte count, platelet count, and serum albumin count. Note: A single lesion is defined as the number of thyroid cancer foci <2, whereas a multiple lesion is defined as the number of cancer foci in 1 or different lobes of the thyroid ≥2; in the case of multiple lesions, the diameter was measured using the largest lesion. Inflammatory index calculation: dNLR = neutrophil count/(white blood cell count - neutrophil count); PNI = albumin (g/L) + 5× lymphocyte count (×10⁹/L); platelet-to-lymphocyte ratio (PLR) = platelet count/lymphocyte count; SII = neutrophil × platelet/lymphocyte count. PIS: Patients with a low neutrophil-lymphocyte ratio (<3) and high serum albumin (≥40 g/L) were scored as 2; patients with a high neutrophil-lymphocyte ratio (≥3) or low serum albumin levels (<40 g/L) were scored as 1.

Statistical Analyses

Statistical analysis was performed using SPSS 26.0 and R 4.2.2: (1) Continuous variables were transformed into binary variables by ROC curve analysis and calculating the best cut-off value; (2) Independent influences factors on predicting LNM in PTC were screened using univariate and multivariate logistic regression analysis; (3) The results of multivariate logistic regression (variables with P < .05) were used to construct nomogram; (4) The nomogram was evaluated by calculating the consistency index (C-index) and plotting the receiver operating characteristic curve (ROC), calibration curve, and decision curve; (5) Finally, Bootstrap validation was performed to internally validate the model and brought into the validation group for external validation.

Results

Baseline Characteristics of the Included Patients

This study included 114 patients, with a range of 24 to 79 years and a mean age of 42 years. The baseline data revealed no statistically significant differences between the training and validation groups (P > .05), indicating that the grouping was random and reasonable. In the training group, there were 36 cases of CLNM and 40 cases of non-metastasis, 15 males and 61 females. In the validation group, there were 26 cases of CLNM and 12 cases of non-metastasis, 8 males and 30 females. Other baseline information is shown in Table 1. Table 2 showed that the mean values of IC, NIC, and λ_HU in the metastatic group were 33.18 ± 6.89, .71 ± .14, and 3.82 ± .84, respectively, and the mean values of IC, NIC, and λ_HU in the non-metastatic group were 31.20 ± 7.43, .66 ± .13, and 3.60 ± .87, respectively. The intraclass correlation coefficient (ICC) of the IC, NIC, and λ_HU were .923, .964, and .903, respectively.

Table 1.

Baseline Information of 114 Patients With Papillary Thyroid Cancer.

Parameters	Training Group (n = 76)		Validation Group (n = 38)		P Value
Parameters	No Metastasis	Metastasis	No Metastasis	Metastasis	P Value
Age (years)					.549
≥45	24 (31.58%)	12 (15.79%)	6 (15.79%)	9 (23.68%)
<45	16 (21.05%)	24 (31.58%)	6 (15.79%)	17 (44.74%)
Gender					>.99
Male	4 (5.26%)	11 (14.47%)	1 (2.63%)	7 (18.42%)
Female	36 (47.37%)	25 (32.89%)	11 (28.95%)	19 (50%)
Number of lesions					.051
Multifocal	11 (14.47%)	15 (19.74%)	8 (21.05%)	13 (34.21%)
Unifocal	29 (38.16%)	21 (27.63%)	4 (10.53%)	13 (34.21%)
Calcification					>.99
Yes	27 (35.53%)	28 (36.84%)	7 (18.42%)	23 (60.53%)
No	13 (17.10%)	8 (10.53%)	5 (13.16%)	3 (7.89%)
Location_of_lesion					.144
Bilateral	7 (9.21%)	8 (10.53%)	4 (10.53%)	9 (23.68%)
Unilateral	33 (43.42%)	28 (36.84%)	8 (21.05%)	17 (44.74%)
Diameter (mm)					.634
≥10	17 (22.37%)	28 (36.84%)	4 (10.53%)	21 (55.26%)
<10	23 (30.26%)	8 (10.53%)	8 (21.05%)	5 (13.16%)
IC (mg/ml)					.946
≥30.53	18 (23.68%)	26 (34.21%)	6 (15.79%)	17 (44.74%)
<30.53	22 (28.95%)	10 (13.16%)	6 (15.79%)	9 (23.68%)
NIC (mg/ml)					.842
≥.68	15 (19.74%)	26 (34.21%)	4 (10.53%)	15 (39.47%)
<.68	25 (32.89%)	10 (13.16%)	8 (21.05%)	11 (28.95%)
λ_HU (HU/keV)					.521
≥3.28	20 (26.32%)	30 (39.47%)	9 (23.68%)	19 (50.00%)
<3.28	20 (26.32%)	6 (7.89%)	3 (7.89%)	7 (18.42%)
TSH (mIU/L)					.729
≥.95	3 (3.95%)	3 (3.95%)	1 (2.63%)	3 (7.89%)
<.95	37 (48.68%)	33 (43.42%)	11 (28.95%)	23 (60.53%)
Tg (ug/L)					.060
≥40	5 (6.76%)	14 (18.92%)	2 (5.56%)	1 (2.78%)
<40	25 (33.78%)	30 (40.54%)	17 (47.22%)	16 (44.44%)
TgAb (IU/ml)					.364
≥115	6 (7.89%)	7 (9.21%)	4 (10.53%)	6 (15.79%)
<115	34 (44.74%)	29 (38.16%)	8 (21.05%)	20 (52.63%)
BMI (kg/m²)					.465
≥23.90	22 (28.95%)	17 (22.37%)	5 (13.16%)	10 (26.32%)
<23.90	18 (23.68%)	19 (25.00%)	7 (18.42%)	16 (42.10%)
dNLR					>.99
≥1.07	26 (34.21%)	33 (43.42%)	8 (21.05%)	21 (55.26%)
<1.07	14 (18.42%)	3 (3.95%)	4 (10.53%)	5 (13.16%)
PLR					.459
≥108.9	24 (31.58%)	23 (30.26%)	5 (13.16%)	15 (39.47%)
<108.9	16 (21.05%)	13 (17.11%)	7 (18.42%)	11 (28.95%)
SII					.364
≥255.73	32 (42.11%)	31 (40.79%)	7 (18.42%)	21 (55.26%)
<255.73	8 (10.52%)	5 (6.58%)	5 (13.16%)	5 (13.16%)
PNI					.396
≥56.08	19 (25%)	27 (35.53%)	10 (26.32%)	17 (44.74%)
<56.08	21 (27.63%)	9 (11.84%)	2 (5.26%)	9 (23.68%)

IC: iodine concentration; NIC: normalized iodine concentration; λ_HU: the slope of energy spectrum curve; BMI: body mass index; dNLR: derived neutrophil-to-lymphocyte ratio; PNI: prognostic nutritional index; SII: systemic immune-inflammation index; PLR: platelet-to-lymphocyte ratio.

Table 2.

Mean Value of Dual-Energy CT Parameters.

Parameters	Metastasis Group	No Metastasis Group
NIC (mg/ml)	.71 ± .14	.66 ± .13
IC (mg/ml)	33.18 ± 6.89	31.20 ± 7.43
λ_HU (HU/keV)	3.82 ± .84	3.60 ± .87

IC: iodine concentration; NIC: normalized iodine concentration; λ_HU: the slope of energy spectrum curve; CT: computed tomography.

Result of Logistic Regression Analysis

A total of 17 variables were included in this study for statistical analysis, Table 3 showed that by univariate logistic regression analysis, age (OR = 3.00; 95% CI: 1.174-7.667; P = .022), gender (OR = 3.960; 95% CI: 1.131-13.863; P = .031), calcification (OR = 5.296; 95% CI: 1.367-20.522; P = .016), tumor diameter (OR = 4.735; 95% CI: 1.733-12.939; P = .002), IC (OR = 3.178; 95% CI: 1.218-8.292; P = .018), NIC (OR = 4.333; 95% CI: 1.642-11.436; P = .003), λ HU (OR = 5.000; 95% CI: 1.709-14.628; P = .003), dNLR (OR = 5.923; 95% CI: 1.537-22.818; P = .001), PNI (OR = 3.316; 95% CI: 1.248-8.807; P = .016) were associated with CLNM (P < .05), the other variables were not associated with CLNM (P > .05). The variables with P < .05 were subjected to multivariate logistic regression analysis, and the results showed that gender, tumor diameter, NIC, dNLR, and PNI were independent predictors of cervical lymph node metastasis in papillary thyroid cancer.

Table 3.

Univariate and Multivariate Logistic Regression Analysis of CLNM in Patients With PTC in Training Group.

Variable	Univariate Logistic Regression Analysis		Multivariate Logistic Regression Analysis
Variable	OR (95% CI)	P Value	OR (95% CI)	P Value
Age	3.000 (1.174-7.667)	.022	.187 (.034-1.041)	.056
Gender	3.960 (1.131-13.863)	.031	11.410 (1.066-122.106)	.044
Number of lesions	1.883 (.721-4.916)	.196
Calcification	5.296 (1.367-20.522)	.016	4.128 (.650-26.198)	.133
Location of lesion	1.347 (.434-4.180)	.606
Diameter	4.735 (1.733-12.939)	.002	8.960 (1.750-45.888)	.009
IC	3.178 (1.218-8.292)	.018	.916 (.070-12.020)	.947
NIC	4.333 (1.642-11.436)	.003	7.211 (1.065-48.828)	.043
λ_HU	5.000 (1.709-14.628)	.003	3.392 (.292-39.435)	.329
TSH	1.121 (.212-5.942)	.893
Tg	.511 (.190-1.375)	.184
TgAb	1.368 (.413-4.531)	.608
BMI	1.366 (.553-3.371)	.499
dNLR	5.923 (1.537-22.818)	.001	40.479 (3.706-442.110)	.002
PLR	1.179 (.466-2.986)	.728
SII	1.550 (.457-5.259)	.482
PNI	3.316 (1.248-8.807)	.016	.178 (.032-.992)	.049

Construction and Evaluation of Nomogram

According to the ROC curves (Figure 3), the combined prediction model outperforms the dual-energy CT parameters and inflammatory indicators alone. Drawing nomogram (Figure 4(A)) and its forest plot (Figure 4(B)) based on the results of multivariate logistic regression analysis. The C-index of the nomogram predicting CLNM was .898 (95% CI: .830-.965), and the area under the curve (AUC) for Bootstrap validation was .896 (95% CI: .891-.900). Further introducing nomogram into the validation group, the C-index was .853 (95% CI: .727-.978), and the AUC was .854 (95% CI: .845-.863). Calibration plots (Figure 4(C)) and DCA curve (Figure 4(D)) showed good predictive effect and clinical benefit of the nomogram, respectively.

Figure 3.

ROC curve for predicting cervical lymph node metastasis in thyroid papillary cancer. The AUC of the combined prediction model was .898 (95% CI: .829-.966), which was the largest. AUC, the area under the curve; ROC, receiver operating characteristic curve; NIC, normalized iodine concentration; dNLR, derived neutrophil-to-lymphocyte ratio; PNI, prognostic nutritional index.

Figure 4.

Nomogram (A) and its forest plot (B), decision curve (C), calibration curve (D) for predicting cervical lymph node metastasis in papillary thyroid cancer.

Discussion

The thyroid gland is an iodine-rich organ, and iodine in the thyroid gland is mainly stored in follicular epithelial cells as thyroglobulin. When the gland becomes malignant, the follicular epithelial cells are replaced by cancer cells, and iodine uptake and storage are reduced, resulting in malignant thyroid nodules with lower overall iodine concentration than normal tissue. In this study, iodograms were obtained by GSI workstation, and the IC of parenchymal components of the lesion was measured quantitatively. Filling tissue microvasculature with iodine contrast is the foundation of iodine basemap imaging. The IC measured by DECT is highly consistent with the actual amount of iodine, so the measured iodine concentration can represent the blood supply to the lesion indirectly,^10,11 ie, the higher the iodine concentration, the richer the blood supply to the lesion. Currently, many studies have shown that dual-energy CT parameters can be used as biomarkers for predicting LNM in cancer. Li et al,¹² in their findings for predicting LNM in gastric cancer noted that the IC of cancer foci was closely associated with LNM. Su et al¹³ indicated significant differences in NIC in esophageal cancer foci in patients with metastatic and non-metastatic lymph. This study included 3 DECT parameters of IC, NIC, and λ_HU of intravenous phase PTC, and all 3 parameters were correlated with CLNM of PTC by univariate statistical analysis (P < .05), and the results of the multivariate analysis showed that only intravenous phase NIC was an independent predictor of CLNM, and when NIC ≥.68, patients with PTC had an increased risk of developing CLNM. The findings of this study are similar to those of Liu et al¹⁴ However, these scholars conducted their study on arterial phase NIC. Meanwhile, the results of this study showed that the IC, NIC, and λ_HU of the primary cancer lesions in the patients with metastatic cervical lymph nodes were 33.18 ± 6.89, .71 ± .14, and 3.82 ± .84, respectively, while the IC, NIC, and λ_HU of the non-metastatic group were 31.20 ± 7.43, .66 ± .13, and 3.60 ± .87, respectively, that is, the DECT parameters in the metastatic group were higher than those in the non-metastatic group. The reason for this result may be due to the increased metabolic rate of cancer cells during the process of cervical lymph node metastasis, as well as the increase of tumor blood vessels in the cancer foci to provide rich nutrients to maintain the growth and proliferation of cancer cells, so the IC and NIC of the cervical lymph node metastasis group were higher than those of the non-metastasis group. This is also consistent with Zou’s findings that differences in iodine intake may lead to differences in the capacity of LNM.¹⁵ The λ_HU reflects the decay of HU values of tissues in the 40-140 keV range and can be used to quantify the different tissue iodine content in different tissues.^16,17 So, it is reasonable to explain that the λ_HU is higher in the metastatic group than in the non-metastatic group based on the above.

Research on the relationship between inflammation and various cancers has continued to heat up in recent years. Numerous previous studies have shown that cancer-associated inflammatory responses play a crucial role in the tumor microenvironment and are key mediators of cancer development, progression and metastasis.⁷ Cancer cells induce an inflammatory response, altering peripheral blood leukocyte and platelet levels and releasing pro-inflammatory molecules, which in turn drive cancer development.^18-20 The major inflammatory factors include IL-1, TNF-a, etc. Cancer cells can secrete vascular endothelial growth factor (VEGF) to promote tumor microvasculature and lymphatic vessel production, and the probability of CLNM is increased when VEGF is overexpressed.²¹ Despite many studies describing the interaction between cancer and inflammation-mediated tumor microenvironment, determining the status of cancer-associated inflammation in clinical practice remains problematic. According to the literature, NLR can be a key predictor of thyroid cancer survival and LNM, with higher NLR indicating a more aggressive tumor and a worse prognosis.^22-24 In this study, we investigated the connection between inflammatory markers and CLNM in PTC. dNLR and PNI were shown to be independent predictors of CLNM in patients with PTC. PNI not only reflects the nutritional and immune status of cancer patients but also predicts prognosis.^25,26 PTC patients with low preoperative PNI were more likely to develop CLNM in the current study. It could be due to the high metabolic rate of cancer cells and the tumor’s aggressiveness during lymph node metastasis, which causes malnutrition and reduced immunological function, resulting in a lower PNI.

Furthermore, in this study, gender and tumor diameter were also independent influencing factors for CLNM in PTC patients. The significance of gender in CLNM may be due to the higher hormone level in women than men in general, which has a potential influence on the metastasis of cancer cells. The larger the tumor diameter, the more likely it is to break through the envelope and invade the surrounding lymphatic vessels and lymph nodes, and when the diameter exceeds 10 mm, CLNM is more likely.

Currently, the nomogram has shown excellent results in predicting the prognosis of various tumors.^27,28 Based on the findings of the preceding study, we attempted to construct a nomogram for predicting CLNM in PTC by combining 5 independent influencing factors (NIC, dNLR, PNI, gender, and tumor diameter). The C-index of this nomogram was .898. The calibration curve showed that the probability of the nomogram predicting CLNM and the actual occurrence of metastasis were in good agreement. The decision curves showed that the model had the greatest net clinical benefit over the most reasonable threshold probability ranges. In the nomogram, each factor is assigned a weighted number of points, and the corresponding probability value for the occurrence of CLNM is obtained by calculating the total number of points for each patient.

In conclusion, NIC, dNLR, PNI, gender, and diameter can be used as independent predictors of CLNM in PTC patients. The nomogram constructed by combining the above 5 factors has higher accuracy and clinical benefit in predicting CLNM in PTC patients, and by scoring the system, it can provide clinicians with a visualized prediction result, which may be beneficial for clinicians to develop personalized treatment plans for patients and avoid unnecessary injury to patients due to prophylactic dissection of the lymph nodes.

This research has the following deficiencies: (1) when patients had multiple cancer foci, only the largest cancer foci were selected for measurement, and the remaining foci were not analyzed. (2) the DECT parameters of the venous phase were only measured and analyzed in this study, and DECT parameters in the arterial phase will be added later for further study. (3) the number of samples used to develop and validate the model in this study was small, and it’s currently not possible to increase the sample size. (4) We did not perform power calculations when estimating the sample size selected for the study. (5) Loss of thyroglobulin information in individual patients.

Footnotes

Acknowledgments

All authors sincerely thank all patients who participated in this study.

Author Contributions

DJN and WYY led the study. WLY conducted the data analysis; BJ, SMJ and FBY implemented the methodology; WLY and XBZ collected the data and discussed the results, WLY and ZY prepared the original draft, WLY and DJN reviewed and edited the final manuscript. The author(s) read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The article is supported by Key Specialties in Clinical Medicine Construction Funding Project of Anhui Province (No. 2019sjlczdzk), Joint Fund for Medical Artificial Intelligence (No. MAI2023Q032) and The National Cancer Center Climbing Foundation for Clinical Research (No. NCC201912B03).

Ethical Statement

ORCID iD

Jiangning Dong

References

Deng

Liu

, et al. Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics. Nat Commun. 2020;11(1):4807. doi:10.1038/s41467-020-18497-3.

Kim

Gosnell

Roman

. Geographic influences in the global rise of thyroid cancer. Nat Rev Endocrinol. 2020;16(1):17-29. doi:10.1038/s41574-019-0263-x.

Sung

Ferlay

Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi:10.3322/caac.21660.

Wei

Wang

Zhang

Dong

. Preoperative prediction of cervical nodal metastasis in papillary thyroid carcinoma: value of quantitative dual-energy CT parameters and qualitative morphologic features. AJR Am J Roentgenol. 2021;216(5):1335-1343. doi:10.2214/AJR.20.23516.

Han

Lei

Luo

Ding

. Differential diagnosis value of the ultrasound gray scale ratio for papillary thyroid microcarcinomas and micronodular goiters. Quant Imag Med Surg. 2018;8(5):507-513. doi:10.21037/qims.2018.06.04.

Ginat

Mayich

Daftari-Besheli

Gupta

. Clinical applications of dual-energy CT in head and neck imaging. Eur Arch Oto-Rhino-Laryngol. 2016;273(3):547-553. doi:10.1007/s00405-014-3417-4.

Kinoshita

Goto

. Links between inflammation and postoperative cancer recurrence. J Clin Med. 2021;10(2):228. doi:10.3390/jcm10020228.

Kinoshita

Goto

. Molecular mechanisms of pulmonary fibrogenesis and its progression to lung cancer: a review. Int J Mol Sci. 2019;20(6):1461. doi:10.3390/ijms20061461.

Collins

Reitsma

Altman

Moons

. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi:10.1136/bmj.g7594.

10.

Stiller

Skornitzke

Fritz

, et al.

Correlation of quantitative dual-energy computed tomography iodine maps and abdominal computed tomography perfusion measurements: are single-acquisition dual-energy computed tomography iodine maps more than a reduced-dose surrogate of conventional computed tomography perfusion?

Invest Radiol. 2015;50(10):703-708. doi:10.1097/RLI.0000000000000176.

11.

Kang

Kim

Bae

Jeon

Han

. Can quantitative iodine parameters on DECT replace perfusion CT parameters in colorectal cancers? Eur Radiol. 2018;28(11):4775-4782. doi:10.1007/s00330-018-5502-3.

12.

Fang

Wang

, et al. Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer. Eur Radiol. 2018;28(12):5241-5249. doi:10.1007/s00330-018-5483-2.

13.

Sun

Niwa

Ozawa

Endo

Hashimoto

. Detecting lymph node metastasis of esophageal cancer on dual-energy computed tomography. Acta Radiol. 2022;63(1):3-10. doi:10.1177/0284185120980144.

14.

Liu

Ouyang

, et al. Papillary thyroid cancer: dual-energy spectral CT quantitative parameters for preoperative diagnosis of metastasis to the cervical lymph nodes. Radiology. 2015;275(1):167-176. doi:10.1148/radiol.14140481.

15.

Zou

Zhang

, et al. Prediction of ipsilateral lateral cervical lymph node metastasis in papillary thyroid carcinoma: a combined dual-energy CT and thyroid function indicators study. BMC Cancer. 2021;21(1):221. doi:10.1186/s12885-021-07951-0.

16.

Yang

Luo

, et al. Differentiation of malignant cervical lymphadenopathy by dual-energy CT: a preliminary analysis. Sci Rep. 2016;6:31020. doi:10.1038/srep31020.

17.

Yang

Zhang

, et al. Preoperative prediction of the aggressiveness of oral tongue squamous cell carcinoma with quantitative parameters from dual-energy computed tomography. Front Oncol. 2022;12:904471. doi:10.3389/fonc.2022.904471.

18.

Wang

Liu

Zhong

. Prediction of lymph node metastasis in oral tongue squamous cell carcinoma using the neutrophil-to-lymphocyte ratio and platelet-to-neutrophil ratio. J Clin Lab Anal. 2021;35(6):e23684. doi:10.1002/jcla.23684.

19.

Itami

Miyake

Tatsumi

, et al. Preoperative predictive factors focused on inflammation-, nutrition-, and muscle-status in patients with upper urinary tract urothelial carcinoma undergoing nephroureterectomy. Int J Clin Oncol. 2019;24(5):533-545. doi:10.1007/s10147-018-01381-y.

20.

Balkwill

Joffroy

. TNF: a tumor-suppressing factor or a tumor-promoting factor? Future Oncol. 2010;6(12):1833-1836. doi:10.2217/fon.10.155.

21.

Takekoshi

Sullivan

Hwang

. Inflammation and tumor microenvironment in lymph node metastasis. Cancers. 2011;3(1):927-944. doi:10.3390/cancers3010927.

22.

Cheong

Hong

Jung

. The diagnostic predictive value of neutrophil-to-lymphocyte ratio in thyroid cancer adjusted for tumor size. PLoS One. 2021;16(5):e0251446. doi:10.1371/journal.pone.0251446.

23.

Ozmen

Timur

Calik

, et al. Neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) may be superior to C-reactive protein (CRP) for predicting the occurrence of differentiated thyroid cancer. Endocr Regul. 2017;51(3):131-136. doi:10.1515/enr-2017-0013.

24.

Kim

Park

Jeong

, et al. Prognostic importance of baseline neutrophil to lymphocyte ratio in patients with advanced papillary thyroid carcinomas. Endocrine. 2014;46(3):526-531. doi:10.1007/s12020-013-0089-6.

25.

Sakurai

Ohira

Tamura

, et al. Predictive potential of preoperative nutritional status in long-term outcome projections for patients with gastric cancer. Ann Surg Oncol. 2016;23(2):525-533. doi:10.1245/s10434-015-4814-7.

26.

Yang

Gao

Song

, et al. The prognostic nutritional index is a predictive indicator of prognosis and postoperative complications in gastric cancer: a meta-analysis. Eur J Surg Oncol. 2016;42(8):1176-1182. doi:10.1016/j.ejso.2016.05.029.

27.

Huang

Balavarca

van der Geest

, et al. Development and validation of a prognostic model to predict the prognosis of patients who underwent chemotherapy and resection of pancreatic adenocarcinoma: a large international population-based cohort study. BMC Med. 2019;17(1):66. doi:10.1186/s12916-019-1304-y.

28.

Zhou

Wan

Luo

. Construction and validation of prognostic markers of liver cancer based on autophagy genes. Anti Cancer Agents Med Chem. 2021;21(14):1921-1930. doi:10.2174/1871520621666210329100052.