
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312368
MD-D-24-03485
00069
10.1097/MD.0000000000039683
3
6800
Research Article
Observational Study
Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study
https://orcid.org/0000-0003-2341-3193
Wang Dawei MM 15530392140@163.com
a
Shang Zeyu MB szy131031@163.com
b
Chen Rong MB 2723710176@qq.com
c
Yang Yue MB hiangfei@126.com
c
Su Yaying MM hiweiyulei@163.com
d
Jia Peng MM 1220673047@qq.com
e
Liu Yanfang MB 93767765@qq.com
f
https://orcid.org/0000-0002-7283-5790
Yang Fei MD g*
a Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
b University College London, London, United Kingdom
c Department of Medicine, Hebei North University, Zhangjiakou, China
d Department of Nuclear medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
e Department of Medical Imaging, Beijing Huairou Hospital, Beijing, China
f Department of Operating rooms, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
g Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.
* Correspondence: Fei Yang, Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, China (e-mail: hiyangfei@126.com).
20 9 2024
20 9 2024
103 38 e3968308 4 2024
21 8 2024
23 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

To explore the feasibility and application value of texture analysis based on computed tomography (CT) for predicting the differentiation of esophageal squamous cell carcinoma (ESCC). Patients diagnosed with ESCC who underwent chest contrast-enhanced CT before treatment were selected. Based on the pathological results, the patients were stratified into poorly differentiated and moderately well-differentiated groups. FireVoxel software was used to analyze the region of interest based on venous phase CT images. Texture parameters including the mean, median, standard deviation (SD), inhomogeneity, skewness, kurtosis, and entropy were obtained automatically. Differences in the texture parameters and their relationship with the degree of differentiation between the 2 groups were analyzed. The value of CT texture parameters in identifying poor differentiation and moderate-well differentiation of esophageal cancer was analyzed using the ROC curve. A total of 48 patients with ESCC were included, including 24 patients in the poorly differentiated group and 24 patients in the moderate-well-differentiated group. There were negative correlations between SD, inhomogeneity, entropy, and the degree of differentiation of esophageal cancer (P < .05). The correlation of inhomogeneity was the highest (r = −0.505, P < .001). SD, inhomogeneity, and entropy could effectively distinguish between the poorly and moderately well-differentiated groups, with statistically significant differences between the 2 groups (P < .05). The best critical values for SD, inhomogeneity, and entropy were 17.538, 0.017, and 3.917, respectively. The areas under the ROC curve were 0.793, 0.792, and 0.729, respectively, with the SD and inhomogeneity being the best. The application of texture analysis on venous phase CT images holds promise as a method for forecasting the degree of differentiation in esophageal cancers, which could significantly contribute to the preoperative noninvasive evaluation of tumor differentiation.

cell differentiation
esophageal squamous cell sarcinoma
texture analysis
tomography
X-ray computed
Hebei Medical Science Research Project20220585 Dawei WangOPEN-ACCESSTRUE
==== Body
pmc 1. Introduction

In recent years, the degree of differentiation has received increasing attention as an important pathological index for esophageal cancer.[1] Researches has demonstrated that precise assessment of the degree of differentiation is instrumental in developing tailored treatment plans for individuals with esophageal cancer and in predicting their prognosis. Patients with the same treatment methods may have different therapeutic effects owing to different degrees of differentiation.[2,3] At present, the preoperative judgment of the degree of differentiation of esophageal cancer still depends on gastroscopic biopsy, although gastroscopy still has many defects such as discomfort of examination, random error of sampling, and difficulty in reflecting the overall situation of the tumor.[4]

Computed tomography (CT) has been widely used in the routine diagnosis and staging of esophageal cancer; however, it is rarely used to study the differential diagnosis of esophageal cancer.[5] Texture analysis has been a rapidly developing image post-processing technology in recent years. An increasing number of studies have shown that texture analysis can noninvasively evaluate the inhomogeneity of various tumors in vitro, such as tumor characteristics, prognosis, and prediction of therapeutic effects.[6] According to a literature review, this is the first time texture analysis based on CT-enhanced venous phase images has been applied to evaluate the differentiation of esophageal carcinoma before surgery. We hypothesized that texture analysis based on CT-enhanced venous phase images could identify the degree of differentiation of esophageal squamous cell carcinoma (ESCC), which could be used as a noninvasive method to quantitatively evaluate the differentiation of esophageal carcinoma before surgery.

2. Materials and methods

2.1. Subjects

Local ethics committee approval was obtained for this retrospective study and the need for informed consent from the patients was waived. Patients with ESCC diagnosed by pathology in our hospital between December 2016 and December 2019 were selected. Most patients with progressive dysphagia are the main symptoms, with or without nausea, loss of appetite, chest and back pain, hoarseness, and weight loss.

The inclusion criteria were as follows: esophageal cancer mass could be seen in the venous phase image of the CT-enhanced scan before surgery, contrast-enhanced CT examination preoperatively (including arterial and venous phases), and a diagnosis of ESCC confirmed by postoperative pathology.

The following criteria were excluded: patients who had undergone any local or systemic treatment prior to CT examination or surgery, including radiation therapy, chemotherapy, or targeted therapy; patients with poor CT image quality that precluded adequate post-processing due to artifacts; patients with tumors with a minimum diameter of <5 mm, which was deemed insufficient to encompass a viable region of interest (ROI) for analysis; patients with multifocal or diffuse esophageal cancer; and concurrent benign esophageal lesions or other malignant tumors.

Based on the pathological degree of tumor differentiation (low differentiation, middle differentiation, and high differentiation), the patients were divided into 2 groups: poorly differentiated group; and moderate-well-differentiated group.

2.2. Scanning protocol

CT imaging was performed using a 320-row CT scanner (Aquilion ONE; Toshiba Medical Systems Corporation, Tokyo, Japan). The scanning protocol used a tube voltage of 120 kV and the tube current was automatically adjusted to maintain consistency. The dosage of the contrast medium was tailored to each patient’s body weight to ensure an optimal concentration for imaging clarity. The patients received an injection of 1.2 mL/kg isotonic contrast agent (iodixanol, 320 mg I/mL, produced by the Yangtze River Pharmaceutical Group in China) at a flow rate of 3 mL/s, followed by a flush of 30 mL 0.9% saline solution administered at the same rate. A dual-shot injector system (OptiVantage DH, Mallinckrodt Tyco Healthcare) was employed to deliver the contrast medium and saline solution through an 18-gauge intravenous catheter placed in the antecubital vein. Contrast-enhanced Computerized tomography imaging was performed in a dual-phase sequence: arterial phase (captured approximately 30 seconds postinjection of the contrast agent) and venous phase (obtained approximately 60 seconds postinjection). The imaging protocol used a slice thickness of 5 mm and an interslice gap of 5 mm.

2.3. Texture analysis

The venous phase images were loaded into FireVoxel software (https://wp.nyu.edu/firevoxel, USA) for texture analysis. A blinded radiologist manually segmented the regions of interest (ROIs) on the CT images, which were then reviewed by an experienced radiologist (10 years of experience) to reach a consensus. Precision was ensured by excluding surrounding non-tumor tissues, water, air, and artifacts and by clearly delineating the lesion edge at least 2 mm from the surrounding normal tissue, ensuring precise exclusion of adjacent non-tumor tissues such as water or air. Once the ROIs were delineated, CT texture analysis was conducted, resulting in the generation of 6 texture parameters derived from the Hounsfield Unit values.

2.4. Pathological analysis

Patients who met the criteria underwent resectable surgery performed by 2 experienced thoracic surgeons with 25 and 10 years of experience in general thoracic surgery. After surgical resection, histopathological evaluation of the excised specimens was performed by a gastrointestinal pathologist with 6 years of expertise. The differentiation grades of the specimens were meticulously assessed and documented in accordance with the criteria set forth by the American Joint Committee on Cancer (7th edition).

2.5. Statistical processing

Statistical analyses were performed with SPSS 22.0 (Chicago). The Shapiro–Wilk test was used to assess the normality assumption for each parameter. The data were analyzed using the χ2 test. Spearman correlation analysis was used to determine the correlation between the above texture parameters and the different degrees of differentiation of esophageal cancer. An independent sample t-test or Mann–Whitney U test was used to compare the differences in texture parameters between the poorly differentiated group and the moderate-well-differentiated group. The ROC curve was used to analyze the efficacy of various texture parameters in differentiating the degree of differentiation of esophageal cancer. A 2-tailed P value <.05 was considered statistically significant.

3. Results

3.1. General clinical information of patients

A total of 48 patients with esophageal cancer were included in this study. There were 24 cases in the poorly differentiated group, including 20 males and 4 females, with an average age of 64.9 ± 5.7 years old, and 24 cases in the moderate-well-differentiated group, including 21 males and 3 females, with an average age of 63.5 ± 8.4 years. There was no significant difference in sex and age between the 2 groups (P = 1.000), and there was no significant difference in age between the 2 groups (t = −0.683, P = .498).

There were 8 cases in the upper esophagus, 18 in the middle esophagus, and 22 in the lower esophagus.

3.2. Correlation between CT texture parameters and different differentiation degrees of esophageal carcinoma

The standard deviation (SD), inhomogeneity, and entropy were negatively correlated with the degree of differentiation of esophageal carcinoma, and the correlation of inhomogeneity was the highest (r = −0.505, P < .001) (Table 1). There was no correlation between other texture parameters (mean, median, skewness, and kurtosis) and the degree of differentiation of esophageal carcinoma(P > .05).

Table 1 Correlation between CT texture parameters and the degree of differentiation of esophageal carcinoma.

Texture parameters	Correlation coefficient	P	
Mean	0.033	.823	
Median	0.045	.760	
SD	−0.508*	<.001	
Inhomogenity	−0.505*	<.001	
Skewness	−0.066	.655	
Kurtosis	0.268	.066	
Entropy	−0.397*	.005	
* Statistically significant.

3.3. The differences of CT texture parameters between poor differentiated group and moderate-well-differentiated group

There were significant differences in the SD, inhomogeneity, and entropy between the poorly differentiated and moderately well-differentiated groups (P < .05) (Table 2). The SD, inhomogeneity, and entropy in the poorly differentiated group were higher than those in the moderately well-differentiated group (Figs. 1 and 2). There were no significant differences in the mean, median, skewness, or kurtosis between the 2 groups (p>= 0.05).

Table 2 Comparison of texture parameters between poor differentiated group and moderate-well-differentiated group.

Group	Mean	Median	SD	Inhomogenity	Skewness	Kurtosis	Entropy	
Moderate and well	1103.13 ± 11.29	1104.00 ± 11.68	16.22 ± 2.62	0.0147 ± 0.0024	−0.33 ± 0.36	0.79 ± 0.56	3.76 ± 0.16	
Poor	1103.17 ± 13.90	1103.92 ± 14.05	18.97 ± 2.37	0.0171 ± 0.0022	-0.31 ± 0.34	0.68 ± 1.10	3.89 ± 0.15	
Z/t	−0.011	0. 022	−3.818*	−3.687*	−0.241	−1.835	−3.016*	
P	.991	.982	<.001	.001	.811	.066	.004	
* Statistically significant.

Figure 1. The CT texture parameters in the moderate-well-differentiated group.

Figure 2. The CT texture parameters in the poor differentiated group.

3.4. Diagnostic efficiency of CT texture parameters in different differentiated esophageal carcinoma

The best critical values of SD, inhomogeneity, and entropy were 17.538, 0.017, and 3.917, respectively, and the areas under the curve (AUC) were 0.793, 0.792, and 0.729, respectively (Table 3 and Figure 3). The AUCs of SD and inhomogeneity were higher than those of entropy, which had a certain value in the differentiation of esophageal cancer.

Table 3 Diagnostic efficiency of CT texture parameters in different differentiated esophageal carcinoma.

Texture parameters	Critical value	AUC	Sensitivity (%)	Specificity (%)	P	
SD	17.538	0.793	79.2	75	<.001	
Inhomogenity	0.016	0.792	70.8	83.3	.001	
Entropy	3.842	0.729	66.7	75	.006	

Figure 3. ROC curve of CT texture parameters in differential diagnosis of poor and moderate-well-differentiated esophageal cancer.

4. Discussion

We confirmed the correlations between texture parameters based on CT-enhanced venous phase images and the degree of differentiation of esophageal cancer. There were negative correlations between SD, inhomogeneity, entropy, and the degree of differentiation of esophageal cancer. The SD, inhomogeneity, and entropy in the poorly differentiated group were higher than those in the moderately well-differentiated group. Our research suggests that SD and inhomogeneity may be optimal indicators for distinguishing between poorly and moderately well-differentiated esophageal carcinomas. We confirmed that texture analysis based on CT-enhanced venous phase images could identify the degree of differentiation of ESCC and could be used as a noninvasive method to quantitatively evaluate the differentiation of esophageal carcinoma before surgery.

Non-contrast-enhanced CT images mainly reflect different tissue components, including cell density, necrosis, cystic degeneration, and bleeding. Contrast-enhanced CT images can also reflect the inhomogeneity of tumor blood supply. The degree of tumor enhancement on contrast-enhanced CT images is closely related to the density and structure of the microvasculature.[7,8] The higher the degree of malignancy of esophageal cancer, the poorer the degree of differentiation, and the more abundant and incomplete the neovascularization in the tumor. The iodine content of the tumor was different from that of a normal esophagus after iodine injection, which resulted in a change in the gray and density values of the tumor on the CT images. The distribution of contrast agents in the tumor at different phases on contrast-enhanced CT images differs, which directly affects the pixel density value of the CT image.[8,9] Some scholars believe that the enhanced state of CT in the portal phase reflects neovascularization with more functional insufficiency, and its higher permeability leads to greater retention of contrast media in the interstitial space.[7] Djuric-Stefanovic A et al found that the average attenuation of the esophageal wall measured on CT-enhanced venous phase images had a stronger diagnostic performance for predicting pathologic complete regression.[10] Zhang YH et al measured the tumor volume in the portal-venous phase using semiautomatic and manual segmentation.[11] This was the theoretical basis for the application of venous phase CT imaging for texture analysis in this study. Therefore, in this study, texture analysis was based on venous phase CT images to explore the relationship between inhomogeneity and the degree of differentiation of esophageal cancer, which has not yet been reported.

We found that SD, inhomogeneity, and entropy were negatively correlated with the degree of esophageal cancer differentiation (P < .05). There were significant differences in the SD, inhomogeneity, and entropy between the poorly differentiated and moderately well-differentiated groups (P < .05). The results showed that there were differences in CT texture features among different degrees of differentiation of esophageal carcinoma, and that SD, inhomogeneity, and entropy could distinguish the different degrees of differentiation of ESCC.

Inhomogeneity and entropy reflect the complexity and inhomogeneity of the pixel distribution in a lesion. The more complex the internal structure of the tumor, the greater are the values of inhomogeneity and entropy. Liu et al reported that CT texture analysis has a great potential for predicting the degree of gastric cancer differentiation.[12] Some scholars have found that entropy and SD are helpful in predicting the degree of lung cancer differentiation.[13] Studies have found that the SD of CT texture analysis parameters may help predict the histological grade of gallbladder cancer.[14] Other scholars have found a significant difference in SD between high- and low-grade endometrial cancers in diffusion-weighted imaging images.[15] The above conclusions are consistent with this study. The results of this study showed that the areas under the ROC curve of SD and inhomogeneity were high and the diagnostic efficiency was excellent, suggesting that these 2 parameters could effectively distinguish the different degrees of differentiation of ESCC.

Skewness and kurtosis reflect the asymmetry and steepness of the distribution of mean voxel values in the ROI. Studies have suggested that differences in skewness and kurtosis are helpful in determining the degree of malignancy of tumors.[4,16,17] This study showed that there was no significant difference in skewness and kurtosis among different degrees of esophageal cancer differentiation. The possible reasons were insufficient sample size, examination methods, and scanning parameters (arterial and portal phases). We could enlarge the sample size and choose different examination methods (MRI and PET/CT) to explore the value of these texture parameters in differentiating different differentiated esophageal cancers.

Furthermore, the pharmacokinetics of CT contrast agents may vary among individuals and there may be errors in the measurement results.

This study had several limitations that warrant consideration. First, the investigation was conducted exclusively among patients with ESCC, which limits the generalizability of the findings to adenocarcinoma. Second, the current methodology involves manual tracing of the ROI, a process that could potentially be enhanced by incorporating automatic segmentation algorithms in future research to facilitate more standardized and efficient lesion demarcation. Moreover, variability in the pharmacokinetics of CT contrast agents across patients may introduce measurement inaccuracies, highlighting the potential benefit of individualized imaging protocols in reducing these errors. The small sample size is another limitation of this preliminary study, and our research team plans to expand the sample size further to advance radiomics research.

In summary, texture analysis derived from venous phase CT images is a promising method for predicting the degree of carcinoma differentiation. This approach does not require specialized CT scanning techniques or subjects patients to additional radiation, making it a viable and potentially widespread tool in the routine clinical assessment of patients with esophageal cancer.

Author contributions

Conceptualization: Fei Yang.

Data curation: Rong Chen.

Formal analysis: Yue Yang.

Funding acquisition: Dawei Wang.

Project administration: Yanfang Liu.

Software: Zeyu Shang, Yaying Su.

Supervision: Peng Jia.

Writing – original draft: Dawei Wang.

Writing – review & editing: Zeyu Shang, Fei Yang.

Abbreviations:

AUC area under the curve

CT computed tomography

ESCC esophageal squamous cell carcinoma

ROC receiver operating curve

ROI region of interest

SD standard deviation

This study was supported by the Hebei Medical Science Research Project (Grant no. 20220585).

Ethical approval for this study was obtained from the Institutional Review Board of the First Affiliated Hospital of Hebei North University (K2021161). Written informed consent for participation was not deemed necessary for this study, as it adhered to national legislation and institutional guidelines.

The authors declare no conflict of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Wang D, Shang Z, Chen R, Yang Y, Su Y, Jia P, Liu Y, Yang F. Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study. Medicine 2024;103:38(e39683).

DW and ZS have contributed equally to the work.
==== Refs
References

[1] Ohashi S Miyamoto S Kikuchi O Goto T Amanuma Y Muto M . Recent advances from basic and clinical studies of esophageal squamous cell carcinoma. Gastroenterology. 2015;149 :1700–15.26376349
[2] Wang Y Tian W Tian S He L Xia J Zhang J . Spectral CT: a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma. BMC Med Imaging. 2023;23 :110.37612644
[3] Cheng L Wu L Chen S Ye W Liu Z Liang C . CT-based radiomics analysis for evaluating the differentiation degree of esophageal squamous carcinoma. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2019;44 :251–6.30971516
[4] Liu S Zheng H Pan X . Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness. J Thorac Dis. 2017;9 :4724–32.29268543
[5] Jayaprakasam VS Yeh R Ku GY . Role of imaging in esophageal cancer management in 2020: update for radiologists. AJR Am J Roentgenol. 2020;215 :1072–84.32901568
[6] Hayano K Tian F Kambadakone AR . Texture analysis of non-contrast-enhanced computed tomography for assessing angiogenesis and survival of soft tissue sarcoma. J Comput Assist Tomogr. 2015;39 :607–12.25793653
[7] Chen XH Ren K Liang P Chai Y-R Chen K-S Gao J-B . Spectral computed tomography in advanced gastric cancer: can iodine concentration non-invasively assess angiogenesis? World J Gastroenterol. 2017;23 :1666–75.28321168
[8] Yano Y Yoshimatsu K Yokomizo H Sagawa M Itagaki H Naritaka Y . Enhancement of the marginal area in colorectal cancer liver metastasis on computed tomography correlates with microvessel density and clinicopathological factors. Anticancer Res. 2019;39 :1301–8.30842162
[9] Umeoka S Koyama T Togashi K . Esophageal cancer: evaluation with triple-phase dynamic CT-initial experience. Radiology. 2006;239 :777–83.16621930
[10] Djuric-Stefanovic A Jankovic A Saponjski D . Analyzing the post-contrast attenuation of the esophageal wall on routine contrast-enhanced MDCT examination can improve the diagnostic accuracy in response evaluation of the squamous cell esophageal carcinoma to neoadjuvant chemoradiotherapy in comparison with the esophageal wall thickness. Abdom Radiol (NY). 2019;44 :1722–33.30758534
[11] Zhang YH Fischer MA Lehmann H . Computed tomography volumetry of esophageal cancer - the role of semiautomatic assessment. BMC Med Imaging. 2019;19 :17.30767773
[12] Liu S Liu S Ji C . Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol. 2017;27 :4951–9.28643092
[13] Digumarthy SR Padole AM Lo GR . CT texture analysis of histologically proven benign and malignant lung lesions. Medicine (Baltim). 2018;97 :e11172.
[14] Gupta P Rana P Ganeshan B . Computed tomography texture-based radiomics analysis in gallbladder cancer: initial experience. Clin Exp Hepatol. 2021;7 :406–14.35402717
[15] Woo S Cho JY Kim SY Kim SH . Histogram analysis of apparent diffusion coefficient map of diffusion-weighted MRI in endometrial cancer: a preliminary correlation study with histological grade. Acta Radiol. 2014;55 :1270–7.24316663
[16] Xing P Chen L Yang Q . Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging. Cancer Imaging. 2021;21 :54.34579789
[17] Guo W Liu J Wang X Yuan H . Predicting the risk of thymic tumors using texture analysis of contrast-enhanced chest computed tomography. J Comput Assist Tomogr. 2023;47 :598–602.36944121
