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CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer

  • Gastrointestinal
  • Published:
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Abstract

Objectives

The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC).

Methods

A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrap** validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index.

Results

LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96–54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39–25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11–17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01–0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06–0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89.

Conclusions

There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection.

Key Points

• LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer.

• CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors.

• The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.

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Abbreviations

AUC:

Area under the curve

CA19-9:

Carbohydrate antigen 19–9

CA72-4:

Carbohydrate antigen 72–4

CEA:

Carcinoembryonic antigen

CECT:

Contrast-enhanced computed tomography

CI:

Confidence interval

CRC:

Colorectal cancer

IQR:

Interquartile range

LN:

Lymph node

LNM:

Lymph node metastasis

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

SD:

Standard deviation

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Funding

This study was supported by the Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (grant number U22A20345), the National Science Fund for Distinguished Young Scholars (grant number 81925023), the National Natural Scientific Foundation of China (grant number 82072090), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (grant number 2022B1212010011), High-level Hospital Construction Project (DFJHBF202105), and Science and Technology Projects in Guangzhou (grant numbers 202201020001 and 202201010513).

Author information

Author notes

  1. Suyun Li, Zhenhui Li, Li Wang, and Mimi Wu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, China

    Suyun Li, Mimi Wu, **aobo Chen, Yao Xu, Yanting Liang & Zaiyi Liu

  2. School of Medicine, South China University of Technology, Guangzhou, 510006, China

    Suyun Li, Yao Xu & Zaiyi Liu

  3. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Suyun Li, **aobo Chen, Yao Xu, Yanting Liang & Zaiyi Liu

  4. Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China

    Zhenhui Li

  5. Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, 511400, China

    Li Wang & Mengyi Dong

  6. Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China

    Chutong He & **n Chen

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  1. Suyun Li
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Corresponding authors

Correspondence to **n Chen or Zaiyi Liu.

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Guarantor

The scientific guarantor of this publication is Zaiyi Liu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Suyun Li, Mimi Wu, and **n Chen kindly provided statistical advice for this manuscript.

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Written informed consent was waived by the Institutional Review Board.

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Study subjects or cohorts overlap

The study subjects or cohorts have never been previously reported.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Li, S., Li, Z., Wang, L. et al. CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer. Eur Radiol 33, 6861–6871 (2023). https://doi.org/10.1007/s00330-023-09688-9

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