Evaluation of the utility of a nomogram for predicting lymph node metastasis in T1 colorectal cancer in shared decision-making in clinical practice: a survey-based study

Hyeon Seung Kim; Kyung Su Han; Min Wan Lee; Dae Kyung Sohn; Chang Won Hong; Dong Woon Lee; Kiho You; Sung Chan Park; Byung Chang Kim; Bun Kim; Jae Hwan Oh

doi:10.3393/ac.2025.00318.0045

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Original Article Colorectal cancer Evaluation of the utility of a nomogram for predicting lymph node metastasis in T1 colorectal cancer in shared decision-making in clinical practice: a survey-based study: Hyeon Seung Kim, Kyung Su Han, Min Wan Lee, Dae Kyung Sohn, Chang Won Hong, Dong Woon Lee, Kiho You, Sung Chan Park, Byung Chang Kim, Bun Kim, Jae Hwan Oh; Annals of Coloproctology 2025;41(4):303-309.
DOI: https://doi.org/10.3393/ac.2025.00318.0045
Published online: August 25, 2025

Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Korea

Correspondence to: Kyung Su Han, MD Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea Email: kshan@ncc.re.kr

Current affiliation of Hyeon Seung Kim: Department of Colorectal Surgery, Hansol Hospital, Seoul, Korea
Current affiliation of Min Wan Lee: Department of Surgery, Suhgwang General Hospital, Gwangju, Korea

• Received: March 21, 2025 • Revised: April 28, 2025 • Accepted: April 29, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract
INTRODUCTION
METHODS
RESULTS
DISCUSSION
ARTICLE INFORMATION
REFERENCES

Abstract

Purpose
In 2019, we reported a novel nomogram to predict lymph node metastasis (LNM) in T1 colorectal cancer. Herein, we conducted a survey-based study to evaluate the clinical utility of this nomogram in determining the need for additional surgery after endoscopic resection for high-risk T1 colorectal cancer.
Methods
A survey was conducted among 77 members of the Korean Society of Coloproctology and 25 members of the Korean Society of Gastrointestinal Endoscopy. The survey assessed decision-making regarding additional surgery after endoscopic resection for high-risk T1 colorectal cancer according to various predicted LNM rates (3%, 10%, and 27%) and tumor locations (anal verge [AV] 2, 7, and 25 cm). Additionally, participants provided feedback regarding the reliability, usefulness, and potential adoptability of the prediction model in patient counseling.
Results
Of the 2,314 surveys distributed, 102 responses were analyzed. A trend was observed in which tumors located closer to the anus and associated with a lower predicted risk of LNM were less likely to lead respondents to opt for surgery (e.g., AV 2 cm and 3% of predicted LNM risk, 21.6% opt for surgery vs. AV 25 cm and 27% of predicted LNM risk, 98.0% opt for surgery). Additionally, 94.1% of the respondents reported that the prediction model would be helpful in clinical decision-making and patient counseling.
Conclusion
Our findings suggest that the nomogram is an effective and reliable tool for guiding treatment strategies and enhancing consultations in patients with T1 colorectal cancer.
Keywords: Colorectal neoplasms; Lymph nodes; Neoplasm metastasis; Nomograms; Surveys and questionnaires

INTRODUCTION

In Korea, colorectal cancer is the fourth most prevalent cancer [1], and from 2000 to the 2020s, the number of colon cancer-related deaths per 100,000 individuals has increased [2]. Early detection of colorectal cancer is important to improve patient outcomes [3]. For T1 colorectal cancer, also referred to as early-stage colorectal cancer, current guidelines recommend curative surgery if one or more risk factors for lymph node metastasis (LNM) are observed, even in the presence of clear resection margins after endoscopic or local resection [4–7]. However, LNM is not detected in over 90% of these surgeries [8]. Owing to the functional and quality of life issues associated with colorectal surgery, a model to accurately predict LNM is required.

Research on prediction models using nomograms has been conducted worldwide, including in Japan [9–12]. In 2019, we developed a novel nomogram-based predictive model for LNM in T1 colorectal cancer, designed for and currently applied in outpatient clinics [13]. However, although these prediction models provide numerical probabilities of LNM based on different risk factors, the decision to opt for additional surgery or close observation remains at the discretion of clinicians. Furthermore, studies reporting these prediction models do not offer appropriate criteria or evidence for recommending additional surgery based on predicted LNM rates [9, 11, 12]. Additionally, clinical research utilizing these prediction models is limited.

The aim of this study was to investigate whether our prediction model influenced the decision-making process of specialists regarding the management of T1 colorectal cancer treated with local resection, considering the tumor location and probability of LNM according to risk factors, and assess the clinical utility of the model. By validating the clinical utility of the prediction model among experts, we can provide patients with information about the probability of LNM during consultations, facilitating shared decision-making between physicians and patients.

METHODS

Ethics statement

This study was approved by the Institutional Review Board of the National Cancer Center, Korea (No. NCC 2017-0189). Informed consent was obtained from all the participants.

Survey design

This study was designed as a survey, and a webpage was created to allow survey participants to utilize the prediction model on an online platform. The survey comprised questions regarding 2 major topics in accordance with the research purpose and was conducted by emailing a Google Forms questionnaire (Google) to members of the Korean Society of Coloproctology and the Korean Society of Gastrointestinal Endoscopy. The survey results were collected between January 11 and 29, 2024.

The survey was structured as follows. One section included details regarding the survey topic, background, and reward for participation, and requested consent for participation in the survey. Additionally, items requesting information regarding age, sex, place of work, department, and career history were included to obtain demographic data of the survey participants.

In another section, we included 3 questions covering 2 major topics. The first and second questions focused on the choice of the treatment approach, this question is illustrated with a simple schema (Fig. 1), and the last question addressed the clinical utility of the prediction model. The English translations of the 3 core questions are as follows:

(1) What would be the different treatment plans used for several scenarios with different predicted LNM risks (3%, 10%, and 27%) and tumor locations (AV 2, 7 and 25 cm)? The AV distances were classified based on postoperative functional outcomes: AV 2 cm carries a risk of impaired sphincter function, AV 7 cm carries a risk of low anterior resection syndrome, and AV 25 cm has no such risk.

(2) What is your cutoff value for the predicted LNM risk to decide on additional surgery in various scenarios with different tumor locations (AV 2, 7, and 25 cm)?

(3) What are your thoughts on the nomogram regarding its reliability and usefulness? Do you intend to adopt this method in patient counseling? Do you think this prediction model will be helpful during consultations with patients?

The first core survey question was designed to evaluate clinicians’ decisions regarding treatment direction based on the prediction model. For this, we devised a hypothetical scenario in which a previously healthy 55-year-old adult underwent local resection for pathologic T1 colorectal cancer with a clear resection margin. Subsequently, we inquired what treatment policy should be established in the following combinations of 3 risk factors and 3 tumor location conditions: the presence of only deep submucosal invasion corresponding to SM2 or SM3 and a 3% predicted LNM risk according to the prediction model; the presence of only vascular invasion and a 10% predicted LNM risk; and the presence of all 3 risk factors (tumor budding, deep submucosal invasion, and vascular invasion) and a 27% predicted LNM risk. The tumor locations were set at 2, 7, and 25 cm from the AV to examine how the treatment policy changed depending on the distance from the AV.

In the second core question, we asked what predicted LNM risk would be used to decide whether to perform additional radical surgery, depending on the location of each tumor.

To evaluate the clinical usability of the prediction model, the third core question addressed the following 3 aspects: the reliability of the prediction model, willingness to use it, and its helpfulness in counseling patients.

Statistical analysis

The data are presented as frequencies and percentages. Independent-sample t-tests were used to compare the choices of surgeons and gastroenterologists regarding the minimum metastasis probability for radical resection, and the Kolmogorov-Smirnov test was used to assess the normality of the data. Statistical significance was set at P<0.05. Analyses were performed using R ver. 4.1.2 (R Foundation for Statistical Computing).

RESULTS

Of the 2,314 emails sent, 117 responses (5.1%) were received. After excluding 12 inappropriate responses and 3 incomplete responses, 102 responses were analyzed (Fig. 2). The demographic details of the participants are summarized in Table 1. Most respondents were male (80.4%) and aged over 40 years (71.6%). Additionally, most respondents were colorectal surgeons (75.5%), and the remainder were gastroenterologists (24.5%). Approximately 90% worked in general, university, or specialized colorectal hospitals, indicating a suitable participant pool for this survey.

Treatment choices according to risk factors and tumor locations were as follows. First, in the presence of only deep submucosal invasion (a predicted LNM risk of 3%), the closer the tumor was to the AV, the more likely clinicians were to opt for follow-up observation than surgical treatment; only 21.6% of the respondents responded that they would conduct additional surgery for the tumors at the AV 2 cm (Fig. 3A). Second, in the presence of only vascular invasion and a predicted LNM rate of 10%, the rate of follow-up observation increased depending on the location of the tumor. In case where vascular invasion was present (a predicted LNM risk of 10%), additional surgery was much more preferred compared to when the predicted LNM risk was 3%; 60.8 % of the respondents responded that they would conduct additional surgery for the tumors at the AV 2 cm (Fig. 3B). Lastly, when deep submucosal invasion, vascular invasion, and tumor buddings were present (a predicted LNM risk of 27%), most respondents opted for additional surgery (Fig. 3C).

Most participants responded positively to the question of whether the prediction rate suggested by the model influenced treatment decision (Fig. 4). The closer the tumor is to the anus, the higher the probability threshold of predicted LNM for considering surgery becomes. In other words, the lower the tumor location and the lower the predicted risk of LNM, the less respondents opted for surgery. And, colorectal surgeons showed a more proactive tendency to choose surgery compared to medical gastroenterologists; however the difference was not significant (Table 2).

Regarding the clinical utility of the prediction model, 65.7% of the respondents trusted the model, and most (94.1%) provided positive feedback regarding its usefulness in clinical practice and patient consultations (Fig. 5).

DISCUSSION

To the best of our knowledge, our nomogram-based model for predicting T1 colorectal cancer LNM is the first model developed using domestic data; additionally, it is the first model to be used in clinical settings in Korea [13]. The previous predicting model classified LNM risk into only 2 categories, low (when no risk factors were present) or high (when at least one risk factor was present) [5, 14, 15]. In contrast, our new predicting model provides a numerical estimate of LNM risk for each of the 32 possible combinations of 5 risk factors, offering clinicians a broader range of options for treatment decision-making [13]. This nationwide expert survey revealed the tendency of clinicians to opt for surveillance over surgical treatment when the predicted LNM rate was low, particularly for tumors closer to the AV. Surgical treatment was preferred more often by surgeons than by gastroenterologists, except for tumors located 2 cm from the AV, where surgeons were more reluctant to operate. This may be because surgeons have more experience performing surgeries for rectal cancer close to the AV, which increases the risk of creating a diverting stoma; this may result in decreased anal function and quality of life, ultimately increasing the risk of sphincter sacrifice [16–19]. Additionally, higher predicted LNM rates are required to decide on surgery for tumors closer to the AV.

We obtained positive responses regarding the helpfulness of the prediction model in deciding treatment direction and patient counseling. Thus, the prediction model can be used for patient counseling to allow doctors and patients to make shared decisions about treatment plans, which not only increases patient satisfaction but also improves clinical outcomes by establishing treatment plans optimized for patients [20, 21]. In practice, our center uses the prediction model for patient consultations; we plan to conduct a study to determine patient satisfaction with the provision of this information and shared decision-making for treatment plans. Further research is required to improve the precision and accuracy of this prediction model.

This study has some limitations. In the questionnaire, additional information such as sphincter tone was not considered. This was owing to the intentional simplification of the questionnaire to prevent the provision of excessive information; an increased complexity of the questionnaire may hinder the identification of overall trends. Additionally, the response rate was low despite offering rewards. Email pre-notification, invitations, and reminders, as well as user-friendly survey designs, can improve response rates [22]. In the future, an on-site survey conducted at an academic conference venue may yield a higher response rate. The positive response to the reliability of the prediction model was approximately 65%, which is unsatisfactory. This may be because the prediction model included deep invasion as a factor determining the probability of LNM. The role of deep submucosal invasion as a risk factor for LNM remains controversial [23, 24]. Moreover, background adenoma, which is defined as benign adenomatous tissue contiguous to resected carcinomas, is included in the routine pathologic report of our institution; however, this may be an unfamiliar concept in other hospitals or a factor that is not routinely included in pathologic reports, which may have also affected the relatively low reliability [25]. If the prediction model is updated and validated for these factors in the future, a higher reliability may be obtained.

In conclusion, our nomogram for predicting LNM in T1 colorectal cancer is helpful for assisting physicians in making personalized treatment decisions. We plan to conduct a survey study on patients to assess its influence on shared decision-making, and further research is necessary to develop and validate the model to increase its reliability.

ARTICLE INFORMATION

Conflict of interest

No potential conflict of interest relevant to this article was reported.

Funding

This study was supported by grants from the National Cancer Center, Korea (No. NCCCDA2023-02, No. NCC2432300-1).

Acknowledgments

The research was supported by the Korean Society of Coloproctology and the Korean Society of Gastrointestinal Endoscopy, which assisted with the mailing process for the survey targeting their members.

Author contributions

Conceptualization: KSH, DKS, CWH, DWL, KHY, SCP, BCK, BK, JHO; Data curation: HSK, MWL; Formal analysis: KSH; Funding acquisition: MWL; Investigation: MWL; Methodology: all authors; Project administration: KSH; Supervision: KSH; Visualization: HSK, KSH, DKS; Writing–original draft: HSK; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Fig. 1.

Characteristics of the 9 samples used in the survey; tumor location, pathologic results, and risk of lymph node metastasis (LNM; calculated based on the pathologic results and nomogram). AV, anal verge; SM2/3, deep submucosal invasion; VI, vascular invasion; TB, tumor budding.

Fig. 2.

Study flowchart.

Fig. 3.

Determination rate of additional surgery according to lymph node metastasis (LNM) risk and location (anal verge [AV] 2, 7, and 25 cm). (A) LNM risk 3% (deep submucosal invasion). (B) LNM risk 10% (vascular invasion). (C) LNM risk 27% (deep submucosal invasion, vascular invasion, and tumor budding).

Fig. 4.

Influence of the prediction model on treatment decisions: “Did the prediction model influence your decision on treatment direction?” (A) Lymph node metastasis (LNM) risk 3% (deep submucosal invasion). (B) LNM risk 10% (vascular invasion). (C) LNM risk 27% (deep submucosal invasion, vascular invasion, and tumor budding).

Fig. 5.

Response to questions regarding reliability and utility of the prediction model in clinical practice and patient consultations. (A) Trust it? (B) Will you use it? (C) Helpful in consultation?

Table 1.

Demographic data of the participants of the survey

Characteristic	No. of participants (%)
Sex
Female	20 (19.6)
Male	82 (80.4)
Age (yr)
30–39	29 (28.4)
40–49	44 (43.1)
50–59	27 (26.5)
≥60	2 (2.0)
Workplace
Primary hospital	9 (8.8)
Secondary hospital	22 (21.6)
Secondary (specialized) hospital	12 (11.8)
Tertiary hospital	57 (55.9)
Other	2 (2.0)
Specialization
Colorectal surgery	77 (75.5)
Gastroenterology	25 (24.5)

Percentages may not total 100 due to rounding.

Table 2.

Risk percentage of predicted LNM to consider surgery

Tumor location	Minimum risk of LNM (%, mean)			P-value
Tumor location	Total (n=102)	Surgeon (n=77)	Gastroenterologist (n=25)	P-value
AV 25 cm	7.0	6.4	9.0	0.30
AV 7 cm	8.2	7.5	10.5	0.27
AV 2 cm	13.9	13.7	14.5	0.76

LNM, lymph node metastasis; AV, anal verge.

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4. Miyachi H, Kudo SE, Ichimasa K, Hisayuki T, Oikawa H, Matsudaira S, et al. Management of T1 colorectal cancers after endoscopic treatment based on the risk stratification of lymph node metastasis. J Gastroenterol Hepatol 2016;31:1126–32. Article PubMed PDF
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7. Ebbehøj AL, Jørgensen LN, Krarup PM, Smith HG. Histopathological risk factors for lymph node metastases in T1 colorectal cancer: meta-analysis. Br J Surg 2021;108:769–76. Article PubMed PDF
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Evaluation of the utility of a nomogram for predicting lymph node metastasis in T1 colorectal cancer in shared decision-making in clinical practice: a survey-based study

Ann Coloproctol. 2025;41(4):303-309. Published online August 25, 2025

DOI: https://doi.org/10.3393/ac.2025.00318.0045

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Evaluation of the utility of a nomogram for predicting lymph node metastasis in T1 colorectal cancer in shared decision-making in clinical practice: a survey-based study

Fig. 1. Characteristics of the 9 samples used in the survey; tumor location, pathologic results, and risk of lymph node metastasis (LNM; calculated based on the pathologic results and nomogram). AV, anal verge; SM2/3, deep submucosal invasion; VI, vascular invasion; TB, tumor budding.

Fig. 2. Study flowchart.

Fig. 3. Determination rate of additional surgery according to lymph node metastasis (LNM) risk and location (anal verge [AV] 2, 7, and 25 cm). (A) LNM risk 3% (deep submucosal invasion). (B) LNM risk 10% (vascular invasion). (C) LNM risk 27% (deep submucosal invasion, vascular invasion, and tumor budding).

Fig. 4. Influence of the prediction model on treatment decisions: “Did the prediction model influence your decision on treatment direction?” (A) Lymph node metastasis (LNM) risk 3% (deep submucosal invasion). (B) LNM risk 10% (vascular invasion). (C) LNM risk 27% (deep submucosal invasion, vascular invasion, and tumor budding).

Fig. 5. Response to questions regarding reliability and utility of the prediction model in clinical practice and patient consultations. (A) Trust it? (B) Will you use it? (C) Helpful in consultation?

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Evaluation of the utility of a nomogram for predicting lymph node metastasis in T1 colorectal cancer in shared decision-making in clinical practice: a survey-based study

Characteristic	No. of participants (%)
Sex
Female	20 (19.6)
Male	82 (80.4)
Age (yr)
30–39	29 (28.4)
40–49	44 (43.1)
50–59	27 (26.5)
≥60	2 (2.0)
Workplace
Primary hospital	9 (8.8)
Secondary hospital	22 (21.6)
Secondary (specialized) hospital	12 (11.8)
Tertiary hospital	57 (55.9)
Other	2 (2.0)
Specialization
Colorectal surgery	77 (75.5)
Gastroenterology	25 (24.5)

Tumor location	Minimum risk of LNM (%, mean)			P-value
Tumor location	Total (n=102)	Surgeon (n=77)	Gastroenterologist (n=25)	P-value
AV 25 cm	7.0	6.4	9.0	0.30
AV 7 cm	8.2	7.5	10.5	0.27
AV 2 cm	13.9	13.7	14.5	0.76

Table 1. Demographic data of the participants of the survey

Percentages may not total 100 due to rounding.

Table 2. Risk percentage of predicted LNM to consider surgery

LNM, lymph node metastasis; AV, anal verge.