Chen SH, Xie C. User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management. World J Gastroenterol 2024; 30(45): 4850-4854 [PMID: 39649545 DOI: 10.3748/wjg.v30.i45.4850]
Corresponding Author of This Article
Chuan Xie, MD, Chief Doctor, Teacher, Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang 330006, Jiangxi Province, China. xcsghhz@ncu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Si-Hai Chen, Department of Gastroenterology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi Province, China
Chuan Xie, Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
Author contributions: Chen SH wrote the manuscript; Xie C revised the manuscript.
Supported by the National Natural Science Foundation of China, No. 82100599 and No. 81960112; the Jiangxi Provincial Department of Science and Technology, No. 20242BAB26122; the Science and Technology Plan of Jiangxi Provincial Administration of Traditional Chinese Medicine, No. 2023Z021; and the Project of Jiangxi Provincial Academic and Technical Leaders Training Program for Major Disciplines, No. 20243BCE51001.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chuan Xie, MD, Chief Doctor, Teacher, Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang 330006, Jiangxi Province, China. xcsghhz@ncu.edu.cn
Received: August 18, 2024 Revised: October 10, 2024 Accepted: October 28, 2024 Published online: December 7, 2024 Processing time: 86 Days and 17 Hours
Abstract
In this letter, we explore into the potential role of the recent study by Zeng et al. Rectal neuroendocrine tumours (rNETs) are rare, originate from peptidergic neurons and neuroendocrine cells, and express corresponding markers. Although most rNETs patients have a favourable prognosis, the median survival period significantly decreases when high-risk factors, such as larger tumours, poorer differentiation, and lymph node metastasis exist, are present. Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care, but their complex calculation formulae limit clinical use. Moreover, the prognostic models that have been developed for rNETs to date still have several limitations, such as insufficient sample sizes and the lack of external validation. A high-quality prognostic model for rNETs would guide treatment and follow-up, enabling the precise formulation of individual patient treatment and follow-up plans. The future development of models for rNETs should involve closer collaboration with statistical experts, which would allow the construction of clinical prediction models to be standardized and robust, accurate, and highly generalizable prediction models to be created, ultimately achieving the goal of precision medicine.
Core Tip: Rectal neuroendocrine tumours are rare, and these patients generally have a favourable prognosis; however, the presence of high-risk factors can significantly reduce patient survival. The development of predictive models such as the global alliance for trade in services score is crucial for identifying high-risk patients and guiding precise treatment strategies. However, prognostic models capable of conducting comprehensive and rigorous sample size calculations along with multicentre external validation prior to model construction remain somewhat less common. In future research, close collaboration with experts in medical statistics is imperative, with the aim of balancing clinical utility and predictive accuracy throughout the model development and optimization processes to more effectively guide stratified management of patients with rectal neuroendocrine tumours.
Citation: Chen SH, Xie C. User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management. World J Gastroenterol 2024; 30(45): 4850-4854
Neuroendocrine neoplasms are rare tumours that originate from peptidergic neurons and neuroendocrine cells[1]. These tumours undergo demonstrate neuroendocrine differentiation and express corresponding markers. Gastroenteropancreatic neuroendocrine neoplasms account for up to 60% of these tumours[2]. With advances in endoscopic technology, the rate of rectal neuroendocrine tumour (rNET) detection has significantly increased[3]. Most patients with rNETs have a good prognosis, but when patients have high-risk factors, such as larger tumours, poorer differentiation types, and lymph node metastasis, the median survival period significantly decreases[4].
Clinical prediction models are essential for guiding diagnosis and prognosis in health care[5]. In recent years, the number of studies focusing on the development of NET prognostic clinical prediction models based on public databases and multicenter data has increased. Despite the numerous guidelines for clinical prediction models[6-8], the recently created rNET models still have many limitations, including an unclear definition of target patients, insufficient sample size, collinearity among variables, and a lack of external validation. These issues have hindered the clinical application of these models.
Creating an rNET prognostic model with strong predictive ability and clinical utility is vital for guiding the precision treatment of rNETs. Close collaboration with statisticians enables us to consider both statistical significance and practical clinical implications. In the future, we can adopt different interventional measures for high-, medium-, and low-risk patients as identified by high-quality rNET prediction models, which could ultimately improve the prognosis of rNET patients.
SHORTCOMINGS OF THE EXISTING rNET PROGNOSTIC MODELS
Although the recently constructed model for rNETs has strong calibration and discrimination capabilities (Table 1), there are still some deficiencies in the actual construction of the prognostic model. In the process of constructing the global alliance for trade in services (GATIS) model, the target population consisted of patients who could undergo resection treatment. Hence, this model is not suitable for patients with distant metastasis. Given that surgery is generally not recommended once rNET patients develop distant metastases[8], a separate prognostic model needs to be developed for these patients. During the construction of rNET-related models, the issue of collinearity is commonly overlooked[9,10]. In the GATIS scoring system, the authors incorporated both tumour size and T stage simultaneously, which are undeniably correlated, resulting in collinearity between model variables and diminishing the model’s generalizability[11]. In addition to the use of several statistical methods, such as least absolute shrinkage and selection operator regression analysis and examining the correlation between variables and the variance inflation factor, clinicians also need to conduct in-depth analyses on more variables from a professional perspective.
Table 1 Prognostic model for rectal neuroendocrine tumours[9-13,16,17].
During model construction, it should be ensured that the sample size is substantial enough to guarantee the reliability of the prediction model when it is applied to a new target population. Consequently, determining an appropriate sample size is one of the crucial factors that influences the stability of the model’s predictive power[5]. Unfortunately, no calculation of sample size was conducted in the research on the rNET prognostic model.
During the construction of predictive models, to increase the practicality of the model for clinical use, continuous variables are often converted into categorical variables. However, this conversion may lead to potential loss of some information; thus, we do not recommend converting continuous variables into binary variables[7]. With respect to the NOVARA score established by Ricci et al[12], these authors binarized age and Ki-67 index on the basis of the receiver operating characteristic curve and constructed a prognostic model using these data[12]. During the construction of the GATIS score, X-tile software was used to binarize age and the prognostic nutritional index[11]. Although this approach simplifies the clinical application of the model, such categorization is applicable only to the specific patient population for which the model was constructed and lacks clinical universality.
OPTIMIZATION OF THE PROGNOSTIC rNET MODEL
rNETs usually have a relatively good prognosis, however[8], in scenarios where the number of positive events is small, a relatively large sample size is needed for model construction. Based on the formula for determining sample size and setting the observation endpoint to 5-year survival rate, the number of candidate predictive parameters to 15, the expected average follow-up time to 5.10 years, the outcome event rate to 0.065, the conservative Cox-Snell R squared statistic value to 0.065, the determined sample size was 1853 individuals[5]. However, in the GATIS scoring model constructed by Zeng et al[11], only data from 1408 patients from 17 centers were incorporated, rendering the sample size slightly insufficient. With the exception of the surveillance, epidemiology, and end results data, this study is thus far the multicenter study with the largest sample size in the construction of rNET prognostic models. Therefore, future rNET prognosis research should focus on expanding to multiple centers, even multinational centers, to achieve a more satisfactory sample size. During the selection of model variables, we usually choose variables with P values less than 0.05 in univariate regression analysis for multivariate regression analysis[13]. However, this raises certain questions. Are variables with P values greater than 0.05 in univariate regression analysis truly irrelevant to prognosis? Is there collinearity among the variables incorporated into the model? Does overfitting occur during the model construction process? Constructing a high-quality rNET model requires the joint participation of clinicians and statisticians to ensure that the resulting model has both statistical and clinical significance.
In addition to the intrinsic characteristics of rNETs, in the establishment of future prognostic models, we should also consider the incorporation of new prognostic indicators related to rNETs, such as chromogranin A[14] and somatostatin receptor 2[15], among others. Preoperative enhanced computed tomography scanning is typically utilized to assess whether patients with rNETs larger than 10 mm have concurrent lymph node metastasis, but studies on rNET prognostic models based on radiomics are still rare. Real-time analysis of artificial intelligence images has been deployed in the diagnosis of early gastric cancer and has high sensitivity and specificity. If this technology can be integrated with endoscopic ultrasound (EUS) and analyzed according to different parts of the EUS image, it may increase the accuracy of rNET prognosis prediction. Notably, in multiomics research, statistical methods such as machine learning are frequently used in model construction, so the issue of sample size still necessitates attention.
CONCLUSION
Creating a high-quality rNET prognostic model enables the grading of rNET patients under varying conditions, thereby achieving refined prognosis management. At present, there are some challenges in the construction of prognostic models for rNETs. We should adhere to the existing guidelines for establishing prognostic models and standardize the model construction process, such as the determination of sample size, the selection and transformation of variables, and internal and external validation of the model. Throughout this process, close collaboration with clinical statistical experts is necessary.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade D
Novelty: Grade B, Grade C
Creativity or Innovation: Grade B, Grade C
Scientific Significance: Grade A, Grade B
P-Reviewer: Tan KZ S-Editor: Fan M L-Editor: A P-Editor: Yu HG
Zeng XY, Zhong M, Lin GL, Li CG, Jiang WZ, Zhang W, Xia LJ, Di MJ, Wu HX, Liao XF, Sun YM, Yu MH, Tao KX, Li Y, Zhang R, Zhang P. GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience.World J Gastroenterol. 2024;30:3403-3417.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Ricci AD, Pusceddu S, Panzuto F, Gelsomino F, Massironi S, De Angelis CG, Modica R, Ricco G, Torchio M, Rinzivillo M, Prinzi N, Rizzi F, Lamberti G, Campana D. Assessment of the Risk of Nodal Involvement in Rectal Neuroendocrine Neoplasms: The NOVARA Score, a Multicentre Retrospective Study.J Clin Med. 2022;11.
[PubMed] [DOI][Cited in This Article: ][Cited by in Crossref: 4][Cited by in F6Publishing: 5][Article Influence: 2.5][Reference Citation Analysis (0)]
Zeng X, Zhang P, Zhu G, Li C, Zhang R, Yu M, Lin G, Di M, Jiang C, Li Y, Sun Y, Xia L, Chi P, Tao K. Lymph node ratio and hematological parameters predict relapse-free survival in patients with high grade rectal neuroendocrine neoplasms after radical resection: a multicenter prognostic study.World J Surg Oncol. 2023;21:300.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Kim JY, Kim J, Kim YI, Yang DH, Yoo C, Park IJ, Ryoo BY, Ryu JS, Hong SM. Somatostatin receptor 2 (SSTR2) expression is associated with better clinical outcome and prognosis in rectal neuroendocrine tumors.Sci Rep. 2024;14:4047.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]
Lee HJ, Seo Y, Oh CK, Lee JM, Choi HH, Gweon TG, Lee SH, Cheung DY, Kim JI, Park SH, Lee HH. Assessing risk stratification in long-term outcomes of rectal neuroendocrine tumors following endoscopic resection: a multicenter retrospective study.Scand J Gastroenterol. 2024;59:868-874.
[PubMed] [DOI][Cited in This Article: ][Reference Citation Analysis (0)]