User-friendly prognostic model for rectal neuroendocrine tumours: In the era of precision management

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.

Keywords: Rectal neuroendocrine tumours; High-risk factors; Prognosis; Clinical prediction models; Precision medicine; Statistical collaboration

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.