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Related Experiment Video

Updated: Jun 21, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

The NET Score: An Interpretable AI-Assisted Prognostic Score for Mortality Risk in Lung Neuroendocrine Tumors.

Luca Bertolaccini1,2, Francesca Spada3, Lavinia Benini3

  • 1Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy.

Endocrine-Related Cancer
|June 16, 2026
PubMed
Summary

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A new NET Score uses routine pathology to predict mortality risk in lung neuroendocrine tumors (NETs). This AI-assisted tool offers a simple, validated method for personalized risk assessment and clinical decisions.

Area of Science:

  • Oncology
  • Pathology
  • Artificial Intelligence

Background:

  • Lung neuroendocrine tumors (NETs) exhibit varied prognoses, with existing nomograms lacking clinical utility due to complexity and insufficient validation.
  • A need exists for a straightforward, reproducible prognostic model utilizing standard pathology data for lung NETs.

Purpose of the Study:

  • To develop and internally validate the NET Score, an AI-assisted, interpretable tool for estimating individualized mortality risk in lung NETs.
  • To create a practical scoring system based on routine pathological findings for better clinical decision-making.

Main Methods:

  • A retrospective cohort of resected pulmonary carcinoids was analyzed.
  • LASSO regression identified key predictors: nodal status, mitotic index, necrosis, and Ki-67.
Keywords:
CarcinoidLung CancerPredictive ModelingPrognostic ScorePulmonary Neuroendocrine Tumors

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  • A logistic regression model generated a point-based NET Score (0-8) for cumulative mortality risk estimation.

    • Main Results:

    • The final NET Score incorporated LODDS, mitotic index (>2), necrosis, and Ki-67 (>5%), yielding moderate discrimination (AUC 0.70).
    • Risk stratification into low (≤5%), intermediate (8-12%), and high (≥18%) mortality groups was achieved.
    • Kaplan-Meier analysis confirmed survival differences across the defined risk groups.

    Conclusions:

    • The NET Score provides a practical, interpretable prognostic tool for lung NETs, aiding risk communication and clinical decisions.
    • This AI-assisted scoring system offers a pragmatic approach to prognostic modeling for rare thoracic malignancies.
    • External validation is recommended to further confirm the generalizability of the NET Score.