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Related Experiment Video

Updated: Jul 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Artificial intelligence-based algorithm for predicting outcomes in early-stage lung cancer: An annotation-free

Keiju Aokage1, Jumpei Ukita2, Mamoru Miura2

  • 1Department of Thoracic Surgery, National Cancer Center Hospital East, Chiba, Japan.

JTCVS Open
|July 1, 2026
PubMed
Summary

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JTCVS open·2026

An artificial intelligence (AI) model using computed tomography (CT) scans and clinical data accurately predicts prognosis for early-stage non-small cell lung cancer (NSCLC) patients. This AI tool enhances risk stratification and supports personalized treatment planning.

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Surgery is the standard treatment for stage I non-small cell lung cancer (NSCLC).
  • Conventional prognostic factors for NSCLC are subjective and variable.
  • Objective prediction methods are needed for stage I NSCLC prognosis.

Purpose of the Study:

  • To develop and validate an annotation-free artificial intelligence (AI) model for prognosis prediction in stage I NSCLC.
  • To integrate computed tomography (CT) imaging and clinical data for enhanced predictive accuracy.
  • To improve risk stratification and support personalized treatment planning.

Main Methods:

  • Developed an AI algorithm to predict pathological classifications from CT and clinical data.
  • Refined and validated the model using multi-institutional prospective trial data and a validation cohort.
Keywords:
annotation-freeeartificial intelligencenon–small cell lung cancerpersonalized caresurgerythin-section computed tomography

Related Experiment Videos

Last Updated: Jul 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

  • Trained models to predict 5-year disease-free and overall survival, evaluating performance using AUC.

    • Main Results:

    • Models integrating CT imaging and clinical data outperformed those using single data types.
    • The AI model achieved an AUC of 0.787 for pathology prediction.
    • Combined AI, clinical, and CT assessment data yielded the highest AUC for 5-year survival prediction (0.757 for DFS, 0.756 for OS).

    Conclusions:

    • Annotation-free AI models integrating CT and clinical data offer accurate, objective prognosis prediction for stage I NSCLC.
    • These AI models complement conventional diagnostics.
    • AI-driven predictions support personalized multidisciplinary treatment planning for NSCLC.