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Natural Language Processing-Assisted Incidental Pulmonary Nodule Evaluation Program: Impact on Lung Cancer Outcomes.

Noa Tamam Shenholz1, Keren Hod2,3, Liat Toderis4,5

  • 1Faculty of Health Sciences, Joyce & Irving Goldman Medical School, Ben Gurion University of the Negev, Beer-Sheva 8410501, Israel.

Medical Sciences (Basel, Switzerland)
|March 27, 2026
PubMed
Summary

Natural language processing (NLP) for incidental pulmonary nodule (IPN) evaluation aids early lung cancer (LC) detection. This NLP-assisted program improved diagnostic efficiency and expedited critical interventions for LC patients.

Keywords:
IPNNLPincidental pulmonary nodulelung cancernatural language processing

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Area of Science:

  • Oncology
  • Radiology
  • Medical Informatics

Background:

  • Early detection and prompt treatment initiation are crucial for improving lung cancer (LC) outcomes.
  • Incidental pulmonary nodules (IPNs) detected on chest CT scans require timely evaluation to rule out malignancy.
  • Traditional referral pathways for IPN evaluation can lead to delays in diagnosis and treatment.

Purpose of the Study:

  • To assess the impact of a natural language processing (NLP)-assisted incidental pulmonary nodule (IPN) evaluation program on lung cancer (LC) diagnosis and patient prognosis.
  • To compare diagnostic work-up and treatment timelines between NLP-assisted IPN evaluation and traditional referral methods.
  • To evaluate the program's effectiveness as a lung cancer screening (LCS) tool.

Main Methods:

  • A retrospective study of 200 consecutive LC patients diagnosed at Assuta Medical Centers (AMC) between January 2019 and December 2022.
  • Patients were divided into two groups: Group A (n=100) received NLP-assisted IPN evaluation, and Group B (n=100) received traditional referral.
  • Analysis included stage at diagnosis, diagnostic work-up and treatment (Tx) timelines, and overall survival (OS).

Main Results:

  • The NLP-assisted IPN evaluation program resulted in a significant stage shift towards earlier diagnosis (Stage I: 48% in Group A vs. 27% in Group B, p=0.013).
  • While overall time from imaging to Tx initiation was similar, time to systemic Tx (p=0.035) and radiotherapy (p=0.044) was significantly shorter in the NLP-assisted group.
  • The NLP program identified suspicious lung findings (SLF) more efficiently, necessitating further investigation.

Conclusions:

  • An NLP-assisted IPN evaluation program can facilitate earlier lung cancer detection, leading to a stage shift towards earlier diagnosis.
  • This approach improves diagnostic efficiency and expedites time-critical interventions for lung cancer patients.
  • Implementing NLP-assisted IPN evaluation is a promising strategy for enhancing lung cancer care.