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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation.

Peng Huang1,2,3, Peter B Illei3,4, Wilbur Franklin5

  • 1Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.

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|September 9, 2022
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Summary

Integrated deep learning evaluation (IDLE) using CT images and histology improves risk prediction for stage IA non-small-cell lung cancer (NSCLC) recurrence. This approach enhances prognostic accuracy beyond traditional TNM staging and tumor grade.

Keywords:
artificial intelligentbiomarkercomputer-aided diagnosispostoperative-stage IA NSCLCtumor grade

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Area of Science:

  • Oncology
  • Radiology
  • Pathology
  • Artificial Intelligence

Background:

  • Prognostic risk factors for stage IA non-small-cell lung cancer (NSCLC) have seen limited advancement.
  • The added value of biomarkers to TNM staging and tumor grade for predicting NSCLC recurrence remains unclear.

Purpose of the Study:

  • To develop and evaluate an integrated deep learning evaluation (IDLE) model for predicting tumor recurrence or progression in stage IA NSCLC.
  • To assess the added value of IDLE to TNM staging and tumor grade in risk prediction and stratification.

Main Methods:

  • Extracted features from preoperative low-dose CT images and histological findings of resected lung tumors from 182 stage IA NSCLC patients.
  • Utilized integrated deep learning evaluation (IDLE) to combine image and histology features for risk prediction.
  • Evaluated IDLE's performance against TNM staging and tumor grade for progression risk prediction and stratification.

Main Results:

  • IDLE achieved a 5-year AUC of 0.817, significantly outperforming TNM stage (0.561) and tumor grade (0.573).
  • The IDLE score was significantly associated with cancer recurrence (p < 0.0001), even after adjusting for TNM staging and tumor grade.
  • Synergy between CT image markers and histological markers drove the deep learning algorithm's enhanced prognostic capability.

Conclusions:

  • Integrating preoperative CT image markers and histological findings via deep learning improves risk stratification for stage IA NSCLC.
  • Combining markers from non-overlapping platforms, like imaging and pathology, increases cancer risk prediction accuracy.
  • IDLE offers a more accurate prognostic tool for stage IA NSCLC compared to TNM staging and tumor grade alone.