AI Article Synopsis

  • - The study examines how various factors (like pathological stage and histological type) affect the likelihood of relapse in Stage I non-small cell lung cancer (NSCLC) patients after surgical treatment, with a focus on different machine-learning methods for prediction.
  • - After analyzing data from 268 patients, the DeepSurv model performed best in predicting recurrence during training, while the Random Survival Forest (RSF) model excelled in the test set.
  • - The findings suggest that machine-learning techniques can effectively predict NSCLC recurrence, helping doctors make informed decisions on additional treatments and follow-up care.

Article Abstract

Background: The curative treatment for Stage I non-small cell lung cancer (NSCLC) is surgical resection. Even for Stage I patients, the probability of recurrence after curative treatment is around 20%.

Methods: In this retrospective study, we included 268 operated Stage I NSCLC patients between January 2008 and June 2018 to analyze the prognostic factors (pathological stage, histological type, number of sampled mediastinal lymph node stations, type of resection, SUVmax of the lesion) that may affect relapse with three different methods, Cox proportional hazard (CoxPH), random survival forest (RSF), DeepSurv, and to compare the performance of these methods with Harrell's C-index. The dataset was randomly split into two sets, training and test sets.

Results: In the training set, DeepSurv showed the best performance among the three models, the C-index of the training set was 0.832, followed by RSF (0.675) and CoxPH (0.672). In the test set, RSF showed the best performance among the three models, followed by DeepSurv with 0.677 and CoxPH methods with 0.625.

Conclusion: In conclusion, machine-learning techniques can be useful in predicting recurrence for lung cancer and guide clinicians both in choosing the adjuvant treatment options and best follow-up programs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557877PMC
http://dx.doi.org/10.1002/cam4.6479DOI Listing

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