RESUMO
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.
RESUMO
A 61-year-old male patient was hospitalized for the evaluation of a lung mass. The patient underwent right pneumonectomy. Although reverse-transcription polymerase chain reaction tests were negative for COVID-19, the diagnosis was supported by thoracic computed tomography. The patient responded to COVID-19 treatment, as evidenced by thoracic computed tomography. This case report highlights the importance of prompt diagnosis and treatment of COVID-19 in a patient who underwent pneumonectomy, which has high mortality and morbidity rates.
