4. Discussion
NSCLC, SCLC, tumor, primary cancer, lung cancer, primary lung cancer
TUMOR, PRIMARY TUMORS, METASTASIS, SCLC, LUNG METASTASIS, DISEASE, LUNG CANCER, SAID, NSCLC, PRIMARY CANCER
The model with six fundamental variables including age, gender, tumor size, T, N, and AJCC stages which were initially built to predict the in-hospital mortality of new lung cancer cases resulted in promising applications in prognosis to enhance the quality-of-life of patients diagnosed with this challenging disease. First, in the internal validation extracted from the SEER database, both training and validation cohorts have good performances with an AUC of above 0.75 (Apart from smoking, sex is likely a risk factor to play a specific role in developing lung cancer [From the statistical standpoint, our study shows the predictive value of the training model as the probability varies from 0 to 50% of in-hospital mortality since the initial diagnosis timepoint. Having said that, Shen et al. [From the clinical standpoint, there are biases and confounding factors can be taken into account. First, in terms of diagnosis, the protocol for the differentiation of primary lung cancer from solitary lung metastasis, especially with unknown primary tumors, is challenging since the lung is considered a frequent metastatic location. Therefore, the model should be applied after the primary lung location is confirmed by careful examination. Second, the policymaking of lung cancer management varies from place to place. Because the model was built based on the SEER database, TCGA-LUAD, and TCGA-LUSC, it partly reflects the outcome of lung cancer management in developed countries. In other words, while the model is helpful to smooth the decision-making among various disease-treatment choices in advanced healthcare settings, it is likely to have less predictive value for patients in a system with fewer resources. Thus, to unveil whether the probability of in-hospital mortality of patients based in developing economies predicted less than 50% is overestimated, further study counting the finance component may need to be conducted. Furthermore, despite the fact “the higher probability, the less predictive value” phenomenon of the model cannot be denied, ultimately the main focus on end-of-life care could be the case in high-risk patients. Likewise, it appears to have an ethic-related controversy surrounding palliative care and dignified death. Hence, a cautious approach should be considered in this group in corresponding to the quality of life. Last, in spite of cutting-edge interventions related to immune-, chemo-radiotherapy, etc., lung cancer is still the leading cause of mortality [Regarding applications, we designed three platforms to put the model into practice for quantitative groundwork. The static nomogram (The study has its limitations. First, the data extracted from SEER and TCGA-LUAD, and TCGA-LUSC with different regimes made them heterogeneous. Since metastasis diagnosis has a possibility to be missed in new primary cancer diagnoses, we hope to minimize this bias by excluding the metastasis variable. However, a pilot validation in clinical may be necessary to examine the model for a comprehensive application. Second, as mentioned in the Results section, the model turned out to have a less accurate value with the probability of an in-hospital mortality rate above 50%. This limitation infers the important role of end-of-life care regardless of the survival rate. Finally, the population-based design of the study without subtype classification of NSCLC and SCLC missed the molecular insight reasoning for immunotherapeutic that has played a game changer for the survival rate improvement in recent decades, especially in countries with advanced healthcare systems. Thus, to figure out how the high- and low-risk patient discrimination are correlated to the subtypes of lung cancer like NSCLC and SCLC, future studies can possibly include that variable for an optimal model.