Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.
BACKGROUND
The impact of prognostic stratification of early stage non-small-cell lung cancer (NSCLC) patients with poor prognosis after surgery is of considerable clinical relevance. The objective of this study was to identify clinical factors associated with long-term overall survival (OS) in early stage NSCLC patients and develop a prognostic model that identifies features associated with poor prognosis and stratifies patients by risk.
METHODS
This is a cohort study including 505 patients, diagnosed with stage I-II NSCLC, who underwent curative surgical procedures at a tertiary hospital in Madrid, Spain.
RESULTS
Median survival was 62.4 in patients submitted to surgery and 65 in patients submitted to surgery plus adjuvant treatment. From the univariate analysis we estimated that a female diagnosed with NSCLC has a 0.967 (95% CI 0.936–0.999) probability of survival one year after diagnosis and a 0.784 (95% CI 0.712–0.863) five years after diagnosis. For males, these probabilities drop to 0.904 (95% CI 0.875–0.934) and 0.613 (95% CI 0.566–0.665), respectively. Multivariable analysis shows that sex, age at diagnosis, type of treatment, ECOG-PS, and Stage are statistically significant variables (p < 0.10). According to the Cox regression model, age over 50, ECOG-PS 1 or 2, and stage ll are risk factors for survival (HR > 1) while adjuvant chemotherapy is a good prognostic variable (HR < 1).
CONCLUSIONS
Surgery plus adjuvant chemotherapy was associated with the best long-term OS in our patients. The prognostic model identified Age, Sex, Stage and ECOG-PS as significant factors to explain the probability of survival.
BACKGROUND
Current prognosis in oncology is reduced to the tumour stage and performance status, leaving out many other factors that may impact the patient´s management. Prognostic stratification of early stage non-small-cell lung cancer (NSCLC) patients with poor prognosis after surgery is of considerable clinical relevance. The objective of this study was to identify clinical factors associated with long-term overall survival in a real-life cohort of patients with stage I-II NSCLC and develop a prognostic model that identifies features associated with poor prognosis and stratifies patients by risk.
METHODS
This is a cohort study including 505 patients, diagnosed with stage I-II NSCLC, who underwent curative surgical procedures at a tertiary hospital in Madrid, Spain.
RESULTS
Median OS (in months) was 63.7 (95% CI, 58.7–68.7) for the whole cohort, 62.4 in patients submitted to surgery and 65 in patients submitted to surgery and adjuvant treatment. The univariate analysis estimated that a female diagnosed with NSCLC has a 0.967 (95% CI 0.936–0.999) probability of survival one year after diagnosis and a 0.784 (95% CI 0.712–0.863) five years after diagnosis. For males, these probabilities drop to 0.904 (95% CI 0.875–0.934) and 0.613 (95% CI 0.566–0.665), respectively. Multivariable analysis shows that sex, age at diagnosis, type of treatment, ECOG-PS, and stage are statistically significant variables (p < 0.10). According to the Cox regression model, age over 50, ECOG-PS 1 or 2, and stage ll are risk factors for survival (HR > 1) while adjuvant chemotherapy is a good prognostic variable (HR < 1). The prognostic model identified a high-risk profile defined by males over 71 years old, former smokers, treated with surgery, ECOG-PS 2.
CONCLUSIONS
Surgery plus adjuvant chemotherapy was associated with the best long-term OS in our patients. The prognostic model identified Age, Sex, Stage and ECOG-PS as significant factors to explain the probability of survival.
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