ANA PAULA DE SOUZA BORGES
Projetos de Pesquisa
Unidades Organizacionais
LIM/24 - Laboratório de Oncologia Experimental, Hospital das Clínicas, Faculdade de Medicina
2 resultados
Resultados de Busca
Agora exibindo 1 - 2 de 2
- Predicting survival in metastatic non-small cell lung cancer patients with poor ECOG-PS: A single-arm prospective study(2023) CUNHA, Mateus Trinconi; BORGES, Ana Paula de Souza; JARDIM, Vinicius Carvalho; FUJITA, Andre; JR, Gilberto de CastroBackground Patients with advanced non-small cell lung cancer (NSCLC) are a heterogeneous population with short lifespan. We aimed to develop methods to better differentiate patients whose survival was >90 days. Methods We evaluated 83 characteristics of 106 treatment-naive, stage IV NSCLC patients with Eastern Cooperative Oncology Group Performance Status (ECOG-PS) >1. Automated machine learning was used to select a model and optimize hyperparameters. 100-fold bootstrapping was performed for dimensionality reduction for a second (""lite"") model. Performance was measured by C-statistic and accuracy metrics in an out-of-sample validation cohort. The ""lite"" model was validated on a second independent, prospective cohort (N = 42). Network analysis (NA) was performed to evaluate the differences in centrality and connectivity of features. Results The selected method was ExtraTrees Classifier, with C-statistic of 0.82 (p < 0.01) and accuracy of 0.81 (p = 0.01). The ""lite"" model had 16 variables and obtained C-statistic of 0.84 (p < 0.01) and accuracy of 0.75 (p = 0.039) in the first cohort, and C-statistic of 0.706 (p < 0.01) and accuracy of 0.714 (p < 0.01) in the second cohort. The networks of patients with lower survival were more interconnected. Features related to cachexia, inflammation, and quality of life had statistically different prestige scores in NA. Conclusions Machine learning can assist in the prognostic evaluation of advanced NSCLC. The model generated with a reduced number of features showed high accessibility and reasonable metrics. Features related to quality of life, cachexia, and performance status had increased correlation and importance scores, suggesting that they play a role at later disease stages, in line with the biological rationale already described.
- Serum Creatinine as a Potential Biomarker of Skeletal Muscle Atrophy in Non-small Cell Lung Cancer Patients(2021) NEVES, Willian das; ALVES, Christiano R. R.; BORGES, Ana Paula de Souza; JR, Gilberto de CastroObjectives: Identifying simple biomarkers to determine muscle atrophy in non-small-cell lung cancer (NSCLC) patients remains a critical research gap. Since creatinine is mainly a product from intramuscular creatine metabolism, we tested the hypothesis that low serum creatinine levels would be associated to skeletal muscle atrophy in NSCLC patients. Materials and Methods: This is a prospective cohort study including 106 treatment-naive patients with histologically confirmed stage IV NSCLC. All patients performed routine serum creatinine laboratory tests. We divided patients into two groups based on low (<0.7 mg/dL for male and <0.5 mg/dL for female) or normal creatinine levels. We compared body mass index (BMI), psoas muscle cross-sectional area, adipose tissue area and complete blood counts between groups. Results: Male and female NSCLC patients with low serum creatinine levels had low muscle cross-sectional area as compared to patients with normal serum creatinine levels. Male NSCLC patients with low serum creatinine also displayed reduced BMI, reduced adipose tissue area, and elevated systemic inflammation compared to NSCLC patients with normal serum creatinine levels. There were no significant differences between female groups for BMI, adipose tissue area and inflammatory markers. Conclusions: Serum creatinine is a potential prognostic biomarker of skeletal muscle atrophy in NSCLC patients. Since serum creatinine is a simple and accessible measurement, we suggest that it should be monitored in longitudinal follow-up of NSCLC patients as a biomarker of muscle atrophy.