RESUMEN
OBJECTIVES: Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis. MATERIALS AND METHODS: This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities. RESULTS: Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9-17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88-0.91), 0.85 (0.84-0.87), 0.83 (0.81-0.85), and 0.85 (0.82-0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84-0.92), 0.84 (0.78-0.88), 0.84 (0.76-0.89), and 0.84 (0.75-0.90) at 180, 360, 720, and 1,080 days, respectively. CONCLUSION: This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.
Asunto(s)
Neoplasias Pulmonares , Aprendizaje Automático , Humanos , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/diagnóstico , Masculino , Femenino , Anciano , Pronóstico , Estudios Retrospectivos , Persona de Mediana Edad , Estadificación de Neoplasias , Algoritmos , Curva ROC , Anciano de 80 o más Años , Estudios de CohortesRESUMEN
1. Flavin-containing monooxygenase 3 (FMO3) in humans is polymorphic in several ethnic groups, including Caucasians, Africans and Asians. Some FMO3 variants are associated with a disorder trimethylaminuria. 2. In the current study, we used the results from urinary phenotyping assays to identify 63 subjects with <85% FMO3 metabolic capacity with respect to trimethylamine N-oxidation among 787 Japanese volunteers with self-reported trimethylaminuria. The 63 subjects with reduced FMO3 activity were screened and investigated in detail to identify novel FMO3 variants. 3. Homozygous or heterozygous individuals for new single nucleotide substitution variants/haplotypes p.(Pro282Leu), p.[(Glu158Lys; Glu308Gly; Thr329Ala)], p.[(Glu158Lys; Glu308Gly; Asp429Gly)], p.[(Val257Met; Leu473Pro)], p.[(Glu158Lys; Glu308Gly; Ile441Thr)], and p.[(Arg205Cys; Gly503Arg)] were identified in six proband subjects and their family members after pedigree analyses. 4. These variant FMO3 proteins recombinantly expressed in Escherichia coli membranes exhibited decreased N-oxygenation activities toward trimethylamine (Vmax/Km < 40% that of the wild-type). 5. Although the allele frequencies of the six new variants and/or haplotypes were low, the present results indicated that individuals homozygous or heterozygous for any of these novel missense FMO3 variants or known nonsense mutations such as p.(Cys197Ter) or p.(Arg205Cys) highly found in this self-reported Japanese trimethylaminuria cohort may have reduced FMO3 activity with respect to the N-oxygenation of trimethylamine.