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Lutetium in prostate cancer: Reconstruction of patient-level data from published trials and generation of a multi-trial Kaplan-Meier curve
BACKGROUND: Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer. Two clinical trials have evaluated lutetium thus far (therap and vision with 99 and 385 patients, respectively), but their results are discordant. AIM: To synth...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9157627/ https://www.ncbi.nlm.nih.gov/pubmed/35721242 http://dx.doi.org/10.5662/wjm.v12.i3.107 |
Sumario: | BACKGROUND: Lutetium has been shown to be an important potential innovation in pre-treated metastatic castration-resistant prostate cancer. Two clinical trials have evaluated lutetium thus far (therap and vision with 99 and 385 patients, respectively), but their results are discordant. AIM: To synthetize the available evidence on the effectiveness of lutetium in pre-treated metastatic castration-resistant prostate cancer; and to test the application of a new artificial intelligence technique that synthetizes effectiveness based on reconstructed patient-level data. METHODS: We employed a new artificial intelligence method (shiny method) to pool the survival data of these two trials and evaluate to what extent the lutetium cohorts differed from one another. The shiny technique employs an original reconstruction of individual patient data from the Kaplan-Meier curves. The progression-free survival graphs of the two lutetium cohorts were analyzed and compared. RESULTS: The hazard ratio estimated was in favor of the vision trial; the difference was statistically significant (P < 0.001). These results indicate that further studies on lutetium are needed because the survival data of the two trials published thus far are conflicting. CONCLUSION: Our study confirms the feasibility of reconstructing patient-level data from survival graphs in order to generate a survival statistics. |
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