Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT
Abstract To develop predictive models of side effect occurrence in GEPNET treated with PRRT. Metastatic GEPNETs patients treated in our centre with PRRT (177Lu-Oxodotreotide) from 2019 to 2020 were considered. Haematological, liver and renal toxicities were collected and graded according to CTCAE v5...
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2021
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oai:doaj.org-article:3380ac4098f3446e97f6596eaacd19af2021-12-02T19:17:05ZApplication of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT10.1038/s41598-021-99048-82045-2322https://doaj.org/article/3380ac4098f3446e97f6596eaacd19af2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99048-8https://doaj.org/toc/2045-2322Abstract To develop predictive models of side effect occurrence in GEPNET treated with PRRT. Metastatic GEPNETs patients treated in our centre with PRRT (177Lu-Oxodotreotide) from 2019 to 2020 were considered. Haematological, liver and renal toxicities were collected and graded according to CTCAE v5. Patients were grouped according with ECOG-PS, number of metastatic sites, previous treatment lines and therapies received before PRRT. A FLIC model with backward selection was used to detect the most relevant predictors. A subsampling approach was implemented to assess variable selection stability and model performance. Sixty-seven patients (31 males, 36 females, mean age 63) treated with PRRT were considered and followed up for 30 weeks from the beginning of the therapy. They were treated with PRRT as third or further lines in 34.3% of cases. All the patients showed at least one G1–G2, meanwhile G3–G5 were rare events. No renal G3–G4 were reported. Line of PRRT administration, age, gender and ECOG-PS were the main predictors of haematological, liver and renal CTCAE. The model performance, expressed by AUC, was > 65% for anaemia, creatinine and eGFR. The application of FLIC model can be useful to improve GEPNET decision-making, allowing clinicians to identify the better therapeutic sequence to avoid PRRT-related adverse events, on the basis of patient characteristics and previous treatment lines.F. ScalorbiG. ArgiroffiM. BacciniL. GherardiniV. FuocoN. PrinziS. PuscedduE. M. GaranziniG. CentonzeM. KirienkoE. SeregniM. MilioneM. MaccauroNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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EN |
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Medicine R Science Q |
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Medicine R Science Q F. Scalorbi G. Argiroffi M. Baccini L. Gherardini V. Fuoco N. Prinzi S. Pusceddu E. M. Garanzini G. Centonze M. Kirienko E. Seregni M. Milione M. Maccauro Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT |
| description |
Abstract To develop predictive models of side effect occurrence in GEPNET treated with PRRT. Metastatic GEPNETs patients treated in our centre with PRRT (177Lu-Oxodotreotide) from 2019 to 2020 were considered. Haematological, liver and renal toxicities were collected and graded according to CTCAE v5. Patients were grouped according with ECOG-PS, number of metastatic sites, previous treatment lines and therapies received before PRRT. A FLIC model with backward selection was used to detect the most relevant predictors. A subsampling approach was implemented to assess variable selection stability and model performance. Sixty-seven patients (31 males, 36 females, mean age 63) treated with PRRT were considered and followed up for 30 weeks from the beginning of the therapy. They were treated with PRRT as third or further lines in 34.3% of cases. All the patients showed at least one G1–G2, meanwhile G3–G5 were rare events. No renal G3–G4 were reported. Line of PRRT administration, age, gender and ECOG-PS were the main predictors of haematological, liver and renal CTCAE. The model performance, expressed by AUC, was > 65% for anaemia, creatinine and eGFR. The application of FLIC model can be useful to improve GEPNET decision-making, allowing clinicians to identify the better therapeutic sequence to avoid PRRT-related adverse events, on the basis of patient characteristics and previous treatment lines. |
| format |
article |
| author |
F. Scalorbi G. Argiroffi M. Baccini L. Gherardini V. Fuoco N. Prinzi S. Pusceddu E. M. Garanzini G. Centonze M. Kirienko E. Seregni M. Milione M. Maccauro |
| author_facet |
F. Scalorbi G. Argiroffi M. Baccini L. Gherardini V. Fuoco N. Prinzi S. Pusceddu E. M. Garanzini G. Centonze M. Kirienko E. Seregni M. Milione M. Maccauro |
| author_sort |
F. Scalorbi |
| title |
Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT |
| title_short |
Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT |
| title_full |
Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT |
| title_fullStr |
Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT |
| title_full_unstemmed |
Application of FLIC model to predict adverse events onset in neuroendocrine tumors treated with PRRT |
| title_sort |
application of flic model to predict adverse events onset in neuroendocrine tumors treated with prrt |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/3380ac4098f3446e97f6596eaacd19af |
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