Your browser doesn't support javascript.
loading
Larynx cancer survival model developed through open-source federated learning.
Rønn Hansen, Christian; Price, Gareth; Field, Matthew; Sarup, Nis; Zukauskaite, Ruta; Johansen, Jørgen; Eriksen, Jesper Grau; Aly, Farhannah; McPartlin, Andrew; Holloway, Lois; Thwaites, David; Brink, Carsten.
Afiliación
  • Rønn Hansen C; Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark; Institute of Medical Physics, School of Physics, University of Sydn
  • Price G; Radiotherapy department, The Christie NHS Foundation Trust, Manchester, United Kingdom.
  • Field M; Ingham Institute for Applied Medical Research, Sydney, Australia.
  • Sarup N; Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.
  • Zukauskaite R; Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Oncology, Odense University Hospital, Odense, Denmark.
  • Johansen J; Department of Oncology, Odense University Hospital, Odense, Denmark.
  • Eriksen JG; Department of Oncology, Odense University Hospital, Odense, Denmark; Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Oncology, Aarhus University Hospital, Denmark.
  • Aly F; Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
  • McPartlin A; Radiotherapy department, The Christie NHS Foundation Trust, Manchester, United Kingdom.
  • Holloway L; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Southwest Sydney Clinical Campus, University of New South Wales, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, Aust
  • Thwaites D; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia.
  • Brink C; Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Radiother Oncol ; 176: 179-186, 2022 11.
Article en En | MEDLINE | ID: mdl-36208652
INTRODUCTION: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, ß regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Laríngeas Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Radiother Oncol Año: 2022 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Laríngeas Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Radiother Oncol Año: 2022 Tipo del documento: Article Pais de publicación: Irlanda