Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis

Author:

Morvan LudivineORCID,Carlier Thomas,Jamet Bastien,Bailly Clément,Bodet-Milin Caroline,Moreau Philippe,Kraeber-Bodéré Françoise,Mateus Diana

Funder

MILCOM (Multi-modal Imaging and Learning for Computational-based Medicine) Connect Talent Region Pays de la Loire + Nantes Metropole

SIRIC ILIAD

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Reference23 articles.

1. Amin SB, Minvielle S, Hanlon B, Shah PK, Li C, Li Y, Swanson D, Moreau P, Magrangeas F, Anderson KC, Avet-Loiseau H, Munshi NC (2014) Gene expression profile alone is inadequate in predicting complete response in multiple myeloma. Leukemia 28:2229–2234

2. Breiman L (1996) Bagging predictors. Mach Learn 42(2):123–140

3. Bühnemann C, Li S, Yu H, White HB, Schäfer K, Llombart-Bosch A, Machado I, Picci P, Hogendoorn P, Athanasou N, Noble J, Hassa A (2014) Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis. Plos ONE 9(9):e107105

4. Carlier T, Bailly C, Leforestier R, Touzeau C, Moreau P, Bodere F, Bodet-Milin C (2017) Prognostic added value of PET textural features at diagnosis in multiple myeloma. J Nuclear Med 58(supplement 1):111

5. Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B (Methodol) 34(2):187–220

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