A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

M. Aldraimli; D. Soria; D. Grishchuck; S. Ingram; R. Lyon; A. Mistry; J. Oliveira; R. Samuel; L.E.A. Shelley; S. Osman; M.V. Dwek; D. Azria; J. Chang-Claude; S. Gutierrez-Enriquez; M.C. De Santis; B.S. Rosenstein; D. De Ruysscher; E. Sperk; R.P. Symonds; H. Stobart; A. Vega; L. Veldeman; A. Webb; C.J. Talbot; C.M. West; T. Rattay; T.J. Chaussalet; REQUITE consortium

doi:10.1016/j.compbiomed.2021.104624

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

M. Aldraimli^*, D. Soria, D. Grishchuck, S. Ingram, R. Lyon, A. Mistry, J. Oliveira, R. Samuel, L.E.A. Shelley, S. Osman, M.V. Dwek, D. Azria, J. Chang-Claude, S. Gutierrez-Enriquez, M.C. De Santis, B.S. Rosenstein, D. De Ruysscher, E. Sperk, R.P. Symonds, H. StobartA. Vega, L. Veldeman, A. Webb, C.J. Talbot, C.M. West^*, T. Rattay, T.J. Chaussalet, REQUITE consortium

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Original language	English
Article number	104624
Number of pages	20
Journal	Computers in Biology and Medicine
Volume	135
DOIs	https://doi.org/10.1016/j.compbiomed.2021.104624
Publication status	Published - 1 Aug 2021

Keywords

Classification
REQUITE
Machine learning
Imbalanced learning
Radiotherapy
SMOTE
Meta-learning
Desquamation
Early toxicities
ACUTE SKIN TOXICITY
BREAST-CANCER
RADIATION
MODEL
PARAMETERS
THERAPY
COHORT
CURVE
NTCP

Access to Document

10.1016/j.compbiomed.2021.104624

Cite this

Aldraimli, M., Soria, D., Grishchuck, D., Ingram, S., Lyon, R., Mistry, A., Oliveira, J., Samuel, R., Shelley, L. E. A., Osman, S., Dwek, M. V., Azria, D., Chang-Claude, J., Gutierrez-Enriquez, S., De Santis, M. C., Rosenstein, B. S., De Ruysscher, D., Sperk, E., Symonds, R. P., ... REQUITE consortium (2021). A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. Computers in Biology and Medicine, 135, [104624]. https://doi.org/10.1016/j.compbiomed.2021.104624

Aldraimli, M. ; Soria, D. ; Grishchuck, D. ; Ingram, S. ; Lyon, R. ; Mistry, A. ; Oliveira, J. ; Samuel, R. ; Shelley, L.E.A. ; Osman, S. ; Dwek, M.V. ; Azria, D. ; Chang-Claude, J. ; Gutierrez-Enriquez, S. ; De Santis, M.C. ; Rosenstein, B.S. ; De Ruysscher, D. ; Sperk, E. ; Symonds, R.P. ; Stobart, H. ; Vega, A. ; Veldeman, L. ; Webb, A. ; Talbot, C.J. ; West, C.M. ; Rattay, T. ; Chaussalet, T.J. ; REQUITE consortium. / A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. In: Computers in Biology and Medicine. 2021 ; Vol. 135.

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title = "A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy",

keywords = "Classification, REQUITE, Machine learning, Imbalanced learning, Radiotherapy, SMOTE, Meta-learning, Desquamation, Early toxicities, ACUTE SKIN TOXICITY, BREAST-CANCER, RADIATION, MODEL, PARAMETERS, THERAPY, COHORT, CURVE, NTCP",

author = "M. Aldraimli and D. Soria and D. Grishchuck and S. Ingram and R. Lyon and A. Mistry and J. Oliveira and R. Samuel and L.E.A. Shelley and S. Osman and M.V. Dwek and D. Azria and J. Chang-Claude and S. Gutierrez-Enriquez and {De Santis}, M.C. and B.S. Rosenstein and {De Ruysscher}, D. and E. Sperk and R.P. Symonds and H. Stobart and A. Vega and L. Veldeman and A. Webb and C.J. Talbot and C.M. West and T. Rattay and T.J. Chaussalet and {REQUITE consortium}",

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day = "1",

doi = "10.1016/j.compbiomed.2021.104624",

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journal = "Computers in Biology and Medicine",

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Aldraimli, M, Soria, D, Grishchuck, D, Ingram, S, Lyon, R, Mistry, A, Oliveira, J, Samuel, R, Shelley, LEA, Osman, S, Dwek, MV, Azria, D, Chang-Claude, J, Gutierrez-Enriquez, S, De Santis, MC, Rosenstein, BS, De Ruysscher, D, Sperk, E, Symonds, RP, Stobart, H, Vega, A, Veldeman, L, Webb, A, Talbot, CJ, West, CM, Rattay, T, Chaussalet, TJ & REQUITE consortium 2021, 'A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy', Computers in Biology and Medicine, vol. 135, 104624. https://doi.org/10.1016/j.compbiomed.2021.104624

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. / Aldraimli, M.; Soria, D.; Grishchuck, D.; Ingram, S.; Lyon, R.; Mistry, A.; Oliveira, J.; Samuel, R.; Shelley, L.E.A.; Osman, S.; Dwek, M.V.; Azria, D.; Chang-Claude, J.; Gutierrez-Enriquez, S.; De Santis, M.C.; Rosenstein, B.S.; De Ruysscher, D.; Sperk, E.; Symonds, R.P.; Stobart, H.; Vega, A.; Veldeman, L.; Webb, A.; Talbot, C.J.; West, C.M.; Rattay, T.; Chaussalet, T.J.; REQUITE consortium.

In: Computers in Biology and Medicine, Vol. 135, 104624, 01.08.2021.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

AU - Aldraimli, M.

AU - Soria, D.

AU - Grishchuck, D.

AU - Ingram, S.

AU - Lyon, R.

AU - Mistry, A.

AU - Oliveira, J.

AU - Samuel, R.

AU - Shelley, L.E.A.

AU - Osman, S.

AU - Dwek, M.V.

AU - Azria, D.

AU - Chang-Claude, J.

AU - Gutierrez-Enriquez, S.

AU - De Santis, M.C.

AU - Rosenstein, B.S.

AU - De Ruysscher, D.

AU - Sperk, E.

AU - Symonds, R.P.

AU - Stobart, H.

AU - Vega, A.

AU - Veldeman, L.

AU - Webb, A.

AU - Talbot, C.J.

AU - West, C.M.

AU - Rattay, T.

AU - Chaussalet, T.J.

AU - REQUITE consortium

PY - 2021/8/1

Y1 - 2021/8/1

KW - Classification

KW - REQUITE

KW - Machine learning

KW - Imbalanced learning

KW - Radiotherapy

KW - SMOTE

KW - Meta-learning

KW - Desquamation

KW - Early toxicities

KW - ACUTE SKIN TOXICITY

KW - BREAST-CANCER

KW - RADIATION

KW - MODEL

KW - PARAMETERS

KW - THERAPY

KW - COHORT

KW - CURVE

KW - NTCP

U2 - 10.1016/j.compbiomed.2021.104624

DO - 10.1016/j.compbiomed.2021.104624

M3 - Article

C2 - 34247131

VL - 135

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

M1 - 104624

ER -