@InProceedings{2015_fargeas301,
	author = "Auréline Fargeas and Amar Kachenoura and Louis h. Liu and Frédéric Commandeur and Gaël Drean and Caroline Lafond and Laurent Albera and Oscar Acosta and Renaud De crevoisier",
	title = "A new voxel principal component analysis for predicting and spatially characterizing rectal toxicity following prostate cancer radiotherapy",
	booktitle = "25° Colloque sur le traitement du signal et des images",
	year = "2015",
	publisher = "GRETSI - Groupe de Recherche en Traitement du Signal et des Images",
	number = "001-0218",
	pages = "p. 873-876",
	month = "Sep # 8--11",
	address = "Lyon",
	doi = "",
	pdf = "2015_fargeas301.pdf",
	abstract = "In prostate cancer radiotherapy, understanding the correlation between the dose distribution and the occurrence of undesirable side-effects is crucial to correlate the treatment outcome with the planning parameters. Most of the current methods addressing the prediction of the
 toxicity events are based on dose volume histogram. However, these methods are not able to correlate the toxicity and the spatial dose distribution at the voxel level. Using the whole 3D planned dose distribution, a principal component analysis based approach was performed to predict late rectal toxicity and to construct a dose pattern characterizing the difference between patients with rectal bleeding and those without. After a non-rigid registration, the method aimed at identifying, from 3D dose distribution, two basis (characterizing patients with/without rectal bleeding). The prediction was performed by evaluating a new variable computed by measuring the distance of a new individual 3D dose distribution to both subspaces spanned by the bases. The method, applied to a total of 118 patients treated for prostate cancer radiotherapy and compared with a recent principal component analysis approach based only on the DVH, showed good performance (AUC=0.87) and suggested that the method is able to establish the correlation between dose and toxicity outcomes..pdf",
}