Get started
Radiotherapy indices
The espadon package offers the possibility of computing many radiotherapy indices. After specifying the target volumes to be treated (if available), the healthy volumes to be protected (if available), the dose distribution from rt-dose file, the functions rt.indices.from.roi and rt.indices.from.bin can calculate, on demand :
- dosimetry indices : minimum, maximum, average dose, standard deviation and requested D.x% or D.x cm3
- volume indices : total volume, surface area and requested V.xGy or V.x%.
- conformity indices : prescription isodose target volume (PITV), prescription dose spillage (PDS), conformity index (CI), conformation number (CN), new conformity index (NCI), Dice similarity coefficient (DSC), conformity index based on distance (CIdistance), conformity distance index (CDI), triple point conformity scale (CS3), underdosed lesion factor (ULF), overdosed healthy tissues factor (OHTF), geometric conformity index (gCI), COIN , critical organ scoring index (COSI), generalized COSI (gCOSI).
- homogeneity indices from RTOG or ICRU.
- gradient indices : gradient index based on volumes ratio, modified gradient index (mGI).
In the following example, the patient folder contains a CT-scan files, a rt-dose file and a rt-struct file in which PTV and optic chiasm are outlined.
library (espadon)
patient.folder <- choose.dir ()
pat <- load.patient.from.dicom (patient.folder, data = FALSE)
S <- load.obj.data (pat$rtstruct[[1]])
CT <- load.obj.data (pat$ct[[1]])
D <- load.obj.data (pat$rtdose[[1]])
# resample D on the cut planes on which the chiasm has been outlined
D.on.CT <- vol.regrid (D, CT)
result <- rt.indices.from.roi (D.on.CT, S, target.roi.sname = "ptv",
healthy.roi.sname = "chiasm",
presc.dose = 0.9 * 70)
result[c("dosimetry", "volume", "conformity", "homogeneity", "gradient")]
for (i in 2:ncol(result$DVH)){
plot(result$DVH[,1],result$DVH[,i], type ="l",
main = colnames(result$DVH)[i-1],
xlab = "Gy", ylab= "%")
grid()
}
The espadon package also allows the calculation of the γ and χ indices from 2D images or 3D volumes using the functions rt.gamma.index and rt.chi.index (see How to compute the Gamma and Chi indices from DMI data?).