dichotomize: Generates a dichotomous ultrasound feature data.frame

In Morphonodepredictivemodel/morphonode: The Morphonode Predictive Model for ultrasound signatures detection and malignancy prediction in vulvar cancer

Generates a dichotomous ultrasound feature data.frame

Description

Convert an ultrasound feature data.frame (each row is an ultrasound vector) into a dichotomous data.frame. Defalut dichotomization cutoffs are computed as described in Fragomeni et al. (2022).

Usage

dichotomize(
  x,
  short = 8,
  cortical = 2,
  ist = 1,
  ecs = 1,
  hab = 0,
  eco = 1,
  vp = c(1, 2, 3),
  vfl = c(2, 3, 4),
  ct = c(2),
  fid = c(1, 2, 3),
  cmid = c(2, 3, 4),
  shape = c(3),
  cs = 3,
  grouping = c(2, 3),
  asFactor = FALSE,
  ...
)

Arguments

x	An (n, 14) ultrasound features data.frame, where n is the number of subjects.
short	Numeric value corresponding to the short axis cutoff in millimeters (default = 8).
cortical	Numeric value corresponding to the cortical thickness cutoff in millimeters (default = 2).
ist	Dichotomous value 0, 1 for the presence of inflammatory stroma (perinodal hyperechogenic ring; default = 1).
ecs	Dichotomous value 0, 1 for the presence of extracapsular spread (cortical interruption; default = 1).
hab	Dichotomous value 0, 1 for the absence of the hilum (nodal core sign; default = 0).
eco	Dichotomous value 0, 1 for heterogeneous echogenicity (echostructure; default = 1).
vp	Categorical value (integers from 0 to 4) associated to a high-risk vascular flow architecture pattern (default = c(1, 2, 3)).
vfl	Categorical value (integers from 0 to 4) associated to a high-risk vascular flow localization (default = c(2, 3, 4)).
ct	Categorical value (integers from 0 to 4) associated to a high-risk cortical thickening (default = 2).
fid	Categorical value (integers from 0 to 3) associated to a high-risk focal intranodal deposit (default = c(1, 2, 3)).
cmid	Categorical value (integers from 0 to 4) associated to a high-risk cortical-medullar interface distortion (default = c(2, 3, 4)).
shape	Categorical value (integers from 1 to 3) associated to a high-risk shape (default = 3).
cs	Ordinal value (integers from 1 to 5) associated to a high-risk color score (default = 3).
grouping	Categorical value (integers from 1 to 3) associated to a high-risk grouping (default = c(2, 3)).
asFactor	Logical value. If TRUE, data.frame columns are converted to factors (default = FALSE).
...	Currently ignored.

Details

This function is internally used to estimate the malignancy risk through the robust binomial model (Morphonode-RBM). Dichotomization is performed to avoid the extremely low frequancy of some levels in categorical variables.

Value

An ultrasound profile with imputed missing values.

Author(s)

Fernando Palluzzi [e-mail]

References

Fragomeni SM, Moro F, Palluzzi F, Mascilini F, Rufini V, Collarino A, Inzani F, Giacò L, Scambia G, Testa AC, Garganese G (2022). Evaluating the risk of inguinal lymph node metastases before surgery using the Morphonode Predictive Model: a prospective diagnostic study. Ultrasound xx Xxxxxxxxxx xxx Xxxxxxxxxx. 00(0):000-000. <https://doi.org/00.0000/00000000000000000000>

See Also

See uss for metastatic risk signature detection (Morphonode-DT module).

Examples

# Create a dichotomous version of subjects with metastatic signature, 
# from the default simulated dataset.

M <- dichotomize(mpm.us[mpm.us$signature == "MET", 2:15])
print(head(M))

Morphonodepredictivemodel/morphonode documentation built on Feb. 15, 2023, 4:51 a.m.