Research Article

Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

Table 6

Two examples of how predictive models work (continued Figure 2).

Radiomic parameters (only 12 of the 100 parameters were shown, based on the predictive model—lymphoma versus cancer)

Sample 1CTvalue_min_CT
−155.00
NGLDM_Coarseness_PET
0.16
HISTO_Entropy _log10_CT1
0.54
FDGuptake _TLG
(mL)_PET
5.35
HISTO_Entropy _log2_CT
4.98
GLRLM_RLNU _PET
17.90
GLCM_Entropy _log10_CT
1.90
SHAPE_Volume (mL) _PET
1.21
GLCM_Entropy _log2_CT
6.32
SHAPE_Volume (vx) _PET
19.00
GLZLM_ZLNU_CT
67.00
GLZLM_LZHGE _PET
467.00
Sample 2CTvalue_min_CT33
0.00
NGLDM_Coarseness_PET
0.06
HISTO_Entropy _log10_CT
0.57
FDGuptake_TLG (mL)_PET
36.80
HISTO_Entropy _log2_CT
1.89
GLRLM_RLNU _PET
86.30
GLCM_Entropy _log10_CT
1.14
SHAPE_Volume (mL)_PET
5.67
GLCM_Entropy _log2_CT
3.80
SHAPE_Volume (vx)_PET
89.00
GLZLM_ZLNU_CT
29.50
GLZLM_LZHGE _PET
798.00

Prediction calculated predictive variables according to the predictive model—lymphoma versus cancer

Sample 1Sample 2
Predictive variablePredictive scores (calculated from the predictive model)ResultPredictive variablePredictive scores (calculated from the predictive model)Result
SUVmax6.41CLNSUVmax12.00LLN
PREct0.29CLNPREct0.12CLN
PREpet0.18CLNPREpet0.49LLN
PREcombination1.00LLNPREcombination0.18CLN
Patient’s disease typeLLNPatient’s disease typeCLN

Abbreviations: CLN, cancerous-lymph nodes; LLN, lymphomatous-lymph nodes; HISTO, histogram; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run-length matrix; HGRE, high gray-level run emphasis; GLZLM, gray-level zone-length matrix; HGZE, high gray-level zone emphasis; RLNU, run-length nonuniformity; LZHGE, long-zone high gray-level emphasis; min, minimum; _CT, this parameter is extracted from the CT image; _PET, this parameter is extracted from the PET image; PREct, CT-predictive variables; PREpet, PET-predictive variables, PREcombination, the combination of PET and CT predictive variables.