BioMed Research International / 2021 / Article / Tab 1

Research Article

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors

Table 1

Preoperative radiographic features and clinical characteristics with potential clinical importance for diagnosis.

FeaturesFeature classPermissible valueICC

LocationCategoricalUpper tibia (a)/inferior femur (b)/upper humerus (c)/middle humerus (d)0.954
LocationCategoricalEpiphysis (a)/metaphysis (b)/diaphysis (c)/not applicable (d)0.854
Eccentric growthBinaryWithout (0)/with (1)0.921
Expansive growthBinaryWithout (0)/with (1)0.888
MarginBinarySharp(0)/ill-defined (1)0.832
Sclerotic borderBinaryWithout (0)/with (1)0.796
Periosteal reactionCategoricalWithout (a)/continuous (b)/interrupted (c)0.899
Radiographic densityCategoricalMixed (a)/low (b)/high (c)0.863
High-density componentsCategoricalWithout (a)/calcification or ossification (b)/tumor bone (c)/unrecognizable (d)0.761
Pattern of bone destructionCategoricalGeographic (a)/moth-eaten (b)/permeated (c)/not applicable (d)0.812
SourceBinaryMedullary (0)/cortical (1)0.909
Pathological fractureBinaryWithout (0)/with (1)0.888
Cortex involvementCategoricalComplete cortex (a)/cortical expansion and thinning (b)/interrupted cortex (c)0.870
Clinical data
GenderBinaryMale (0)/female (1)
Redness and hyperemiaBinaryWithout (0)/with (1)
SwellingBinaryWithout (0)/with (1)
WarmthBinaryWithout (0)/with (1)
PainBinaryWithout (0)/with (1)
Palpable massBinaryWithout (0)/with (1)
DyskinesiaBinaryWithout (0)/with (1)

Note: The location details are shown in supplement section.

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