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European Radiology

, Volume 28, Issue 8, pp 3570–3571 | Cite as

Correction to: A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

  • Zijian Zhang
  • Jinzhong Yang
  • Angela Ho
  • Wen Jiang
  • Jennifer Logan
  • Xin Wang
  • Paul D. Brown
  • Susan L. McGovern
  • Nandita Guha-Thakurta
  • Sherise D. Ferguson
  • Xenia Fave
  • Lifei Zhang
  • Dennis Mackin
  • Laurence E. Court
  • Jing Li
Correction
  • 478 Downloads

Correction to: Eur Radiol

  https://doi.org/10.1007/s00330-017-5154-8

The original version of this article, published on 24 November 2017, unfortunately contained a mistake. The following correction has therefore been made in the original:

The presentation of Table 2 was incorrect. The corrected table is given below. The original article has been corrected.
Table 2

Radiomic features used in this study

Direct intensity and intensity histogram [112]*

Grey level co-occurrence matrix [132]

Grey level run length matrix [11]

Energy**

Inter-quartile range

Auto correlation**

Grey level non-uniformity

Global entropy

Kurtosis

Cluster prominence**

High grey level run emphasis**

Global max

Mean absolute deviation

Cluster shade**

Low grey level run emphasis

Global mean

Median absolute deviation

Cluster tendency**

Long-run emphasis

Global median

Percentile

Contrast**

Long-run high grey level emphasis

Global min

Percentile area

Correlation

Long-run low grey level emphasis

Global standard deviation

Quantile

Difference entropy

Short-run emphasis

Global uniformity

Range

Dissimilarity

Short-run high grey level emphasis**

Local entropy max

Skewness

Energy

Short-run low grey level emphasis

Local entropy mean

Gaussian fit amplitude

Entropy

Run length non-uniformity

Local entropy median

Gaussian fit area

Homogeneity

Run percentage

Local entropy min

Gaussian fit mean

Information measure correlation

 

Local entropy standard deviation

Gaussian fit standard deviation

Inverse different moment norm

 

Local range max

Histogram area

Inverse different norm

 

Local range mean

Local standard deviation median

Inverse variance

 

Local range median

Local standard deviation min

Max probability

 

Local range min

Local standard deviation standard

Sum average

 

Local range standard deviation

deviation

Sum entropy

 

Local standard deviation max

Root mean square

Sum variance**

 

Local standard deviation mean

Variance**

Variance**

 

Geometric shape [14]

Neighbourhood grey-tone difference matrix [10]

Histogram of oriented gradients [6]

Compactness

Roundness

Busyness

Inter-quartile range

Convex

Spherical disproportion

Coarseness

Kurtosis

Convex hull volume

Sphericity

Complexity

Mean absolute deviation

Mass

Surface area

Contrast

Median absolute deviation

Max 3D-diameter

Surface area density

Texture strength

Range

Mean breadth

Orientation

 

Skewness**

*Numbers of features selected from each category are shown in brackets (total = 285 features)

**Radiomic features selected for feature modelling by using concordance correlation coefficients (total = 43 features)

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  • Zijian Zhang
    • 1
    • 2
  • Jinzhong Yang
    • 2
  • Angela Ho
    • 2
    • 3
  • Wen Jiang
    • 4
  • Jennifer Logan
    • 4
  • Xin Wang
    • 2
  • Paul D. Brown
    • 4
  • Susan L. McGovern
    • 4
  • Nandita Guha-Thakurta
    • 5
  • Sherise D. Ferguson
    • 6
  • Xenia Fave
    • 2
  • Lifei Zhang
    • 2
  • Dennis Mackin
    • 2
  • Laurence E. Court
    • 2
  • Jing Li
    • 4
  1. 1.Central South University Xiangya HospitalChangshaChina
  2. 2.Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  3. 3.University of HoustonHoustonUSA
  4. 4.Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  5. 5.Department of Diagnostic RadiologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  6. 6.Department of NeurosurgeryThe University of Texas MD Anderson Cancer CenterHoustonUSA

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