diff --git a/data/baseline/baseline_glcm.csv b/data/baseline/baseline_glcm.csv
index 1f33d1d6..8737ebc2 100644
--- a/data/baseline/baseline_glcm.csv
+++ b/data/baseline/baseline_glcm.csv
@@ -1,6 +1,6 @@
-Patient ID,general_info_BoundingBox,general_info_GeneralSettings,general_info_ImageHash,general_info_ImageSpacing,general_info_InputImages,general_info_MaskHash,general_info_Version,general_info_VolumeNum,general_info_VoxelNum,Homogeneity1,Homogeneity2,ClusterShade,MaximumProbability,Idmn,SumVariance,Contrast,DifferenceEntropy,InverseVariance,Entropy,Dissimilarity,DifferenceVariance,Idn,Idm,Correlation,Autocorrelation,SumEntropy,AverageIntensity,Energy,SumSquares,ClusterProminence,SumAverage,Imc2,Imc1,DifferenceAverage,Id,ClusterTendency
-brain1,"(162, 84, 11, 47, 70, 7)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{'original': {}},9dc2c3137b31fd872997d92c9a92d5178126d9d3,0.post403.dev0+gd611f07,2,4137,0.28722572382985151,0.20022255640475706,19.605083427287216,0.008193101942105081,0.961402169623227,108.73139325453903,47.492125114429797,3.7440609780664209,0.19881884197093194,8.6484091908787004,5.2844687898663176,16.655637050270975,0.87260521573971694,0.20022255640475706,0.39219743588570566,289.54369940172597,5.354241321485615,16.60808368859043,0.003151601429689756,40.55236483913805,27995.937591943148,33.107615448855022,0.79188798671101246,-0.12298081261731311,5.2844687898663176,0.28722572382985151,108.73139325453907
-brain2,"(205, 155, 8, 20, 15, 3)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",f2b8fbc4d5d1da08a1a70e79a301f8a830139438,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{'original': {}},b41049c71633e194bee4891750392b72eabd8800,0.post403.dev0+gd611f07,1,453,0.34588791642687811,0.26177848833510498,127.13809223840238,0.0597732663763283,0.97092709086072326,42.317452064295658,29.293839050702996,3.3390497846557254,0.25338046923906615,6.2202489517449395,3.9826729595606403,11.757829127963504,0.89204192659477488,0.26177848833510498,0.31298362781586209,276.9390375378519,4.3841213671951538,17.596367643690353,0.021296604313774989,16.074173100898356,7743.448102798925,33.084919329182931,0.94806637990321463,-0.30140675421432073,3.9826729595606398,0.34588791642687811,42.317452064295665
-breast1,"(21, 64, 8, 9, 12, 3)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",016951a8f9e8e5de21092d9d62b77262f92e04a5,"(0.664062, 0.664062, 2.1)",{'original': {}},5aa7d57fd57e83125b605c036c40f4a0d0cfd3e4,0.post403.dev0+gd611f07,1,143,0.73109230839874451,0.72442214443357622,-0.016711211805404854,0.29665628002001326,0.93749593957145183,1.1696127286855562,0.67121866250587736,1.1846976792586774,0.48362030098551811,2.713185137166831,0.5711662030283523,0.32725642574315206,0.86221107221678805,0.72442214443357622,0.28882475722469503,3.9663379593499664,2.0621987981343954,2.0314251665935505,0.18015057742115115,0.45588441376750161,3.2994515259044608,3.9170802211991651,0.46586325566090836,-0.10371357440316083,0.5711662030283523,0.73109230839874451,1.1696127286855562
-lung1,"(206, 347, 32, 24, 26, 3)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",34dca4200809a5e76c702d6b9503d958093057a3,"(0.5703125, 0.5703125, 5.0)",{'original': {}},054d887740012177bd1f9031ddac2b67170af0f3,0.post403.dev0+gd611f07,1,837,0.41736196376573204,0.34435017672215545,-345.71336725148899,0.08912560596848712,0.97258239381784684,58.74756668430426,20.713449304070664,3.1875245017550018,0.27869716669593048,6.6556227958601175,3.2166030915302346,9.38199581278284,0.89963070737626616,0.34435017672215545,0.4968957940703928,411.4164748228452,4.6355019464777119,19.570210424032926,0.019550376521576325,21.652368580855146,9732.6943959902492,40.09024968089912,0.89429023840987421,-0.22722644766515582,3.2166030915302346,0.41736196376573204,58.747566684304267
-lung2,"(318, 333, 15, 87, 66, 11)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",14f57fd04838eb8c9cca2a0dd871d29971585975,"(0.6269531, 0.6269531, 5.0)",{'original': {}},e284ff05593bc6cb2747261882e452d4efbccb3a,0.post403.dev0+gd611f07,1,24644,0.67488828052808181,0.65209771741148159,-498.73190629686474,0.32422085412558954,0.99472500276765008,28.339974380216191,12.115468558259831,2.1805601671536725,0.35136435757987061,4.3065420577384446,1.5501077045805824,9.4711179692288354,0.97084505602500215,0.65209771741148159,0.36291098448004738,1250.7846278881173,3.1809315369280782,35.275329629029159,0.15022980791791918,10.631815040073569,13254.853382802215,70.617306920893128,0.57027092518828848,-0.13541543952656429,1.5501077045805824,0.67488828052808181,28.339974380216198
+Patient ID,general_info_BoundingBox,general_info_GeneralSettings,general_info_ImageHash,general_info_ImageSpacing,general_info_InputImages,general_info_MaskHash,general_info_Version,general_info_VolumeNum,general_info_VoxelNum,Homogeneity1,Homogeneity2,ClusterShade,MaximumProbability,Idmn,Contrast,DifferenceEntropy,InverseVariance,JointEntropy,Dissimilarity,DifferenceVariance,Idn,Idm,Correlation,Autocorrelation,SumEntropy,JointAverage,JointEnergy,SumSquares,ClusterProminence,SumAverage,Imc2,Imc1,DifferenceAverage,Id,ClusterTendency
+brain1,"(162, 84, 11, 47, 70, 7)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",5c9ce3ca174f0f8324aa4d277e0fef82dc5ac566,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{'original': {}},9dc2c3137b31fd872997d92c9a92d5178126d9d3,0.post403.dev0+gd611f07,2,4137,0.287225724,0.200222556,19.60508343,0.008193102,0.96140217,47.49212511,3.744060978,0.198818842,8.648409191,5.28446879,16.65563705,0.872605216,0.200222556,0.392197436,289.5436994,5.354241321,16.60808369,0.003151601,40.55236484,27995.93759,33.10761545,0.791887987,-0.122980813,5.28446879,0.287225724,108.7313933
+brain2,"(205, 155, 8, 20, 15, 3)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",f2b8fbc4d5d1da08a1a70e79a301f8a830139438,"(0.7812499999999999, 0.7812499999999999, 6.499999999999998)",{'original': {}},b41049c71633e194bee4891750392b72eabd8800,0.post403.dev0+gd611f07,1,453,0.345887916,0.261778488,127.1380922,0.059773266,0.970927091,29.29383905,3.339049785,0.253380469,6.220248952,3.98267296,11.75782913,0.892041927,0.261778488,0.312983628,276.9390375,4.384121367,17.59636764,0.021296604,16.0741731,7743.448103,33.08491933,0.94806638,-0.301406754,3.98267296,0.345887916,42.31745206
+breast1,"(21, 64, 8, 9, 12, 3)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",016951a8f9e8e5de21092d9d62b77262f92e04a5,"(0.664062, 0.664062, 2.1)",{'original': {}},5aa7d57fd57e83125b605c036c40f4a0d0cfd3e4,0.post403.dev0+gd611f07,1,143,0.731092308,0.724422144,-0.016711212,0.29665628,0.93749594,0.671218663,1.184697679,0.483620301,2.713185137,0.571166203,0.327256426,0.862211072,0.724422144,0.288824757,3.966337959,2.062198798,2.031425167,0.180150577,0.455884414,3.299451526,3.917080221,0.465863256,-0.103713574,0.571166203,0.731092308,1.169612729
+lung1,"(206, 347, 32, 24, 26, 3)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",34dca4200809a5e76c702d6b9503d958093057a3,"(0.5703125, 0.5703125, 5.0)",{'original': {}},054d887740012177bd1f9031ddac2b67170af0f3,0.post403.dev0+gd611f07,1,837,0.417361964,0.344350177,-345.7133673,0.089125606,0.972582394,20.7134493,3.187524502,0.278697167,6.655622796,3.216603092,9.381995813,0.899630707,0.344350177,0.496895794,411.4164748,4.635501946,19.57021042,0.019550377,21.65236858,9732.694396,40.09024968,0.894290238,-0.227226448,3.216603092,0.417361964,58.74756668
+lung2,"(318, 333, 15, 87, 66, 11)","{'verbose': False, 'voxelArrayShift': 2000, 'binWidth': 25, 'label': 1, 'interpolator': None, 'symmetricalGLCM': False, 'resampledPixelSpacing': None, 'gldm_a': 0, 'weightingNorm': None, 'padDistance': 5}",14f57fd04838eb8c9cca2a0dd871d29971585975,"(0.6269531, 0.6269531, 5.0)",{'original': {}},e284ff05593bc6cb2747261882e452d4efbccb3a,0.post403.dev0+gd611f07,1,24644,0.674888281,0.652097717,-498.7319063,0.324220854,0.994725003,12.11546856,2.180560167,0.351364358,4.306542058,1.550107705,9.471117969,0.970845056,0.652097717,0.362910984,1250.784628,3.180931537,35.27532963,0.150229808,10.63181504,13254.85338,70.61730692,0.570270925,-0.13541544,1.550107705,0.674888281,28.33997438
diff --git a/docs/features.rst b/docs/features.rst
index e77bce11..44b82cc1 100644
--- a/docs/features.rst
+++ b/docs/features.rst
@@ -9,7 +9,7 @@ subdivided into the following classes:
 
 * :ref:`radiomics-firstorder-label` (19 features)
 * :ref:`radiomics-shape-label` (16 features)
-* :ref:`radiomics-glcm-label` (27 features)
+* :ref:`radiomics-glcm-label` (26 features)
 * :ref:`radiomics-glszm-label` (16 features)
 * :ref:`radiomics-glrlm-label` (16 features)
 
diff --git a/docs/index.rst b/docs/index.rst
index 271cbe5f..8922fc92 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -41,6 +41,7 @@ Table of Contents
    customization
    radiomics
    features
+   removedfeatures
    contributing
    developers
    FAQs <faq>
diff --git a/docs/removedfeatures.rst b/docs/removedfeatures.rst
new file mode 100644
index 00000000..25e8c294
--- /dev/null
+++ b/docs/removedfeatures.rst
@@ -0,0 +1,88 @@
+.. _radiomics-removed-features-label:
+
+==========================
+Excluded Radiomic Features
+==========================
+
+Some commonly know features are not supported (anymore) in PyRadiomics. These features are listed here, so as to provide
+a complete overview, as well as argumentation for why these features are excluded from PyRadiomics
+
+Excluded GLCM Features
+----------------------
+
+For included features and class definition, see :ref:`radiomics-glcm-label`.
+
+.. _radiomics-excluded-sumvariance-label:
+
+1. Sum Variance
+###############
+
+.. math::
+    \textit{sum variance} = \displaystyle\sum^{2N_g}_{k=2}{(k-SA)^2p_{x+y}(k)}
+
+Sum Variance is a measure of heterogeneity that places higher weights on
+neighboring intensity level pairs that deviate more from the mean.
+
+This feature has been removed, as it is mathematically identical to Cluster Tendency (see
+:py:func:`~radiomics.glcm.RadiomicsGLCM.getClusterTendencyFeatureValue()`).
+
+The mathematical proof is as follows:
+
+(1) As defined in GLCM,
+    :math:`p_{x+y}(k) = \sum^{N_g}_{i=1}\sum^{N_g}_{j=1}{p(i,j)},\text{ where }i+j=k, k \in \{2, 3, \dots, 2N_g\}`
+
+(2) Starting with cluster tendency as defined in GLCM:
+
+.. math::
+    \textit{cluster tendency} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
+      {\big(i+j-\mu_x-\mu_y\big)^2p(i,j)}
+
+    = \displaystyle\sum^{2N_g}_{k=2}{\Big[\displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
+      {\big(i+j-\mu_x-\mu_y\big)^2p(i,j)} \text{, where }i+j=k\Big]}
+
+    = \displaystyle\sum^{2N_g}_{k=2}{\Big[\displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
+      {\big(k-(\mu_x+\mu_y)\big)^2p(i,j)} \text{, where }i+j=k \Big]}
+
+.. note::
+    Because inside the sum :math:`\sum^{2N_g}_{k=2}`, :math:`k` is a constant, and so are :math:`\mu_x` and
+    :math:`\mu_y`, :math:`\big(k-(\mu_x+\mu_y)\big)^2` is constant and can be taken outside the inner sum
+    :math:`\sum^{N_g}_{i=1}\sum^{N_g}_{j=1}`.
+
+.. math::
+    = \displaystyle\sum^{2N_g}_{k=2}{\Big[\big(k-(\mu_x+\mu_y)\big)^2\displaystyle\sum^{N_g}_{i=1}
+      \displaystyle\sum^{N_g}_{j=1}{p(i,j)} \text{, where }i+j=k \Big]}
+
+(3) Using (1.) and (2.)
+
+.. math::
+    \textit{cluster tendency} = \displaystyle\sum^{2N_g}_{k=2}{\Big[\big(k-(\mu_x+\mu_y)\big)^2p_{x+y}(k)\Big]}
+
+(4) As defined in GLCM, :math:`p_x(i) = \sum^{N_g}_{j=1}{P(i,j)}` and :math:`\mu_x = \sum^{N_g}_{i=1}{p_x(i)i}`,
+    therefore :math:`\mu_x = \sum^{N_g}_{i=1}\sum^{N_g}_{j=1}{P(i,j)i}`
+
+(5) Similarly as in (4.), :math:`\mu_y = \sum^{N_g}_{j=1}\sum^{N_g}_{i=1}{P(i,j)j}`
+
+(6) Using (4.) and (5.), :math:`\mu_x` and :math:`\mu_y` can then be combined as follows:
+
+.. math::
+    \mu_x + \mu_y = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{P(i,j)i} +
+    \displaystyle\sum^{N_g}_{j=1}\displaystyle\sum^{N_g}_{i=1}{P(i,j)j}
+
+    = \displaystyle\sum^{N_g}_{j=1}\displaystyle\sum^{N_g}_{i=1}{P(i,j)i + P(i, j)j}
+
+    = \displaystyle\sum^{N_g}_{j=1}\displaystyle\sum^{N_g}_{i=1}{P(i,j)(i + j)}
+
+    = \displaystyle\sum^{2N_g}_{k=2}{\Big[\displaystyle\sum^{N_g}_{j=1}
+    \displaystyle\sum^{N_g}_{i=1}{P(i,j)(i + j)} \text{, where } k = i + j\Big]}
+
+    = \displaystyle\sum^{2N_g}_{k=2}{p_{x+y}(k)k} = \textit{sum average (SA)}
+
+(7) Combining (3.) and (6.) yields the following formula:
+
+.. math::
+
+    \text{Cluster Tendency} =
+    \displaystyle\sum^{2N_g}_{k=2}{\Big[\big(k-SA\big)^2p_{x+y}(k)\Big]} =
+    \textit{ sum variance}
+
+Q.E.D
diff --git a/examples/exampleSettings/Params.yaml b/examples/exampleSettings/Params.yaml
index b63a6d47..645f39f5 100644
--- a/examples/exampleSettings/Params.yaml
+++ b/examples/exampleSettings/Params.yaml
@@ -31,7 +31,7 @@ imageType:
 # Featureclasses, from which features must be calculated. If a featureclass is not mentioned, no features are calculated
 # for that class. Otherwise, the specified features are calculated, or, if none are specified, all are calculated.
 featureClass:
-  shape:
+  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
     - 'Volume'
     - 'SurfaceArea'
     - 'SurfaceVolumeRatio'
@@ -45,8 +45,33 @@ featureClass:
     - 'MinorAxis'
     - 'LeastAxis'
     - 'Elongation'
-    - 'Flatness'  # Only enable these shape descriptors (disables redundant Compactness 1 and Compactness 2)
+    - 'Flatness'
   firstorder: [] # specifying an empty list has the same effect as specifying nothing.
-  glcm: # for lists none values are allowed, in this case, all features are enabled
-  glrlm:
+  glcm:  # Disable SumAverage by specifying all other GLCM features available
+    - 'Autocorrelation'
+    - 'JointAverage'
+    - 'ClusterProminence'
+    - 'ClusterShade'
+    - 'ClusterTendency'
+    - 'Contrast'
+    - 'Correlation'
+    - 'DifferenceAverage'
+    - 'DifferenceEntropy'
+    - 'DifferenceVariance'
+    - 'Dissimilarity'
+    - 'JointEnergy'
+    - 'JointEntropy'
+    - 'Homogeneity1'
+    - 'Homogeneity2'
+    - 'Imc1'
+    - 'Imc2'
+    - 'Idm'
+    - 'Idmn'
+    - 'Id'
+    - 'Idn'
+    - 'InverseVariance'
+    - 'MaximumProbability'
+    - 'SumEntropy'
+    - 'SumSquares'
+  glrlm: # for lists none values are allowed, in this case, all features are enabled
   glszm:
diff --git a/examples/exampleSettings/exampleCT.yaml b/examples/exampleSettings/exampleCT.yaml
index d5366baa..28898c66 100644
--- a/examples/exampleSettings/exampleCT.yaml
+++ b/examples/exampleSettings/exampleCT.yaml
@@ -14,22 +14,48 @@ imageType:
   Wavelet: {}
 
 featureClass:
-  shape: ['Volume',
-          'SurfaceArea',
-          'SurfaceVolumeRatio',
-          'Sphericity',
-          'SphericalDisproportion',
-          'Maximum3DDiameter',
-          'Maximum2DDiameterSlice',
-          'Maximum2DDiameterColumn',
-          'Maximum2DDiameterRow',
-          'MajorAxis',
-          'MinorAxis',
-          'LeastAxis',
-          'Elongation',
-          'Flatness'] # Only enable these shape descriptors (disables redundant Compactness 1 and Compactness 2)
+  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
+    - 'Volume'
+    - 'SurfaceArea'
+    - 'SurfaceVolumeRatio'
+    - 'Sphericity'
+    - 'SphericalDisproportion'
+    - 'Maximum3DDiameter'
+    - 'Maximum2DDiameterSlice'
+    - 'Maximum2DDiameterColumn'
+    - 'Maximum2DDiameterRow'
+    - 'MajorAxis'
+    - 'MinorAxis'
+    - 'LeastAxis'
+    - 'Elongation'
+    - 'Flatness'
   firstorder:
-  glcm:
+  glcm:  # Disable SumAverage by specifying all other GLCM features available
+    - 'Autocorrelation'
+    - 'JointAverage'
+    - 'ClusterProminence'
+    - 'ClusterShade'
+    - 'ClusterTendency'
+    - 'Contrast'
+    - 'Correlation'
+    - 'DifferenceAverage'
+    - 'DifferenceEntropy'
+    - 'DifferenceVariance'
+    - 'Dissimilarity'
+    - 'JointEnergy'
+    - 'JointEntropy'
+    - 'Homogeneity1'
+    - 'Homogeneity2'
+    - 'Imc1'
+    - 'Imc2'
+    - 'Idm'
+    - 'Idmn'
+    - 'Id'
+    - 'Idn'
+    - 'InverseVariance'
+    - 'MaximumProbability'
+    - 'SumEntropy'
+    - 'SumSquares'
   glrlm:
   glszm:
 
diff --git a/examples/exampleSettings/exampleMR_3mm.yaml b/examples/exampleSettings/exampleMR_3mm.yaml
index 4d71f983..9b991029 100644
--- a/examples/exampleSettings/exampleMR_3mm.yaml
+++ b/examples/exampleSettings/exampleMR_3mm.yaml
@@ -16,22 +16,48 @@ imageType:
   Wavelet: {}
 
 featureClass:
-  shape: ['Volume',
-          'SurfaceArea',
-          'SurfaceVolumeRatio',
-          'Sphericity',
-          'SphericalDisproportion',
-          'Maximum3DDiameter',
-          'Maximum2DDiameterSlice',
-          'Maximum2DDiameterColumn',
-          'Maximum2DDiameterRow',
-          'MajorAxis',
-          'MinorAxis',
-          'LeastAxis',
-          'Elongation',
-          'Flatness'] # Only enable these shape descriptors (disables redundant Compactness 1 and Compactness 2)
+  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
+    - 'Volume'
+    - 'SurfaceArea'
+    - 'SurfaceVolumeRatio'
+    - 'Sphericity'
+    - 'SphericalDisproportion'
+    - 'Maximum3DDiameter'
+    - 'Maximum2DDiameterSlice'
+    - 'Maximum2DDiameterColumn'
+    - 'Maximum2DDiameterRow'
+    - 'MajorAxis'
+    - 'MinorAxis'
+    - 'LeastAxis'
+    - 'Elongation'
+    - 'Flatness'
   firstorder:
-  glcm:
+  glcm:  # Disable SumAverage by specifying all other GLCM features available
+    - 'Autocorrelation'
+    - 'JointAverage'
+    - 'ClusterProminence'
+    - 'ClusterShade'
+    - 'ClusterTendency'
+    - 'Contrast'
+    - 'Correlation'
+    - 'DifferenceAverage'
+    - 'DifferenceEntropy'
+    - 'DifferenceVariance'
+    - 'Dissimilarity'
+    - 'JointEnergy'
+    - 'JointEntropy'
+    - 'Homogeneity1'
+    - 'Homogeneity2'
+    - 'Imc1'
+    - 'Imc2'
+    - 'Idm'
+    - 'Idmn'
+    - 'Id'
+    - 'Idn'
+    - 'InverseVariance'
+    - 'MaximumProbability'
+    - 'SumEntropy'
+    - 'SumSquares'
   glrlm:
   glszm:
 
diff --git a/examples/exampleSettings/exampleMR_5mm.yaml b/examples/exampleSettings/exampleMR_5mm.yaml
index 329b131e..fc6c3bbc 100644
--- a/examples/exampleSettings/exampleMR_5mm.yaml
+++ b/examples/exampleSettings/exampleMR_5mm.yaml
@@ -15,22 +15,48 @@ imageType:
   Wavelet: {}
 
 featureClass:
-  shape: ['Volume',
-          'SurfaceArea',
-          'SurfaceVolumeRatio',
-          'Sphericity',
-          'SphericalDisproportion',
-          'Maximum3DDiameter',
-          'Maximum2DDiameterSlice',
-          'Maximum2DDiameterColumn',
-          'Maximum2DDiameterRow',
-          'MajorAxis',
-          'MinorAxis',
-          'LeastAxis',
-          'Elongation',
-          'Flatness'] # Only enable these shape descriptors (disables redundant Compactness 1 and Compactness 2)
+  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
+    - 'Volume'
+    - 'SurfaceArea'
+    - 'SurfaceVolumeRatio'
+    - 'Sphericity'
+    - 'SphericalDisproportion'
+    - 'Maximum3DDiameter'
+    - 'Maximum2DDiameterSlice'
+    - 'Maximum2DDiameterColumn'
+    - 'Maximum2DDiameterRow'
+    - 'MajorAxis'
+    - 'MinorAxis'
+    - 'LeastAxis'
+    - 'Elongation'
+    - 'Flatness'
   firstorder:
-  glcm:
+  glcm:  # Disable SumAverage by specifying all other GLCM features available
+    - 'Autocorrelation'
+    - 'JointAverage'
+    - 'ClusterProminence'
+    - 'ClusterShade'
+    - 'ClusterTendency'
+    - 'Contrast'
+    - 'Correlation'
+    - 'DifferenceAverage'
+    - 'DifferenceEntropy'
+    - 'DifferenceVariance'
+    - 'Dissimilarity'
+    - 'JointEnergy'
+    - 'JointEntropy'
+    - 'Homogeneity1'
+    - 'Homogeneity2'
+    - 'Imc1'
+    - 'Imc2'
+    - 'Idm'
+    - 'Idmn'
+    - 'Id'
+    - 'Idn'
+    - 'InverseVariance'
+    - 'MaximumProbability'
+    - 'SumEntropy'
+    - 'SumSquares'
   glrlm:
   glszm:
 
diff --git a/examples/exampleSettings/exampleMR_NoResampling.yaml b/examples/exampleSettings/exampleMR_NoResampling.yaml
index 7360b71e..e43f25a1 100644
--- a/examples/exampleSettings/exampleMR_NoResampling.yaml
+++ b/examples/exampleSettings/exampleMR_NoResampling.yaml
@@ -19,22 +19,48 @@ imageType:
   Wavelet: {}
 
 featureClass:
-  shape: ['Volume',
-          'SurfaceArea',
-          'SurfaceVolumeRatio',
-          'Sphericity',
-          'SphericalDisproportion',
-          'Maximum3DDiameter',
-          'Maximum2DDiameterSlice',
-          'Maximum2DDiameterColumn',
-          'Maximum2DDiameterRow',
-          'MajorAxis',
-          'MinorAxis',
-          'LeastAxis',
-          'Elongation',
-          'Flatness'] # Only enable these shape descriptors (disables redundant Compactness 1 and Compactness 2)
+  shape:  # disable redundant Compactness 1 and Compactness 2 features by specifying all other shape features
+    - 'Volume'
+    - 'SurfaceArea'
+    - 'SurfaceVolumeRatio'
+    - 'Sphericity'
+    - 'SphericalDisproportion'
+    - 'Maximum3DDiameter'
+    - 'Maximum2DDiameterSlice'
+    - 'Maximum2DDiameterColumn'
+    - 'Maximum2DDiameterRow'
+    - 'MajorAxis'
+    - 'MinorAxis'
+    - 'LeastAxis'
+    - 'Elongation'
+    - 'Flatness'
   firstorder:
-  glcm:
+  glcm:  # Disable SumAverage by specifying all other GLCM features available
+    - 'Autocorrelation'
+    - 'JointAverage'
+    - 'ClusterProminence'
+    - 'ClusterShade'
+    - 'ClusterTendency'
+    - 'Contrast'
+    - 'Correlation'
+    - 'DifferenceAverage'
+    - 'DifferenceEntropy'
+    - 'DifferenceVariance'
+    - 'Dissimilarity'
+    - 'JointEnergy'
+    - 'JointEntropy'
+    - 'Homogeneity1'
+    - 'Homogeneity2'
+    - 'Imc1'
+    - 'Imc2'
+    - 'Idm'
+    - 'Idmn'
+    - 'Id'
+    - 'Idn'
+    - 'InverseVariance'
+    - 'MaximumProbability'
+    - 'SumEntropy'
+    - 'SumSquares'
   glrlm:
   glszm:
 
diff --git a/radiomics/glcm.py b/radiomics/glcm.py
index bfb1db80..f285ffa2 100644
--- a/radiomics/glcm.py
+++ b/radiomics/glcm.py
@@ -50,9 +50,9 @@ class RadiomicsGLCM(base.RadiomicsFeaturesBase):
   - :math:`p_x(i) = \sum^{N_g}_{j=1}{P(i,j)}` be the marginal row probabilities
   - :math:`p_y(j) = \sum^{N_g}_{i=1}{P(i,j)}` be the marginal column probabilities
   - :math:`\mu_x` be the mean gray level intensity of :math:`p_x` and defined as
-    :math:`\mu_x = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)i}`
+    :math:`\mu_x = \displaystyle\sum^{N_g}_{i=1}{p_x(i)i}`
   - :math:`\mu_y` be the mean gray level intensity of :math:`p_y` and defined as
-    :math:`\mu_y = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)j}`
+    :math:`\mu_y = \displaystyle\sum^{N_g}_{j=1}{p_y(j)j}`
   - :math:`\sigma_x` be the standard deviation of :math:`p_x`
   - :math:`\sigma_y` be the standard deviation of :math:`p_y`
   - :math:`p_{x+y}(k) = \sum^{N_g}_{i=1}\sum^{N_g}_{j=1}{p(i,j)},\text{ where }i+j=k,\text{ and }k=2,3,\dots,2N_g`
@@ -318,13 +318,13 @@ def getAutocorrelationFeatureValue(self):
     ac = numpy.sum(self.P_glcm * (i * j)[:, :, None], (0, 1))
     return ac.mean()
 
-  def getAverageIntensityFeatureValue(self):
+  def getJointAverageFeatureValue(self):
     r"""
     **2. Joint Average**
 
     .. math::
 
-      \mu_x = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)i}
+      \textit{joint average} = \mu_x = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{p(i,j)i}
 
     Returns the mean gray level intensity of the :math:`i` distribution.
 
@@ -344,7 +344,7 @@ def getClusterProminenceFeatureValue(self):
     .. math::
 
       \textit{cluster prominence} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
-      {\big( i+j-\mu_x(i)-\mu_y(j)\big)^4p(i,j)}
+      {\big( i+j-\mu_x-\mu_y\big)^4p(i,j)}
 
     Cluster Prominence is a measure of the skewness and asymmetry of the GLCM. A higher values implies more asymmetry
     about the mean while a lower value indicates a peak near the mean value and less variation about the mean.
@@ -363,7 +363,7 @@ def getClusterShadeFeatureValue(self):
     .. math::
 
       \textit{cluster shade} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
-      {\big(i+j-\mu_x(i)-\mu_y(j)\big)^3p(i,j)}
+      {\big(i+j-\mu_x-\mu_y\big)^3p(i,j)}
 
     Cluster Shade is a measure of the skewness and uniformity of the GLCM.
     A higher cluster shade implies greater asymmetry about the mean.
@@ -382,9 +382,13 @@ def getClusterTendencyFeatureValue(self):
     .. math::
 
       \textit{cluster tendency} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
-      {\big(i+j-\mu_x(i)-\mu_y(j)\big)^2p(i,j)}
+      {\big(i+j-\mu_x-\mu_y\big)^2p(i,j)}
 
     Cluster Tendency is a measure of groupings of voxels with similar gray-level values.
+
+    .. note::
+      Cluster Tendency is mathematically identical to Sum Variance, the latter has therefore been removed from
+      PyRadiomics. See :ref:`here <radiomics-excluded-sumvariance-label>` for the proof.
     """
     i = self.coefficients['i']
     j = self.coefficients['j']
@@ -415,7 +419,7 @@ def getCorrelationFeatureValue(self):
 
     .. math::
 
-      \textit{correlation} = \frac{\sum^{N_g}_{i=1}\sum^{N_g}_{j=1}{p(i,j)ij-\mu_x(i)\mu_y(j)}}{\sigma_x(i)\sigma_y(j)}
+      \textit{correlation} = \frac{\sum^{N_g}_{i=1}\sum^{N_g}_{j=1}{p(i,j)ij-\mu_x\mu_y}}{\sigma_x(i)\sigma_y(j)}
 
     Correlation is a value between 0 (uncorrelated) and 1 (perfectly correlated) showing the
     linear dependency of gray level values to their respective voxels in the GLCM.
@@ -477,7 +481,7 @@ def getDifferenceVarianceFeatureValue(self):
 
     .. math::
 
-      \textit{difference variance} = \displaystyle\sum^{N_g-1}_{k=0}{(1-DA)^2p_{x-y}(k)}
+      \textit{difference variance} = \displaystyle\sum^{N_g-1}_{k=0}{(k-DA)^2p_{x-y}(k)}
 
     Difference Variance is a measure of heterogeneity that places higher weights on
     differing intensity level pairs that deviate more from the mean.
@@ -505,13 +509,13 @@ def getDissimilarityFeatureValue(self):
     dis = numpy.sum((self.P_glcm * (numpy.abs(i - j))[:, :, None]), (0, 1))
     return dis.mean()
 
-  def getEnergyFeatureValue(self):
+  def getJointEnergyFeatureValue(self):
     r"""
     **12. Joint Energy**
 
     .. math::
 
-      \textit{energy} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{\big(p(i,j)\big)^2}
+      \textit{joint energy} = \displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}{\big(p(i,j)\big)^2}
 
     Energy is a measure of homogeneous patterns
     in the image. A greater Energy implies that there are more instances
@@ -524,13 +528,13 @@ def getEnergyFeatureValue(self):
     ene = numpy.sum((self.P_glcm ** 2), (0, 1))
     return ene.mean()
 
-  def getEntropyFeatureValue(self):
+  def getJointEntropyFeatureValue(self):
     r"""
     **13. Joint Entropy**
 
     .. math::
 
-      \textit{entropy} = -\displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
+      \textit{joint entropy} = -\displaystyle\sum^{N_g}_{i=1}\displaystyle\sum^{N_g}_{j=1}
       {p(i,j)\log_2\big(p(i,j)+\epsilon\big)}
 
     Entropy is a measure of the randomness/variability in neighborhood intensity values.
@@ -745,6 +749,12 @@ def getSumAverageFeatureValue(self):
     Sum Average measures the relationship between occurrences of pairs
     with lower intensity values and occurrences of pairs with higher intensity
     values.
+
+    .. warning::
+      When GLCM is symmetrical, :math:`\mu_x = \mu_y`, and therefore :math:`\text{Sum Average} = \mu_x + \mu_y =
+      2 \mu_x = 2 * Joint Average`. See formulas (4.), (5.) and (6.) defined
+      :ref:`here <radiomics-excluded-sumvariance-label>` for the proof that :math:`\text{Sum Average} = \mu_x + \mu_y`.
+      In the default parameter files provided in the ``examples/exampleSettings``, this feature has been disabled.
     """
     pxAddy = self.coefficients['pxAddy']
     kValuesSum = self.coefficients['kValuesSum']
@@ -766,26 +776,9 @@ def getSumEntropyFeatureValue(self):
     sumentr = (-1) * numpy.sum((pxAddy * numpy.log2(pxAddy + eps)), 0)
     return sumentr.mean()
 
-  def getSumVarianceFeatureValue(self):
-    r"""
-    **26. Sum Variance**
-
-    .. math::
-
-      \textit{sum variance} = \displaystyle\sum^{2N_g}_{k=2}{(k-SA)^2p_{x+y}(k)}
-
-    Sum Variance is a measure of heterogeneity that places higher weights on
-    neighboring intensity level pairs that deviate more from the mean.
-    """
-    pxAddy = self.coefficients['pxAddy']
-    kValuesSum = self.coefficients['kValuesSum']
-    sumavg = numpy.sum((kValuesSum[:, None] * pxAddy), 0, keepdims=True)
-    sumvar = numpy.sum((pxAddy * ((kValuesSum[:, None] - sumavg) ** 2)), 0)
-    return sumvar.mean()
-
   def getSumSquaresFeatureValue(self):
     r"""
-    **27. Sum of Squares**
+    **26. Sum of Squares**
 
     .. math::