Prediction of Rapid Early Progression and Survival Risk with PreRadiation MRI in WHO Grade 4 Glioma Patients
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patient Data
2.2. Algorithm Pipeline for Prediction of Rapid Early Progression (REP)
2.3. MRI Preprocessing, Tumor Volume Segmentation and Feature Extraction
2.3.1. MRI Preprocessing
2.3.2. Tumor Volume Segmentation
2.3.3. Feature Extraction
2.4. Selection of Radiomics Features and Model Building
2.5. Survival Analysis Modeling under Dependent Censoring
2.6. Survival Prediction
3. Results
3.1. Predictive Performance of Rapid Early Progression (REP) Classification
3.2. Survival Probability Analysis under Dependent Censoring
(2.46 × T1C_ptpsa_GLZSM_LargeZoneLowGrayEmphasis).
3.3. Binary Prediction of Survival
3.4. Analysis of Prognostic Groups and Its Association with REP Status
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Algorithm A1: Subject Independent random sampling with i iteration for feature ranking. 
1: Input: total iteration number I, number of folds n, Radiomics feature data frame D, data frame with REP status D_{p}, data frame with nonREP status D_{N} 2: Define S_{N} as 20 patients randomly sampled from 57 nonREP D_{N} in each iteration i, S_{p} 13 patients with REP status in each iteration i 3: Define Y as target class vector and $\left\{{X}_{j}\right\}$_{j=1 to k} the feature matrix in the data frame D, k is number of total features. 4: for iteration I = 1 to I do 5: Initialize S_{N} D_{N}, randomly sample 20 patients from 57 nonREP patients. 6: Initialize S_{P =} D_{P} 7: Initialize D = {D_{N,} D_{P}} 8: Save D after each ith iteration 9: within D split the target variable vector y and feature variable matrix as $\left\{{X}_{j}\right\}$_{j=1 to k} 10: for fold = 1 to n do 11: enumerate train and test indices for nthfold in $\left\{{X}_{j}\right\}$_{j=1 to k} and y 12: for j = 1 to k do 13: fit a DTon the train indices of $\left\{{X}_{j}\right\}$ 14: predict ŷ on the test indices of $\left\{{X}_{j}\right\}$ 15: calculate F1score based on true y and predicted ŷ of the test indices of $\left\{{X}_{j}\right\}$ 16: assign the score as the feature score of each $\left\{{X}_{j}\right\}$ 17: end for 18: end for 19: Output: Cumulative F1score after nfold crossvalidation 20: end for 21: Output: Ranked features based on cumulative score after I iterations 
Appendix A.1. Model Building Algorithm
Algorithm A2: Subject independent nfold crossvalidation with i iteration for model evaluation. 
1: Input: D after each i iterations, ranking score R for each feature in matrix $\left\{{X}_{j}\right\}$_{j=1 to k} 2: Define {X_{S}} selected feature matrix based on R which is a subset of $\left\{{X}_{j}\right\}$_{j=1 to k}, Classification model C, Model performance evaluation metrics P 3: for iteration = 1 to I do 4: Initialize D = {D_{N}, D_{P}} 5: Initialize {X_{S}} based on R and target vector y from D 6: for fold = 1 to n do 7: enumerate train and test index for nfold in {X_{S}} and y 8: fit classifier model C to the train index of {X_{S}} and y 9: evaluate C on the test index of {X_{S}} 10: Save P after each n 11: end for 12: Output: Cumulative P after n fold 13: end for 14: Output: Mean values of P after I iterations 
Original Extracted Features for NonFractal Model  Significant (pValue < 0.05) Features from SecondStep Feature Selection (Rank Score) 

41 texture features extracted from raw T1C 

12 volumetric features  None 
9 arearelated features 

6 histogram statistics  None 
Original Extracted Features for Fractal Model  Significant (pValue < 0.05) Features from SecondStep Feature Selection (Rank Score) 

41 texture features extracted from PTPSA, mBm and GmBm of T1C (fractal features) 

41 texture features extracted from raw T1C 

12 volumetric features  None 
9 arearelated features  None 
6 histogram statistics  None 
Appendix A.2. Detailed Statistical Analysis of Selected Features
NonREP (n = 57)  REP (n = 13)  pValue  

Eccentricity in edema region  0.0446  
Mean (±std)  0.8320 ± 0.1771  0.7779 ± 0.1184  
Standard error  0.0235  0.0328  
Median  0.8922  0.8109  
Second axis (yaxis) length in necrosis region  0.0116  
Mean (±std)  0.5445 ± 0.1995  0.7072 ± 0.2239  
Standard error  0.0264  0.0621  
Median  0.5597  0.8085  
Autocorrelation of GTSDM from T1C  0.0262  
Mean (±std)  0.3731 ± 0.2125  0.5137 ± 0.1381  
Standard error  0.0281  0.0383  
Median  0.3522  0.4984 
NonREP (n = 57)  REP (n = 13)  pValue  

GmBm * of T1C  0.0296  
Mean (±std)  0.3587 ± 0.2012  0.2372 ± 0.1272  
Standard error  0.0267  0.0353  
Median  0.3325  0.1918  
Strength of NGTDM from 37th direction of T1C  0.0019  
Mean (±std)  1.9390 ± 0.5780  0.8260 ± 0.4864  
Standard error  0.0415  0.1349  
Median  1.2292  0.6970  
Strength of NGTDM  0.0013  
Mean (±std)  0.1224 ± 0.0665  0.0389 ± 0.0299  
Standard error  0.0221  0.0083  
Median  0.0752  0.0252 
Appendix B
Features Name  Coefficient  pValue 

T1C_mBm_GLZSM_LargeZoneLowGrayEmphasis ^{1}  5.46  0.002 
T1C_ptpsa_GLZSM_LargeZoneLowGrayEmphasis ^{2}  2.46  0.038 
Fractal Model  NonFractal Model  

Feature Numbers  Difference in Survival Curves (pValue *)  Difference in Survival Curves (pValue *) 
3  0.261 (0.0001)  0.198 (0.003) 
5  0.168 (0.010)  0.161 (0.013) 
7  0.169 (0.009)  0.173 (0.007) 
9  0.183 (0.004)  0.128 (0.039) 
10  0.169 (0.008)  0.127 (0.04) 
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Total (n = 70)  REP (n = 13)  NonREP (n = 57)  

Survival Days from Surgery  
Present (patient dead/expired)  45  8  37 
Lost followup (censored) *  22  5  17 
Not dead nor censored  3  0  3 
MGMT Promoter Status  
Hypermethylated  23  5  18 
Unmethylated  33  6  27 
Indeterminate  14  2  12 
IDH1 Mutation Status  
Wild type  59  12  47 
Mutant  8  1  7 
Indeterminate  3  0  3 
1p19qCodeletion Status  
Codeletion  2  0  2 
Negative  18  1  17 
Indeterminate  50  12  38 
Expired/Dead (Denoted as 1)  Alive (Denoted as 0)  

NonCensored Patients (n = 45)  45  None 
Censored/Lost Followup Patients (n = 22)  9  13 
Model Configurations  Area under Curve (AUC)  Accuracy (%)  Positive Predictive Value (PPV)  False Positive Rate (FPR) 

NonFractal Model  0.673 ± 0.082  63.5 ± 0.069  0.617 ± 0.067  0.262 ± 0.177 
Fractal Model  0.793 ± 0.082  78.1 ± 0.071  0.761 ± 0.069  0.145 ± 0.107 
Fractal Model Features  
Features Name  CoEfficient  pValue 
ET2 ^{1}  −1.58  0.0045 
T1C_ptpsa_GLZSM_Low_Gray_Level_Zone_Emphasis  1.33  0.0110 
L2_Orientation ^{2}  0.74  0.0183 
edema_FirstAxisLength ^{3}  −0.91  0.0194 
wt_MajorAxisLength ^{4}  −0.93  0.0198 
L1_Extent ^{2}  0.76  0.0218 
L3_Orientation ^{2}  0.70  0.0261 
T1C_mBm_GLZSM_LargeZoneLowGrayEmphasis  2.27  0.0316 
nec_SecondAxis_1 ^{5}  −1.06  0.0355 
T1C_ED_Histogram_Mean ^{6}  1.09  0.0434 
NonFractal Model Features  
Features Name  CoEfficient  pValue 
ET2 ^{1}  −1.58  0.0045 
L2_Orientation ^{2}  0.74  0.0183 
edema_FirstAxisLength ^{3}  −0.91  0.0194 
wt_MajorAxisLength ^{4}  −0.93  0.0198 
L1_Extent ^{2}  0.76  0.0218 
L3_Orientation ^{2}  0.70  0.0261 
nec_SecondAxis_1  −1.06  0.0355 
T1C_ED_Histogram_Mean ^{6}  1.09  0.0434 
ED_up_left_y *  0.83  0.0585 
T1C_ED_Histogram_Skewness *  −1.18  0.0718 
Number of Features  Model Configurations  Area under Curve (AUC)  Accuracy (%)  Positive Predicted Value (PPV)  False Positive Rate (FPR) 

Top 3 features  NonFractal Model  0.730 ± 0.235  74.018 ± 0.045  0.843 ± 0.054  0.351 ± 0.088 
Fractal Model  0.659 ± 0.241  67.67 ± 0.040  0.817 ± 0.058  0.453 ± 0.088  
Top 5 features  NonFractal Model  0.783 ± 0.199  73.22 ± 0.048  0.847 ± 0.057  0.356 ± 0.098 
Fractal Model  0.658 ± 0.243  70.03 ± 0.048  0.811 ± 0.059  0.420 ± 0.090  
Top 7 features  NonFractal Model  0.735 ± 0.219  73.05 ± 0.045  0.872 ± 0.054  0.339 ± 0.106 
Fractal Model  0.762 ± 0.214  74.39 ± 0.046  0.881 ± 0.056  0.311 ± 0.109  
Top 9 features  NonFractal Model  0.725 ± 0.224  71.00 ± 0.049  0.861 ± 0.059  0.378 ± 0.115 
Fractal Model  0.719 ± 0.214  70.37 ± 0.048  0.844 ± 0.061  0.397 ± 0.107 
Model Configurations  Area under Curve  Accuracy (%)  Positive Predictive Value (PPV)  False Positive Rate (FPR) 

NonFractal Molecular  0.757 ± 0.214  72.36 ± 0.046  0.866 ± 0.058  0.354 ± 0.109 
Fractal Molecular  0.762 ± 0.214  73.48 ± 0.047  0.883 ± 0.057  0.322 ± 0.114 
Group Name  Number of Cases  Mean  Standard Deviation  Standard Error  Median  Range  pValue 

Bad Prognostic  34  420.382  335.353  57.513  329.00  48.00–1211.00  0.02 
Good Prognostic  33  678.424  494.209  86.031  511.00  57.00–1821.00  
NonREP  54  604.259  441.961  60.143  474.50  48.00–1821.00  0.006 
REP  13  311.615  340.626  94.472  172.00  57.00–1211.00 
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Farzana, W.; Basree, M.M.; Diawara, N.; Shboul, Z.A.; Dubey, S.; Lockhart, M.M.; Hamza, M.; Palmer, J.D.; Iftekharuddin, K.M. Prediction of Rapid Early Progression and Survival Risk with PreRadiation MRI in WHO Grade 4 Glioma Patients. Cancers 2023, 15, 4636. https://doi.org/10.3390/cancers15184636
Farzana W, Basree MM, Diawara N, Shboul ZA, Dubey S, Lockhart MM, Hamza M, Palmer JD, Iftekharuddin KM. Prediction of Rapid Early Progression and Survival Risk with PreRadiation MRI in WHO Grade 4 Glioma Patients. Cancers. 2023; 15(18):4636. https://doi.org/10.3390/cancers15184636
Chicago/Turabian StyleFarzana, Walia, Mustafa M. Basree, Norou Diawara, Zeina A. Shboul, Sagel Dubey, Marie M. Lockhart, Mohamed Hamza, Joshua D. Palmer, and Khan M. Iftekharuddin. 2023. "Prediction of Rapid Early Progression and Survival Risk with PreRadiation MRI in WHO Grade 4 Glioma Patients" Cancers 15, no. 18: 4636. https://doi.org/10.3390/cancers15184636