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DCE‐MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro‐fuzzy‐based model and nested model selection technique
Author(s) -
Dehkordi Azimeh N.V.,
KamaliAsl Alireza,
Wen Ning,
Mikkelsen Tom,
Chetty Indrin J.,
BagherEbadian Hassan
Publication year - 2017
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.3739
Subject(s) - adaptive neuro fuzzy inference system , magnetic resonance imaging , glioblastoma , fuzzy inference system , computer science , nuclear medicine , fuzzy logic , mathematics , artificial intelligence , medicine , fuzzy control system , radiology , cancer research
This pilot study investigates the construction of an Adaptive Neuro‐Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) data analysis, and patient age. DCE‐MRI investigations of 33 treatment‐naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume ( v p ) for Model 1; v p and volume transfer constant ( K trans ) for Model 2; v p , K trans and rate constant ( k ep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K ‐fold cross‐validation ( K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, k ep and v e ( K trans / k ep ) of Model 3, and K trans of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off‐diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE‐MRI PK analysis, combined with the MS technique and ANFIS, allows the construction of a DCE‐MRI‐based fuzzy integrated predictor for the prediction of the survival of patients with GBM.