DCE‐MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro‐fuzzy‐based model and nested model selection technique

• Published in: NMR in biomedicine 2017-09, Vol.30 (9), p.n/a
• Main Authors: Dehkordi, Azimeh N.V., Kamali‐Asl, Alireza, Wen, Ning, Mikkelsen, Tom, Chetty, Indrin J., Bagher‐Ebadian, Hassan
• Format: Article
• Language: English
• Subjects: adaptive neuro‐fuzzy inference system; Adaptive systems; Adolescent; Adult; Age; Aged; Aged, 80 and over; Artificial neural networks; Biological products; Blood plasma; Brain Neoplasms - mortality; Classification; Confusion; Contrast Media - chemistry; Contrast Media - pharmacokinetics; Data analysis; Data processing; dynamic contrast enhanced‐MRI; Efflux; Female; Fuzzy Logic; Fuzzy systems; Glioblastoma; Glioblastoma - mortality; glioblastoma multiforme; Humans; Leakage; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Models, Neurological; NMR; Nuclear magnetic resonance; Optimization; Parameter estimation; Parameter identification; Patients; pharmacokinetic analysis; Predictions; Proportional Hazards Models; Regression analysis; Regression models; Survival; Survival Analysis; survival prediction; Time Factors; Training; Young Adult
• ISSN: 0952-3480; 1099-1492
• DOI: 10.1002/nbm.3739

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 (vp) for Model 1; vp and volume transfer constant (Ktrans) for Model 2; vp, Ktrans and rate constant (kep) 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, kep and ve (Ktrans/kep) of Model 3, and Ktrans 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. This pilot study recruits a model selection (MS) technique in dynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) data processing, together with Cox regression statistical analysis, to identify the most effective vascular permeability parameters for training a neuro‐fuzzy system for the prediction of the survival time in patients with glioblastoma multiforme (GBM). This study