Background: After radiochemotherapy, 30% of patients with early worsening MRI experience pseudoprogression (Psp) which is not distinguishable from early progression (EP). We aimed to assess the diagnostic value of radiomics in patients with suspected EP or Psp.
Methods: Radiomics features (RF) of 76 patients (53 EP and 23 Psp) retrospectively identified were extracted from conventional MRI based on four volumes-of-interest. Subjects were randomly assigned into training and validation groups. Classification model (EP versus Psp) consisted of a random forest algorithm after univariate filtering. Overall (OS) and progression-free survivals (PFS) were predicted using a semi-supervised principal component analysis, and forecasts were evaluated using C-index and integrated Brier scores (IBS).
Results: Using 11 RFs, radiomics classified patients with 75.0% and 76.0% accuracy, 81.6% and 94.1% sensitivity, 50.0% and 37.5% specificity, respectively, in training and validation phases. Addition of MGMT promoter status improved accuracy to 83% and 79.2%, and specificity to 63.6% and 75%. OS model included 14 RFs and stratified low- and high-risk patients both in the training (hazard ratio [HR], 3.63; P = .002) and the validation (HR, 3.76; P = .001) phases. Similarly, PFS model stratified patients during training (HR, 2.58; P = .005) and validation (HR, 3.58; P = .004) phases using 5 RF. OS and PFS forecasts had C-index of 0.65 and 0.69, and IBS of 0.122 and 0.147, respectively.
Conclusions: Conventional MRI radiomics has promising diagnostic value, especially when combined with MGMT promoter status, but with moderate specificity. In addition, our results suggest a potential for predicting OS and PFS.
Keywords: artificial intelligency; deep learning; glioblastoma; magnetic resonance imaging; pseudoprogression.
© The Author(s) 2019. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.