Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network

Abstract

Background: The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI.

Methods: Four hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed by manual correction. DSC perfusion MRI was performed to obtain T2* susceptibility signal intensity-time curves from each subregion of the tumors: enhancing tumor, non-enhancing tumor, peritumoral edema, and whole tumor. These, with arterial input functions, were fed into a neural network as multidimensional inputs. A convolutional long short-term memory model with an attention mechanism was developed to predict IDH genotypes. Receiver operating characteristics analysis was performed to evaluate the model.

Results: The IDH genotype predictions had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (area under the curve [AUC], 0.98; 95% confidence interval [CI], 0.969-0.991) and 91.7%, 92.1%, and 91.5%, respectively, in the test set (AUC, 0.95; 95% CI, 0.898-0.982). In temporal feature analysis, T2* susceptibility signal intensity-time curves obtained from DSC perfusion MRI with attention weights demonstrated high attention on the combination of the end of the pre-contrast baseline, up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype.

Conclusions: We developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas.

Keywords: angiogenesis; dynamic susceptibility contrast perfusion-weighted imaging; gliomas; isocitrate dehydrogenase mutations; recurrent neural network.

Fig. 1

MRI preprocessing pipeline. All the conventional MRI data were skull stripped, co-registered, and resampled. They were then submitted to tumor segmentation using convolutional neural networks with manual correction. The raw T2* signal intensity-time curve was obtained from DSC perfusion MRI using the subregion mask: peritumoral edema (blue), enhancing tumor core (green), and non-enhancing tumor core (red).

Fig. 2

(A) Schematic of the feed-forward neural network attention mechanism and (B) overview of the convolutional LSTM with an attention model network structure.

Fig. 3

Diagnostic performance and ROC analysis. (A) The diagnostic performance in both the validation and test sets and (B) the normalized confusion matrix for the test set. (C) ROC curve for the validation set (AUC, 0.98), (D) 95% CI AUC for the validation set using bootstrapping: 0.969–0.991, (E) ROC curve for the test set (AUC, 0.95), and (F) 95% CI AUC for the test set using bootstrapping: 0.898–0.982.

Fig. 4

Temporal patterns according to IDH genotype. (A) Graphical definition of the temporal patterns on the heat maps for attention weights overlaid upon T2* susceptibility intensity-time curves according to the segments with the highest and second highest attention weights: if the high attention weights were on segments 1 and 2; segments 1 and 3; segments 1 and 4; segments 2 and 3; segments 2 and 4; and segments 3 and 4 (red), the corresponding temporal patterns were defined as TP 1, TP 2, TP 3, TP 4, TP 5, and TP 6, respectively. (B) The frequencies of the temporal patterns of the heat maps of attention weights according to IDH genotype. The numbers represent percentages.Note: segment 1, pre-contrast baseline; segment 2, downslope of the signal drop; segment 3, upslope of the signal drop; and segment 4, post-bolus plateau.

Fig. 5

(A) Boxplots for the mean rCBV values corresponding to IDH genotypes. The vertical axis represents the mean rCBV values averaged from the whole tumor in arbitrary units. The asterisk (*) indicates that the mean rCBV values were significantly different between the groups (P = 0.005). Note that mean rCBV values according to IDH genotype largely overlap. Solid and hollow squares indicate the following cases of IDH-wildtype and IDH-mutant gliomas that had nearly the same rCBV values, respectively. (B), (C) Representative T2* susceptibility signal intensity-time curves from DSC perfusion MRI according to IDH genotype. The heatmap shows the normalized attention weights for each time step of the case belonging to the correct IDH genotype, as indicated by the color bar to the right of the graph. Brighter time steps indicate higher attention weights for IDH genotype prediction on the signal intensity-time curve. The vertical axis represents the z-normalized signal intensity in arbitrary units. The horizontal axis represents repetition time (the unit of time steps). Note that the T2* susceptibility signal intensity-time curve averaged from the whole tumor had a less steep upslope for the signal drop portion and an attenuated post-bolus plateau as well as a larger signal drop with a steeper downslope in the IDH-wildtype group than in the IDH-mutant group. The model therefore focused on the upslopes of the signal drop portion and post-bolus plateau in the IDH-wildtype group and on the pre-contrast baseline and the downslope of the signal drop in the IDH-mutant glioma. Corresponding CET1WI and FLAIR images for (D) IDH-wildtype and (E) IDH-mutant gliomas: note that the IDH-wildtype glioma shows thick, irregular enhancement with central necrosis, whereas the IDH-mutant glioma shows well-defined margins, reduced enhancement, and cyst-like necrosis.