Multimodal MRI radiomic models to predict genomic mutations in diffuse intrinsic pontine glioma with missing imaging modalities

Purpose

Predicting H3.1, TP53, and ACVR1 mutations in DIPG could aid in the selection of therapeutic options. The contribution of clinical data and multi-modal MRI were studied for these three predictive tasks. To keep the maximum number of subjects, which is essential for a rare disease, missing data were considered. A multi-modal model was proposed, collecting all available data for each patient, without performing any imputation.

Methods

A retrospective cohort of 80 patients with confirmed DIPG and at least one of the four MR modalities (T1w, T1c, T2w, and FLAIR), acquired with two different MR scanners was built. A pipeline including standardization of MR data and extraction of radiomic features within the tumor was applied. The values of radiomic features between the two MR scanners were realigned using the ComBat method. For each prediction task, the most robust features were selected based on a recursive feature elimination with cross-validation. Five different models, one based on clinical data and one per MR modality, were developed using logistic regression classifiers. The prediction of the multi-modal model was defined as the average of all possible prediction results among five for each patient. The performances of the models were compared using a leave-one-out approach.

Results

The percentage of missing modalities ranged from 6 to 11% across modalities and tasks. The performance of each individual model was dependent on each specific task, with an AUC of the ROC curve ranging from 0.63 to 0.80. The multi-modal model outperformed the clinical model for each prediction tasks, thus demonstrating the added value of MRI. Furthermore, regardless of performance criteria, the multi-modal model came in the first place or second place (very close to first). In the leave-one-out approach, the prediction of H3.1 (resp. ACVR1 and TP53) mutations achieved a balanced accuracy of 87.8% (resp. 82.1 and 78.3%).

Conclusion

Compared with a single modality approach, the multi-modal model combining multiple MRI modalities and clinical features was the most powerful to predict H3.1, ACVR1, and TP53 mutations and provided prediction, even in the case of missing modality. It could be proposed in the absence of a conclusive biopsy.

DIPG-23. Artificial intelligence for detecting ACVR1 mutations in patients with DIPG using MRI and clinical data

INTRODUCTION: ACVR1 mutations are found in about 25% of patients with diffuse intrinsic pontine glioma (DIPG). Recent work has identified the combination of vandetanib and everolimus as a promising therapeutic approach for these patients. We investigate the predictive power of an AI model integrating clinical and radiomic information to predict ACVR1 mutation. METHODS: This retrospective monocentric study includes 65 patients with known ACVR1 status. Patients were scanned at the diagnosis time with at least one of the four structural MRI modalities (pre- and post-contrast T1, T2, FLAIR) and basic clinical information (age and sex) was collected. Radiomic features were extracted within the tumor region from each modality. For each modality, a recursive feature elimination method was used to select the most relevant features. Inside a leave-one-out framework, up to five logistic regression models were built: one per MRI modality and one for the clinical information. The final prediction for each patient was computed as the mean of the probabilities of ACVR1 mutation for the up to 5 different models. Assigning a different weight to clinical data according to age, (more or less than 10 years old) was also tested. RESULTS: Out of the 65 patients (mean age 7.9±3.7, 15 patients older then 10 years), ACVR1 mutations were identified with a 78% accuracy (sensitivity = 92% and specificity = 75%) in the leave-out-out process. Accounting for the clinical data in the model increase the accuracy to 82% (resp. sensitivity = 86% and specificity = 80%). CONCLUSION: The proposed multi model approach compensates for missing MR modalities while taking advantage of all the available information. Our first results suggest that a dedicated model could be developed for younger patients to improve the prediction. The different models will now be tested using additional data coming from the ongoing multi-centric BIOMEDE trials.

Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns

Background

Translation of predictive and prognostic image‐based learning models to clinical applications is challenging due in part to their lack of interpretability. Some deep‐learning‐based methods provide information about the regions driving the model output. Yet, due to the high‐level abstraction of deep features, these methods do not completely solve the interpretation challenge. In addition, low sample size cohorts can lead to instabilities and suboptimal convergence for models involving a large number of parameters such as convolutional neural networks.

Purpose

Here, we propose a method for designing radiomic models that combines the interpretability of handcrafted radiomics with a sub‐regional analysis.

Materials and methods

Our approach relies on voxel‐wise engineered radiomic features with average global aggregation and logistic regression. The method is illustrated using a small dataset of 51 soft tissue sarcoma (STS) patients where the task is to predict the risk of lung metastasis occurrence during the follow‐up period.

Results

Using positron emission tomography/computed tomography and two magnetic resonance imaging sequences separately to build two radiomic models, we show that our approach produces quantitative maps that highlight the signal that contributes to the decision within the tumor region of interest. In our STS example, the analysis of these maps identified two biological patterns that are consistent with STS grading systems and knowledge: necrosis development and glucose metabolism of the tumor.

Conclusions

We demonstrate how that method makes it possible to spatially and quantitatively interpret radiomic models amenable to sub‐regions identification and biological interpretation for patient stratification.