Lipomatous soft-tissue tumors grow from mesenchymal tissue, referred as lipoma and liposarcoma for benign and malignant tumors respectively. Five subclasses of liposarcoma exist, requiring different patient treatments. Most of the types are easily distinguishable relying on Magnetic Resonance Imaging (MRI). But lipoma and Atypical Lipomatous Tumor / Well-Differentiated Liposarcoma (ALT/WDL) have overlapping MR imaging characteristics. A biopsy is usually performed to detect cancerous cells, and classify the tumor. However, these biopsies are invasive, costly and unnecessary in most cases as the malignant/benign ratio is significantly low (1/100). This work aim to provide efficient MRI-based decision support tools to discriminate cancerous tumors, as an alternative to biopsies.
85 MRI scans from patients with lipoma or ALT/WDL were gathered from 43 different centers with non-uniform protocols. We compared different approaches based on three versions of the MRI dataset: a 2D version with only one slice per patient (where the tumor is the largest), a 3D version (with all the slices where the tumor is visible), and a 3D version with batch-effect correction, to remove the inter-site technical variability. Radiomic features were extracted from the datasets, producing respectively 35 and 92 features for the 2D and 3D collection. In parallel, the MR images were normalized for pixel intensity, and inhomogeneities were corrected. We compared traditional machine
learning algorithms based on radiomic data, to deep learning approaches using Convolutional Neural Networks (CNN) applied directly on MR images. Three CNN-based architectures were compared: a custom CNN learned from scratch, a fine-tuned pre-trained ResNet50 model, and a XGBoost classifier based on a CNN feature extraction.
The models performance were assessed using 10 cross-validation folds. On the batch-corrected 3D radiomic dataset, we achieve to classify correctly all the validation folds (mean AUROC = 1) using linear Support Vector Machine (SVM) with feature selection, as well as using a Random Forest classifier. The best image classification performance was obtained by fine-tuning the pre-trained ResNet50 (mean AUROC = 0.878, std AUROC = 0.11).
In our context of very limited observations, radiomic-based models outperformed the image-based approaches. Importantly, the batcheffect normalization, that removed the inter-site technical variability on the radiomic data, had a huge effect on the models performance. With such a small dataset, it was a hard task to train complex architectures like CNN. Moreover, the MRI scans were acquired on various body regions,
resulting in high heterogeneity in the images, making the generalization even harder. These exciting results on the radiomics need to be confirmed on an external validation cohort, but could have an impact on clinical practice to differentiate lipoma from ALT/WDL based only on MRI, and in a wider approach, to classify all types of lipomatous soft-tissue tumors.
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