Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model

Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional radiomics, where a large number of radiomic features are directly used to train and test models that predict genotypes or clinical outcomes. In order to tackle this problem, we propose an unsupervised learning pipeline composed of an autoencoder for representation learning of radiomic features and a Gaussian mixture model based on minimum message length criterion for clustering. By incorporating probabilistic modeling, disease heterogeneity has been taken into account. The performance of the proposed pipeline was evaluated on an institutional MRI cohort of 108 patients with colorectal cancer liver metastases. Our approach is capable of automatically selecting the optimal number of clusters and assigns patients into clusters (imaging subtypes) with significantly different survival rates. Our method outperforms other unsupervised clustering methods that have been used for radiomics analysis and has comparable performance to a state-of-the-art imaging biomarker.Comment: Accepted at MICCAI 201

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma

We proposed a pooling-based radiomics feature selection method and showed how it would be applied to the clinical question of predicting one-year survival in 130 patients treated for glioma by radiotherapy. The method combines filter, wrapper and embedded selection in a comprehensive process to identify useful features and build them into a potentially predictive signature. The results showed that non-invasive CT radiomics were able to moderately predict overall survival and predict WHO tumour grade. This study reveals an associative inter-relationship between WHO tumour grade, CT-based radiomics and survival, that could be clinically relevant

MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award

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A case study on the prediction of pCR in breast tumours and the axilla

Carrasquinha, E., Santinha, J., Mongolin, A., Lisitskiya, M., Ribeiro, J., Cardoso, F., Matos, C., Vanneschi, L., & Papanikolaou, N. (2020). Regularization techniques in radiomics: A case study on the prediction of pCR in breast tumours and the axilla. In P. Cazzaniga, D. Besozzi, I. Merelli, & L. Manzoni (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics: 16th International Meeting, CIBB 2019, Revised Selected Papers (pp. 271-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12313 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63061-4_24Clinicians have shown an increasing interest in quantitative imaging for precision medicine. Imaging features can extract distinct phenotypic differences of tumours, potentially they can be used as a non-invasive prognostic tool and contribute for a better prediction of pathological Complete Response (pCR). However, the high-dimensional nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. In this work, classic lasso, ridge and the recently proposed priority-lasso are applied to high-dimensional imaging data, regarding a binary outcome. A breast cancer dataset, with radiomics, clinical and pathological information as features, was used. The application of sparsity techniques to the dataset enabled the selection of relevant features extracted in MRI of breast cancer patients, in order to identify the accuracy of those features and predict the pCR in the breast and the axilla.authorsversionpublishe

Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction

In this paper, a two-step convolutional neural network (CNN) for brain tumor segmentation in brain MR images with a random forest regressor for survival prediction of high-grade glioma subjects are proposed. The two-step CNN consists of three 2D U-nets for utilizing global information on axial, coronal, and sagittal axes, and a 3D U-net that uses local information in 3D patches. In our two-step setup, an initial segmentation probability map is first obtained using the ensemble 2D U-nets; second, a 3D U-net takes as input both the MR image and initial segmentation map to generate the final segmentation. Following segmentation, radiomics features from T1-weighted, T2-weighted, contrast enhanced T1-weighted, and T2-FLAIR images are extracted with the segmentation results as a prior. Lastly, a random forest regressor is used for survival time prediction. Moreover, only a small number of features selected by the random forest regressor are used to avoid overfitting. We evaluated the proposed methods on the BraTS 2019 challenge dataset. For the segmentation task, we obtained average dice scores of 0.74, 0.85 and 0.80 for enhanced tumor core, whole tumor, and tumor core, respectively. In the survival prediction task, an average accuracy of 50.5% was obtained showing the effectiveness of the proposed methods. © Springer Nature Switzerland AG 2020