Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

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

Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional GE data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (~document) as a mixture over cancer-topics, where each cancer-topic is a mixture over GE values (~words). This required some extensions to the standard LDA model eg: to accommodate the "real-valued" expression values - leading to our novel "discretized" Latent Dirichlet Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which describes breast cancer patients using the r=49,576 GE values, from microarrays. Our results show that our approach provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this approach by running it on the Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq modality - and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach

Using survival prediction techniques to learn consumer-specific reservation price distributions

A consumer’s “reservation price” (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, including personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able to predict these values, based on each consumer’s specific information, using a model learned from earlier consumer transactions. Here, we view each such (non)transaction as a censored observation, which motivates us to use techniques from survival analysis/prediction, to produce models that can generate a consumer-specific RP distribution, based on features of each new consumer. To validate this framework of RP, we run experiments on realistic data, with four survival prediction methods. These models performed very well (under three different criteria) on the task of estimating consumer-specific RP distributions, which shows that our RP framework can be effective

The Cure: Making a game of gene selection for breast cancer survival prediction

Motivation: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Here, we developed and evaluated a game called The Cure on the task of gene selection for breast cancer survival prediction. Our central hypothesis was that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from game players. We envisioned capturing knowledge both from the players prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data clearly demonstrated the accumulation of relevant expert knowledge. In terms of predictive accuracy, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure

Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction

Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed by survival regression and classification using these abnormal tumor tissue segments and other relevant clinical features. The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI. The survival prediction step includes two representative survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) global challenges in 2017 and 2018. The best overall survival pipeline in the proposed framework achieves leave-one-out cross-validation (LOOCV) accuracy of 0.73 for training datasets and 0.68 for validation datasets, respectively. These training and validation accuracies for tumor patient survival prediction are among the highest reported in literature. Finally, a critical analysis of radiomics features and efficacy of these features in segmentation and survival prediction performance is presented as lessons learned

Deep learning for cancer survival prediction

Cancer claimed 18.1 millions deaths worldwide in 2018 and $87.8 billion for health-care in 2014 in USA. The tremendous impact this disease supposes worldwide, combined with the increasingly availability of genomic and transcriptomic data, have aroused the interest on incorporating cutting edge technologies, such as Deep Learning (DL), in the fight against cancer. DL has stand out in the last years, particularly because of the performance of the Convolutional Neural Networks (ConvNets) models in image recognition. The problem for which all models in this project have been trained is the prediction of cancer survival in a discrete set of time intervals, from RNA-Seq data, because of the importance survival analysis have in the study of cancer treatment and its improvement. The very nature of biological data brings some inconvenients when using it for training a ConvNet model. These data are usually composed by a much bigger number of features (M) than observations (N). This is known as the Curse of Dimensionality (M>>N). Other inconvenient is the lack, a priori, of spatial information among biological features. ConvNet is a DL model which is specially designed for image processing, in which the pixels composing them are related to its neighbour. This relation is used by ConvNets to extract more knowledge from observations and have, in consequence, a better performance. This project proposes some strategies to try to solve these two inconvenients. In order to equip gene-expression-profiles with structure, five strategies have been proposed, applied and compared. Similarly, the transfer learning technique known as fine-tuning have been applied to try to solve the inconvenient which we refer to as the Curse of Dimensionality. The comparison of these models, all trained with the same set of features and observations, has been made by calculating the Concordance Index (C-index) metric for each of them.El cáncer se cobró 18,1 millones de muertes a nivel mundial en 2018 y$87,8 billones para cuidados de salud durante el año 2014 en EEUU. El tremendo impacto que esta enfermedad supone a nivel mundial, junto con la disponibilidad cada vez mayor de datos genómicos y transcriptómicos, han potenciado el interés en incorporar tecnologías de vanguardia, como es el Aprendizaje Profundo (AI), a la lucha contra el cáncer. AI ha destacado en los últimos años, particularmente por el rendimiento de los modelos de Redes Neuronales Convolucionales (RNC) en reconocimiento de imágenes. El problema para el cual todos los modelos de este proyecto han sido entrenados es la predicción de supervivencia en cáncer en un conjunto discreto de intervalos de tiempo a partir de datos de RNA-Seq, debido a la importancia que el análisis de la supervivencia tiene en cuanto al estudio de los tratamientos contra el cáncer y su mejora. La propia naturaleza de los datos biológicos trae consigo algunos inconvenientes cuando se usan para entrenar modelos de RNC. Estos datos normalmente est´an formados por un número mucho mayor de variables (M) que de observaciones (N). Esto se conoce como la maldición de la dimensionalidad (en inglés, the Curse of Dimensionality) (M>>N). Otro inconveniente es la falta, a priori, de información espacial entre las variables biológicas. RNC son un tipo de modelo concreto de Aprendizaje Profundo que está especialmente pensado para el procesado de imágenes, en las cuales los píxeles que las componen se relacionan con sus píxeles vecinos. Esta relación se usa en las RNC para extraer más conocimientos de las observaciones y tener, en consecuencia, un mejor rendimiento. En este proyecto se proponen algunas estrategias para tratar de resolver estos dos inconvenientes. Con el objetivo de equipar a los perfiles de expresión génica con estructura, cinco estrategias han sido propuestas, aplicadas y comparadas. ..