The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent raise of radiomics and pathomics, i.e. the extraction of quantitative features from radiology and histopathology images routinely collected to predict clinical outcomes or to guide clinical decisions using artificial intelligence algorithms. Nevertheless, how to combine them into a single multimodal framework is still an open issue. In this work we therefore develop a multimodal late fusion approach that combines hand-crafted features computed from radiomics, pathomics and clinical data to predict radiation therapy treatment outcomes for non-small-cell lung cancer patients. Within this context, we investigate eight different late fusion rules (i.e. product, maximum, minimum, mean, decision template, Dempster-Shafer, majority voting, and confidence rule) and two patient-wise aggregation rules leveraging the richness of information given by computer tomography images and whole-slide scans. The experiments in leave-one-patient-out cross-validation on an in-house cohort of 33 patients show that the proposed multimodal paradigm with an AUC equal to $90.9\%$ outperforms each unimodal approach, suggesting that data integration can advance precision medicine. As a further contribution, we also compare the hand-crafted representations with features automatically computed by deep networks, and the late fusion paradigm with early fusion, another popular multimodal approach. In both cases, the experiments show that the proposed multimodal approach provides the best results.
Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.