Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.

Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach.

Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland-Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil's size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals.

Portable Oxygen Therapy: Is the 6-Minute Walking Test Overestimating the Actual Oxygen Needs?

The appropriate titration for the personalized oxygen needs of patients with chronic obstructive pulmonary disease (COPD) and severe hypoxemia is a determining factor in the success of long-term oxygen therapy. There are no standardized procedures to assist in determining the patient's needs during the physical activities of daily life. Despite that effort tests are a wide broad approach, further research concerning the development of protocols to titrate O2 therapy is needed. The main objective of this study was to assess whether the level of oxygen titrated through the 6-minute walking test (6MWT) for patients with COPD and exertional hypoxemia is adequate to meet the patients' demand during their activities of daily living. Physiological and subjective variables were estimated for a study population during two walking tests: a 6MWT and a 20-minute walking circuit (20MWC), designed ad-hoc to reproduce daily physical activities more truthfully. The results indicate that in a significant proportion of patients, the 6MWT might not accurately predict their oxygen needs at a domiciliary environment. Therefore, the titration of the portable O2 therapy could not be optimal in these cases, with the detrimental impact on the patient's health (hyperoxia episodes), the autonomy of the oxygen device, and the decrease of time out of the home.