Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review.

BACKGROUND: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Multiscale model of the human cardiovascular system: Description of heart failure and comparison of contractility indices.

A multiscale model of the cardiovascular system is presented. Hemodynamics is described by a lumped parameter model, while heart contraction is described at the cellular scale. An electrophysiological model and a mechanical model were coupled and adjusted so that the pressure and volume of both ventricles are linked to the force and length of a half-sarcomere. Particular attention was paid to the extreme values of the sarcomere length, which must keep physiological values. This model is able to reproduce healthy behavior, preload variations experiments, and ventricular failure. It also allows to compare the relevance of standard cardiac contractility indices. This study shows that the theoretical gold standard for assessing cardiac contractility, namely the end-systolic elastance, is actually load-dependent and therefore not a reliable index of cardiac contractility.

NIDCAP improves brain function and structure in preterm infants with severe intrauterine growth restriction.

OBJECTIVE: The effect of NIDCAP (Newborn Individualized Developmental Care and Assessment Program) was examined on the neurobehavioral, electrophysiological and neurostructural development of preterm infants with severe intrauterine growth restriction (IUGR). STUDY DESIGN: A total of 30 infants, 27-33 weeks gestation, were randomized to control (C; N=17) or NIDCAP/experimental (E; N=13) care. Baseline health and demographics were assessed at intake; electroencephalography (EEG) and magnetic resonance imaging (MRI) at 35 and 42 weeks postmenstrual age; and health, growth and neurobehavior at 42 weeks and 9 months corrected age (9 months). RESULTS: C and E infants were comparable in health and demographics at baseline. At follow-up, E infants were healthier, showed significantly improved brain development and better neurobehavior. Neurobehavior, EEG and MRI discriminated between C and E infants. Neurobehavior at 42 weeks correlated with EEG and MRI at 42 weeks and neurobehavior at 9 months. CONCLUSION: NIDCAP significantly improved IUGR preterm infants' neurobehavior, electrophysiology and brain structure. Longer-term outcome assessment and larger samples are recommended.

Is the Newborn Individualized Developmental Care and Assessment Program (NIDCAP) effective for preterm infants with intrauterine growth restriction?

OBJECTIVE: This study investigates the effectiveness of the Newborn Individualized Developmental Care and Assessment Program (NIDCAP) on neurobehavioral and electrophysiological functioning of preterm infants with severe intrauterine growth restriction (IUGR). STUDY DESIGN: Thirty IUGR infants, 28 to 33 weeks gestational age, randomized to standard care (control/C=18), or NIDCAP (experimental/E=12), were assessed at 2 weeks corrected age (2wCA) and 9 months corrected age (9mCA) in regard to health, anthropometrics, and neurobehavior, and additionally at 2wCA in regard to electrophysiology (EEG). RESULT: The two groups were comparable in health and anthropometrics at 2wCA and 9mCA. The E-group at 2wCA showed significantly better autonomic, motor, and self-regulation functioning, improved motility, intensity and response thresholds, and reduced EEG connectivity among several adjacent brain regions. At 9mCA, the E-group showed significantly better mental performance. CONCLUSION: This is the first study to show NIDCAP effectiveness for IUGR preterm infants.