Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Biobank for craniosynostosis and faciocraniosynostosis, rare pediatric congenital craniofacial disorders: a study protocol.

PURPOSE: Craniosynostosis (CRS) is a rare congenital cranial malformation in which 1 or more cranial or facial sutures are fused in utero or rapidly fused in early infancy. The cranial sutures separate the skull bone plates and enable rapid growth of the skull in the first 2 years of life, in which growth is largely dictated by growth of the brain. CRS is a rare disease that occurs in 1 in 2100 to 1 in 2500 births and may be either nonsyndromic (also referred to as isolated) or syndromic. In syndromic CRS, other birth defects are present next to the CRS. The distinction between nonsyndromic and syndromic manifestations is made on the basis of dysmorphologic evaluation and genetic evaluation. Owing to advances in genetic diagnostics, nonsyndromic patients are increasingly recognized as syndromic patients. CRS treatment is almost entirely surgical and is sometimes paired with postoperative helmet therapy for maintenance. Corrective procedures are complex, long, and associated with the risk of numerous complications, including heavy blood loss and its sequelae. Although surgery may restore a normal appearance, even in nonsyndromic patients, patients may experience persistent deficits in intellectual ability and cognitive function. The European Commission (EC) has prioritized rare diseases in recent horizon European research programs; indeed, collections or even individual samples may be extremely valuable for research. METHODS AND RESULTS: Here, we present a study protocol in which the combined expertise of clinicians and researchers will be exploited to generate a biobank dedicated to CRS. The generation of the CRS biobank presented in this study will include the collection of different types of biological materials as well as advanced radiological images available to the scientific community. CONCLUSION: The activation of a CRS biobank will provide an opportunity to improve translational research on CRS and to share its benefits with the scientific community and patients and their families.

The impact of COVID-19 on cancer patients.

The COVID-19 pandemic poses a significant challenge for individuals with compromised immune systems, such as patients with cancer, as they face a heightened susceptibility to severe infections compared to the general population. Such severe infections substantially increase the risk of morbidity and mortality among these patients. Notable risk factors for mortality include advanced age (> 70 years), current or past smoking history, advanced disease stage, the use of cytotoxic chemotherapy, and an Eastern Cooperative Oncology Group (ECOG) score of 2 or higher. Multiple types of vaccines have been developed and implemented, demonstrating remarkable efficacy in preventing infections. However, there have been observable reductions in their ability to elicit an immune response, particularly among individuals with hematological malignancies. The situation becomes more challenging due to the emergence of viral variants of concern (VOCs). Despite the increase in neutralizing antibody levels after vaccination, they remain lower in response to these evolving variants. The need for booster vaccinations has become apparent, particularly for this vulnerable population, due to the suboptimal immune response and waning of immunity post-vaccination. Examining and comprehending how the immune system reacts to various vaccination regimens for SARS-CoV-2 and its VOCs in cancer patients is crucial for designing clinical and public health strategies. This review aims to provide an updated overview of the effectiveness of COVID-19 vaccines in cancer patients, including those undergoing treatments such as hematopoietic stem cell transplantation (HCT) or chimeric antigen receptor (CAR) T cell therapy, by exploring the extent of both humoral and cellular immune responses to COVID-19 vaccination. Furthermore, it outlines risk factors and potential biomarkers that are associated with severe SARS-CoV-2 infection and vaccine responses, and offers suggestions for improving SARS-CoV-2 protection in cancer patients.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

Primary adrenal insufficiency induced by immune checkpoint inhibitors: biological, clinical, and radiological aspects.

Immune checkpoint inhibitors (ICI) have become a cornerstone in medical oncology, with evolving therapeutic strategies and applications. These monoclonal antibodies, designed to enhance immune responses, have revealed a spectrum of immune-related adverse events (irAEs). While many irAEs exhibit favorable responses to corticosteroid or immunosuppressive therapy, most ICI-related endocrinopathies necessitate lifelong replacement therapy and pose significant clinical challenges. Adrenal insufficiency (AI), a noteworthy endocrine irAE, can manifest as primary AI (PAI) or secondary AI (SAI), resulting from adrenal or pituitary gland dysfunction, respectively. ICI-induced AI, albeit relatively infrequent, occurs in 1-2% of patients receiving single-agent anti-Programmed Death-1/Programmed Death-Ligand 1 (PD-1/PD-L1) or Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) therapies and in a higher range of 4-9% when ICIs are used in combinations. Recognizing and addressing ICI-induced PAI is crucial, as it often presents with acute and potentially life-threatening symptoms, especially considering the expanding use of ICI therapy. This review provides an updated overview of ICI-induced PAI, exploring its clinical, diagnostic, and radiological aspects.