SenNet recommendations for detecting senescent cells in different tissues.

Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.

Applications of Artificial Intelligence for Pediatric Cancer Imaging.

Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric cancers are rare, and imaging technologies are evolving rapidly, leading to insufficient data of a particular type to effectively train these algorithms. The small market size of pediatric patients compared with adult patients could also contribute to this challenge, as market size is a driver of commercialization. This review provides an overview of the current state of AI applications for pediatric cancer imaging, including applications for medical image acquisition, processing, reconstruction, segmentation, diagnosis, staging, and treatment response monitoring. Although current developments are promising, impediments due to the diverse anatomies of growing children and nonstandardized imaging protocols have led to limited clinical translation thus far. Opportunities include leveraging reconstruction algorithms to achieve accelerated low-dose imaging and automating the generation of metric-based staging and treatment monitoring scores. Transfer learning of adult-based AI models to pediatric cancers, multiinstitutional data sharing, and ethical data privacy practices for pediatric patients with rare cancers will be keys to unlocking the full potential of AI for clinical translation and improving outcomes for these young patients.

Musculoskeletal imaging of senescence.

Senescent cells play a vital role in the pathogenesis of musculoskeletal (MSK) diseases, such as chronic inflammatory joint disorders, rheumatoid arthritis (RA), and osteoarthritis (OA). Cellular senescence in articular joints represents a response of local cells to persistent stress that leads to cell-cycle arrest and enhanced production of inflammatory cytokines, which in turn perpetuates joint damage and leads to significant morbidities in afflicted patients. It has been recently discovered that clearance of senescent cells by novel "senolytic" therapies can attenuate the chronic inflammatory microenvironment of RA and OA, preventing further disease progression and supporting healing processes. To identify patients who might benefit from these new senolytic therapies and monitor therapy response, there is an unmet need to identify and map senescent cells in articular joints and related musculoskeletal tissues. To fill this gap, new imaging biomarkers are being developed to detect and characterize senescent cells in human joints and musculoskeletal tissues. This review article will provide an overview of these efforts. New imaging biomarkers for senescence cells are expected to significantly improve the specificity of state-of-the-art imaging technologies for diagnosing musculoskeletal disorders.

Reproducibility and repeatability of quantitative T2 and T2* mapping of osteosarcomas in a mouse model.

BACKGROUND: New immunotherapies activate tumor-associated macrophages (TAMs) in the osteosarcoma microenvironment. Iron oxide nanoparticles (IONPs) are phagocytosed by TAMs and, therefore, enable TAM detection on T2*- and T2-weighted magnetic resonance images. We assessed the repeatability and reproducibility of T2*- and T2-mapping of osteosarcomas in a mouse model. METHODS: Fifteen BALB/c mice bearing-murine osteosarcomas underwent magnetic resonance imaging (MRI) on 3-T and 7-T scanners before and after intravenous IONP infusion, using T2*-weighted multi-gradient-echo, T2-weighted fast spin-echo, and T2-weighted multi-echo sequences. Each sequence was repeated twice. Tumor T2 and T2* relaxation times were measured twice by two independent investigators. Repeatability and reproducibility of measurements were assessed. RESULTS: We found excellent agreement between duplicate acquisitions for both T2* and T2 measurements at either magnetic field strength, by the same individual (repeatability), and between individuals (reproducibility). The repeatability concordance correlation coefficient (CCC) for T2* values were 0.99 (coefficients of variation (CoV) 4.43%) for reader 1 and 0.98 (CoV 5.82%) for reader 2. The reproducibility of T2* values between the two readers was 0.99 (CoV 3.32%) for the first acquisitions and 0.99 (CoV 6.30%) for the second acquisitions. Regarding T2 values, the repeatability of CCC was similar for both readers, 0.98 (CoV 3.64% for reader 1 and 4.45% for reader 2). The CCC of the reproducibility of T2 was 0.99 (CoV 3.1%) for the first acquisition and 0.98 (CoV 4.38%) for the second acquisition. CONCLUSIONS: Our results demonstrated high repeatability and reproducibility of quantitative T2* and T2 mapping for monitoring the presence of TAMs in osteosarcomas. RELEVANCE STATEMENT: T2* and T2 measurements of osteosarcomas on IONP-enhanced MRI could allow identifying patients who may benefit from TAM-modulating immunotherapies and for monitoring treatment response. The technique described here could be also applied across a wide range of other solid tumors. KEY POINTS: • Optimal integration of TAM-modulating immunotherapies with conventional chemotherapy remains poorly elucidated. • We found high repeatability of T2* and T2 measurements of osteosarcomas in a mouse model, both with and without IONPs contrast, at 3-T and 7-T MRI field strengths. • T2 and T2* mapping may be used to determine response to macrophage-modulating cancer immunotherapies.

Two-Photon Intravital Microscopy of Glioblastoma in a Murine Model.

The delivery of intravenously administered cancer therapeutics to brain tumors is limited by the blood-brain barrier. A method to directly image the accumulation and distribution of macromolecules in brain tumors in vivo would greatly enhance our ability to understand and optimize drug delivery in preclinical models. This protocol describes a method for real-time in vivo tracking of intravenously administered fluorescent-labeled nanoparticles with two-photon intravital microscopy (2P-IVM) in a mouse model of glioblastoma (GBM). The protocol contains a multi-step description of the procedure, including anesthesia and analgesia of experimental animals, creating a cranial window, GBM cell implantation, placing a head bar, conducting 2P-IVM studies, and post-surgical care for long-term follow-up studies. We show representative 2P-IVM imaging sessions and image analysis, examine the advantages and disadvantages of this technology, and discuss potential applications. This method can be easily modified and adapted for different research questions in the field of in vivo preclinical brain imaging.

Multimodal Pediatric Lymphoma Detection using PET and MRI.

Lymphoma is one of the most common types of cancer for children (ages 0 to 19). Due to the reduced radiation exposure, PET/MR systems that allow simultaneous PET and MR imaging have become the standard of care for diagnosing cancers and monitoring tumor response to therapy in the pediatric population. In this work, we developed a multimodal deep learning algorithm for automatic pediatric lymphoma detection using PET and MRI. Through innovative designs such as standardized uptake value (SUV) guided tumor candidate generation, location aware classification model learning and weighted multimodal feature fusion, our algorithm can be effectively trained with limited data and achieved superior tumor detection performance over the state-of-the-art in our experiments.