Immune checkpoint inhibitors (ICIs) and targeted therapies have revolutionized the management of metastatic renal cell carcinoma (mRCC). Currently, the frontline standard of care for patients with mRCC involves the provision of systemic ICI-based combination therapy with no clear guidelines on holding or de-escalating treatment, even with a complete or partial radiological response. Treatments usually continue until disease progression or unacceptable toxicity, frequently leading to overtreatment, which can elevate the risk of toxicity without providing a corresponding increase in therapeutic efficacy. In addition, the ongoing use of expensive antineoplastic drugs increases the financial burden on the already overstretched health care systems and on patients and their families. De-escalation strategies could be designed by integrating contemporary technologies, such as circulating tumor DNA, and advanced imaging techniques, such as computed tomography (CT) scans, positron emission tomography CT, magnetic resonance imaging, and machine learning models. Treatment de-escalation, when appropriate, can minimize treatment-related toxicities, reduce health care costs, and optimize the patients' quality of life while maintaining effective cancer control. This paper discusses the advantages, challenges, and clinical implications of de-escalation strategies in the management of mRCC. PATIENT SUMMARY: In this report, we describe the burden of overtreatment in patients who are never able to stop treatments for metastatic kidney cancer. We discuss the application of the latest technology that can help in making de-escalation decisions.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a potentially life-threatening condition, classified as group 4 pulmonary hypertension (PH), caused by stenosis or occlusion of the pulmonary arteries due to unresolved thromboembolic material. The prognosis for untreated CTEPH patients is poor because it leads to elevated pulmonary artery pressure and right heart failure. Early and accurate diagnosis of CTEPH is crucial because it remains the only form of PH that is potentially curable. However, diagnosing CTEPH is often challenging and frequently delayed or misdiagnosed. This review discusses the current role of multimodal imaging in diagnosing CTEPH, guiding clinical decision-making, and monitoring post-treatment outcomes. The characteristic findings, strengths, and limitations of various imaging modalities, such as computed tomography, ventilation-perfusion lung scintigraphy, digital subtraction pulmonary angiography, and magnetic resonance imaging, are evaluated. Additionally, the role of artificial intelligence in improving the diagnosis and treatment outcomes of CTEPH is explored. Optimal patient assessment and therapeutic decision-making should ideally be conducted in specialized centers by a multidisciplinary team, utilizing data from imaging, pulmonary hemodynamics, and patient comorbidities.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nuclear neuroimaging. The current and emerging applications of ML and DL in the processing, analysis, enhancement and interpretation of SPECT and PET imaging are explored for brain imaging. Key developments include automated image segmentation, disease classification, and radiomic feature extraction, including lower dimensionality first and second order radiomics, higher dimensionality third order radiomics and more abstract fourth order deep radiomics. DL-based reconstruction, attenuation correction using pseudo-CT generation, and denoising of low-count studies have a role in enhancing image quality. AI has a role in sustainability through applications in radioligand design and preclinical imaging while federated learning addresses data security challenges to improve research and development in nuclear cerebral imaging. There is also potential for generative AI to transform the nuclear cerebral imaging space through solutions to data limitations, image enhancement, patient-centered care, workflow efficiencies and trainee education. Innovations in ML and DL are re-engineering the nuclear neuroimaging ecosystem and reimagining tomorrow's precision medicine landscape.

Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imaging methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), struggle to assess tumor characteristics and vascular involvement, which are crucial for treatment planning. This paper explores the potential of diffusion magnetic resonance imaging (dMRI) in enhancing pancreatic cancer diagnosis and treatment. Diffusion-based techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), combined with emerging AI‑powered analysis, provide insights into tissue microstructure, allowing for earlier detection and improved evaluation of tumor cellularity. These methods may help assess prognosis and monitor therapy response by tracking diffusion and perfusion metrics. However, challenges remain, such as standardized protocols and robust data analysis pipelines. Ongoing research, including deep learning applications, aims to improve reliability, and dMRI shows promise in providing functional insights and improving patient outcomes. Further clinical validation is necessary to maximize its benefits.

Epilepsy is a multifaceted neurological disorder marked by seizures that can present with a wide range of symptoms. Despite the prevalent use of anti-epileptic drugs, drug resistance and adverse effects present considerable obstacles. Despite advancements in anti-epileptic drugs (AEDs), approximately 20-30% of patients remain drug-resistant, highlighting the need for innovative therapeutic strategies. This study aimed to explore advancements in epilepsy diagnosis and treatment utilizing modern technology and medicines. The literature survey was carried out using Scopus, ScienceDirect, and Google Scholar. Data from the last 10 years were preferred to include in the study. Emerging technologies, such as artificial intelligence, gene therapy, and wearable gadgets, have transformed epilepsy care. EEG and MRI play essential roles in diagnosis, while AI aids in evaluating big datasets for more accurate seizure identification. Machine learning and artificial intelligence are increasingly integrated into diagnostic processes to enhance seizure detection and classification. Wearable technology improves patient self-monitoring and helps clinical research. Furthermore, gene treatments offer promise by treating the fundamental causes of seizure activity, while stem cell therapies give neuroprotective and regenerative advantages. Dietary interventions, including ketogenic diets, are being examined for their ability to modify neurochemical pathways implicated in epilepsy. Recent technological and therapeutic developments provide major benefits in epilepsy assessment and treatment, with AI and wearable devices enhancing seizure detection and patient monitoring. Nonetheless, additional study is essential to ensure greater clinical application and efficacy. Future perspectives include the potential of optogenetics and advanced signal processing techniques to revolutionize treatment paradigms, emphasizing the importance of personalized medicine in epilepsy care. Overall, a comprehensive understanding of the multifaceted nature of epilepsy is essential for developing effective interventions and improving patient outcomes.

Alzheimer's disease (AD) is a devastating neurological disorder that affects humans and is a major contributor to dementia. It is characterized by cognitive dysfunction, impairing an individual's ability to perform daily tasks. In AD, nerve cells in areas of the brain related to cognitive function are damaged. Despite extensive research, there is currently no specific therapeutic or diagnostic approach for this fatal disease. However, scientists worldwide have developed effective techniques for diagnosing and managing this challenging disorder. Among the various methods used to diagnose AD are feedback from blood relatives and observations of changes in an individual's behavioral and cognitive abilities. Biomarkers, such as amyloid beta and measures of neurodegeneration, aid in the early detection of Alzheimer's disease (AD) through cerebrospinal fluid (CSF) samples and brain imaging techniques like Magnetic Resonance Imaging (MRI). Advanced medical imaging technologies, including X-ray, CT, MRI, ultrasound, mammography, and PET, provide valuable insights into human anatomy and function. MRI, in particular, is non-invasive and useful for scanning both the structural and functional aspects of the brain. Additionally, Machine Learning (ML) and deep learning (DL) technologies, especially Convolutional Neural Networks (CNNs), have demonstrated high accuracy in diagnosing AD by detecting brain changes. However, these technologies are intended to support, rather than replace, clinical assessments by medical professionals.

Medical image super-resolution (SR) is a classic regression task in low-level vision. Limited by hardware limitations, acquisition time, low radiation dose, and other factors, the spatial resolution of some medical images is not sufficient. To address this problem, many different SR methods have been proposed. Especially in recent years, medical image SR networks based on deep learning have been vigorously developed. This survey provides a modular and detailed introduction to the key components of medical image SR technology based on deep learning. In this paper, we first introduce the background concepts of deep learning and medical image SR task. Subsequently, we present a comprehensive analysis of the key components from the perspectives of effective architecture, upsampling module, learning strategy, and image quality assessment of medical image SR networks. Furthermore, we focus on the urgent problems that need to be addressed in the medical image SR task based on deep learning. And finally we summarize the trends and challenges of future development.

Tumor-infiltrating lymphocytes (TILs) are capable of recognizing tumor antigens, impacting tumor prognosis, predicting the efficacy of neoadjuvant therapies, contributing to the development of new cell-based immunotherapies, studying the tumor immune microenvironment, and identifying novel biomarkers. Traditional methods for evaluating TILs primarily rely on histopathological examination using standard hematoxylin and eosin staining or immunohistochemical staining, with manual cell counting under a microscope. These methods are time-consuming and subject to significant observer variability and error. Recently, artificial intelligence (AI) has rapidly advanced in the field of medical imaging, particularly with deep learning algorithms based on convolutional neural networks. AI has shown promise as a powerful tool for the quantitative evaluation of tumor biomarkers. The advent of AI offers new opportunities for the automated and standardized assessment of TILs. This review provides an overview of the advancements in the application of AI for assessing TILs from multiple perspectives. It specifically focuses on AI-driven approaches for identifying TILs in tumor tissue images, automating TILs quantification, recognizing TILs subpopulations, and analyzing the spatial distribution patterns of TILs. The review aims to elucidate the prognostic value of TILs in various cancers, as well as their predictive capacity for responses to immunotherapy and neoadjuvant therapy. Furthermore, the review explores the integration of AI with other emerging technologies, such as single-cell sequencing, multiplex immunofluorescence, spatial transcriptomics, and multimodal approaches, to enhance the comprehensive study of TILs and further elucidate their clinical utility in tumor treatment and prognosis.