Predicting early stage lung cancer recurrence and survival from combined tumor motion amplitude and radiomics on free-breathing 4D-CT.

Background: Cancer control outcomes of lung cancer are hypothesized to be affected by several confounding factors, including tumor heterogeneity and patient history, which have been hypothesized to mitigate the dose delivery effectiveness when treated with radiation therapy. Providing an accurate predictive model to identify patients at risk would enable tailored follow-up strategies during treatment.

Purpose: Our goal is to demonstrate the added prognostic value of including tumor displacement amplitude in a predictive model that combines clinical features and computed tomography (CT) radiomics for 2-year recurrence and survival in non-small-cell lung cancer (NSCLC) patients treated with curative-intent stereotactic body radiation therapy.

Biochemical changes precede affective and cognitive anomalies in aging adult C57BL/6J mice with a prior history of adolescent alcohol binge-drinking.

The early initiation of binge-drinking and biological sex are critical risk factors for the development of affective disturbances and cognitive decline, as well as neurodegenerative diseases including Alzheimer's disease. Further, a history of excessive alcohol consumption alters normal age-related changes in the pattern of protein expression in the brain, which may relate to an acceleration of cognitive decline. Here, we aimed to disentangle the interrelation between a history of binge-drinking during adolescence, biological sex and normal aging on the manifestation of negative affect, cognitive decline and associated biochemical pathology.

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction.

Colorectal liver metastases (CLM) affect almost half of all colon cancer patients and the response to systemic chemotherapy plays a crucial role in patient survival. While oncologists typically use tumor grading scores, such as tumor regression grade (TRG), to establish an accurate prognosis on patient outcomes, including overall survival (OS) and time-to-recurrence (TTR), these traditional methods have several limitations. They are subjective, time-consuming, and require extensive expertise, which limits their scalability and reliability.

Image-level supervision and self-training for transformer-based cross-modality tumor segmentation.

Deep neural networks are commonly used for automated medical image segmentation, but models will frequently struggle to generalize well across different imaging modalities. This issue is particularly problematic due to the limited availability of annotated data, both in the target as well as the source modality, making it difficult to deploy these models on a larger scale. To overcome these challenges, we propose a new semi-supervised training strategy called MoDATTS.

Evaluation of stenoses using AI video models applied to coronary angiography.

The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years.

Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room.

Purpose: Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence.

A Comprehensive Comparison of Tissue Processing Methods for High-Quality MALDI Imaging of Lipids in Reconstructed Human Epidermis.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has become an important tool for skin analysis, as it allows the simultaneous detection and localization of diverse molecular species within a sample. The use of and human skin models is costly and presents ethical issues; therefore, reconstructed human epidermis (RHE) models, which mimic the upper part of native human skin, represent a suitable alternative to investigate adverse effects of chemicals applied to the skin. However, there are few publications investigating the feasibility of using MALDI MSI on RHE models.

Imaging With PET/CT of Diffuse CD8 T-Cell Infiltration of Skeletal Muscle in Patients With Inclusion Body Myositis.

Background And Objectives: Inclusion body myositis (IBM) is a progressive autoimmune skeletal muscle disease in which cytotoxic CD8 T cells infiltrate muscle and destroy myofibers. IBM has required a muscle biopsy for diagnosis. Here, we administered to patients with IBM a novel investigational PET tracer Zr-Df-crefmirlimab for in vivo imaging of whole body skeletal muscle CD8 T cells.

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy.

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life. Deep learning offers an advantage over traditional radiomics for medical image processing by learning salient features from training data originating from multiple datasets. However, while their large capacity allows to combine high-level medical imaging data for outcome prediction, they lack generalization to be used across institutions.

Performance of an integrated multimodality image guidance and dose-planning system supporting tumor-targeted HDR brachytherapy for prostate cancer.

Background And Purpose: Advances in high-dose-rate brachytherapy to treat prostate cancer hinge on improved accuracy in navigation and targeting while optimizing a streamlined workflow. Multimodal image registration and electromagnetic (EM) tracking are two technologies integrated into a prototype system in the early phase of clinical evaluation. We aim to report on the system's accuracy and workflow performance in support of tumor-targeted procedures.

Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy.

Radiotherapy is a widely used treatment modality for various types of cancers. A challenge for precise delivery of radiation to the treatment site is the management of internal motion caused by the patient's breathing, especially around abdominal organs such as the liver. Current image-guided radiation therapy (IGRT) solutions rely on ionising imaging modalities such as X-ray or CBCT, which do not allow real-time target tracking.

CD8-Targeted PET Imaging of Tumor-Infiltrating T Cells in Patients with Cancer: A Phase I First-in-Humans Study of Zr-Df-IAB22M2C, a Radiolabeled Anti-CD8 Minibody.

There is a need for in vivo diagnostic imaging probes that can noninvasively measure tumor-infiltrating CD8+ leukocytes. Such imaging probes could be used to predict early response to cancer immunotherapy, help select effective single or combination immunotherapies, and facilitate the development of new immunotherapies or immunotherapy combinations. This study was designed to optimize conditions for performing CD8 PET imaging with Zr-Df-IAB22M2C and determine whether CD8 PET imaging could provide a safe and effective noninvasive method of visualizing the whole-body biodistribution of CD8+ leukocytes.

A feature invariant generative adversarial network for head and neck MRI/CT image synthesis.

With the emergence of online MRI radiotherapy treatments, MR-based workflows have increased in importance in the clinical workflow. However proper dose planning still requires CT images to calculate dose attenuation due to bony structures. In this paper, we present a novel deep image synthesis model that generates in an unsupervised manner CT images from diagnostic MRI for radiotherapy planning.

Machine Learning Applications for Head and Neck Imaging.

The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. This article reviews the recent applications of machine learning (ML) in HN imaging with a focus on deep learning approaches.

Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.

Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the main deep learning architectures such as multilayer perceptron, convolutional neural networks, autoencoders, recurrent neural networks, and generative adversarial neural networks.

First-in-Humans Imaging with Zr-Df-IAB22M2C Anti-CD8 Minibody in Patients with Solid Malignancies: Preliminary Pharmacokinetics, Biodistribution, and Lesion Targeting.

Immunotherapy is becoming the mainstay for treatment of a variety of malignancies, but only a subset of patients responds to treatment. Tumor-infiltrating CD8-positive (CD8+) T lymphocytes play a central role in antitumor immune responses. Noninvasive imaging of CD8+ T cells may provide new insights into the mechanisms of immunotherapy and potentially predict treatment response.

Targeted expression of HvHMA2 increases the mineral content of the inner endosperm in barley.

Cereals are a major source of dietary energy and protein but are nutritionally poor in micronutrients. Zinc (Zn) biofortification of staple crops has been proposed as a promising strategy to combat the global challenge of human Zn-deficiency. The aim of this study was to improve the Zn content in the edible part of the barley (Hordeum vulgare L.

Serum apoptosis markers in acute liver failure: a pilot study.

Background & Aims: We sought to determine whether circulating apoptotic markers are altered in acute liver failure (ALF), differ with etiology, or predict clinical outcome in this condition.

Methods: Serum levels of soluble Fas (sFas), tumor necrosis factor-alpha (TNF-alpha), hepatocyte growth factor (HGF), and interleukin-6 (IL-6) were measured in 67 acute liver failure patients, as well as controls. In a subset of the groups, we measured serum M-30 antigen, an exposed neoepitope from caspase cleavage.