Articles tagged with Radiology
FLT3 mutations significantly impact AML prognosis and treatment response; next-gen sequencing enhances mutation detection and MRD monitoring. FLT3 inhibitors offer promising avenues for durable remission.
The IOF Advanced Course on Osteoporosis is a comprehensive, 18-hour online training programme designed to equip healthcare professionals with the latest knowledge in osteoporosis prevention, diagnosis, and management. The course aims to promote timely diagnosis and treatment, reduce patient burden, and improve global outcomes.
Researchers at TUM developed an AI-powered algorithm to predict kidney damage in prostate cancer patients undergoing lutetium-177 PSMA therapy. Early detection could enable personalized treatment adjustments to prevent organ damage.
The treatment demonstrated early signals of efficacy, with 65.7% of patients experiencing lasting stable disease, and was generally well-tolerated, with most adverse events being mild and manageable.
A Phase II trial found durable antitumor activity in patients with BRCA1/2 mutations treated with olaparib and pembrolizumab, with 8.3% complete response rate
A survey of patients who underwent breast cancer screening mammograms found most support AI's role as a second reader, but concerns about data privacy and bias persisted. Demographic factors such as education and racial background played a significant role in shaping patient perceptions of AI.
Dr. Elizabeth M. Curtis has been awarded the ESCEO-IOF Pierre Meunier Young Scientist Award for her groundbreaking research on musculoskeletal health and bone disease. Her work focuses on the epidemiology of bone, muscle health, and joint health across the lifespan.
A new study suggests that CT scans could account for 5% of all cancer cases each year, primarily due to radiation exposure. The largest risk is among infants, followed by children and adolescents. To mitigate this risk, reducing the number of scans and doses per scan can help save lives.
Twelve young researchers from around the world receive ESCEO-IOF Young Investigator Awards for their groundbreaking studies on osteoporosis. The prestigious awards were presented during the World Congress on Osteoporosis, Osteoarthritis & Musculoskeletal Diseases (WCO-IOF-ESCEO 2025) in Rome, Italy.
Professor Atsushi Suzuki, Chair of Endocrinology at Fujita Health University School of Medicine, Japan, received the IOF President's Award for his exceptional contributions to IOF. He has shared his expertise as a mentor and engaged member of the Capture the Fracture Governance.
Dr Ambrish Mithal, an Indian endocrinologist, has been awarded the prestigious IOF CSA Medal of Achievement for his groundbreaking work in advancing the field of osteoporosis. His pioneering efforts have significantly increased understanding and awareness of osteoporosis globally.
AMMOM has been recognized by the IOF for its significant contributions to osteoporosis awareness and bone health in Mexico. The organization has collaborated extensively with the IOF on various initiatives, including joint scientific meetings and advocacy efforts.
A new editorial in Oncotarget discusses how artificial intelligence can improve liver imaging by recognizing when it might be wrong. The approach, called 'uncertainty quantification,' helps clinicians better detect liver cancer and other diseases by pointing out areas in medical scans that need a second look.
A new framework helps clinicians choose phrases that more accurately reflect the likelihood of certain pathologies in X-rays, improving the reliability of critical clinical information. The approach treats certainty phrases as probability distributions, capturing nuances of what each word means.
The new Keck Medicine of USC – Newport Beach Radiation Oncology and Imaging Center offers biology-guided radiation therapy, using up-to-the-minute signals to track and treat cancer cells in real time. The center is equipped with state-of-the-art technology and will be the first in Orange County to provide this treatment option.
A new study published in Radiology found that higher blast exposure correlated with changes in brain function and structure, even when standard MRI exams appeared normal. The researchers discovered weaker connectivity in key areas and more severe symptoms such as memory problems and post-traumatic stress disorder.
Researchers found that medical imaging experts can solve common optical illusions, including judging the size of objects. Training to improve visual perception can also make experts less susceptible to these illusions. This study has implications for training medical image analysts.
Dr. David Winchester will lead the American College of Cardiology's Board of Governors, guiding chapters representing all 50 states and promoting heart health improvement in communities. His term aims to address practice challenges through advocacy and chapter support.
A new study has developed CoLDiT, a conditional latent diffusion model capable of generating high-resolution breast ultrasound images. The model achieved comparable performance to real images in BI-RADS classification and realism evaluation, while safeguarding patient privacy through nearest neighbor analysis.
Researchers developed a deep learning algorithm to denoise ultra-low dose CT scans, improving image quality and accuracy. The study found that this approach can diagnose pneumonia in immunocompromised patients using only 2% of the radiation dose of standard CT scans.
A team of researchers from Chiba University has developed a novel radioactive drug that targets and treats metastatic melanoma. The treatment utilizes astatine-211 labeled peptide analog, which shows high accumulation in tumors, rapid clearance from non-target organs, and significant tumor suppression.
Scientists repurposed the drug edaravone as an imaging probe to detect oxidative stress in brain tissue using PET scans. This technique can help diagnose neurological conditions such as ALS and Alzheimer's disease earlier, when treatment is more effective.
A new radiology screening tool can help detect intimate partner violence (IPV) in patients, enabling healthcare providers to identify victims earlier. The study found that imaging studies and injury patterns differ between women who have reported IPV and those who have not.
Researchers developed ItpCtrl-AI, a transparent AI framework that reads chest X-rays like a radiologist, providing accurate diagnoses and increasing trust in medical technology. The framework uses a gaze heat map to show the computer where to search for abnormalities and what section of the image requires less attention.
A consensus statement by the Chinese College of Interventionalists promotes CBCT use in liver malignancy therapies, addressing limitations and challenges. The integration of artificial intelligence and technological innovations are expected to refine CBCT's efficacy and safety.
A study of Viking skulls using CT scans reveals a range of diseases including sinus and ear infections, osteoarthritis, and dental diseases. The results provide greater understanding of the health and wellbeing of the Viking population.
A rare case of adenomyosis in an 81-year-old postmenopausal woman closely resembles invasive endometrial cancer, emphasizing the challenges of diagnosing this condition. Further studies are needed to refine diagnostic protocols and determine risk factors for postmenopausal patients.
Researchers developed a robust AI model that automates segmenting of MRI images, reducing radiologist workload and improving consistency. The TotalSegmentator MRI model achieved high performance on various anatomical structures, with a Dice score of 0.839.
A study found racial and ethnic minorities are less likely to receive standard-of-care diagnostic imaging after abnormal screening mammograms compared to white patients. Minority groups were also less likely to have access to same-day biopsy services, despite similar availability of most diagnostic services.
A new study published in The Lancet Digital Health found that AI-supported breast cancer screening increased the detection of early-stage invasive cancers by 24% compared to traditional screening. AI also identified pre-cancerous lesions with a 51% higher rate, without increasing false positives.
A study by Bonn researchers has shown that local Large Language Models (LLMs) can structure radiological findings in a privacy-safe manner, without transferring data to external servers. The open-source LLMs performed equally well as commercial models with advantages of being developed locally.
Mayo Clinic is developing foundation models with Microsoft Research and Cerebras Systems to personalize patient care, accelerating diagnostic time and improving accuracy. These multimodal models integrate radiology images and genomic sequencing data to transform clinical diagnosis and treatment.
A recent study published in Nature Medicine found that AI significantly improves breast cancer detection rates, detecting an additional cancer case per 1,000 women screened. The results show improved efficiency without increasing false positives or unnecessary follow-ups.
A deep learning model developed by researchers at San Diego State University can accurately diagnose chronic obstructive pulmonary disease (COPD) using a single inhalation lung CT scan. The study found that the model performed similarly to traditional two-phase CT measurements, with added clinical data improving accuracy.
Fogarty's research aims to monitor language function and recovery in post-stroke patients using DOT. She hopes to establish the feasibility of brain-computer interfaces to restore inter-personal communication for post-stroke patients.
The study found a significant increase in late-stage breast cancer diagnoses among U.S. women, with annual percentage increases of up to 2.9% between 2004 and 2021. The researchers attribute the trend to factors such as lack of national screening programs, inconsistent guidelines, and increasing obesity rates.
A new research paper published in Oncotarget introduces an innovative AI tool combining CT scans and body composition data to predict severe liver problems in primary sclerosing cholangitis (PSC) patients. The model achieved impressive results, correctly identifying at-risk patients with 97% accuracy.
Teletrix's VIZRAD AR platform simulates ionizing radiation behavior using augmented reality technology, providing a safe and immersive way to learn about radiation without exposure. The platform offers improved training capabilities for workers in radiation-controlled areas.
Researchers have developed a new coronary risk score specifically for women, accurately predicting and categorizing the risk of major adverse cardiovascular events. The findings suggest that this novel approach can help identify high-risk women earlier, reducing the risk of heart attacks and sudden cardiac death.
A new study found significantly higher injury rates among transgender women compared to cisgender women, with increased risk of head, facial, and chest injuries. The findings emphasize the need for timely IPV screening and support for vulnerable populations.
Priscilla Slanetz, a breast radiologist and professor at Boston University School of Medicine, has received the 2024 Marie Sklodowska-Curie Award for her outstanding contributions to advancing women in radiology. She is recognized for her clinical research, educational efforts, and leadership in promoting diversity and inclusion.
A study found that skeletal muscle loss, particularly in the temporalis muscle, is associated with an increased risk of developing Alzheimer's disease dementia. Participants with smaller temporalis muscle size were more than twice as likely to develop dementia over a median follow-up period of 5.8 years.
Researchers at the University of Bonn are developing an AI foundation model to improve radiology and detect Parkinson's disease earlier. The 'Re-Start PD' project aims to identify the condition before symptoms appear, with potential applications in diagnosis, treatment, and prevention.
A seminal life cycle assessment (LCA) study found that diagnostic services generate the equivalent of nearly 1,100 gas-powered cars annually, with CT scans accounting for the majority of GHG emissions. The team identified areas to reduce energy consumption and greenhouse gas emissions without compromising patient care.
This editorial introduces persistence landscapes as a mathematical method to identify and correct biases in medical imaging. Persistence landscapes offer a way to reduce random noise while preserving important details, making it easier for clinicians to focus on meaningful image parts.
Researchers found lower pulmonary gas exchange may be associated with impaired cognitive function in patients with long COVID. The study also showed a potential treatment strategy using methods that target improved gas exchange, suggesting a causative relationship between cognitive dysfunction and lung dysfunction.
Researchers introduce a new tool called 'persistence images' that uncovers hidden biases in medical imaging data, advancing fairness in healthcare AI. The technology helps detect bias, filter out noise, and improve overall accuracy, leading to more reliable diagnoses and better patient outcomes.
Researchers introduce persistence barcodes, a tool that reduces diagnostic bias and uncovers subtle details in medical images. The method preserves key structural details, enabling clearer insights into the data.
A study published in Radiology found that radiologists and physicians who received local explanations from artificial intelligence (AI) systems performed better and made faster diagnoses than those who received global explanations. The researchers also found that AI advice was trusted more quickly when it provided local explanations.
A 10-year study found that short-term exposure to heat and air pollution was associated with an increase in medical imaging exams, particularly for chest, neuro, and musculoskeletal imaging. High temperatures had a greater impact on X-ray utilization, while high particulate matter levels were linked to increased CT scans.
Researchers have trained AI models to distinguish brain tumors from healthy tissue using convolutional neural networks and transfer learning. The models achieved an average accuracy of 85.99% at detecting brain cancer, with the ability to generate images showing specific areas in its tumor-positive or negative classification.
A new approach using topological data analysis (TDA) enhances the reliability and reduces bias in AI systems used for medical diagnosis in radiology. TDA captures intricate features and provides a holistic view of medical images, leading to more accurate diagnoses and equitable patient care.
A novel deep learning model called Co-Plane Attention Across MRI Sequences (CoPAS) has been developed to assist with classifying 12 common types of knee abnormalities. The model achieved high classification accuracy comparable to that of radiologists, improving diagnostic efficiency and reducing errors.
Neuro-oncology experts developed guidelines to standardize AI use in brain cancer diagnosis and treatment. The recommendations aim to ensure reliable clinical trial results and protect patients.
Researchers developed a new AI-powered diagnostic system, FastGlioma, which reveals invisible cancerous tissue in brain tumor surgery. The technique may delay or prevent recurrence of high-grade tumors and improve patient survival.
A study by Niigata University found that AI analysis of PET/CT images can predict the occurrence of interstitial lung disease, a serious side effect of immunotherapy. Patients with high inflammation in non-cancerous lungs are at a higher risk of developing this condition.
The International Osteoporosis Foundation's Capture the Fracture campaign highlights top-performing Fracture Liaison Services that deliver gold-standard care. These services prioritize continuous care, tailored treatment plans, and teamwork to reduce secondary fractures and improve patient outcomes.
Researchers developed an AI model that can identify and measure aggressive prostate cancer lesions with high accuracy. The model's estimates of tumor size were associated with the likelihood of cancer recurrence or metastasis.
A new study suggests that MRI can predict patient outcomes and the risk of tumor reccurrence or spread for patients with rectal cancer who have undergone chemotherapy and radiation. This information could help doctors determine the best course of treatment, including whether to spare patients from invasive surgery.
Researchers develop Knowledge-enhanced Bottlenecks (KnoBo) method to emulate human physicians' education, resulting in more accurate and interpretable AI models for medical image recognition. KnoBo-based models outperform existing best-in-class models on accuracy and robustness, especially in handling confounded data.