An international team has developed an AI model that brings together medical imaging data and clinical information to calculate the risk of breast cancer tumour recurrence more accurately.
Using radiomics on mammograms of over 30,000 women, researchers have identified six breast texture patterns that may be associated with increased cancer risk.
Results of a new retrospective study demonstrate the potential of a novel, CT-based deep learning-driven tool to enhance liver cancer diagnosis, treatment planning, and response evaluation.
A new deep learning model shows promise in detecting and segmenting lung tumors. The findings of the study could have important implications for lung cancer treatment.
Complex diseases could require complex biomarkers for accurate diagnosis in the years ahead, according to a leading pathologist. In a keynote address to the 36th European Congress of Pathology in Florence, Italy, Professor Manuel Salto-Tellez highlighted this as one of the major future challenges for the discipline. However, he also believes pathologists will sit at the core of modern medicine.
Using specific radiomics features from 70 characteristics in MRI images, researchers develop an objective method to predict the hearing status of patients with vestibular schwannoma.
Experts at Newcastle University have discovered artificial intelligence can be used to analyse medical imaging from CT scans, which is aimed at predicting survival in patients with laryngeal cancer.
In diagnostics, there used to be a hard divide between radiology and pathology, where methods were largely considered incompatible with one another. However, to pave the way for next-generation diagnosis, Professor Regina Beets-Tan urged both sides to come out from their trenches and appreciate the synergies the fields have to offer. In her presentation at the European Congress of Radiology (ECR)…
A novel AI-based, non-invasive diagnostic tool enables accurate brain tumor diagnosis, outperforming current classification methods. The tool leverages MRI information to aid clinical decision making.
Breast cancer has no “one size fits all” therapy approach: subtypes differ significantly in malignancy, progression, and treatment response. Therefore, the more is known about the type of carcinoma in a patient, the better the outcome. At the annual scientific EUSOBI meeting in Valencia, Dr Ramona Woitek pointed out the potential of novel imaging techniques and computational image analysis…
‘Next Generation Radiology: Embracing the future and redefining the field of radiology’: In the run-up to the European Congress of Radiology 2024, we spoke with ESR President Professor Carlo Catalano from Rome, Italy, about the meeting’s content and its intriguing theme.
With a combination of radiomics and deep learning, researchers aim to noninvasively determine lymph node metastasis before surgery. This could lead to more accurate diagnosis and treatment strategies.
The climate crisis and AI – arguably two of the most hotly-debated and relevant topics of our time – share an intricate relationship: While computation of complex AI routines commands an immense carbon footprint, it is these algorithms that might be the very key to mitigate the effects of global warming. In a dedicated session at ECR 2023, radiologists explored the societal and environmental…
What will the future structure for artificial intelligence in health imaging across Europe look like? While the algorithms show great promise in collecting, storing, analysing, and using data to advance healthcare, delegates to a session on the topic at ECR 2023 in Vienna, also heard that it was important for the use of AI to move from research and more toward practical applications for patients.…
Researchers developed a radiomics model that uses information from coronary CT angiography images to assess coronary plaque vulnerability, a common cause of heart attacks.
The distinction between primary tumors and metastases can be made quickly and accurately in brain tumors using radiomics and deep learning algorithms, a new study shows.
AI-based models for multimodality hybrid imaging have the potential to be a potent clinical tool but are currently held back by a lack of transparency and maturity, says Dr Irène Buvat, from the Laboratory of translational Imaging in Oncology, Institute Curie in Paris, France.
This summer, The European Commission launched I3lung, a new research initiative as a part of Horizon Europe, the EU’s research and innovation program. This research initiative aims to create a cutting-edge, decision-making tool to help clinicians and patients select the best lung cancer treatment based on each patient’s specific needs and circumstances.
The classification of brain tumors—and thus the choice of optimal treatment options—can become more accurate and precise through the use of artificial intelligence in combination with physiological imaging.
An artificial intelligence (AI) model combining four methods of machine learning (ML) to accurately detect thyroid cancer from routine ultrasound image data has been developed by US researchers.
A simple blood test could be a better predictor of whether cancer immunotherapy will be successful for a patient with lung cancer than an invasive tumor biopsy procedure.
Triage HF Plus, highlighted in the BCS conference session ‘Digital Innovation in Cardiology - What's new?’ is a digital heart failure care project that uses a customised algorithm to detect early signs of deterioration in patients with implanted devices. During her presentation ‘Digital solutions to identify worsening heart failure’, consultant cardiologist Dr Fozia Ahmed discussed the…
Artificial Intelligence (AI) is providing numerous opportunities across clinical care in the field of cardiovascular imaging. While challenges remain, AI is being applied in terms of diagnosis and prognosis, defining cardiovascular imaging pathways, and image acquisition and analysis. It can also help cardiologists predict which patients may do well, or which treatments are best applied to those…
A solid diagnosis has always been the first step on any patient’s journey to health. However, diagnostic categories are necessarily oversimplifications. In the last decades, medical professionals and scientists have begun to uncover the true variability in patients’ physiological and biochemical make-up that is the principal cause for individual variations in the way diseases present…
Cancer care and the treatment clinicians can offer patients is being increasingly enhanced by Artificial Intelligence (AI). The technology has a role in diagnosis, with algorithms trained to design and deliver patient care, can match patients to clinical trials they may benefit from, and even help predict outcomes and those at greatest risk.
Is artificial intelligence (AI) technology ready to be utilized as a clinical tool by interventional oncologists? Not yet, but when it is, AI technology’s clinical impact may be as profound as advanced imaging is today, two leading experts agree.
AI is revamping workflows and experts showed how radiologists can integrate it into their department to improve daily practice and healthcare at ECR. The panel also discussed the health economics side of AI to help radiologists define which products make more economic sense for their department. The session tackled automated organ segmentation, an interesting application for AI in radiology.
Harnessing the power of radiomics, and adopting an integrated approach to combine imaging and patient data could lead to better clinical cancer outcomes. The move has opened the door for clinicians to explore a non-invasive approach to identify the heterogeneity of a tumour and more accurately target regions for biopsy. During a presentation at ECR 2021 in March, Professor Evis Sala will…
A new EU-wide repository for health-related imaging data could boost development and marketing of AI tools for better cancer management. The open-source database will collect and harmonise images acquired from 40,000 patients, spanning different countries, modalities and equipment. This approach could eliminate one of the major bottlenecks in the clinical adoption of AI today: Data bias.
The advent of digital pathology is offering a unique opportunity to develop computerized image analysis methods to diagnose disease and predict outcomes for cancer patients from histopathology tissue sections. Such advances can help predict risk of recurrence, disease aggressiveness and long-term survival, according to a leading expert in the field, Professor Anant Madabhushi from Case Western…
A new advanced computing technique using routine medical scans to enable doctors to take fewer, more accurate tumour biopsies, has been developed by cancer researchers at the University of Cambridge. This is an important step towards precision tissue sampling for cancer patients to help select the best treatment. In future the technique could even replace clinical biopsies with ‘virtual…
Covid-19 causes characteristic changes in lung tissue visible in CT scans and chest radiographs, known as “ground-glass” opacities. Imaging is now considered a valid alternative, possibly even superior to RT-PCR. ‘This sparked an international debate about the role of CT in the diagnostic work-up of Covid-19,’ said radiologist Professor Cornelia Schäfer-Prokop.
Men die about five years earlier than women across the world. As initiatives to boost awareness of men’s health unfolded in November, an international project is bringing the forefront of AI research to tackle prostate cancer (PC), the second most frequent type of cancer in men and the third most lethal in Europe.
A software tool to predict the most effective therapy for non-small cell lung cancer (NSCLC) developed by applying deep learning artificial intelligence (AI) to positron emission tomography/computed tomography (PET/CT) images has been developed by researchers at H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida. The tool is designed to provide a noninvasive, accurate method to…
The field of AI-enhanced imaging provides radiologists with an unprecedented opportunity to shape patient care, leading Austrian radiologist Katja Pinker-Domenig explained at ECR 2020.
In recent years, technological and regulatory advances have made digital pathology a viable alternative to the conventional microscope. The obtention of a digital replica of the traditional glass slide and its use for primary diagnosis has revolutionized pathology and is shaping the future of the discipline. A digital pathology lab uses digital histology slides for routine diagnosis, and these…
To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study…
The complex form of machine learning DLIR (Deep Learning Image Reconstruction) is based on a deep neuronal network which is similar to the human brain. The artificial neurons of this network learn according to their biological model through intensive training. For the DLIR image reconstruction, the network is fed with sample data from phantom images on the one hand and high-resolution images of…
Luis Martí-Bonmatí, Director of the Medical Imaging Department at La Fe Hospital in Valencia, highlighted the need to assess utility when developing AI tools during ECR 2020. Artificial intelligence (AI) can impact and improve many aspects of clinical practice. But current expectations are too great and need to be toned down by looking at opportunities.
A new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report in the journal IEEE Access. Scientists in India and Japan, including from Kyoto University’s Institute for Integrated Cell-Material Sciences (iCeMS), developed the method to help clinicians choose the most effective treatment strategy for individual…
Examining the safety of AI integration into clinical workflow during at the British Institute of Radiology (BIR) annual congress in London, this November, Professor Nicola Strickland focused on issues of data quantity and quality, regulation, validation and testing of algorithms. She also urged radiologists and computer scientists to work more closely together to develop safe, effective and…
Computed tomography (CT) plays an increasingly important role in assessing pelvic disease, particularly when patients present with acute abdominal pain. In addition, radiomic approaches on CT are being developed to increase the characterisation of ovarian cancer for optimising treatment planning.
Researchers at Osaka University have developed a computer method that uses magnetic resonance imaging (MRI) and machine learning to rapidly forecast genetic mutations in glioma tumors, which occur in the brain or spine. The work may help glioma patients to receive more suitable treatment faster, giving better outcomes. The research was recently published in Scientific Reports. Cancer treatment…
An enthralling insight into the care that could be offered to cancer patients of the future was presented by cancer imaging expert Professor Regina Beets-Tan during her a keynote presentation at the recent British Institute of Radiology congress. In the session ‘Oncologic imaging: Future perspectives’, the professor outlined what a Multi-Disciplinary Team (MDT) of the future – a team in…
Radiomics is one of the most exciting topics in radiology. It involves data and artificial intelligence (AI) but very few people know or understand the details. In her lecture ‘How does Radiomics work?’, presented at the German Radiology Congress in Leipzig, Professor Ulrike Attenberger outlined how radiomics will advance radiology but also the obstacles faced along the way.
Scientists from the Case Western Reserve University digital imaging lab use Artificial Intelligence (AI) to predict which lung-cancer patients will benefit from expensive immunotherapy. This is done by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy…
Although some people foresee artificial intelligence (AI) easing medical workloads, many challenges arise before that dream can begin. Dr Felix Nensa and Dr Bram Stieltjes described such hurdles in a session held during a SITEM School Symposium in Bern, Switzerland. Whilst AI has potential, actually delivering that asset in to routine medical practice remains a major challenge.
Technology developed using artificial intelligence (AI) could identify people at high risk of a fatal heart attack at least five years before it strikes, according to new research funded by the British Heart Foundation (BHF). The findings are being presented at the European Society of Cardiology (ESC) Congress in Paris and published in the European Heart Journal. Researchers at the University of…
La Fe University and Polytechnic Hospital in Valencia, Spain, is coordinating EU-funded program PRIMAGE, which uses precision information from medical imaging to advance knowledge of the most lethal paediatric tumours, by establishing their prognosis and expected treatment response using radiomics, imaging biomarkers and artificial intelligence (AI).
In view of the advent of personalised medicine and holistic therapy many experts predict the end of healthcare as we know it. However, in many places it is ‘healthcare business as usual’. In our interview, Dr Christoph Zindel, President Diagnostic Imaging at Siemens Healthineers, explains where he sees radiology bridging the gap between symptom-centred treatment today and the systemic…
During a European Society of Hybrid, Molecular and Translational Imaging (ESHI) session at ECR 2019, three speakers discussed the role of artificial intelligence (AI) in hybrid imaging. While AI and machine learning is supporting clinicians using hybrid techniques such as PET/CT, MR/PET, or ultrasound and CT, challenges remain in ‘training the machines’ to add value to radiologists’ and…
Using artificial intelligence (AI) to push development of imaging biomarkers shows great promise to improve disease understanding. This alliance could be a game changer in healthcare but, to advance research, clinical validation and variability of results must be factored in, a prominent Spanish radiologist advises. In clinical practice efforts are already ongoing to apply AI to obtain new…
Tumours change over time – and not only in size. They also evolve genetically, mutate and spread through equally diverse metastases. Each is unique and present with a more or less complex structure, but rarely as a unified entity. Characterising them from A to Z and from detection to neutralisation remains a challenge for modern medicine.
A new radiomics study could help unlock one of the more challenging issues facing thoracic radiologists. Distinguishing non-small cell lung cancer from benign nodules is a major challenge due to their similar appearance on CT images. Now, however, researchers from Case Western Reserve University in Cleveland, Ohio, have used radiomic features extracted from CT images to differentiate between…
‘Is it possible to know whether a treatment will work before even starting it – in other words, to predict the truth? That’s the great promise of holomics, a concept that everyone has been involved in without even noticing,’ said leading French physicist Irène Buvat, from the In Vivo Molecular Imaging French lab, who is set to focus on this subject at ECR 2019. ‘The truth,’ said…
Keeping data within the hospital by sending the learning modules to each hospital database might prove a game-changer in radiomics, a leading Dutch researcher will show at ECR 2019. Radiomics, a field that aims to extract large amounts of quantitative features from medical images using data-characterisation algorithms, is a major advance for healthcare, according to Philippe Lambin, a radiation…
Researchers have created a new machine learning software that can forecast the survival rates and response to treatments of patients with ovarian cancer. The artificial intelligence software, created by researchers at Imperial College London and the University of Melbourne, has been able to predict the prognosis of patients with ovarian cancer more accurately than current methods. It can also…
A team of researchers from the Icahn School of Medicine at Mount Sinai and Keck School of Medicine at the University of Southern California (USC) have developed a novel machine-learning framework that distinguishes between low- and high-risk prostate cancer with more precision than ever before. The framework, described in a Scientific Reports paper, is intended to help physicians—in particular,…
Neuroscientist Lynda Chin MD, Founder and CEO of Real-world Education Detection and Intervention, has little doubt: ‘Artificial intelligence to the rescue,’ she proclaimed in her keynote address at the Artificial Intelligence and Machine Learning Summit, held in Las Vegas this spring. ‘We need a system and analytics to interpret data!’ she urged, despite being well aware that building a…
Confirming or infirming hypotheses has long driven scientific research; however, this traditional and costly approach is giving way to data-driven initiatives, according to Prof. Laure Fournier, a leading radiologist at Georges Pompidou European Hospital in Paris. “Usually we formulate the hypothesis first, then take an image and analyze it. We like that in France, it comes from Descartes. The…
‘Breast cancer rates are continuously increasing, and we don’t yet have a means of prevention,’ said Dr Clemens Kaiser, from the Medical Faculty Mannheim, at Heidelberg University, who believes the only way to save more patients from death, after providing the best possible diagnostics procedures, is via precision medicine: the right treatment at the earliest possible time. The radiologist…
Recognising malignant tissue remains a tricky task. While today, most patients undergo a biopsy, an invasive procedure where tissue is sampled, stained and assessed, researchers are exploring the potential of optical biopsy, the visual assessment of suspect tissue. The interest in optical biopsy ‘is indeed enormous,’ confirms Dr Thomas Bocklitz, physicist at Friedrich-Schiller University in…
Some time in the distant future artificial intelligence (AI) systems may displace radiologists and many other medical specialists. However, in a far more realistic future AI tools will assist radiologists by performing very complex functions with medical imaging data that are impossible or unfeasible today, according to a presentation at the RSNA/AAPM Symposium during the Radiological Society of…
In the next five to 10 years, artificial intelligence is likely to fundamentally transform diagnostic imaging. This will by no means replace radiologists, but rather help to meet the rising demand for imaging examinations, prevent diagnostic errors, and enable sustained productivity increases.
Population imaging is key to determining disease prediction and risk prevention, and Big Data will be key to extracting information and drawing analysis from imaging results, experts highlighted during the annual meeting of the European Society of Magnetic Resonance in Medicine and Biology (ESMRMB) held in Barcelona in October. Interest in cohort studies has been increasing over the years and…
The development of new procedures to monitor cancer treatments is gathering momentum. One such innovation is liquid biopsy. This new lab technique allows non-invasive identification, characterisation and monitoring of circulating tumour DNA. Thus, liquid biopsy can potentially revolutionise oncological diagnostics – and put a spoke in the wheel of radiology. High time to act, says Professor Dr…
What effect does a particular cancer medicine or radiation therapy have on patients? To find out, physicians use CT images to determine whether a tumor’s size changes during the course of treatment. In the PANTHER project, a joint team of experts aims at gaining further valuable information from these images. In the future, doctors will be able to find out at an early stage whether a cancer…
The emerging field of psychoradiology is taking a major step ahead. A new study highlights MRI’s role in identifying people with attention deficit and hyperactivity disorder (ADHD) and classifies subtypes of the condition, a leading Chinese researcher explained at the ESMRMB annual meeting.
By definition, biobanks are collections of specimens of human bodily substances, such as tissue, cells, DNA, proteins, blood or other fluids, with data on the donor attached.
The billions of medical images taken and diagnoses made every year across the globe are as varied in quality, detail and structure as the situations in which they are obtained.
Computer programs have defeated humans in Jeopardy!, chess and Go. Now a program developed at Case Western Reserve University has outperformed physicians on a more serious matter.
England’s NHS has received an advanced medical imaging and genomic data integration platform from a global healthcare information systems specialist.
SIGNA Pioneer, a new 3.0 T Magnetic Resonance Imaging (MRI) system, embodies the exploration and expansion of modern medical imaging and blazes a trail to the future of MRI. Dr. Ahlers, general manager of radiomed, shares his experience with SIGNA Pioneer recently installed at radiomed practice in Wiesbaden, Germany – one of the first installations worldwide.
Andrea G. Rockall, Consultant Radiologist at the Royal Marsden Hospital and Visiting Professor of Radiology at Imperial College in London, delivered the prestigious Wilhelm Conrad Röntgen Honorary Lecture at ECR 2016 on ‘Imaging the invisible killer: towards personalisation of ovarian cancer care’.
Professor Hedvig Hricak MD PhD, Chair of the Department of Radiology at the Memorial Sloan-Kettering Cancer Centre, New York, and Professor of Radiology at Cornell University, Ithaca, New York, is a notable expert on crosssectional anatomic and molecular imaging, particularly of gynaecologic and prostate cancers. EH interviewed her about the potential and impact of more precise viewing of inter-…
