Background: Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. However, it remains underreported and subject to significant interobserver variability amongst pathologists. We aimed to develop and validate an AI-based system to improve cribriform pattern detection. Methods: We created a deep learning model using an EfficientNetV2-S encoder with multiple instance learning for end-to-end whole-slide classification. The model was trained on 640 digitised prostate core needle biopsies from 430 patients, collected across three cohorts. It was validated internally (261 slides from 171 patients) and externally (266 slides, 104 patients from three independent cohorts). Internal validation cohorts included laboratories or scanners from the development set, while external cohorts used completely independent instruments and laboratories. Annotations were provided by three expert uropathologists with known high concordance. Additionally, we conducted an inter-rater analysis and compared the model's performance against nine expert uropathologists on 88 slides from the internal validation cohort. Results: The model showed strong internal validation performance (AUC: 0.97, 95% CI: 0.95-0.99; Cohen's kappa: 0.81, 95% CI: 0.72-0.89) and robust external validation (AUC: 0.90, 95% CI: 0.86-0.93; Cohen's kappa: 0.55, 95% CI: 0.45-0.64). In our inter-rater analysis, the model achieved the highest average agreement (Cohen's kappa: 0.66, 95% CI: 0.57-0.74), outperforming all nine pathologists whose Cohen's kappas ranged from 0.35 to 0.62. Conclusion: Our AI model demonstrates pathologist-level performance for cribriform morphology detection in prostate cancer. This approach could enhance diagnostic reliability, standardise reporting, and improve treatment decisions for prostate cancer patients.

Breakthroughs, discoveries, and DIY tips sent every weekday. Your body is constantly generating new cells. In your digestive tract, the colon's lining turns over every five to seven days. Your red blood cells replace themselves every few weeks, skin cells about once a month. But certain organs are a big exception.

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models.

The largest prostate cancer biopsy dataset – involving over 95,000 images – has been created by researchers in Sweden to ensure AI can be trained to diagnose and grade prostate cancer for real world clinical applications. The researchers will call today, at the European Association of Urology annual congress (EAU22), for large-scale clinical trials of artificial intelligence (AI) algorithms and greater global coordination to ensure that AI enhanced diagnostics, prognostication, and treatment selection can help save lives. There is a shortage of pathologists around the world, both generalists and those specialised in urology. AI can help in detecting prostate cancer at an early stage, but because of the vast differences in the way clinics prepare samples, scan images and in the diverse patient populations they serve, many algorithms do not have universal application. The team, from Karolinska Institutet, worked with colleagues from Radboud University Medical Center in the Netherlands, University of Turku in Finland and Google Health in the US to run an AI competition involving nearly 1,300 developers from around the world.

Many parents feel guilty when their children spend hours on end staring at screens – and some even worry it could make them less clever. But a new study suggests that spending an above-average time playing video games can actually help boost children's intelligence. Researchers from the Karolinska Institutet in Sweden carried out psychological tests on more than 5,000 children in the US aged between ten and 12, to gauge their general cognitive abilities. The children and their parents were also asked about how much time the children spent watching TV and videos, playing video games and engaging with social media. The researchers then followed up with the children two years later, at which point they were asked to repeat the psychological tests.

Vara, the Berlin-based deep tech startup on a mission to provide every woman worldwide with life-saving access to better breast cancer screening, is today announcing a collaboration with researchers from Sweden's world-renowned medical university Karolinska Institutet. The objective of the collaboration is to independently evaluate Vara's AI model for mammography screening, including comparisons with other similar AI systems. Following publication, the results will be used in the creation of a platform to validate AI systems being used in breast imaging, known as the VAI-B Platform (Validation of AI in Breast Imaging). The VAI-B Platform is part of a Swedish-born project financed by Vinnova, Sweden's innovation agency, and Regional Cancer Centers in Collaboration. It is intended to be used as a national and, possibly, international resource for the validation of AI systems in breast imaging.

The international validation was performed via a competition called PANDA. The competition lasted for three months and challenged more than 1000 AI experts to develop systems for accurately grading prostate cancer. "Only ten days into the competition, algorithms matching average pathologists were developed. Organising PANDA shows how competitions can accelerate rapid innovation for solving specific problems in healthcare with the help of AI," says Kimmo Kartasalo, a researcher at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet and corresponding author of the study. A problem in today's prostate cancer diagnostics is that different pathologists can arrive at different conclusions even for the same tissue samples, which means that treatment decisions are based on uncertain information.

An AI-based tool that improves breast cancer diagnosis and predicts the risk of recurrence has been developed by researchers in Sweden. The advance from a team at the Karolinska Institutet could lead to more personalised treatment for breast cancer patients with intermediate risk tumours. The results are published in Annals of Oncology. In the diagnostic procedure for breast cancer, tissue samples of the tumour are analysed and graded by a pathologist and categorised by risk as low (grade 1), medium (grade 2) or high (grade 3), which guides decisions on the most suitable treatment. "Roughly half of breast cancer patients have a grade 2 tumour, which unfortunately gives no clear guidance on how the patient is to be treated," said Yinxi Wang, a doctoral student at the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet.

Researchers at Karolinska Institutet and Karolinska University Hospital in Sweden have compared the ability of three artificial intelligence (AI) algorithms to identify breast cancer based on previously taken mammograms. The best algorithm proved to be as accurate as the average radiologist. The results, published in JAMA Oncology, may lead the way in reorganising breast cancer screening for the future. "This is the first independent comparison conducted to assess the accuracy of several different AI algorithms," says study author Fredrik Strand, a researcher at the Department of Oncology-Pathology at Karolinska Institutet and a radiologist at Karolinska University Hospital. "We can demonstrate that one of the three algorithms is significantly better than the others and that it equals the accuracy of the average radiologist."

LONDON: Scientists have developed an artificial intelligence (AI) based method that is as good at identifying and grading prostate cancer as world-leading uro-pathologists. The AI-system has the potential to solve one of the bottlenecks in today's prostate cancer histopathology by providing more accurate diagnosis and better treatment decisions, according to the study published in The Lancet Oncology journal. "Our results show that it is possible to train an AI-system to detect and grade prostate cancer on the same level as leading experts," said Martin Eklund, an associate professor at Karolinska Institutet in Sweden. "This has the potential to significantly reduce the workload of uro-pathologists and allow them to focus on the most difficult cases," Eklund said. A problem in today's prostate pathology is that there is a certain degree of subjectivity in the assessments of the biopsies, researchers said. Different pathologists can reach different conclusions even though they are studying the same samples, they said.