Low-dose computed tomography (LDCT) is the current standard for lung cancer screening, yet its adoption and accessibility remain limited. Many regions lack LDCT infrastructure, and even among those screened, early-stage cancer detection often yield false positives, as shown in the National Lung Screening Trial (NLST) with a sensitivity of 93.8 percent and a false-positive rate of 26.6 percent. We aim to investigate whether X-ray dark-field imaging (DFI) radiograph, a technique sensitive to small-angle scatter from alveolar microstructure and less susceptible to organ shadowing, can significantly improve early-stage lung tumor detection when coupled with deep-learning segmentation. Using paired attenuation (ATTN) and DFI radiograph images of euthanized mouse lungs, we generated realistic synthetic tumors with irregular boundaries and intensity profiles consistent with physical lung contrast. A U-Net segmentation network was trained on small patches using either ATTN, DFI, or a combination of ATTN and DFI channels. Results show that the DFI-only model achieved a true-positive detection rate of 83.7 percent, compared with 51 percent for ATTN-only, while maintaining comparable specificity (90.5 versus 92.9 percent). The combined ATTN and DFI input achieved 79.6 percent sensitivity and 97.6 percent specificity. In conclusion, DFI substantially improves early-tumor detectability in comparison to standard attenuation radiography and shows potential as an accessible, low-cost, low-dose alternative for pre-clinical or limited-resource screening where LDCT is unavailable.

The discovery began, as many breakthroughs do, with an observation that didn't quite make sense. In 1948, two French researchers, Paul Mandel and Pierre Métais, published a little-noticed paper in a scientific journal. Working in a laboratory in Strasbourg, they had been cataloguing the chemical contents of blood plasma--that river of life teeming with proteins, sugars, waste, nutrients, and cellular debris. Amid this familiar inventory, they'd spotted an unexpected presence: fragments of DNA drifting freely. The finding defied biological orthodoxy. DNA was thought to remain locked inside the nuclei of cells, and not float around on its own.

The incidence rate of early - onset colorectal cancer (EoCRC, age < 45) has increased every year, but this populanullon is younger than the recommended age established by nanullonal guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance w ith advanced large language models (LLM), using panullent condinullons, lab results, and observanullons within 6 months of panullent journey prior to the CRC diagnoses. The results demonstrated that the fine - tuned LLM achieved an average of 73% sensinullvity and 91% specificity. Introducnullon Colorectal cancer (CRC) is a significant public health concern, ranking as the second leading cause of cancer - related deaths and the 4th most common new cancer diagnosis in the U.S. in 2024. While CRC has historically been considered a disease of older adults, there has been an increase in colorectal cancer diagnosed in individuals under 50. Between 2011 and 2019, CRC incidence rates increased by 1.9% per year in people younger than 50 years. Furthermore, between 2012 and 2021, among individuals aged 20 to 49, the incidence of advanced - stage colorectal cancer increased by approximately 3% per year .

Mount Sinai urology chair Dr. Ash Tewari joins'Fox News Live' to discuss the PSA test designed to catch the'silent killer.' A new means of prostate cancer screening could emerge as an alternative to the PSA test, which has long been the first-line option. Using machine learning, a form of artificial intelligence, Swedish researchers analyzed urine samples from more than 2,000 men with prostate cancer, along with a control group. They determined that the simple, non-invasive urine test was able to detect biomarkers of prostate cancer with a high degree of accuracy -- and could also determine the grade (stage) of the disease. The results were published in the journal Cancer Research.

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths worldwide. Survival largely depends on tumor stage at diagnosis, and early detection with low-dose CT can significantly reduce mortality in high-risk patients. AI can improve the detection, measurement, and characterization of pulmonary nodules while reducing assessment time. However, the training data, functionality, and performance of available AI systems vary considerably, complicating software selection and regulatory evaluation. Manufacturers must specify intended use and provide test statistics, but they can choose their training and test data, limiting standardization and comparability. Under the EU AI Act, consistent quality assurance is required for AI-based nodule detection, measurement, and characterization. This position paper proposes systematic quality assurance grounded in a validated reference dataset, including real screening cases plus phantom data to verify volume and growth rate measurements. Regular updates shall reflect demographic shifts and technological advances, ensuring ongoing relevance. Consequently, ongoing AI quality assurance is vital. Regulatory challenges are also adressed. While the MDR and the EU AI Act set baseline requirements, they do not adequately address self-learning algorithms or their updates. A standardized, transparent quality assessment - based on sensitivity, specificity, and volumetric accuracy - enables an objective evaluation of each AI solution's strengths and weaknesses. Establishing clear testing criteria and systematically using updated reference data lay the groundwork for comparable performance metrics, informing tenders, guidelines, and recommendations.

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

High rates of false positive test results may be keeping women from sticking to recommended mammogram screenings for breast cancer, a new study has found. Researchers from UC Davis Comprehensive Cancer Center in Sacramento, California, reviewed more than 3.5 million screening mammograms performed among more than one million women between 2005 and 2017. Women who received a true-negative result were more likely to return for future screenings, with a 77% compliance rate. THESE 17 CANCER TYPES ARE MORE COMMON IN GEN X AND MILLENNIALS, AS STUDY NOTES'ALARMING TREND' By comparison, among those who received a false positive, only 61% returned for another mammogram in six months, and 67% returned for a recommended biopsy. The women, who ranged in age from 40 to 73, had not previously received a breast cancer diagnosis.

The absence of adequately sufficient expert-level tumor annotations hinders the effectiveness of supervised learning based opportunistic cancer screening on medical imaging. Clinical reports (that are rich in descriptive textual details) can offer a "free lunch'' supervision information and provide tumor location as a type of weak label to cope with screening tasks, thus saving human labeling workloads, if properly leveraged. However, predicting cancer only using such weak labels can be very changeling since tumors are usually presented in small anatomical regions compared to the whole 3D medical scans. Weakly semi-supervised learning (WSSL) utilizes a limited set of voxel-level tumor annotations and incorporates alongside a substantial number of medical images that have only off-the-shelf clinical reports, which may strike a good balance between minimizing expert annotation workload and optimizing screening efficacy. In this paper, we propose a novel text-guided learning method to achieve highly accurate cancer detection results. Through integrating diagnostic and tumor location text prompts into the text encoder of a vision-language model (VLM), optimization of weakly supervised learning can be effectively performed in the latent space of VLM, thereby enhancing the stability of training. Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability, and produce reliable pseudo tumor masks to improve cancer detection. Our extensive quantitative experimental results on a large-scale cancer dataset, including 1,651 unique patients, validate that our approach can reduce human annotation efforts by at least 70% while maintaining comparable cancer detection accuracy to competing fully supervised methods (AUC value 0.961 versus 0.966).

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Among the latest artificial intelligence innovations in health care, a routine chest X-ray could help identify non-smokers who are at a high risk for lung cancer. The study findings will be presented this week at the annual meeting of the Radiological Society of North America (RSNA) in Chicago. Researchers from the Cardiovascular Imaging Research Center (CIRC) at Massachusetts General Hospital (MGH) and Harvard Medical School in Boston developed a deep learning AI model using 147,497 chest X-rays of asymptomatic smokers and never-smokers.

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.