This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting and Data System (Lung-RADS) has been widely used as the standard framework for patient management and follow-up. Nevertheless, Lung-RADS faces trade-offs between sensitivity and specificity, as it stratifies risk solely based on lung nodule characteristics without incorporating various risk factors. Here we propose a reasoning language model (RLM) to integrate radiology findings with longitudinal medical records for individualized lung cancer risk assessment. Through a systematic study including dataset construction and distillation, supervised fine-tuning, reinforcement learning, and comprehensive evaluation, our model makes significant improvements in risk prediction performance on datasets in the national lung screening trial. Notably, RLM can decompose the risk evaluation task into sub-components, analyze the contributions of diverse risk factors, and synthesize them into a final risk score computed using our data-driven system equation. Our approach improves both predictive accuracy and monitorability through the chain of thought reasoning process, thereby facilitating clinical translation into lung cancer screening.

Summit Consortium a uthors and affiliations listed at end of file. Low - dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect canc er and non - cancer - related early - stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine - tuned off the shelf for a wide range of disease - specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE dem onstrates fast convergence during fine - tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine - tuning data. Pretrained using self - supervised learni ng on over 98,000 thoracic LDCTs, including the UK ' s largest LCS initiative to date and 27 public datasets, TANGERINE achieves strong performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while general ising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource - intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open - source lightweight design lays the foundation for rapid integration into next - generation medical imaging tools that could transform LCS initiatives, allowing them to pivot f rom a singular focus on lung cancer detection to comprehensive respiratory disease management in high - risk populations. National lung cancer screening (LCS) programs herald a generational opportunity to identify early pre - symptomatic disease phenotypes for some of the most common chronic respiratory diseases in the world. In contrast, LCS programmes afford the opportunity to detect preclinical stages of airways or interstitial lung damage, where imaging abnormalities are radiologically visible despite lung function tests remaining normal. Moreover, methods have often relied on patch - based approaches that risk losing contextual information and require prior knowledge of disease location for model development. These limit ations constrain the utility of such models in research and clinical environments, where computational resources are often limited. Hence, t here remains a pressing need for foundation models that are not only accurate and generalisable, but also lightweigh t, open - access, and computationally efficient - enabling fine - tuning with limited data and resources.

Lung cancer is a significant cause of mortality worldwide, emphasizing the importance of early detection for improved survival rates. In this study, we propose a machine learning (ML) tool trained on data from the PLCO Cancer Screening Trial and validated on the NLST to estimate the likelihood of lung cancer occurrence within five years. The study utilized two datasets, the PLCO (n=55,161) and NLST (n=48,595), consisting of comprehensive information on risk factors, clinical measurements, and outcomes related to lung cancer. Data preprocessing involved removing patients who were not current or former smokers and those who had died of causes unrelated to lung cancer. Additionally, a focus was placed on mitigating bias caused by censored data. Feature selection, hyper-parameter optimization, and model calibration were performed using XGBoost, an ensemble learning algorithm that combines gradient boosting and decision trees. The ML model was trained on the pre-processed PLCO dataset and tested on the NLST dataset. The model incorporated features such as age, gender, smoking history, medical diagnoses, and family history of lung cancer. The model was well-calibrated (Brier score=0.044). ROC-AUC was 82% on the PLCO dataset and 70% on the NLST dataset. PR-AUC was 29% and 11% respectively. When compared to the USPSTF guidelines for lung cancer screening, our model provided the same recall with a precision of 13.1% vs. 9.3% on the PLCO dataset and 3.2% vs. 3.1% on the NLST dataset. The developed ML tool provides a freely available web application for estimating the likelihood of developing lung cancer within five years. By utilizing risk factors and clinical data, individuals can assess their risk and make informed decisions regarding lung cancer screening. This research contributes to the efforts in early detection and prevention strategies, aiming to reduce lung cancer-related mortality rates.

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.

Specifically, Google Health will provide scientific expertise and their existing model, while Aidence will use deep learning methods to further develop the existing model in an effort to quickly and accurately predict malignancy of CT-detected lung nodules in a clinical setting. "We are enthusiastic about working on a powerful deep learning model for lung nodule malignancy prediction based on the work of the Aidence and Google teams, as well as making sure that all the other requirements that contribute to the successful deployment of AI in clinical practice are in place, like clinical validation, certification and integration into the clinical workflow," said Aidence cofounder Mark-Jan Harte. Osarogiagbon stressed the potential of a commercially available solution to help identify cancerous cases: "The world looks forward to the rapid development and validation of software that will enhance our ability to find the many lung cancer needles in the giant haystack that is CT-detected lung nodules in today's clinical practice," he said.

LOS ANGELES, Nov. 28, 2022 (GLOBE NEWSWIRE) -- RadNet, Inc. (NASDAQ: RDNT), a national leader in providing high-quality, cost-effective, fixed-site outpatient diagnostic imaging services today reported that its lung artificial intelligence subsidiary, Aidence, and Google Health, a division of Alphabet, Inc. (NASDAQ: GOOG), announce an agreement to license Google Health's AI research model for lung nodule malignancy prediction on CT imaging. Aidence will develop, validate and bring this model to the market to support the early and accurate diagnosis of lung cancer and the reduction of unnecessary procedures in screening programs. Lung cancer screening with low-dose CT has been shown to significantly reduce lung cancer mortality by as high as 24% for men and 33% for women, according to the 2020 NELSON trial. Screening initiatives are increasingly being implemented in Europe, such as the UK's Targeted Lung Health Checks. In the United States, eligibility criteria have recently been broadened, further reflecting the benefit of lung cancer screening.

Lung cancer is one of the most common cancers worldwide. According to a study published in Nature called "Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography", researchers got to know that with the help of AI (artificial intelligence), lung cancer and cardiovascular health can be screened through the usage of low-dose computed tomography. This can help to reduce the risk of developing lung and cardiovascular diseases. The study was a result of a combined effort by Rensselaer Polytechnic Institute and Massachusetts General Hospital. Dr Colin Jacobs, Ph.D. assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen said "As it does not require manual interpretation of nodule imaging characteristics, the proposed algorithm may reduce the substantial interobserver variability in CT interpretation," .

The use of artificial intelligence (AI) can provide an automated and accurate tool to measure a common marker of heart disease in patients undergoing lung cancer screening, according to a study presented today at the annual meeting of the Radiological Society of North America (RSNA). "The new cholesterol guidelines encourage using the calcium score to help physicians and patients decide whether to take a statin," said study co-senior author Michael T. Lu, M.D., M.P.H., director of AI in the Cardiovascular Imaging Research Center (CIRC) at Massachusetts General Hospital (MGH) in Boston in a press release about the findings. "For select patients at intermediate risk of heart disease, if the calcium score is 0, statin can be deferred. If the calcium score is high, then those patients should be on a statin." In this study, researchers trained a deep-learning system on cardiac CTs and chest CTs in which the coronary artery calcium had been measured manually.

One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.