Awaiting the support of artificial intelligence in lung cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Bidzinska J
PROVIDER: S-EPMC10087998 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
Publications
Awaiting the support of artificial intelligence in lung cancer.
Bidzińska Joanna J Szurowska Edyta E
Translational lung cancer research 20230310 3
Similar Datasets
Project description: Not available
| S-EPMC11304431 | biostudies-literature
Project description:Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.
| S-EPMC7947422 | biostudies-literature
Project description:Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.
| S-EPMC7760157 | biostudies-literature
Project description:Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
| S-EPMC10650618 | biostudies-literature
Project description:Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
| S-EPMC10663598 | biostudies-literature
Project description:This paper explores the potential of artificial intelligence (AI) in lung cancer screening programs, particularly in the interpretation of computed tomography (CT) scans. The authors acknowledge the benefits of AI, including faster and potentially more accurate analysis of scans, but also raise concerns about clinician trust, transparency, and the deskilling of radiologists due to decreased scan exposure. The rise of AI in medicine and the introduction of national lung cancer screening programs are both increasing contemporarily and naturally the overlap and interplay between the two in the future is ensured. The paper highlights the importance of human-AI collaboration, emphasizing the need for interpretable models and ongoing validation through clinical trials. The promising results and problems uncovered the current pilot studies is explored. Building trust with patients and clinicians is also crucial, considering factors like disease risk perception and the human element of patient interaction. The authors conclude that while AI offers significant promise, widespread adoption hinges on addressing ethical considerations and ensuring a balanced, synergistic relationship between AI and medical professionals. This report aims to provide a talking point to inspire conversations around, and prepare clinicians for the rapidly approaching frontier that is AI in healthcare.
| S-EPMC11635207 | biostudies-literature
Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description: Not available
| S-EPMC6710912 | biostudies-literature
Project description:BackgroundDue to the rapid advancement of technology, artificial intelligence (AI) has become extensively used for the diagnosis and prognosis of various diseases, such as lung cancer. Research in the field of literature has demonstrated that artificial intelligence (AI) can be valuable in the timely detection of lung cancer and the formulation of an effective treatment plan. This study aims to conduct a bibliometric analysis to examine and illustrate the specific areas of focus, research frontiers, evolutionary processes, and trends in existing research on artificial intelligence in the context of lung cancer.MethodsPublications on AI in lung cancer were selected from the SCIE and ESCI indexes on September 19, 2023. The examination of nations, academic publications, organizations, writers, citations, and terms in this domain was visually analyzed with InCites and VOSviewer.ResultsIn this study, a total of 4275 publications were selected and analyzed. Artificial intelligence-related lung cancer publications have increased significantly in the last 5 years. China and the USA have contributed the most to the literature in this field (1418 publications with 13.92 citation impacts and 1117 publications with 37.34 citation impacts, respectively). The institution with the highest contribution was "Chinese Academy of Sciences," with 118 publications and 29.09 citation impacts. Among the research categories, "Radiology, Nuclear Medicine & Imaging", "Oncology", and "Engineering, Biomedical" were in first place.ConclusionThe USA and China have always been leaders in this field and will continue to be for some time. Research in countries such as the Netherlands is increasing. However, research collaboration has to be strengthened in developing countries.
| S-EPMC10835254 | biostudies-literature
Project description: Not available
| S-EPMC10997848 | biostudies-literature