ABSTRACT
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.
ABSTRACT
Neurocritical care is a multidisciplinary field managing patients with a wide range of aliments. Specifically, neurotrauma is a rapidly growing field with increasing demands. The history of how neurotrauma management came to its current form has not been extensively explored before. Our review delves into the history, timeline, and noteworthy pioneers of neurotrauma-focused neurocritical care. We explore the historical development during early times, the 18th-20th centuries, and modern times, as well as warfare- and sports-related concussions. Research is ever growing in this budding field, with several promising innovations on the horizon.
Subject(s)
Critical Care , Trauma, Nervous System , HumansABSTRACT
Numerous studies indicated that patients with tobacco use disorder (TUD) are inversely associated with mortality in what is known as the smoker's paradox. However, limited studies have been conducted on the impact of TUD on the in-hospital mortality rates of patients with secondary pulmonary hypertension (PH, Non-Group 1 PH). Using the 2019 National Inpatient Sample, we identified PH and divided it into TUD and non-TUD to compare the comorbidities and in-hospital mortality between the two after 1:1 propensity-score matching. Of 1,129,440 PH hospitalizations, 12.1 % had TUD. After matching (n=133545, each group), TUD had lower median age (62 vs. 63), higher females (49 vs. 46.6 %), blacks (25.9 vs. 25.3 %), lower household income (40.8 vs. 32.7 %), Medicaid (22.4 vs. 14.8 %), non-elective (93.5 vs. 89.8 %), rural (9.3 vs. 6.7 %), urban non-teaching (17.2 vs 15.8 %) admissions. All CV comorbidities and other substance use were higher in TUD except CHF and valvular heart disease, TUD+ cohort and lower mortality (3.3 vs. 4.2 %, OR 0.78, p<0.001), higher routine discharges (53.8 vs. 51.3 %, p<0.001) and lower total charges ($47155 vs. 51909, p<0.001) than non-TUD. Although PH patients with TUD had a higher comorbidity burden, they had lower in-hospital mortality rates along with lower total charges of hospitalization, mandating real-world data to validate these results. See also the Graphical abstract(Fig. 1).
