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.

Atrial fibrillation (AF) recurrence has become common in patients who have undergone catheter ablation. High neutrophil lymphocyte ratios (NLR) have been linked to an increased risk of recurrent AF. The research is, however, not conclusive. This meta‐analysis addressed the value of easily accessible and affordable pre‐ and postablation NLR levels as indicators of AF recurrence in patients who had undergone ablation. We searched PubMed, SCOPUS, and Google Scholar for pertinent studies through May 2023. Using random effects models, the aggregated odds ratio (OR) of pre‐ and post‐NLR and AF recurrence was estimated. Inter‐study heterogeneity was described using I² statistics and leave‐one‐out sensitivity analysis. A p‐value < .05 was considered statistically significant. The literature search yielded 270 studies, seven of which were included in this meta‐analysis of 1923 patients who experienced AF recurrence after undergoing ablation. There are five retrospective and two prospective studies with a mean follow‐up of 20.5 months. The unadjusted odds ratio (OR) of AF recurrence for preablation NLR was 1.33 (95% CI: 1.04–1.71, p < .01, I² = 95.49%), while the adjusted OR was 1.45 (95% CI: 0.87–2.43, p < .01, I² = 95.1%). The unadjusted odds ratio (OR) for postablation NLR was 1.21 (95% CI: 1.09–1.36, p < .01, I² = 85.9%), and the adjusted odds ratio (OR) was 1.28 (95% CI: 0.93–1.76), demonstrating significant heterogeneity (I² = 95.32%) with a p‐value < .01. NLR was significantly associated with AF recurrence prediction. To detect AF recurrence, we recommend that clinicians add a simple NLR blood test to their diagnostic modalities.

BACKGROUND There exists a link between irritable bowel syndrome (IBS) and depression. Similarly, chronic depression is known to increase the risk of cancer in general. In this population-based analysis, we investigated the prevalence and the odds of colorectal cancer (CRC) in young-depressed patients with IBS. AIM To investigate the relationship between IBS and CRC in young, depressed patients using a nationally representative United States inpatient sample. METHODS The 2019 National Inpatient Sample was used to identify young (18-44 years) patients admitted with comorbid depression in the presence vs absence of IBS using relevant International Classification of Diseases, Tenth Revision, Clinical Modification codes. Primary endpoint was the prevalence and odds of CRC in age matched (1:1) young-depressed cohort hospitalized with IBS (IBS+) vs without IBS (IBS-). Multivariable regression analysis was performed adjusting for potential confounders. RESULTS Age-matched (1:1) young-depressed IBS+ (83.9% females, median age 36 years) and IBS- (65.8% females, median age 36 years) cohorts consisted of 14370 patients in each group. IBS+ cohort had higher rates of hypertension, uncomplicated diabetes, hyperlipidemia, obesity, peripheral vascular disease, chronic obstructive pulmonary disease, hypothyroidism, prior stroke, prior venous thromboembolism, anxiety, bipolar disorder, and borderline personality disorder (P < 0.005) vs the IBS- cohort. However, prior myocardial infarction, acquired immunodeficiency syndrome, dementia, smoking, alcohol abuse, and drug abuse (P < 0.005) are high in IBS- cohort. The rate of CRC was comparable in both cohorts [IBS+ n = 25 (0.17%) vs IBS- n = 35 (0.24%)]. Compared to the IBS- cohort, the odds ratio (OR) of developing CRC was not significantly higher [OR 0.71, 95% confidence interval (CI) 0.23-2.25)] in IBS+ cohort. Also, adjusting for baseline sociodemographic and hospital characteristics and relevant comorbidities, the OR was found to be non-significant (OR 0.89, 95%CI 0.21-3.83). CONCLUSION This nationwide propensity-matched analysis revealed comparable prevalence and risk of CRC in young-depressed patients with vs without IBS. Future large-scale prospective studies are needed to evaluate the long-term effects of depression and its treatment on CRC risk and outcomes in IBS patients.