ABSTRACT
Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.
ABSTRACT
BACKGROUND: The growth in the elderly population is predicted to expand exponentially and developing countries like Pakistan have about two-third of the global elderly population. It is vital to maintain the health of the elderly aged population to reduce disabilities and health-care cost. AIM: This study aimed to determine the health promotion practices among the older aged population in Pakistan and to explore the factors associate with adopting healthy lifestyle practices. MATERIALS AND METHODS: This was a cross-sectional study spanning from 2019 to 2020 conducted on 317 participants of age more than 60 years in Pakistan. The participants included healthy attendants of patients visiting the outpatient clinics of different disciplines in the Liaquat National Hospital Karachi through purposive sampling technique. The health-promoting practices were assessed using Health-Promoting Lifestyle Profile II Questionnaire. The factors that determined the healthy practices among the elderly population were identified using independent t-test and analysis of variance and Tukey test, with a significance level of P < 0.05. IBM SPSS Statistics 22 was used for data entry and analysis. RESULTS: The highest subscale was detected from interpersonal relationships and spiritual growth. The lowest score was detected from physical activity. The scores differed significantly by occupation, education, and the marital status of the participants. Females, unmarried people, those who were less educated, and participants relying on others for financial support had lower health-promoting lifestyle scores. CONCLUSION: The overall health-promoting practices were good among the old-aged population of Pakistan. These practices differed particularly for physical activity, spiritual growth, and interpersonal relationships.
