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Life sciences · Journal article
International Journal of Drug Delivery Technology · October 7, 2026
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Lung and oesophageal cancer are among the most lethal malignancies in the world.Their treatment faces huge challenges due to high occurrence, poor prognosis, and complicated protocols.Lung cancer is currently the leading cause of cancer-related deaths.However, there have been significant improvements in early diagnosis and precision treatments.AI-driven imaging and machine learning now help determine non-small cell lung cancer (NSCLC) subtypes without extra staining.The rise in lung cancer among never smokers, potentially due to factors like air pollution, points to environmental risks beyond tobacco.New targeted therapies, such as HER2 and c-Met inhibitors, offer extra precision oncology options for specific NSCLC subgroups.Oesophageal cancer, mainly oesophageal squamous cell carcinoma (ESCC) and oesophageal adenocarcinoma (EAC), remains an important global health burden.It tends to be diagnosed at an advanced stage, resulting in poor survival rates despite multimodal intervention.Most studies on oesophageal cancer focus on optimizing existing therapies, including minimally invasive surgery and post-treatment quality of life interventions.Additionally, high rates of comorbidity with pulmonary complications and the presence of synchronous tumors in the lung and esophagus highlight the need for integrated oncological therapy.Progress in creating predictive nomograms and guidelines to assess prognosis in such cases is a significant advancement toward better patient outcomes.Recent statistics show that lung and oesophageal cancers share risk factors and overlap clinically.This highlights the need for integrated research.Oxidative stress, chronic inflammation, and immune disregard, as seen in diseases like COPD, play a central role in lung carcinogenesis.These factors may also alter the tumor microenvironment of both thoracic organs.Rare cases of dual primary malignancies, such as oesophageal malignant melanoma with lung adenocarcinoma, show the complexity of treating co-existing tumors.Nevertheless, the connections between the pathogenesis of lung and oesophageal cancer, shared risk factors in the environments and genetics, and the difficulties of their concomitant treatment still have gaps.There is an urgent need to integrate screening processes, expand molecular profiling across both tumor types, and develop effective therapeutic options to address these complexities.To address this, this paper introduces an Aggregated Joint Assembly of Decision-making (AJAD) classifier that combines Random Forests, Gradient Boosting, and Extra Trees via soft voting to improve diagnostic accuracy and support more robust clinical decision-making for both cancers.By leveraging this classifier, clinicians may achieve more accurate patient-specific risk stratification and treatment planning through integration of bioinformatics, oncology, and thoracic surgery expertise.AI-guided diagnostics and biomarker-based therapies are expected to be central in advancing clinical paradigms and improving survival rates and quality of life in patients with lung and oesophageal cancers.