Possible role of nutrition in the prevention of inflammatory bowel disease-related colorectal cancer: A focus on human studies.

Patients with inflammatory bowel disease (IBD) are at substantially high risk for colorectal cancer (CRC). IBD-associated CRC accounts for roughly 10% to 15% of the annual mortality in patients with IBD. IBD-related CRC also affects younger patients compared with sporadic CRC, with a 5-y survival rate of 50%. Regardless of medical therapies, the persistent inflammatory state characterizing IBD raises the risk for precancerous changes and CRC, with additional input from several elements, including genetic and environmental risk factors, IBD-associated comorbidities, intestinal barrier dysfunction, and gut microbiota modifications. It is well known that nutritional habits and dietary bioactive compounds can influence IBD-associated inflammation, microbiome abundance and composition, oxidative stress balance, and gut permeability. Additionally, in recent years, results from broad epidemiologic and experimental studies have associated certain foods or nutritional patterns with the risk for colorectal neoplasia. The present study aimed to review the possible role of nutrition in preventing IBD-related CRC, focusing specifically on human studies. It emerges that nutritional interventions based on healthy, nutrient-dense dietary patterns characterized by a high intake of fiber, vegetables, fruit, ω-3 polyunsaturated fatty acids, and a low amount of animal proteins, processed foods, and alcohol, combined with probiotic supplementation have the potential of reducing IBD-activity and preventing the risk of IBD-related CRC through different mechanisms, suggesting that targeted nutritional interventions may represent a novel promising approach for the prevention and management of IBD-associated CRC.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.