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Wu D, Lu J, Zheng N, Elsehrawy MG, Alfaiz FA, Zhao H, Alqahtani MS, Xu H. Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery. ENVIRONMENTAL RESEARCH 2024; 245:117784. [PMID: 38065392 DOI: 10.1016/j.envres.2023.117784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 01/06/2024]
Abstract
Nanotechnology has emerged as a promising frontier in revolutionizing the early diagnosis and surgical management of gastric cancers. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high surgical and pharmacological therapy recurrence rates. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for detecting and treating cancer. Considering the limitations of GIC MRI and endoscopy and the complexity of gastric surgery, the early diagnosis and prompt treatment of gastric illnesses by nanotechnology has been a promising development. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. In this research, the boosting technique of machine learning was utilized to capture nonlinear interactions between a large number of input variables and outputs by using XGBoost and RNN-CNN as a classification method. The research sample included 350 patients, comprising 200 males and 150 females. The patients' mean ± SD was 50.34 ± 13.04 with a mean age of 50.34 ± 13.04. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.034), distant metastasis (P = 0.004), and tumor stage (P = 0.014) were shown to have a statistically significant link with GC patient survival. AUC was 93.54%, Accuracy 93.54%, F1-score 93.57%, Precision 93.65%, and Recall 93.87% when analyzing stomach pictures. Integrating nanotechnology with advanced machine learning techniques holds promise for improving the diagnosis and treatment of gastric cancer, providing new avenues for precision medicine and better patient outcomes.
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Affiliation(s)
- Dan Wu
- Department of Gastrointestinal Surgery, Lishui Municipal Central Hospital, Lishui, 323000, Zhejiang, China
| | - Jianhua Lu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Nan Zheng
- School of Pharmacy, Wenzhou Medicine University, Wenzhou, 325000, China
| | - Mohamed Gamal Elsehrawy
- Prince Sattam Bin Abdulaziz University, College of Applied Medical Sciences, Kingdom of Saudi Arabia; Nursing Faculty, Port-Said University, Egypt.
| | - Faiz Abdulaziz Alfaiz
- Department of Biology, College of Science, Majmaah University, Al-Majmaah, 11952, Saudi Arabia.
| | - Huajun Zhao
- School of Pharmacy, Wenzhou Medicine University, Wenzhou, 325000, China.
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia; BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, UK
| | - Hongtao Xu
- Department of Gastrointestinal Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.
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