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Unveiling the best predictive models for early­onset metastatic cancer: Insights and innovations (Review).
Yu, Liqing; Huang, Zhenjun; Xiao, Ziqi; Tang, Xiaofu; Zeng, Ziqiang; Tang, Xiaoli; Ouyang, Wenhao.
Affiliation
  • Yu L; Department of Medical Oncology, Sun Yat­sen Memorial Hospital, Sun Yat­sen University, Guangzhou, Guangdong 510120, P.R. China.
  • Huang Z; Department of Medical Oncology, Sun Yat­sen Memorial Hospital, Sun Yat­sen University, Guangzhou, Guangdong 510120, P.R. China.
  • Xiao Z; The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
  • Tang X; The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
  • Zeng Z; Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
  • Tang X; School of Basic Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
  • Ouyang W; Department of Medical Oncology, Sun Yat­sen Memorial Hospital, Sun Yat­sen University, Guangzhou, Guangdong 510120, P.R. China.
Oncol Rep ; 51(4)2024 04.
Article in En | MEDLINE | ID: mdl-38456540
ABSTRACT
Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early­onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Nomograms / Neoplasms Limits: Humans Language: En Journal: Oncol Rep Journal subject: NEOPLASIAS Year: 2024 Document type: Article Country of publication: Greece

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Nomograms / Neoplasms Limits: Humans Language: En Journal: Oncol Rep Journal subject: NEOPLASIAS Year: 2024 Document type: Article Country of publication: Greece