Oncology Reports - Review - Unveiling the best predictive models for early‑onset metastatic cancer - Insights and innovations

Sep 21, 2026

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Cancer metastasis is the primary cause of cancer deaths, driven by the spread of malignant cells from primary tumors to distant body parts through lymphatic and vascular pathways. Early metastasis, which can occur in initial cancer stages, is frequently misdiagnosed and therefore worsens survival and prognosis. This review summarizes clinicopathological and molecular characteristics of early-onset metastatic cancer, highlighting predictors including age, race, tumor size and location, histological type, and pathological grade. It emphasizes the need for improved detection and management strategies and proposes better predictive models, such as nomograms and machine learning, while noting current limitations and the need for further study.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Early-onset metastases of different types of cancer

The clinicopathological and molecular features of early-onset metastatic types of cancer (Table I)

Factors affecting early-onset metastatic types of cancer

Biochemical characteristics of early-onset metastatic types of cancer

Predictable factors and indicators of early-onset metastatic types of cancer

Association with gene mutation profiles

NGS

ctDNA

Cytokines

Risk analysis and assessment (Table II)

Discussion and conclusion

ONCOLOGY REPORTS 51: 60, 2024 Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review) LIQING YU 1,2* , ZHENJUN HUANG 1* , ZIQI XIAO2 , XIAOFU TANG2 , ZIQIANG ZENG3,4 , XIAOLI TANG5 and WENHAO OUYANG 1 1Department of Medical Oncology, Sun Yat‑sen Memorial Hospital, Sun Yat‑sen University, Guangzhou, Guangdong 510120; 2The Second Clinical Medical College; 3Jiangxi Provincial Key Laboratory of Preventive Medicine; 4School of Public Health and 5School of Basic Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China Received October 8, 2023; Accepted January 22, 2024 DOI: 10. 3892/or.2024.8719 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 demon‑ strated the clinicopathological and molecular characteristics of early‑onset 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 conclu‑ sion, 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. Correspondence to: Ms. Xiaoli Tang, School of Basic Medicine, Jiangxi Medical College, Nanchang University, 461 Bayi Avenue, Nanchang, Jiangxi 330006, P.R. China E‑mail: [xltangmail@163.com](xltangmail@163.com) Dr Wenhao Ouyang, Department of Medical Oncology, Sun Yat‑sen Memorial Hospital, Sun Yat‑sen University, 107 Yanjiang West Road, Guangzhou, Guangdong 510120, P.R. China E‑mail: [auyeung3@126.com](auyeung3@126.com); [auyeung3@mail2.sysu.edu.cn](auyeung3@mail2.sysu.edu.cn) *Contributed equally Key words: metastatic cancer, early‑onset cancer, clinicopatho‑ logical features, the predictable factors, the predictable models Contents 1. Introduction 2. Early‑onset metastases of different types of cancer 3. The clinicopathological and molecular features of early‑onset metastatic types of cancer (Table I) 4. Factors affecting the metastases of early‑onset metastatic types of cancer 5. Biochemical characteristics of early‑onset metastatic types of cancer 6. Predictable factors and indicators of early‑onset metastatic types of cancer 7. Association of early‑onset metastatic types of cancer with gene mutation profiles 8. NGS 9. ctDNA 10. Cytokines 11. Risk analysis and assessment of early‑onset metastatic types of cancer (Table II) 12. Discussion and conclusion 1. Introduction The primary reason for cancer deaths is metastasis, which occurs when tumor cells from the originating site infiltrate into lymphatic veins, blood vessels or other passageways and are transported to other places for continued growth, resulting in tumors of the same type as the primary‑site tumors (1‑3) and the original tumors transform into metastatic ones. Metastasis is one of the defining characteristics of types of cancer (4) . Lymphatic, vascular and implant metastases are some of the main transmission mechanisms (5). The majority of cancer cells enter the local lymph nodes (LNs) through lymphatic vessels and

FAQs

Why is early-onset metastatic cancer particularly challenging to diagnose?

Early metastasis often occurs in the initial stages of cancer and can be misdiagnosed because clinically effective identification is limited. The review notes that minimal residual lesions (MRDs) are commonly used instead.

Which patient and tumor factors are discussed as predictors for early-onset metastasis?

Age, race, tumor size and location are highlighted, along with histological and pathological grade. These clinicopathological variables are presented as significant predictors.

What predictive modeling approaches does the review recommend to improve patient outcomes?

The review calls for improved predictive models, including advanced techniques such as nomograms and machine learning. It also acknowledges current research challenges and limitations and the need for additional studies.

Sep 21, 2026

Uploaded by Finn