Author(s)

Yixiang Hepeng ¹, Xu Wu ¹, Zhi Xu ², Kailin Qu ¹

Affiliation(s)

¹ Department of Thoracic Surgery, Southern Medical University Nanfang Hospital, Guangzhou, 510515, China
² Medical Center for Overseas Patients, Southern Medical University Nanfang Hospital, Guangzhou, 510515, China

Corresponding Author

Xu Wu

ABSTRACT

Esophageal cancer is one of the eight most common malignant tumors, with high incidence and mortality rates. Most patients are already in the advanced or late stage at the time of diagnosis, missing the best treatment period and resulting in extremely poor prognosis. Factors affecting the prognosis of esophageal cancer include clinical staging, lymph node status, and pathological type. Early diagnosis and personalized treatment plans, as well as controlling the risk factors for esophageal cancer, can effectively improve the prognosis of patients. Therefore, early diagnosis and treatment of esophageal cancer have become extremely important. This paper proposes a model that comprehensively predicts lymph node metastasis status through multi-level image features. It utilizes sparse self-encoded feature fusion networks to process high-dimensional features from different levels, including machine vision features, imaging genomics features, and perceptual features. The model is constructed using statistical methods and experimentally verified for its discriminative ability, identification capability, and clinical practicality.

KEYWORDS

Esophageal cancer; Multi-scale features; Self-coding fusion model; State prediction

CITE THIS PAPER

Yixiang Hepeng, Xu Wu, Zhi Xu, Kailin Qu, Prediction of Preoperative Lymph Node Metastasis of Esophageal Cancer by Self-coding Fusion Model Based on Multi-scale Features. MEDS Clinical Medicine (2024) Vol. 5: 37-43. DOI: http://dx.doi.org/10.23977/medsc.2024.050107.

REFERENCES

[1] Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2018, 68(6): 394-424.
[2] Zheng R S, Sun KX, Zhang Z, et al. Prevalence of malignant tumors in China in 2015 [J]. Chinese Journal of Cancer, 2019, 41(1): 19-28.
[3] Chen S-B, Weng H-R, Wang G, et al. Prognostic Factors and Outcome for Patients with Esophageal Squamous Cell Carcinoma Underwent Surgical Resection Alone: Evaluation of the Seventh Edition of the American Joint Committee on Cancer Staging System for Esophageal Squamous Cell Carcinoma [J]. Journal of Thoracic Oncology, 2013, 8(4): 495-501.
[4] Chen M-F, Chen P-T, Lu M-S, et al. Survival benefit of surgery to patients with esophageal squamous cell carcinoma [J]. Scientific Reports, 2017, 7(1): 46139.
[5] Jiang H, Learned-Miller E. Face Detection with the Faster R-CNN[C]. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017: 650-657.

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