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Vietnamese cohort study on non-renal cell carcinoma kidney cancers: Identifying tumor size as an independent
Kinh Luan Thai1,2, Doan Ngoc Tran Nguyen1, Nhu Quang Tran1
1University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam.
Introduction:
Kidney cancer is the third most common malignancy of the urinary system. Non-renal cell carcinoma kidney cancer (non-RCC kidney cancer), though accounting for only 15%-20% of cases, displays distinct biological features and prognosis, require specific diagnosis and therapeutic strategies. This study aims to determine the clinical and paraclinical characteristics, the histopathological subtypes and factors associated with overall survival of patients diagnosed with non-RCC kidney cancer.
Patients And Methods:
We conducted a retrospective single-center cohort study on 81 patients diagnosed with non-RCC kidney cancer at Cho Ray Hospital between January 2019 and April 2024. Data were collected from medical records, including clinical, imaging, and histopathological characteristics. Statistical analysis was performed using Stata 14.0 software.
Results:
The mean age of the patients was 57.2 ± 15.2 years, with males comprising 65.4% of the cohort. A significant 95.1% of patients were diagnosed after being presented with symptoms. The most common symptoms were flank pain (58.9%) and gross hematuria (25.9%). The average tumor size was 8.3 ± 4.5 cm, with renal sarcoma showing the largest average tumor size of 13.32 ± 4.13 cm. The histopathological distribution was as follows: squamous cell carcinoma (SCC; 40.7%), upper tract urothelial carcinoma (UTUC; 30.9%), sarcoma (21%), lymphoma (3.7%), and metastatic kidney cancer (2.5%). Larger tumor size was associated with worse overall survival (OS) in exploratory analysis (HR = 1.12, 95% CI: 1.001-1.26, p = 0.049).
Conclusions:
Non-RCC kidney cancers present with a variety of clinical features, predominantly in males, and are generally diagnosed after symptom onset. SCC and UTUC are the most common histopathological subtypes. Early identification and accurate pathological classification are essential for optimal treatment strategies and prognosis.
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