Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data.

I-Jung Tsai,Chia-Ling Lee,Horng-Dar Wang,Wen-Chi Shen,Ching-Yu Lin

doi:10.3390/diagnostics12010203

I-Jung Tsai, Chia-Ling Lee + Show 3 more

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https://doi.org/10.3390/diagnostics12010203

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Abstract

Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

Highlights

Bladder cancer has been noted as the 10th most common cancer in the world [1]
alkaline phosphatase (ALP), BUN, chloride, creatinine, direct bilirubin, eGFR, pH, potassium, total protein, nitrite, strip WBC, and urine occult blood were significantly different in patients with kidney cancer compared to patients with cystitis
We discovered that ALP, AST, BUN, calcium, creatinine, sodium, urine epithelium counts, and urine occult blood had significant differences in patients with prostate cancer compared to patients with cystitis

Summary

Introduction

Bladder cancer has been noted as the 10th most common cancer in the world [1]. Bladder cancer is observed in men more than in women, with respective incidence and mortality rates of 9.5 and 3.3 per 100,000 among men, which are four times those among women globally [2]. Smoking is considered the major risk factor in patients with bladder cancer [3]. The gold standard procedure for diagnosing bladder cancer is cystoscopy, with a sensitivity 88–100% and specificity 77.1–97% [4]. Urinary cytology is considered a major non-invasive method to diagnose bladder cancer with high specificity, but only 38% sensitivity [5]. A screening method with high sensitivity and high specificity is urgently needed for the diagnosis of bladder cancer

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