An intelligent decision support algorithm for diagnosis of colorectal cancer through serum tumor markers

Authors:
Jinghua Shi;Qiang Su;Chenpeng Zhang;Gang Huang;Yan Zhu
Affiliations:
Department of Industrial Engineering and Logistics Management, Shanghai Jiao Tong University, Dong Chuan Road 800, Minhang District, Shanghai 200240, China;School of Economics & Management, Tongji University, Shanghai 200092, China;Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200001, China;Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200001, China;School of Economics and Management, Tsinghua University, Beijing 100084, China
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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Abstract

Nowadays, a wide range of serum tumor markers can be applied in the diagnosis of colorectal cancer. There exists a wide variability in the type and number of routinely used markers so that, sometimes, patients may receive redundant or insufficient checks. Furthermore, the traditional single cutoff point also hinders the efficient utilization of the continuous check value of a tumor marker. In order to improve the diagnostic accuracy (DA) and decrease the cost, it is necessary to optimize the check combinations and exploit the check values fully. To this end, focusing on colorectal cancer (CRC), an artificial intelligent algorithm entitled DS-STM (diagnosis strategy of serum tumor makers) is developed in this paper. DS-STM can provide decision support for physicians on the usage of different tumor markers and diagnosis of colorectal cancer (CRC). The study demonstrates that, instead of five or more tumor markers, two markers are already enough for diagnosis for most CRC patients. The experimental study shows, compared to the traditional serial test, DS-STM can improve DA from 67.53% to 73.87% for the same validation dataset. In addition, a significant cost reduction can be achieved with the new developed diagnosis strategy.