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基于创新模糊逻辑方法的结直肠源性腹膜癌病诊断

Diagnosis of Peritoneal Carcinomatosis of Colorectal Origin Based on an Innovative Fuzzy Logic Approach.

作者信息

Bejan Valentin, Pîslaru Marius, Scripcariu Viorel

机构信息

Department of Surgery, Faculty of Medicine, "Gr. T. Popa" University of Medicine and Farmacy of Iași, 700115 Iasi, Romania.

Department of Engineering and Management, Faculty of Industrial Design and Business Management, "Gheorghe Asachi" Technical University of Iași, 700050 Iasi, Romania.

出版信息

Diagnostics (Basel). 2022 May 21;12(5):1285. doi: 10.3390/diagnostics12051285.

DOI:10.3390/diagnostics12051285
PMID:35626439
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9140813/
Abstract

Colorectal cancer represents one of the most important causes worldwide of cancer related morbidity and mortality. One of the complications which can occur during cancer progression, is peritoneal carcinomatosis. In the majority of cases, it is diagnosed in late stages due to the lack of diagnostic tools capable of revealing the early-stage peritoneal burden. Therefore, still associates with poor prognosis and quality of life, despite recent therapeutic advances. The aim of the study was to develop a fuzzy logic approach to assess the probability of peritoneal carcinomatosis presence using routine blood test parameters as input data. The patient data was acquired retrospective from patients diagnosed between 2010-2021. The developed model focuses on the specific quantitative alteration of these parameters in the presence of peritoneal carcinomatosis, which is an innovative approach as regards the literature in the field and validates the feasibility of using a fuzzy logic approach in the noninvasive diagnosis of peritoneal carcinomatosis.

摘要

结直肠癌是全球范围内癌症相关发病率和死亡率的最重要原因之一。癌症进展过程中可能出现的并发症之一是腹膜癌转移。在大多数情况下,由于缺乏能够揭示早期腹膜负荷的诊断工具,它在晚期才被诊断出来。因此,尽管最近治疗有进展,但它仍然与不良预后和生活质量相关。该研究的目的是开发一种模糊逻辑方法,以常规血液检测参数作为输入数据来评估腹膜癌转移存在的概率。患者数据是从2010年至2021年期间诊断的患者中回顾性获取的。所开发的模型侧重于这些参数在腹膜癌转移存在时的特定定量变化,这在该领域的文献中是一种创新方法,并验证了使用模糊逻辑方法进行腹膜癌转移无创诊断的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60e0/9140813/9a96bd2cab84/diagnostics-12-01285-g013.jpg
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