Neuro-fuzzy modeling: An accurate and interpretable method for predicting bladder cancer progression
Neuro-fuzzy modeling: An accurate and interpretable method for predicting bladder cancer progression
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DOI：
10.1016/s0022-5347(05)00246-6
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发表时间：
2006-02-01
影响因子：
6.6
通讯作者：
Hamdy, FC
中科院分区：
文献类型：
--
作者：
Catto, JWF;Abbod, MF;Hamdy, FC
目的：由于传统统计测试的准确性有限，需要新的方法来改进癌症进展的预测。准确的预测将使医生能够根据个体患者的风险提供特定的治疗。虽然使用人工神经网络可以获得预测改进，但这些网络的隐藏性质阻碍了洞察力并阻碍了其广泛实施。 NFM 是使用模糊逻辑（一种在不确定性下提供推理的多值逻辑）的人工智能的另一种形式。通过去模糊化，NFM 规则库变得透明，以克服 ANN 的黑匣子性质。材料和方法：结合来自 117 名患者的临床病理学（肿瘤分期和分级、患者年龄、性别和吸烟状况）和分子（p53 的免疫组织化学表达和 11 个基因座的甲基化状态）数据，使用 NFM、ANN 和 LR 开发和比较肿瘤进展的预测模型。结果：NFM（88％ 100% 的敏感性、97% 至 100% 的特异性和 94% 至 100% 的准确度）比 ANN（81% 至 87%、95% 至 100% 和 89% 至 90%，p = 0.002）和 LR（3%、61% 至 72% 和 47% 至 53%，p = 0.00005）。 NFM 能够询问临床病理学和分子数据，并选择最重要的参数（年龄、分级、分期、吸烟、甲基化）进行进展预测。结论：智能系统和分子生物标志物提高了癌症进展预测的准确性。 NFM 在准确性、敏感性、特异性和透明度方面似乎优于 ANN。 NFM 在常规临床实践中的使用值得进一步验证。
Purpose: New methods are required to improve the prediction of cancer progression as traditional statistical tests have limited accuracy. Accurate predictions would allow physicians to offer specific treatment according to individual patient risk. While predictive improvements are obtained using ANN, the hidden nature of these networks prevents insight and has hindered their widespread implementation. NFM is an alternate form of artificial intelligence using fuzzy logic (which is a multivalued logic which provides reasoning under uncertainty). By defuzzification the NFM rule base becomes transparent to overcome the black box nature of ANN.Materials and Methods: Combinations of clinicopathological (tumor stage and grade, patient age, gender, and smoking status) and molecular (immunohistochemical expression of p53 and methylation status of 11 loci) data from 117 patients were used to develop and compare predictive models of tumor progression using NFM, ANN and LR.Results: NFM (88% to 100% sensitivity, 97% to 100% specificity and 94% to 100% accuracy) predicted the presence and timing of cancer progression more accurately than ANN (81% to 87%, 95% to 100% and 89% to 90%, p = 0.002) and LR (3%, 61% to 72% and 47% to 53%, p = 0.00005). NFM was able to interrogate the clinicopathological and molecular data, and select the most important parameters (age, grade, stage, smoking, methylation) for progression prediction.Conclusions: Intelligent systems and molecular biomarkers improved the accuracy of cancer progression predictions. NFM appeared superior to ANN in terms of accuracy, sensitivity, specificity and transparency. The use of NFM in routine clinical practice warrants further validation.