Abstract To develop an effective and objective diagnostic method for detecting the malignancy is of great importance. Considering that near-infrared (NIR) spectroscopy has many advantages such as being inexpensive and simple in sample preparation and class-modeling is a rather new strategy, the present paper investigates the feasibility of combining class-modeling technique other than classic classification and NIR spectroscopy for colorectal diagnosis. A total of 162 colorectal tissue slices were prepared and used to collect NIR spectra. A special variable importance (VI) index was defined to pick out 20 most significant variables. The Kennard-Stone (KS) algorithm was used to select representative 57 cancerous samples as the training set for building one-class model and the other samples served as the test set. The results showed that on the independent test set, it can achieve acceptable performance, i.e., the total accuracy of 95.2 %, the sensitivity of 96 %, and the specificity of 94.5 %. It indicates that the combination of NIR spectroscopy and one-class classifier is a potential tool for automatic cancer diagnosis.

中文翻译：

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基于近红外光谱和分类建模技术的癌症自动判别

摘要 开发一种有效、客观的诊断方法来检测恶性肿瘤具有重要意义。考虑到近红外 (NIR) 光谱具有成本低廉、样品制备简单等诸多优点，并且类建模是一种较新的策略，本文研究了结合经典分类和 NIR 光谱之外的类建模技术的可行性。用于结肠直肠诊断。共制备了 162 个结直肠组织切片并用于收集 NIR 光谱。定义了一个特殊的变量重要性 (VI) 指数来挑选 20 个最重要的变量。Kennard-Stone（KS）算法用于选择具有代表性的57个癌样本作为训练集构建一类模型，其他样本作为测试集。结果表明，在独立测试集上，它可以达到可接受的性能，即总准确率为95.2%，敏感性为96%，特异性为94.5%。这表明近红外光谱和一类分类器的结合是癌症自动诊断的潜在工具。