Background and Aims

Magnifying narrow-band imaging (M-NBI) is important in the diagnosis of early gastric cancers (EGCs) but requires expertise to master. We developed a computer-aided diagnosis (CADx) system to assist endoscopists in identifying and delineating EGCs.

Methods

We retrospectively collected and randomly selected 66 EGC M-NBI images and 60 non-cancer M-NBI images into a training set and 61 EGC M-NBI images and 20 non-cancer M-NBI images into a test set. After preprocessing and partition, we determined 8 gray-level co-occurrence matrix (GLCM) features for each partitioned 40 × 40 pixel block and calculated a coefficient of variation of 8 GLCM feature vectors. We then trained a support vector machine (SVM_Lv1) based on variation vectors from the training set and examined in the test set. Furthermore, we collected 2 determined P and Q GLCM feature vectors from cancerous image blocks containing irregular microvessels from the training set, and we trained another SVM (SVM_Lv2) to delineate cancerous blocks, which were compared with expert-delineated areas for area concordance.

Results

The diagnostic performance revealed accuracy of 96.3%, precision (positive predictive value [PPV]) of 98.3%, recall (sensitivity) of 96.7%, and specificity of 95%, at a rate of 0.41 ± 0.01 seconds per image. The performance of area concordance, on a block basis, demonstrated accuracy of 73.8% ± 10.9%, precision (PPV) of 75.3% ± 20.9%, recall (sensitivity) of 65.5% ± 19.9%, and specificity of 80.8% ± 17.1%, at a rate of 0.49 ± 0.04 seconds per image.

Conclusions

This pilot study demonstrates that our CADx system has great potential in real-time diagnosis and delineation of EGCs in M-NBI images.

中文翻译：

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在放大窄带成像中识别和描绘早期胃癌的计算机辅助诊断

背景和目标

放大窄带成像（M-NBI）在早期胃癌（EGC）的诊断中很重要，但需要掌握专业知识。我们开发了一种计算机辅助诊断（CADx）系统，以协助内镜医师识别和描绘EGC。

方法

我们回顾性收集并随机选择66张EGC M-NBI图像和60张非癌M-NBI图像进入训练集，并选择61张EGC M-NBI图像和20张非癌M-NBI图像进入测试集。经过预处理和分割后，我们为每个分割的40×40像素块确定了8个灰度共现矩阵（GLCM）特征，并计算了8个GLCM特征向量的变异系数。然后，我们基于来自训练集的变异向量训练了支持向量机（SVM _Lv1），并在测试集中进行了检查。此外，我们从训练集中从包含不规则微血管的癌性图像块中收集了2个确定的P和Q GLCM特征向量，并训练了另一个SVM（SVM _Lv2）划定癌块，然后将其与专家划定的区域进行区域一致性的比较。

结果

诊断性能显示准确度为96.3％，准确度（阳性预测值[PPV]）为98.3％，召回率（灵敏度）为96.7％，特异性为95％，每张图像的速度为0.41±0.01秒。整体上表现出区域一致的表现，准确性为73.8％±10.9％，精度（PPV）为75.3％±20.9％，召回率（灵敏度）为65.5％±19.9％，特异性为80.8％±17.1％ ，每张图像的速度为0.49±0.04秒。

结论

这项初步研究表明，我们的CADx系统在M-NBI图像中EGC的实时诊断和描绘方面具有很大的潜力。