Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network.
Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network.
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DOI：
10.1002/cam4.5343
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发表时间：
2023-03
期刊：
影响因子：
4
通讯作者：
Hiraoka, Shin-Ichiro
中科院分区：
文献类型：
--
作者：
Kawamura, Kohei;Lee, Chonho;Yoshikawa, Takashi;Hani, Al-Shareef;Usami, Yu;Toyosawa, Satoru;Tanaka, Susumu;Hiraoka, Shin-Ichiro
虽然颈淋巴转移是口腔癌的一个重要预后因素，但即使通过诊断性成像，隐匿性转移仍未被发现。我们利用多层感知器神经网络(MNN)对舌癌切除标本中血管生成和淋巴管生成相关蛋白的免疫组织化学(IHC)染色水平进行分类，建立了一个预测舌癌切除标本淋巴转移的学习模型。我们获得了76例舌鳞状细胞癌患者的数据集，这些患者接受了初次肿瘤切除。所有76例标本均进行了上述6种类型(VEGF-C、VEGF-D、Nrp1、NRP2、CCR7和SEMA3E)的IHC染色，并制备了456张切片。我们对所有幻灯片上的染色级别进行了可视评分。我们创建了虚拟幻灯片(4560张图像)，并将MNN模型与色调-饱和度(HS)直方图进行比较，验证了MNN模型的准确性。HS直方图将人工确定的视觉信息量化。用MNN训练模型的准确率为98.6%，当训练图像转换为灰度时，准确率下降到52.9%。这表明我们的MNN充分评估了IHC图像的染色程度，而不是形态特征。多因素分析显示，在HS直方图和MNN中，CCR7染色水平和T分期是影响颈淋巴转移的独立因素。这些结果表明，使用MNN进行IHC评估可能有助于确定舌癌患者的淋巴转移。从切除的舌癌标本中预测淋巴结转移在临床上是有用的；MNN可以用少量的训练数据来进行简单的分类，具有很高的准确性。这种方法允许对免疫化学染色标本的染色水平进行自动分类，并发现CCR7染色水平与淋巴结转移之间存在相关性。这是一种潜在的评估口腔癌患者淋巴结转移的新系统。
Although cervical lymph node metastasis is an important prognostic factor for oral cancer, occult metastases remain undetected even by diagnostic imaging. We developed a learning model to predict lymph node metastasis in resected specimens of tongue cancer by classifying the level of immunohistochemical (IHC) staining for angiogenesis‐ and lymphangiogenesis‐related proteins using a multilayer perceptron neural network (MNN). We obtained a dataset of 76 patients with squamous cell carcinoma of the tongue who had undergone primary tumor resection. All 76 specimens were IHC stained for the six types shown above (VEGF‐C, VEGF‐D, NRP1, NRP2, CCR7, and SEMA3E) and 456 slides were prepared. We scored the staining levels visually on all slides. We created virtual slides (4560 images) and the accuracy of the MNN model was verified by comparing it with a hue–saturation (HS) histogram, which quantifies the manually determined visual information. The accuracy of the training model with the MNN was 98.6%, and when the training image was converted to grayscale, the accuracy decreased to 52.9%. This indicates that our MNN adequately evaluates the level of staining rather than the morphological features of the IHC images. Multivariate analysis revealed that CCR7 staining level and T classification were independent factors associated with the presence of cervical lymph node metastasis in both HS histograms and MNN. These results suggest that IHC assessment using MNN may be useful for identifying lymph node metastasis in patients with tongue cancer. Prediction of lymph node metastasis from resected specimens of tongue cancer is clinically useful; MNN can be highly accurate for simple classification with a small amount of training data. This method allows an automatic classification of the staining levels of immunochemically stained specimens, and a correlation was found between CCR7 staining levels and lymph node metastases. This is a potential new system for the assessment of lymph node metastases in patients with oral cancer.
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影响因子：
3.8
作者：
Mascitti, Marco;Togni, Lucrezia;Troiano, Giuseppe
通讯作者：
Troiano, Giuseppe
影响因子：
2.1
作者：
Le Campion ACOV;Ribeiro CMB;Luiz RR;da Silva Júnior FF;Barros HCS;Dos Santos KCB;Ferreira SJ;Gonçalves LS;Ferreira SMS
通讯作者：
Ferreira SMS
DOI：
10.1002/hed.10130
发表时间：
2002-08-01
影响因子：
2.9
作者：
Kurokawa, H;Yamashita, Y;Takahashi, T
通讯作者：
Takahashi, T
影响因子：
2.5
作者：
Chien, Chih-Yen;Su, Chih-Ying;Huang, Chao-Cheng
通讯作者：
Huang, Chao-Cheng
影响因子：
4.2
作者：
Al-Shareef H;Hiraoka SI;Tanaka N;Shogen Y;Lee AD;Bakhshishayan S;Kogo M
通讯作者：
Kogo M