Hattori Hideharu, Sakashita Shingo, Tsuboi Masahiro, Ishii Genichiro, Tanaka Toshiyuki
Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa 223-8522, Japan.
Research & Development Group, Hitachi, Ltd., 1-280 Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan.
J Pathol Inform. 2022 Dec 23;14:100175. doi: 10.1016/j.jpi.2022.100175. eCollection 2023.
Lung cancer is one of the cancers with the highest morbidity and mortality in the world. Recurrence often occurs even after complete resection of early-stage lung cancer, and prediction of recurrence after resection is clinically important. However, the pathological characteristics of the recurrence of pathological stage IB lung adenocarcinoma (LAIB) have not yet been elucidated. Therefore, the problem is what type of histological image of lung adenocarcinoma recurs, and it is important to examine the histological image of recurrence. We attempted to predict recurrence of early lung adenocarcinoma after resection on the basis of digital pathological images of hematoxylin and eosin-stained specimens and machine learning applying a convolutional neural network. We constructed a model that extracts the features of two-color spaces and a switching model that automatically switches between our extraction model and one that extracts the features of one-color space for each image. We then developed a tumor-identification method for predicting the presence or absence of LAIB recurrence using these models. We conducted an experiment involving 55 patients with LAIB who underwent surgical resection to evaluate the proposed method. The proposed method determined LAIB recurrence with an accuracy of 84.8%. The use of digital pathology and machine learning can be used for highly accurate prediction of LAIB recurrence after surgical resection. The proposed method has the potential for objective postoperative follow-up observation.
肺癌是全球发病率和死亡率最高的癌症之一。即使早期肺癌完全切除后也常出现复发，因此术后复发的预测在临床上具有重要意义。然而，病理分期为IB期的肺腺癌（LAIB）复发的病理特征尚未阐明。所以，问题在于肺腺癌复发的是哪种组织学图像，检查复发的组织学图像很重要。我们试图基于苏木精和伊红染色标本的数字病理图像以及应用卷积神经网络的机器学习来预测早期肺腺癌切除术后的复发情况。我们构建了一个提取双色空间特征的模型以及一个针对每张图像在我们的提取模型和提取单色空间特征的模型之间自动切换的切换模型。然后，我们开发了一种使用这些模型预测LAIB复发与否的肿瘤识别方法。我们对55例接受手术切除的LAIB患者进行了实验，以评估所提出的方法。该方法确定LAIB复发的准确率为84.8%。数字病理学和机器学习的应用可用于手术切除后LAIB复发的高精度预测。所提出的方法具有进行客观术后随访观察的潜力。
