Akashi Takahisa, Okumura Tomoyuki, Terabayashi Kenji, Yoshino Yuki, Tanaka Haruyoshi, Yamazaki Takeyoshi, Numata Yoshihisa, Fukuda Takuma, Manabe Takahiro, Baba Hayato, Miwa Takeshi, Watanabe Toru, Hirano Katsuhisa, Igarashi Takamichi, Sekine Shinichi, Hashimoto Isaya, Shibuya Kazuto, Hojo Shozo, Yoshioka Isaku, Matsui Koshi, Yamada Akane, Sasaki Tohru, Fujii Tsutomu
Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-0194, Japan.
Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan.
Oncol Lett. 2023 Jun 8;26(1):320. doi: 10.3892/ol.2023.13906. eCollection 2023 Jul.
Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.
尽管食管鳞状细胞癌（ESCC）的多学科治疗最近取得了进展，但患者术后仍经常发生远处转移。对于多种类型的癌症，循环肿瘤细胞（CTC）被认为是远处转移、治疗反应和预后的预测指标。然而，随着越来越多细胞病理学异质性标志物的发现，在CTC中检测这些标志物表达的整体过程变得越来越复杂且耗时。在本研究中，使用基于卷积神经网络（CNN）的人工智能（AI）对ESCC患者的KYSE ESCC细胞系和血样进行CTC检测评估。该AI算法能够将KYSE细胞与健康志愿者外周血来源的单核细胞（PBMC）区分开来，并伴有上皮细胞粘附分子（EpCAM）和细胞核DAPI染色，当AI在同一KYSE细胞系上进行训练时，准确率>99.8%。此外，在KYSE520上训练的AI能够以99.8%的准确率将KYSE30与PBMC区分开来，尽管这两种KYSE细胞系之间EpCAM表达存在显著差异。AI和四位研究人员将KYSE细胞与PBMC区分开的平均准确率分别为100%和91.8%（P = 0.011）。AI和研究人员对100张图像完成细胞分类的平均时间分别为0.74秒和630.4秒（P = 0.012）。AI在10例ESCC患者的血样中检测到的EpCAM阳性/DAPI阳性细胞的平均数量为44.5个，在5名健康志愿者中为2.4个（P = 0.019）。这些结果表明，基于CNN的CTC检测图像处理算法与人类相比具有更高的准确率和更短的分析时间，表明其适用于ESCC患者的临床应用。此外，AI能够准确识别即使是EpCAM阴性的KYSE细胞这一发现表明，AI算法可能基于尚未知晓的特征来区分CTC，而不依赖于已知的标志物表达。
