The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer

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

doi:10.3892/ol.2023.13906

The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer

Authors:
- Takahisa Akashi
- Tomoyuki Okumura
- Kenji Terabayashi
- Yuki Yoshino
- Haruyoshi Tanaka
- Takeyoshi Yamazaki
- Yoshihisa Numata
- Takuma Fukuda
- Takahiro Manabe
- Hayato Baba
- Takeshi Miwa
- Toru Watanabe
- Katsuhisa Hirano
- Takamichi Igarashi
- Shinichi Sekine
- Isaya Hashimoto
- Kazuto Shibuya
- Shozo Hojo
- Isaku Yoshioka
- Koshi Matsui
- Akane Yamada
- Tohru Sasaki
- Tsutomu Fujii
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Affiliations: Department of Surgery and Science, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930‑0194, Japan, 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, Department of Mechanical and Intellectual Systems Engineering, Faculty of Engineering, University of Toyama, Toyama 930‑8555, Japan
Published online on: June 8, 2023 https://doi.org/10.3892/ol.2023.13906
Article Number: 320
Copyright: © Akashi et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.

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1	Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 71:209–249. 2021. View Article : Google Scholar : PubMed/NCBI
2	Watanabe M, Otake R, Kozuki R, Toihata T, Takahashi K, Okamura A and Imamura Y: Recent progress in multidisciplinary treatment for patients with esophageal cancer. Surg Today. 50:12–20. 2020. View Article : Google Scholar : PubMed/NCBI
3	Castro-Giner F and Aceto N: Tracking cancer progression: From circulating tumor cells to metastasis. Genome Med. 12:312020. View Article : Google Scholar : PubMed/NCBI
4	Ganesh K and Massague J: Targeting metastatic cancer. Nat Med. 27:34–44. 2021. View Article : Google Scholar : PubMed/NCBI
5	Rodrigues P and Vanharanta S: Circulating tumor cells: Come together, right now, over metastasis. Cancer Discov. 9:22–24. 2019. View Article : Google Scholar : PubMed/NCBI
6	Ujiie D, Matsumoto T, Endo E, Okayama H, Fujita S, Kanke Y, Watanabe Y, Hanayama H, Hayase S, Saze Z, et al: Circulating tumor cells after neoadjuvant chemotherapy are related with recurrence in esophageal squamous cell carcinoma. Esophagus. 18:566–573. 2021. View Article : Google Scholar : PubMed/NCBI
7	Xu HT, Miao J, Liu JW, Zhang LG and Zhang QG: Prognostic value of circulating tumor cells in esophageal cancer. World J Gastroenterol. 23:1310–1318. 2017. View Article : Google Scholar : PubMed/NCBI
8	Ohnaga T, Shimada Y, Takata K, Obata T, Okumura T, Nagata T, Kishi H, Muraguchi A and Tsukada K: Capture of esophageal and breast cancer cells with polymeric microfluidic devices for CTC isolation. Mol Clin Oncol. 4:599–602. 2016. View Article : Google Scholar : PubMed/NCBI
9	Watanabe T, Okumura T, Hirano K, Yamaguchi T, Sekine S, Nagata T and Tsukada K: Circulating tumor cells expressing cancer stem cell marker CD44 as a diagnostic biomarker in patients with gastric cancer. Oncol Lett. 13:281–288. 2017. View Article : Google Scholar : PubMed/NCBI
10	Yamaguchi T, Okumura T, Hirano K, Watanabe T, Nagata T, Shimada Y and Tsukada K: Detection of circulating tumor cells by p75NTR expression in patients with esophageal cancer. World J Surg Oncol. 14:402016. View Article : Google Scholar : PubMed/NCBI
11	Kojima H, Okumura T, Yamaguchi T, Miwa T, Shimada Y and Nagata T: Enhanced cancer stem cell properties of a mitotically quiescent subpopulation of p75NTR-positive cells in esophageal squamous cell carcinoma. Int J Oncol. 51:49–62. 2017. View Article : Google Scholar : PubMed/NCBI
12	Correnti M and Raggi C: Stem-like plasticity and heterogeneity of circulating tumor cells: Current status and prospect challenges in liver cancer. Oncotarget. 8:7094–7115. 2017. View Article : Google Scholar : PubMed/NCBI
13	Semaan A, Bernard V, Kim DU, Lee JJ, Huang J, Kamyabi N, Stephens BM, Qiao W, Varadhachary GR, Katz MH, et al: Characterisation of circulating tumour cell phenotypes identifies a partial-EMT sub-population for clinical stratification of pancreatic cancer. Br J Cancer. 124:1970–1977. 2021. View Article : Google Scholar : PubMed/NCBI
14	Shimizu H and Nakayama KI: Artificial intelligence in oncology. Cancer Sci. 111:1452–1460. 2020. View Article : Google Scholar : PubMed/NCBI
15	Elemento O, Leslie C, Lundin J and Tourassi G: Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer. 21:747–752. 2021. View Article : Google Scholar : PubMed/NCBI
16	Zeune LL, Boink YE, van Dalum G, Nanou A, de Wit S, Andree KC, Swennenhuis JF, van Gils SA, Terstappen LWMM and Brune C: Deep learning of circulating tumour cells. Nat Machine Intelligence. 2:124–133. 2020. View Article : Google Scholar
17	Russakovsky O, Deng J, Su H, Krause J, Bernstein M, Berg A and Fei-Fei L: ImageNet large scale visual recognition challenge. Int J Computer Vision. 115:211–252. 2015. View Article : Google Scholar
18	Sobin LH, Gospodarowicz M and Wittekind C: TNM Classification of Malignant Tumours, 7th edition, UICC International Union Against Cancer 2010. Hoboken, NJ: Wiley-Blackwell; 2010
19	Nobuyuki O: A Threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 9:pp62–66. 1979. View Article : Google Scholar : PubMed/NCBI
20	Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, et al: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digit Health. 1:e271–e297. 2019. View Article : Google Scholar : PubMed/NCBI
21	Kamba S, Tamai N, Saitoh I, Matsui H, Horiuchi H, Kobayashi M, Sakamoto T, Ego M, Fukuda A, Tonouchi A, et al: Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: A multicenter randomized controlled trial. J Gastroenterol. 56:746–757. 2021. View Article : Google Scholar : PubMed/NCBI
22	Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM and Thrun S: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542:115–118. 2017. View Article : Google Scholar : PubMed/NCBI
23	Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW and Dietrich CF: Artificial intelligence in medical imaging of the liver. World J Gastroenterol. 25:672–682. 2019. View Article : Google Scholar : PubMed/NCBI
24	Zheng S, Lin HK, Lu B, Williams A, Datar R, Cote RJ and Tai YC: 3D microfilter device for viable circulating tumor cell (CTC) enrichment from blood. Biomed Microdevices. 13:203–213. 2011. View Article : Google Scholar : PubMed/NCBI
25	Moore MJ, Sebastian JA and Kolios MC: Determination of cell nucleus-to-cytoplasmic ratio using imaging flow cytometry and a combined ultrasound and photoacoustic technique: A comparison study. J Biomed Opt. 24:1–10. 2019. View Article : Google Scholar : PubMed/NCBI
26	Guo Z, Lin X, Hui Y, Wang J, Zhang Q and Kong F: Circulating tumor cell identification based on deep learning. Front Oncol. 12:8438792022. View Article : Google Scholar : PubMed/NCBI
27	Habli Z, AlChamaa W, Saab R, Kadara H and Khraiche ML: Circulating tumor cell detection technologies and clinical utility: Challenges and opportunities. Cancers (Basel). 12:19302020. View Article : Google Scholar : PubMed/NCBI
28	Miki Y, Yashiro M, Kuroda K, Okuno T, Togano S, Masuda G, Kasashima H and Ohira M: Circulating CEA-positive and EpCAM-negative tumor cells might be a predictive biomarker for recurrence in patients with gastric cancer. Cancer Med. 10:521–528. 2021. View Article : Google Scholar : PubMed/NCBI
29	Han D, Chen K, Che J, Hang J and Li H: Detection of epithelial-mesenchymal transition status of circulating tumor cells in patients with esophageal squamous carcinoma. Biomed Res Int. 2018:76101542018. View Article : Google Scholar : PubMed/NCBI
30	Gao Y, Fan WH, Song Z, Lou H and Kang X: Comparison of circulating tumor cell (CTC) detection rates with epithelial cell adhesion molecule (EpCAM) and cell surface vimentin (CSV) antibodies in different solid tumors: A retrospective study. PeerJ. 9:e107772021. View Article : Google Scholar : PubMed/NCBI
31	Chaw SY, Abdul Majeed A, Dalley AJ, Chan A, Stein S and Farah CS: Epithelial to mesenchymal transition (EMT) biomarkers-E-cadherin, beta-catenin, APC and Vimentin-in oral squamous cell carcinogenesis and transformation. Oral Oncol. 48:997–1006. 2012. View Article : Google Scholar : PubMed/NCBI
32	Shi Y, Ge X, Ju M, Zhang Y, Di X and Liang L: Circulating tumor cells in esophageal squamous cell carcinoma-Mini review. Cancer Manag Res. 13:8355–8365. 2021. View Article : Google Scholar : PubMed/NCBI
33	Fischer KR, Durrans A, Lee S, Sheng J, Li F, Wong ST, Choi H, El Rayes T, Ryu S, Troeger J, et al: Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature. 527:472–476. 2015. View Article : Google Scholar : PubMed/NCBI
34	Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, Brooks M, Reinhard F, Zhang CC, Shipitsin M, et al: The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 133:704–715. 2008. View Article : Google Scholar : PubMed/NCBI
35	Deng Z, Wu S, Wang Y and Shi D: Circulating tumor cell isolation for cancer diagnosis and prognosis. EBioMedicine. 83:1042372022. View Article : Google Scholar : PubMed/NCBI
36	Gkountela S, Castro-Giner F, Szczerba BM, Vetter M, Landin J, Scherrer R, Krol I, Scheidmann MC, Beisel C, Stirnimann CU, et al: Circulating tumor cell clustering shapes DNA methylation to enable metastasis seeding. Cell. 176:98–112. 2019. View Article : Google Scholar : PubMed/NCBI
37	Allard WJ, Matera J, Miller MC, Repollet M, Connelly MC, Rao C, Tibbe AG, Uhr JW and Terstappen LW: Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin Cancer Res. 10:6897–6904. 2004. View Article : Google Scholar : PubMed/NCBI