Application progress of artificial intelligence in the diagnosis of esophageal cancer_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

TANG Chuanjun , YUAN Xianglei , ZHANG Qiongying ,  HU Bing

Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

HU Bing, Email: hubingnj@163.com

Keywords：

Artificial intelligence; esophageal cancer; precancerous lesions; diagnosis; review

DOI：

10.7507/1007-4848.202306032

Video：

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Abstract Full text Figures/Tables Video References Cited by

Esophageal cancer is an aggressive malignancy with high morbidity and poor prognosis. Symptoms of early esophageal cancer are insidious and difficult to detect, while advanced esophageal obstruction, lesion infiltration and metastasis seriously affect patients’ quality of life. Early detection and treatment can help to increase the survival chance of patients. Recently, artificial intelligence (AI) has shown remarkable success in diagnosis of esophageal cancer, highlighting the great potential of new AI-assisted diagnostic modalities. This paper aims to review recent progress of AI in the diagnosis of esophageal cancer and to prospect its clinical application.

Citation： TANG Chuanjun, YUAN Xianglei, ZHANG Qiongying, HU Bing. Application progress of artificial intelligence in the diagnosis of esophageal cancer. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2024, 31(1): 153-159. doi: 10.7507/1007-4848.202306032 Copy

1.	Thuss-Patience P, Stein A. Immunotherapy in squamous cell cancer of the esophagus. Curr Oncol, 2022, 29(4): 2461-2471.
2.	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
3.	Eyck BM, van Lanschot JJB, Hulshof MCCM, et al. Ten-year outcome of neoadjuvant chemoradiotherapy plus surgery for esophageal cancer: The randomized controlled CROSS trial. J Clin Oncol, 2021, 39(18): 1995-2004.
4.	Mao YS, Gao SG, Wang Q, et al. Analysis of a registry database for esophageal cancer from high-volume centers in China. Dis Esophagus, 2020, 33(8): doz091.
5.	He J, Chen WQ, Li ZS, et al. China guideline for the screening, early detection and early treatment of esophageal cancer (2022, Beijing). Zhonghua Zhong Liu Za Zhi, 2022, 44(6): 491-522.
6.	Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc, 2020, 91(2): 301-309.
7.	Gillies RJ, Schabath MB. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomarkers Prev, 2020, 29(12): 2556-2567.
8.	Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett, 2017, 401: 63-71.
9.	Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
10.	Patel V, Khan MN, Shrivastava A, et al. Artificial intelligence applied to gastrointestinal diagnostics: A review. J Pediatr Gastroenterol Nutr, 2020, 70(1): 4-11.
11.	Syed T, Doshi A, Guleria S, et al. Artificial intelligence and its role in identifying esophageal neoplasia. Dig Dis Sci, 2020, 65(12): 3448-3455.
12.	Mehrer J, Spoerer CJ, Jones EC, et al. An ecologically motivated image dataset for deep learning yields better models of human vision. Proc Natl Acad Sci U S A, 2021, 118(8): e2011417118.
13.	Feng Y, Liang Y, Li P, et al. Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system. Discov Oncol, 2023, 14(1): 73.
14.	Tajiri A, Ishihara R, Kato Y, et al. Utility of an artificial intelligence system for classification of esophageal lesions when simulating its clinical use. Sci Rep, 2022, 12(1): 6677.
15.	Inoue H, Kaga M, Ikeda H, et al. Magnification endoscopy in esophageal squamous cell carcinoma: A review of the intrapapillary capillary loop classifification. Ann Gastroenterol, 2015, 28(1): 41-48.
16.	Oyama T, Ishihara R, Takeuchi M, et al. Usefulness of Japan Esophageal Society classification of magnified endoscopy for the diagnosis of superficial esophageal squamous cell carcinoma. Gastrointest Endosc, 2012, 75(Suppl): AB456.
17.	Yuan XL, Liu W, Liu Y, et al. Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: A multicenter study. Surg Endosc, 2022, 36(11): 8651-8662.
18.	Shimamoto Y, Ishihara R, Kato Y, et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol, 2020, 55(11): 1037-1045.
19.	Guo L, Xiao X, Wu C, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc, 2020, 91(1): 41-51.
20.	Liu W, Yuan X, Guo L, et al. Artificial intelligence for detecting and delineating margins of early ESCC under WLI endoscopy. Clin Transl Gastroenterol, 2022, 13(1): e00433.
21.	Yuan XL, Zeng XH, Liu W, et al. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointest Endosc, 2023, 97(4): 664-672.
22.	Tani Y, Ishihara R, Inoue T, et al. A single-center prospective study evaluating the usefulness of artificial intelligence for the diagnosis of esophageal squamous cell carcinoma in a real-time setting. BMC Gastroenterol, 2023, 23(1): 184.
23.	Rustgi AK, El-Serag HB. Esophageal carcinoma. N Engl J Med, 2014, 371(26): 2499-2509.
24.	Spadaccini M, Vespa E, Chandrasekar VT, et al. Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do. World J Gastroenterol, 2022, 28(11): 1113-1122.
25.	Madabhushi A, Toro P, Willis JE. Artificial intelligence in surveillance of Barrett's esophagus. Cancer Res, 2021, 81(13): 3446-3448.
26.	Struyvenberg MR, de Groof AJ, Fonollà R, et al. Prospective development and validation of a volumetric laser endomicroscopy computer algorithm for detection of Barrett's neoplasia. Gastrointest Endosc, 2021, 93(4): 871-879.
27.	Tan JL, Chinnaratha MA, Woodman R, et al. Diagnostic accuracy of artificial intelligence (AI) to detect early neoplasia in Barrett's esophagus: A non-comparative systematic review and meta-analysis. Front Med (Lausanne), 2022, 22: 890720.
28.	de Groof J, van der Sommen F, van der Putten J, et al. The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterol J, 2019, 7(4): 538-547.
29.	Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). Gastrointest Endosc, 2020, 91(6): 1264-1271.
30.	Ebigbo A, Mendel R, Probst A, et al. Multimodal imaging for detection and segmentation of Barrett's esophagus-related neoplasia using artificial intelligence. Endoscopy, 2022, 54(10): E587.
31.	Ebigbo A, Mendel R, Probst A, et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus. Gut, 2020, 69(4): 615-616.
32.	Ali S, Bailey A, Ash S, et al. A pilot study on automatic three-dimensional quantification of Barrett's esophagus for risk stratification and therapy monitoring. Gastroenterology, 2021, 161(3): 865-878.
33.	Pan W, Li X, Wang W, et al. Identification of Barrett's esophagus in endoscopic images using deep learning. BMC Gastroenterol, 2021, 21(1): 479.
34.	Qumseya BJ, Brown J, Abraham M, et al. Diagnostic performance of EUS in predicting advanced cancer among patients with Barrett's esophagus and high-grade dysplasia/early adenocarcinoma: Systematic review and meta-analysis. Gastrointest Endosc, 2015, 81(4): 865-874.
35.	Ebigbo A, Mendel R, Rückert T, et al. Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: A pilot study. Endoscopy, 2021, 53(9): 878-883.
36.	Ebigbo A, Mendel R, Probst A, et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut, 2019, 68(7): 1143-1145.
37.	Hussein M, González-Bueno Puyal J, Lines D, et al. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks. United European Gastroenterol J, 2022, 10(6): 528-537.
38.	Hasegawa S, Yoshikawa T, Cho H, et al. Is adenocarcinoma of the esophagogastric junction different between Japan and western countries? The incidence and clinicopathological features at a Japanese high-volume cancer center. World J Surg, 2009, 33(1): 95-103.
39.	Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol, 2021, 36(1): 131-136.
40.	Liu H, Ren H, Wu Z, et al. CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: Compared with CO-RADS. J Transl Med, 2021, 19(1): 29.
41.	Sung YS, Park B, Park HJ, et al. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol, 2021, 36(3): 561-568.
42.	Jiang C, Luo Y, Yuan J, et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol, 2020, 30(7): 4050-4057.
43.	Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 12611.
44.	Min BH, Yang JW, Min YW, et al. Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma. J Gastroenterol Hepatol, 2020, 35(6): 1009-1015.
45.	Zheng H, Tang H, Wang H, et al. Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma. Br J Surg, 2018, 105(11): 1464-1470.
46.	Zhang W, Chen H, Zhang G, et al. A nomogram for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma. J Biomed Res, 2021, 35(5): 361-370.
47.	Tan X, Ma Z, Yan L, et al. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol, 2019, 29(1): 392-400.
48.	Zhang C, Shi Z, Kalendralis P, et al. Prediction of lymph node metastases using pre-treatment PET radiomics of the primary tumour in esophageal adenocarcinoma: An external validation study. Br J Radiol, 2021, 94(1118): 20201042.
49.	Jin X, Zheng X, Chen D, et al. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol, 2019, 29(11): 6080-6088.
50.	Larue RTHM, Klaassen R, Jochems A, et al. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol, 2018, 57(11): 1475-1481.
51.	Ypsilantis PP, Siddique M, Sohn HM, et al. Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One, 2015, 10(9): e0137036.
52.	van Rossum PS, Fried DV, Zhang L, et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med, 2016, 57(5): 691-700.
53.	Foley KG, Hills RK, Berthon B, et al. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. Eur Radiol, 2018, 28(1): 428-436.
54.	Qu J, Shen C, Qin J, et al. The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer. Eur Radiol, 2019, 29(2): 906-914.
55.	Hou Z, Li S, Ren W, et al. Radiomic analysis in T2W and SPAIR T2W MRI: Predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis, 2018, 10(4): 2256-2267.
56.	Guo YN, Tian DP, Gong QY, et al. Perineural invasion is a better prognostic indicator than lymphovascular invasion and a potential adjuvant therapy indicator for pN0M0 esophageal squamous cell carcinoma. Ann Surg Oncol, 2020, 27(11): 4371-4381.
57.	Wang C, Wang J, Chen Z, et al. Immunohistochemical prognostic markers of esophageal squamous cell carcinoma: A systematic review. Chin J Cancer, 2017, 36(1): 65.
58.	Tomita N, Abdollahi B, Wei J, et al. Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw Open, 2019, 2(11): e1914645.
59.	Kouzu K, Nearchou IP, Kajiwara Y, et al. Deep-learning-based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma. Histopathology, 2022, 81(2): 255-263.

1. Thuss-Patience P, Stein A. Immunotherapy in squamous cell cancer of the esophagus. Curr Oncol, 2022, 29(4): 2461-2471.
2. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
3. Eyck BM, van Lanschot JJB, Hulshof MCCM, et al. Ten-year outcome of neoadjuvant chemoradiotherapy plus surgery for esophageal cancer: The randomized controlled CROSS trial. J Clin Oncol, 2021, 39(18): 1995-2004.
4. Mao YS, Gao SG, Wang Q, et al. Analysis of a registry database for esophageal cancer from high-volume centers in China. Dis Esophagus, 2020, 33(8): doz091.
5. He J, Chen WQ, Li ZS, et al. China guideline for the screening, early detection and early treatment of esophageal cancer (2022, Beijing). Zhonghua Zhong Liu Za Zhi, 2022, 44(6): 491-522.
6. Ohmori M, Ishihara R, Aoyama K, et al. Endoscopic detection and differentiation of esophageal lesions using a deep neural network. Gastrointest Endosc, 2020, 91(2): 301-309.
7. Gillies RJ, Schabath MB. Radiomics improves cancer screening and early detection. Cancer Epidemiol Biomarkers Prev, 2020, 29(12): 2556-2567.
8. Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett, 2017, 401: 63-71.
9. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
10. Patel V, Khan MN, Shrivastava A, et al. Artificial intelligence applied to gastrointestinal diagnostics: A review. J Pediatr Gastroenterol Nutr, 2020, 70(1): 4-11.
11. Syed T, Doshi A, Guleria S, et al. Artificial intelligence and its role in identifying esophageal neoplasia. Dig Dis Sci, 2020, 65(12): 3448-3455.
12. Mehrer J, Spoerer CJ, Jones EC, et al. An ecologically motivated image dataset for deep learning yields better models of human vision. Proc Natl Acad Sci U S A, 2021, 118(8): e2011417118.
13. Feng Y, Liang Y, Li P, et al. Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system. Discov Oncol, 2023, 14(1): 73.
14. Tajiri A, Ishihara R, Kato Y, et al. Utility of an artificial intelligence system for classification of esophageal lesions when simulating its clinical use. Sci Rep, 2022, 12(1): 6677.
15. Inoue H, Kaga M, Ikeda H, et al. Magnification endoscopy in esophageal squamous cell carcinoma: A review of the intrapapillary capillary loop classifification. Ann Gastroenterol, 2015, 28(1): 41-48.
16. Oyama T, Ishihara R, Takeuchi M, et al. Usefulness of Japan Esophageal Society classification of magnified endoscopy for the diagnosis of superficial esophageal squamous cell carcinoma. Gastrointest Endosc, 2012, 75(Suppl): AB456.
17. Yuan XL, Liu W, Liu Y, et al. Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: A multicenter study. Surg Endosc, 2022, 36(11): 8651-8662.
18. Shimamoto Y, Ishihara R, Kato Y, et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol, 2020, 55(11): 1037-1045.
19. Guo L, Xiao X, Wu C, et al. Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos). Gastrointest Endosc, 2020, 91(1): 41-51.
20. Liu W, Yuan X, Guo L, et al. Artificial intelligence for detecting and delineating margins of early ESCC under WLI endoscopy. Clin Transl Gastroenterol, 2022, 13(1): e00433.
21. Yuan XL, Zeng XH, Liu W, et al. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointest Endosc, 2023, 97(4): 664-672.
22. Tani Y, Ishihara R, Inoue T, et al. A single-center prospective study evaluating the usefulness of artificial intelligence for the diagnosis of esophageal squamous cell carcinoma in a real-time setting. BMC Gastroenterol, 2023, 23(1): 184.
23. Rustgi AK, El-Serag HB. Esophageal carcinoma. N Engl J Med, 2014, 371(26): 2499-2509.
24. Spadaccini M, Vespa E, Chandrasekar VT, et al. Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do. World J Gastroenterol, 2022, 28(11): 1113-1122.
25. Madabhushi A, Toro P, Willis JE. Artificial intelligence in surveillance of Barrett's esophagus. Cancer Res, 2021, 81(13): 3446-3448.
26. Struyvenberg MR, de Groof AJ, Fonollà R, et al. Prospective development and validation of a volumetric laser endomicroscopy computer algorithm for detection of Barrett's neoplasia. Gastrointest Endosc, 2021, 93(4): 871-879.
27. Tan JL, Chinnaratha MA, Woodman R, et al. Diagnostic accuracy of artificial intelligence (AI) to detect early neoplasia in Barrett's esophagus: A non-comparative systematic review and meta-analysis. Front Med (Lausanne), 2022, 22: 890720.
28. de Groof J, van der Sommen F, van der Putten J, et al. The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy. United European Gastroenterol J, 2019, 7(4): 538-547.
29. Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). Gastrointest Endosc, 2020, 91(6): 1264-1271.
30. Ebigbo A, Mendel R, Probst A, et al. Multimodal imaging for detection and segmentation of Barrett's esophagus-related neoplasia using artificial intelligence. Endoscopy, 2022, 54(10): E587.
31. Ebigbo A, Mendel R, Probst A, et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus. Gut, 2020, 69(4): 615-616.
32. Ali S, Bailey A, Ash S, et al. A pilot study on automatic three-dimensional quantification of Barrett's esophagus for risk stratification and therapy monitoring. Gastroenterology, 2021, 161(3): 865-878.
33. Pan W, Li X, Wang W, et al. Identification of Barrett's esophagus in endoscopic images using deep learning. BMC Gastroenterol, 2021, 21(1): 479.
34. Qumseya BJ, Brown J, Abraham M, et al. Diagnostic performance of EUS in predicting advanced cancer among patients with Barrett's esophagus and high-grade dysplasia/early adenocarcinoma: Systematic review and meta-analysis. Gastrointest Endosc, 2015, 81(4): 865-874.
35. Ebigbo A, Mendel R, Rückert T, et al. Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: A pilot study. Endoscopy, 2021, 53(9): 878-883.
36. Ebigbo A, Mendel R, Probst A, et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut, 2019, 68(7): 1143-1145.
37. Hussein M, González-Bueno Puyal J, Lines D, et al. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks. United European Gastroenterol J, 2022, 10(6): 528-537.
38. Hasegawa S, Yoshikawa T, Cho H, et al. Is adenocarcinoma of the esophagogastric junction different between Japan and western countries? The incidence and clinicopathological features at a Japanese high-volume cancer center. World J Surg, 2009, 33(1): 95-103.
39. Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol, 2021, 36(1): 131-136.
40. Liu H, Ren H, Wu Z, et al. CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: Compared with CO-RADS. J Transl Med, 2021, 19(1): 29.
41. Sung YS, Park B, Park HJ, et al. Radiomics and deep learning in liver diseases. J Gastroenterol Hepatol, 2021, 36(3): 561-568.
42. Jiang C, Luo Y, Yuan J, et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol, 2020, 30(7): 4050-4057.
43. Bibault JE, Giraud P, Housset M, et al. Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci Rep, 2018, 8(1): 12611.
44. Min BH, Yang JW, Min YW, et al. Nomogram for prediction of lymph node metastasis in patients with superficial esophageal squamous cell carcinoma. J Gastroenterol Hepatol, 2020, 35(6): 1009-1015.
45. Zheng H, Tang H, Wang H, et al. Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma. Br J Surg, 2018, 105(11): 1464-1470.
46. Zhang W, Chen H, Zhang G, et al. A nomogram for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma. J Biomed Res, 2021, 35(5): 361-370.
47. Tan X, Ma Z, Yan L, et al. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol, 2019, 29(1): 392-400.
48. Zhang C, Shi Z, Kalendralis P, et al. Prediction of lymph node metastases using pre-treatment PET radiomics of the primary tumour in esophageal adenocarcinoma: An external validation study. Br J Radiol, 2021, 94(1118): 20201042.
49. Jin X, Zheng X, Chen D, et al. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol, 2019, 29(11): 6080-6088.
50. Larue RTHM, Klaassen R, Jochems A, et al. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol, 2018, 57(11): 1475-1481.
51. Ypsilantis PP, Siddique M, Sohn HM, et al. Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One, 2015, 10(9): e0137036.
52. van Rossum PS, Fried DV, Zhang L, et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med, 2016, 57(5): 691-700.
53. Foley KG, Hills RK, Berthon B, et al. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. Eur Radiol, 2018, 28(1): 428-436.
54. Qu J, Shen C, Qin J, et al. The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer. Eur Radiol, 2019, 29(2): 906-914.
55. Hou Z, Li S, Ren W, et al. Radiomic analysis in T2W and SPAIR T2W MRI: Predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis, 2018, 10(4): 2256-2267.
56. Guo YN, Tian DP, Gong QY, et al. Perineural invasion is a better prognostic indicator than lymphovascular invasion and a potential adjuvant therapy indicator for pN0M0 esophageal squamous cell carcinoma. Ann Surg Oncol, 2020, 27(11): 4371-4381.
57. Wang C, Wang J, Chen Z, et al. Immunohistochemical prognostic markers of esophageal squamous cell carcinoma: A systematic review. Chin J Cancer, 2017, 36(1): 65.
58. Tomita N, Abdollahi B, Wei J, et al. Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw Open, 2019, 2(11): e1914645.
59. Kouzu K, Nearchou IP, Kajiwara Y, et al. Deep-learning-based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma. Histopathology, 2022, 81(2): 255-263.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Application progress of artificial intelligence in the diagnosis of esophageal cancer

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