|
1
|
Fatehi Hassanabad A, Chehade R, Breadner D
and Raphael J: Esophageal carcinoma: Towards targeted therapies.
Cell Oncol (Dordr). 43:195–209. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Qu HT, Li Q, Hao L, Jing Ni Y, Luan W,
Yang Z, Chen XD, Zhang TT, Miao YD and Zhang F: Esophageal cancer
screening, early detection and treatment: Current insights and
future directions. World J Gastrointest Oncol. 16:1180–1191. 2024.
View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Qi JH, Huang SL and Jin SZ: Novel
milestones for early esophageal carcinoma: From bench to bed. World
J Gastrointest Oncol. 16:1104–1118. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Yang YM, Hong P, Xu WW, He QY and Li B:
Advances in targeted therapy for esophageal cancer. Signal
Transduct Target Ther. 5:2292020. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Li S, Chen H, Man J, Zhang T, Yin X, He Q,
Yang X and Lu M: Changing trends in the disease burden of
esophageal cancer in China from 1990 to 2017 and its predicted
level in 25 years. Cancer Med. 10:1889–1899. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Ganaie MA, Hu M, Malik AK, Tanveer M and
Suganthan PN: Ensemble deep learning: A review. Eng Appl Artif
Intell. 115:1051512022. View Article : Google Scholar
|
|
7
|
Shrestha A and Mahmood A: Review of deep
learning algorithms and architectures. IEEE Access. 7:53040–53065.
2019. View Article : Google Scholar
|
|
8
|
Zhang YH, Guo LJ, Yuan XL and Hu B:
Artificial intelligence-assisted esophageal cancer management: Now
and future. World J Gastroenterol. 26:5256–5271. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Tao Y, Fang L, Qin G, Xu Y, Zhang S, Zhang
X and Du S: Efficiency of endoscopic artificial intelligence in the
diagnosis of early esophageal cancer. Thoracic Cancer.
15:1296–1304. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Ding Z, Li H, Guo Y, Zhou D, Liu Y and Xie
S: M4FNet: Multimodal medical image fusion network via
Multi-receptive-field and Multi-scale feature integration. Comput
Biol Med. 159:1069232023. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Tokat M, van Tilburg L, Koch AD and
Spaander MCW: Artificial intelligence in upper gastrointestinal
endoscopy. Dig Dis. 40:395–408. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Zhang S, Mu W, Dong D, Wei J, Fang M, Shao
L, Zhou Y, He B, Zhang S, Liu Z, et al: The applications of
artificial intelligence in digestive system neoplasms: A review.
Health Data Sci. 3:00052023. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Li H, Hou X, Lin R, Fan M, Pang S, Jiang
L, Liu Q and Fu L: Advanced endoscopic methods in gastrointestinal
diseases: A systematic review. Quant Imaging Med Surg. 9:905–920.
2019. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
DiSiena M, Perelman A, Birk J and
Rezaizadeh H: Esophageal cancer: An updated review. South Med J.
114:161–168. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Mohan A, Asghar Z, Abid R, Subedi R,
Kumari K and Kumar S, Majumder K, Bhurgri AI, Tejwaney U and Kumar
S: Revolutionizing healthcare by use of artificial intelligence in
esophageal carcinoma-a narrative review. Ann Med Surg (Lond).
85:4920–4927. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Ohmori M, Ishihara R, Aoyama K, Nakagawa
K, Iwagami H, Matsuura N, Shichijo S, Yamamoto K, Nagaike K,
Nakahara M, et al: Endoscopic detection and differentiation of
esophageal lesions using a deep neural network. Gastrointest
Endosc. 91:301–309.e1. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Yang XX, Li Z, Shao XJ, Ji R, Qu JY, Zheng
MQ, Sun YN, Zhou RC, You H, Li LX, et al: Real-time artificial
intelligence for endoscopic diagnosis of early esophageal squamous
cell cancer (with video). Dig Endosc. 33:1075–1084. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Goda K and Irisawa A: Japan esophageal
society classification for predicting the invasion depth of
superficial esophageal squamous cell carcinoma: Should it be
modified now? Digestive Endoscopy. 32:37–38. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Shiroma S, Yoshio T, Kato Y, Horie Y,
Namikawa K, Tokai Y, Yoshimizu S, Yoshizawa N, Horiuchi Y, Ishiyama
A, et al: Ability of artificial intelligence to detect T1
esophageal squamous cell carcinoma from endoscopic videos and the
effects of real-time assistance. Sci Rep. 11:77592021. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Guo L, Xiao X, Wu C, Zeng X, Zhang Y, Du
J, Bai S, Xie J, Zhang Z, Li Y, et al: Real-time automated
diagnosis of precancerous lesions and early esophageal squamous
cell carcinoma using a deep learning model (with videos).
Gastrointest Endosc. 91:41–51. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Yan Y, Zhang S, Jin Y, Cheng F, Qian Z and
Lu S: Spatial and temporal detection with attention for real-time
video analytics at edges. IEEE Transactions Mobile Computing.
23:9254–9270. 2024. View Article : Google Scholar
|
|
22
|
Kunzmann AT, Coleman HG, Johnston BT,
Turkington RC, McManus D, Anderson LA and Thrift AP: Does risk of
progression from Barrett's esophagus to esophageal adenocarcinoma
change based on the number of Non-dysplastic Endoscopies? Dig Dis
Sci. 66:1965–1973. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Bhatti KM, Khanzada ZS, Kuzman M, Ali SM,
Iftikhar SY and Small P: Diagnostic performance of artificial
Intelligence-based models for the detection of early esophageal
cancers in Barret's esophagus: A Meta-analysis of Patient-based
studies. Cureus. 13:e154472021.PubMed/NCBI
|
|
24
|
Hussein M, González-Bueno Puyal J, Lines
D, Sehgal V, Toth D, Ahmad OF, Kader R, Everson M, Lipman G,
Fernandez-Sordo JO, 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. 10:528–537. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Knabe M, Welsch L, Blasberg T, Müller E,
Heilani M, Bergen C, Herrmann E and May A: Artificial
Intelligence-assisted staging in Barrett's carcinoma. Endoscopy.
54:1191–1197. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Tsai MC, Yen HH, Tsai HY, Huang YK, Luo
YS, Kornelius E, Sung WW, Lin CC, Tseng MH and Wang CC: Artificial
intelligence system for the detection of Barrett's esophagus. World
J Gastroenterol. 29:6198–6207. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Tu JX, Lin XT, Ye HQ, Yang SL, Deng LF,
Zhu RL, Wu L and Zhang XQ: Global research trends of artificial
intelligence applied in esophageal carcinoma: A bibliometric
analysis (2000–2022) via CiteSpace and VOSviewer. Front Oncol.
12:9723572022. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Kumar CA and Mubarak MND: A review on
esophageal cancer detection and classification using deep learning
techniques. Int J Curr Res Rev. 13:51–57. 2021. View Article : Google Scholar
|
|
29
|
Horie Y, Yoshio T, Aoyama K, Yoshimizu S,
Horiuchi Y, Ishiyama A, Hirasawa T, Tsuchida T, Ozawa T, Ishihara
S, et al: Diagnostic outcomes of esophageal cancer by artificial
intelligence using convolutional neural networks. Gastrointest
Endosc. 89:25–32. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Shimamoto Y, Ishihara R, Kato Y, Shoji A,
Inoue T, Matsueda K, Miyake M, Waki K, Kono M, Fukuda H, et al:
Real-time assessment of video images for esophageal squamous cell
carcinoma invasion depth using artificial intelligence. J
Gastroenterol. 55:1037–1045. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Mine S, Tanaka K, Kawachi H, Shirakawa Y,
Kitagawa Y, Toh Y, Yasuda T, Watanabe M, Kamei T, Oyama T, et al:
Japanese classification of esophageal cancer, 12th edition: Part I.
Esophagus. 21:179–215. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Li H, Liu D, Zeng Y, Liu S, Gan T, Rao N,
Yang J and Zeng B: Single-Image-based deep learning for
segmentation of early esophageal cancer lesions. IEEE Trans Image
Process. 33:2676–2688. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Nakagawa K, Ishihara R, Aoyama K, Ohmori
M, Nakahira H, Matsuura N, Shichijo S, Nishida T, Yamada T,
Yamaguchi S, et al: Classification for invasion depth of esophageal
squamous cell carcinoma using a deep neural network compared with
experienced endoscopists. Gastrointest Endosc. 90:407–414. 2019.
View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Howard AG, Zhu M, Chen B, Kalenichenko D,
Wang W, Weyand T, Andreetto M and Adam H: MobileNets: Efficient
convolutional neural networks for mobile vision applications.
arXiv. 57:342017.
|
|
35
|
Sandler M, Howard A, Zhu M, Zhmoginov A
and Chen LC: MobileNetV2: Inverted residuals and linear
bottlenecks. Conference on Computer Vision and Pattern Recognition
(CVPR). 4510–4520. 2018.
|
|
36
|
Howard A, Sandler M, Chu G, Chen CL, Chen
B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, et al: Searching for
MobileNetV3. IEEE/CVF International Conference on Computer Vision
(ICCV) Seoul, Korea (South): IEEE; pp. 1314–1324. 2019
|
|
37
|
Li SW, Zhang LH, Cai Y, Zhou XB, Fu XY,
Song YQ, Xu SW, Tang SP, Luo RQ, Huang Q, et al: Deep learning
assists detection of esophageal cancer and precursor lesions in a
prospective, randomized controlled study. Sci Transl Med.
16:eadk53952024. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Yasaka K, Hatano S, Mizuki M, Okimoto N,
Kubo T, Shibata E, Watadani T and Abe O: Effects of deep learning
on radiologists' and radiology residents' performance in
identifying esophageal cancer on CT. Br J Radiol. 96:202206852023.
View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Takeuchi M, Seto T, Hashimoto M, Ichihara
N, Morimoto Y, Kawakubo H, Suzuki T, Jinzaki M, Kitagawa Y, Miyata
H and Sakakibara Y: Performance of a deep learning-based
identification system for esophageal cancer from CT images.
Esophagus. 18:612–620. 2021. View Article : Google Scholar : PubMed/NCBI
|
|
40
|
Sui H, Ma R, Liu L, Gao Y, Zhang W and Mo
Z: Detection of incidental esophageal cancers on chest CT by deep
learning. Front Oncol. 11:7002102021. View Article : Google Scholar : PubMed/NCBI
|
|
41
|
Lin C, Guo Y, Huang X, Rao S and Zhou J:
Esophageal cancer detection via Non-contrast CT and deep learning.
Front Med (Lausanne). 11:13567522024. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Hosseini F, Asadi F, Emami H and Harari
RE: Machine learning applications for early detection of esophageal
cancer: A systematic review. BMC Med Inform Decis Mak. 23:1242023.
View Article : Google Scholar : PubMed/NCBI
|
|
43
|
Baxi V, Edwards R, Montalto M and Saha S:
Digital pathology and artificial intelligence in translational
medicine and clinical practice. Mod Pathol. 35:23–32. 2022.
View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Gehrung M, Crispin-Ortuzar M, Berman AG,
O'Donovan M, Fitzgerald RC and Markowetz F: Triage-driven diagnosis
of Barrett's esophagus for early detection of esophageal
adenocarcinoma using deep learning. Nat Med. 27:833–841. 2021.
View Article : Google Scholar : PubMed/NCBI
|
|
45
|
Sharma P, Dent J, Armstrong D, Bergman JJ,
Gossner L, Hoshihara Y, Jankowski JA, Junghard O, Lundell L, Tytgat
GN and Vieth M: The development and validation of an endoscopic
grading system for Barrett's esophagus: The Prague C & M
criteria. Gastroenterology. 131:1392–1399. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
Pisula JI, Datta RR, Valdez LB, Avemarg
JR, Jung JO, Plum P, Löser H, Lohneis P, Meuschke M, Dos Santos DP,
et al: Predicting the HER2 status in oesophageal cancer from tissue
microarrays using convolutional neural networks. Br J Cancer.
128:1369–1376. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Kumar N, Gupta R and Gupta S: Whole slide
imaging (WSI) in pathology: Current perspectives and future
directions. J Digit Imaging. 33:1034–1040. 2020. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Faghani S, Codipilly DC, David Vogelsang,
Moassefi M, Rouzrokh P, Khosravi B, Agarwal S, Dhaliwal L, Katzka
DA, Hagen C, et al: Development of a deep learning model for the
histologic diagnosis of dysplasia in Barrett's esophagus.
Gastrointest Endosc. 96:918–925.e3. 2022. View Article : Google Scholar : PubMed/NCBI
|
|
49
|
Bouzid K, Sharma H, Killcoyne S, Castro
DC, Schwaighofer A, Ilse M, Salvatelli V, Oktay O, Murthy S,
Bordeaux L, et al: Enabling large-scale screening of Barrett's
esophagus using weakly supervised deep learning in histopathology.
Nat Commun. 15:20262024. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Wan J and Zeng Y: Prediction of hepatic
metastasis in esophageal cancer based on machine learning. Sc Rep.
14:145072024. View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Chen L, Ouyang Y, Liu S, Lin J, Chen C,
Zheng C, Lin J, Hu Z and Qiu M: Radiomics analysis of lymph nodes
with esophageal squamous cell carcinoma based on deep learning. J
Oncol. 2022:132022. View Article : Google Scholar
|
|
52
|
Pan Y, Sun Z, Wang W, Yang Z, Jia J, Feng
X, Wang Y, Fang Q, Li J, Dai H, et al: Automatic detection of
squamous cell carcinoma metastasis in esophageal lymph nodes using
semantic segmentation. Clin Transl Med. 10:e1292020. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Xiang J, Wang X, Zhang X, Xi Y, Eweje F,
Chen Y, Li Y, Bergstrom C, Gopaulchan M, Kim T, et al: A
vision-language foundation model for precision oncology. Nature.
638:769–778. 2025. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Yang Z, Guan F, Bronk L and Zhao L:
Multi-omics approaches for biomarker discovery in predicting the
response of esophageal cancer to neoadjuvant therapy: A
multidimensional perspective. Pharmacol Ther. 254:1085912024.
View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Sheen AR and Saqib HWU: ‘Harnessing AI for
treatment optimization: Neoadjuvant chemotherapy in
gastroesophageal cancer’. Eur J Surg Oncol. 50:1082282024.
View Article : Google Scholar : PubMed/NCBI
|
|
56
|
Duan Y, Wang J, Wu P, Shao Y, Chen H, Wang
H, Cao H, Gu H, Feng A, Huang Y, et al: AS-NeSt: A Novel 3D deep
learning model for radiation therapy dose distribution prediction
in esophageal cancer treatment with multiple prescriptions. Int J
Radiat Oncol Biol Phys. 119:978–989. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Zhang S, Li K, Sun Y, Wan Y, Ao Y, Zhong
Y, Liang M, Wang L, Chen X, Pei X, et al: Deep learning for
automatic gross tumor volumes contouring in esophageal cancer based
on contrast-enhanced computed tomography images: A
multi-institutional study. Int J Radiat Oncol Biol Phys.
119:1590–1600. 2024. View Article : Google Scholar : PubMed/NCBI
|
|
58
|
Matsuda S, Irino T, Kawakubo H, Takeuchi
M, Nishimura E, Hisaoka K, Sano J, Kobayashi R, Fukuda K, Nakamura
R, et al: Evaluation of endoscopic response using deep neural
network in esophageal cancer patients who received neoadjuvant
chemotherapy. Ann Surg Oncol. 30:3733–3742. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Li Z, Wang F, Zhang H, Xie S, Peng L, Xu H
and Wang Y: A radiomics strategy based on CT intra-tumoral and
peritumoral regions for preoperative prediction of neoadjuvant
chemoradiotherapy for esophageal cancer. Eur J Surg Oncol.
50:1080522024. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Roisman LC, Kian W, Anoze A, Fuchs V,
Spector M, Steiner R, Kassel L, Rechnitzer G, Fried I, Peled N and
Bogot NR: Radiological artificial intelligence-predicting
personalized immunotherapy outcomes in lung cancer. NPJ Precis
Oncol. 7:1252023. View Article : Google Scholar : PubMed/NCBI
|
|
61
|
Nardone V, Boldrini L, Grassi R,
Franceschini D, Morelli I, Becherini C, Loi M, Greto D and Desideri
I: Radiomics in the setting of neoadjuvant radiotherapy: A new
approach for tailored treatment. Cancers. 13:35902021. View Article : Google Scholar : PubMed/NCBI
|
|
62
|
Zhao M, Xue G, He B, Deng J, Wang T, Zhong
Y, Li S, Wang Y, He Y, Chen T, et al: Integrated multiomics
signatures to optimize the accurate diagnosis of lung cancer. Nat
Commun. 16:842025. View Article : Google Scholar : PubMed/NCBI
|
|
63
|
Tada T, Hirasawa T and Yoshio T: The role
for artificial intelligence in evaluation of upper GI cancer.
Techniques Innovations Gastrointestinal Endoscopy. 22:66–70. 2020.
View Article : Google Scholar : PubMed/NCBI
|
|
64
|
Merchán Gómez B, Milla Collado L and
Rodríguez M: Artificial intelligence in esophageal cancer diagnosis
and treatment: Where are we now?-a narrative review. Ann Transl
Med. 11:353. 2023. View Article : Google Scholar : PubMed/NCBI
|
|
65
|
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
|
|
66
|
Kassem K, Sperti M, Cavallo A, Vergani AM,
Fassino D, Moz M, Liscio A, Banali R, Dahlweid M, Benetti L, et al:
An innovative artificial intelligence-based method to compress
complex models into explainable, model-agnostic and reduced
decision support systems with application to healthcare (NEAR).
Artif Intell Med. 151:1028412024. View Article : Google Scholar : PubMed/NCBI
|
|
67
|
Knapič S, Malhi A, Saluja R and Främling
K: Explainable artificial intelligence for human decision support
system in the medical domain. Machine Learning Knowledge
Extraction. 3:740–770. 2021. View Article : Google Scholar
|
|
68
|
Lutnick B, Ramon AJ, Ginley B, Csiszer C,
Kim A, Flament I, Damasceno PF, Cornibe J, Parmar C, Standish K, et
al: Accelerating pharmaceutical R&D with a user-friendly AI
system for histopathology image analysis. J Pathol Inform.
14:1003372023. View Article : Google Scholar : PubMed/NCBI
|
|
69
|
Guidozzi N, Menon N, Chidambaram S and
Markar SR: The role of artificial intelligence in the endoscopic
diagnosis of esophageal cancer: A systematic review and
meta-analysis. Dis Esophagus. 36:doad0482023. View Article : Google Scholar : PubMed/NCBI
|
