scholarly journals Artificial intelligence for the real‐time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof‐of‐concept study

2019 ◽  
Vol 7 (2) ◽  
pp. 297-306 ◽  
Author(s):  
M Everson ◽  
LCGP Herrera ◽  
W Li ◽  
I Muntion Luengo ◽  
O Ahmad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Real Time ◽  
Squamous Cell ◽  
Oesophageal Squamous Cell Carcinoma ◽  
Proof Of Concept ◽  
Capillary Loop ◽  
Concept Study

Polyamine Inhibitors for Treatment of Feline Oral Squamous Cell Carcinoma: A Proof-of-Concept Study

2013 ◽  
Vol 30 (3) ◽  
pp. 140-145 ◽  
Author(s):  
John R. Lewis ◽  
Thomas G. O'Brien ◽  
Katherine A. Skorupski ◽  
Erika L. Krick ◽  
Alexander M. Reiter ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Proof Of Concept ◽  
Concept Study ◽  
Polyamine Inhibitors

scholarly journals Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sho Shiroma ◽  
Toshiyuki Yoshio ◽  
Yusuke Kato ◽  
Yoshimasa Horie ◽  
Ken Namikawa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Real Time ◽  
Squamous Cell ◽  
Narrow Band Imaging ◽  
High Sensitivity ◽  
Detection Accuracy

AbstractDiagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25–70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support.

scholarly journals Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos: supportive effects of real-time assistance

2020 ◽  
Author(s):  
Sho Shiroma ◽  
Toshiyuki Yoshio ◽  
Yusuke Kato ◽  
Yoshimasa Horie ◽  
Ken Namikawa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Real Time ◽  
Squamous Cell ◽  
Narrow Band Imaging ◽  
High Sensitivity ◽  
Detection Accuracy

Abstract Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCC using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25-70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p<0.05). AI can detect superficial ESCC from EGD videos with high sensitivity and improve endoscopists’ detection of ESCC with real-time support.

scholarly journals Aspirin enhances the therapeutic efficacy of cisplatin in oesophageal squamous cell carcinoma by inhibition of putative cancer stem cells

2021 ◽  
Author(s):  
Zhigeng Zou ◽  
Wei Zheng ◽  
Hongjun Fan ◽  
Guodong Deng ◽  
Shih-Hsin Lu ◽  
...  
Keyword(s):  
Stem Cells ◽  
Squamous Cell Carcinoma ◽  
Cancer Stem Cells ◽  
Cell Carcinoma ◽  
Cell Lines ◽  
Squamous Cell ◽  
High Throughput Sequencing ◽  
Combined Treatment ◽  
Oesophageal Squamous Cell Carcinoma

Abstract Background Cancer stem cells (CSCs) are related to the patient’s prognosis, recurrence and therapy resistance in oesophageal squamous cell carcinoma (ESCC). Although increasing evidence suggests that aspirin (acetylsalicylic acid, ASA) could lower the incidence and improve the prognosis of ESCC, the mechanism(s) remains to be fully understood. Methods We investigated the role of ASA in chemotherapy/chemoprevention in human ESCC cell lines and an N-nitrosomethylbenzylamine-induced rat ESCC carcinogenesis model. The effects of combined treatment with ASA/cisplatin on ESCC cell lines were examined in vitro and in vivo. Sphere-forming cells enriched with putative CSCs (pCSCs) were used to investigate the effect of ASA in CSCs. Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq) was performed to determine the alterations in chromatin accessibility caused by ASA in ESCC cells. Results ASA inhibits the CSC properties and enhances cisplatin treatment in human ESCC cells. ATAC-seq indicates that ASA treatment results in remarkable epigenetic alterations on chromatin in ESCC cells, especially their pCSCs, through the modification of histone acetylation levels. The epigenetic changes activate Bim expression and promote cell death in CSCs of ESCC. Furthermore, ASA prevents the carcinogenesis of NMBzA-induced ESCC in the rat model. Conclusions ASA could be a potential chemotherapeutic adjuvant and chemopreventive drug for ESCC treatment.

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