scholarly journals Sa2057 COMPARISON OF ARTIFICIAL INTELLIGENCE AND EXPERT ENDOSCOPIST TOWARD REAL-TIME ASSISTED DIAGNOSIS OF ESOPHAGEAL SQUAMOUS CELL CARCINOMA.

2020 ◽  
Vol 91 (6) ◽  
pp. AB262-AB263
Author(s):  
Hiromu Fukuda ◽  
Ryu Ishihara ◽  
Yusuke Kato ◽  
Ayaka Shoji ◽  
Muneaki Miyake ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Real Time ◽  
Squamous Cell ◽  
Assisted Diagnosis

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 Auraptene Attenuates Malignant Properties of Esophageal Stem-Like Cancer Cells

2016 ◽  
Vol 16 (4) ◽  
pp. 519-527 ◽  
Author(s):  
Saffiyeh Saboor-Maleki ◽  
Fatemeh B. Rassouli ◽  
Maryam M. Matin ◽  
Mehrdad Iranshahi
Keyword(s):  
Polymerase Chain Reaction ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Esophageal Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Cells ◽  
Real Time ◽  
Squamous Cell ◽  
Chain Reaction ◽  
Polymerase Chain

The high incidence of esophageal squamous cell carcinoma has been reported in selected ethnic populations including North of Iran. Low survival rate of esophageal carcinoma is partially due to the presence of stem-like cancer cells with chemotherapy resistance. In the current study, we aimed to determine the effects of auraptene, an interesting dietary coumarin with various biological activities, on malignant properties of stem-like esophageal squamous cell carcinoma, in terms of sensitivity to anticancer drugs and expression of specific markers. To do so, the half maximal inhibitory concentration values of auraptene, cisplatin, paclitaxel, and 5-fluorouracil were determined on esophageal carcinoma cells (KYSE30 cell line). After administrating combinatorial treatments, including nontoxic concentrations of auraptene + cisplatin, paclitaxel, or 5-fluorouracil, sensitivity of cells to chemical drugs and also induced apoptosis were assessed. In addition, quantitative real-time polymerase chain reaction was used to study changes in the expression of tumor suppressor proteins 53 and 21 ( P53 and P21), cluster of differentiation 44 ( CD44), and B cell-specific Moloney murine leukemia virus integration site 1 ( BMI-1) upon treatments. Results of thiazolyl blue assay revealed that auraptene significantly ( P < .05) increased toxicity of cisplatin, paclitaxel, and 5-fluorouracil in KYSE30 cells, specifically 72 hours after treatment. Conducting an apoptosis assay using flow cytometry also confirmed the synergic effects of auraptene. Results of quantitative real-time polymerase chain reaction revealed significant ( P < .05) upregulation of P53 and P21 upon combinatorial treatments and also downregulation of CD44 and BMI-1 after auraptene administration. Current study provided evidence, for the first time, that auraptene attenuates the properties of esophageal stem-like cancer cells through enhancing sensitivity to chemical agents and reducing the expression of CD44 and BMI-1 markers.

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