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3. A novel artificial intelligence–assisted “vascular healing” diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study (with video)

Author

Kuroki, Takanori, Maeda, Yasuharu, Kudo, Shin-ei, Ogata, Noriyuki, Iacucci, Marietta, Takishima, Kazumi, Ide, Yutaro, Shibuya, Tomoya, Semba, Shigenori, Kawashima, Jiro, Kato, Shun, Ogawa, Yushi, Ichimasa, Katsuro, Nakamura, Hiroki, Hayashi, Takemasa, Wakamura, Kunihiko, Miyachi, Hideyuki, Baba, Toshiyuki, Nemoto, Tetsuo, Ohtsuka, Kazuo, and Misawa, Masashi

Published
2024
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9. Comprehensive Diagnostic Performance of Real-Time Characterization of Colorectal Lesions Using an Artificial Intelligence–Assisted System: A Prospective Study

Author

Mori, Yuichi, Mochida, Kentaro, Akimoto, Yoshika, Ishiyama, Misaki, Ogura, Yohei, Abe, Masahiro, Sato, Yuta, Ogawa, Yushi, Yasuharu, Maeda, Tanaka, Kenta, Ichimasa, Katsuro, Nakamura, Hiroki, Ogata, Noriyuki, Hisayuki, Tomokazu, Kudo, Toyoki, Hayashi, Takemasa, Wakamura, Kunihiko, Miyachi, Hideyuki, Baba, Toshiyuki, Ishida, Fumio, Itoh, Hayato, Oda, Masahiro, Minegishi, Yosuke, Kudo, Shin-Ei, Miyata, Yuki, Nemoto, Tetsuo, Mori, Kensaku, and Misawa, Masashi

Published
2022
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11. Survey on the perceptions of Asian endoscopists to artificial intelligence

Author

Lee, Jonathan, primary, QUEK, Sabrina Xin Zi, additional, KOO, Chieh Sian, additional, LIU, Lin, additional, LEUNG, Wai Keung, additional, TANG, Raymond Shing Yan, additional, Chung, Hyunsoo, additional, ZHANG, Xuesong, additional, ICHIMASA, Katsuro, additional, NGO, Denis, additional, MARALIT, Ruter, additional, DAO, Viet Hang, additional, KHIEN, Vu Van, additional, DUSEJA, Ajay, additional, PRATAP, Nitesh, additional, LEE, Yeong Yeh, additional, RENALDI, Kaka, additional, SIM, Shi Min, additional, WU, Clement Chun Ho, additional, LI, James Weiquan, additional, TAN, Jarrod Kah Hwee, additional, KOH, Calvin Jianyi, additional, and SO, Jimmy Bok Yan, additional

Published
2024
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12. Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node

Author

Kudo, Shin-ei, Ichimasa, Katsuro, Villard, Benjamin, Mori, Yuichi, Misawa, Masashi, Saito, Shoichi, Hotta, Kinichi, Saito, Yutaka, Matsuda, Takahisa, Yamada, Kazutaka, Mitani, Toshifumi, Ohtsuka, Kazuo, Chino, Akiko, Ide, Daisuke, Imai, Kenichiro, Kishida, Yoshihiro, Nakamura, Keiko, Saiki, Yasumitsu, Tanaka, Masafumi, Hoteya, Shu, Yamashita, Satoshi, Kinugasa, Yusuke, Fukuda, Masayoshi, Kudo, Toyoki, Miyachi, Hideyuki, Ishida, Fumio, Itoh, Hayato, Oda, Masahiro, and Mori, Kensaku

Published
2021
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14. Efficacy of a whole slide image‐based prediction model for lymph node metastasis in T1 colorectal cancer: A systematic review.

Author

Ichimasa, Katsuro, Kouyama, Yuta, Kudo, Shin‐ei, Takashina, Yuki, Nemoto, Tetsuo, Watanabe, Jun, Takamatsu, Manabu, Maeda, Yasuharu, Yeoh, Khay Guan, Miyachi, Hideyuki, and Misawa, Masashi

Subjects
*RECEIVER operating characteristic curves, *LYMPHATIC metastasis, *ARTIFICIAL intelligence, *COLORECTAL cancer, *LYMPH nodes
Abstract

Background and Aim Methods Results Conclusions Accurate stratification of the risk of lymph node metastasis (LNM) following endoscopic resection of submucosal invasive (T1) colorectal cancer (CRC) is imperative for determining the necessity for additional surgery. In this systematic review, we evaluated the efficacy of prediction of LNM by artificial intelligence (AI) models utilizing whole slide image (WSI) in patients with T1 CRC.In accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, a systematic review was conducted through searches in PubMed (MEDLINE), Embase, and the Cochrane Library for relevant studies published up to December 2023. The inclusion criteria were studies assessing the accuracy of hematoxylin and eosin‐stained WSI‐based AI models for predicting LNM in patients with T1 CRC.Four studies met the criteria for inclusion in this systematic review. The area under the receiver operating characteristic curve for these AI models ranged from 0.57 to 0.76. In the three studies in which AI performance was compared directly with current treatment guidelines, AI consistently exhibited a higher area under the receiver operating characteristic curve. At a fixed sensitivity of 100%, specificities ranged from 18.4% to 45.0%.Artificial intelligence models based on WSI can potentially address the issue of diagnostic variability between pathologists and exceed the predictive accuracy of current guidelines. However, these findings require confirmation by larger studies that incorporate external validation. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms

Author

Kudo, Shin-ei, Misawa, Masashi, Mori, Yuichi, Hotta, Kinichi, Ohtsuka, Kazuo, Ikematsu, Hiroaki, Saito, Yutaka, Takeda, Kenichi, Nakamura, Hiroki, Ichimasa, Katsuro, Ishigaki, Tomoyuki, Toyoshima, Naoya, Kudo, Toyoki, Hayashi, Takemasa, Wakamura, Kunihiko, Baba, Toshiyuki, Ishida, Fumio, Inoue, Haruhiro, Itoh, Hayato, Oda, Masahiro, and Mori, Kensaku

Published
2020
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19. Left-sided location is a risk factor for lymph node metastasis of T1 colorectal cancer: a single-center retrospective study

Author

Mochizuki, Kenichi, Kudo, Shin-ei, Ichimasa, Katsuro, Kouyama, Yuta, Matsudaira, Shingo, Takashina, Yuki, Maeda, Yasuharu, Ishigaki, Tomoyuki, Nakamura, Hiroki, Toyoshima, Naoya, Mori, Yuichi, Misawa, Masashi, Ogata, Noriyuki, Kudo, Toyoki, Hayashi, Takemasa, Wakamura, Kunihiko, Sawada, Naruhiko, Ishida, Fumio, and Miyachi, Hideyuki

Published
2020
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21. Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta‐analysis.

Author

Watanabe, Jun, Ichimasa, Katsuro, Kataoka, Yuki, Miki, Atsushi, Someko, Hidehiro, Honda, Munenori, Tahara, Makiko, Yamashina, Takeshi, Yeoh, Khay Guan, Kawai, Shigeo, Kotani, Kazuhiko, and Sata, Naohiro

Subjects
*LYMPHATIC metastasis, *COLORECTAL cancer, *RECEIVER operating characteristic curves, *TUMOR budding, *ENDOSCOPIC surgery, *COHEN'S kappa coefficient (Statistics)
Abstract

Objectives: Lymphovascular invasion (LVI) is a critical risk factor for lymph node metastasis (LNM), which requires additional surgery after endoscopic resection of T1 colorectal cancer (CRC). However, the impact of additional staining on estimating LNM is unclear. This systematic review aimed to evaluate the impact of additional staining on determining LNM in T1 CRC. Methods: We searched five electronic databases. Outcomes were diagnostic odds ratio (DOR), assessed using hierarchical summary receiver operating characteristic curves, and interobserver agreement among pathologists for positive LVI, assessed using Kappa coefficients (κ). We performed a subgroup analysis of studies that simultaneously included a multivariable analysis for other risk factors (deep submucosal invasion, poor differentiation, and tumor budding). Results: Among the 64 studies (18,097 patients) identified, hematoxylin–eosin (HE) and additional staining for LVI had pooled sensitivities of 0.45 (95% confidence interval [CI] 0.32–0.58) and 0.68 (95% CI 0.44–0.86), specificities of 0.88 (95% CI 0.78–0.94) and 0.76 (95% CI 0.62–0.86), and DORs of 6.26 (95% CI 3.73–10.53) and 6.47 (95% CI 3.40–12.32) for determining LNM, respectively. In multivariable analysis, the DOR of additional staining for LNM (DOR 5.95; 95% CI 2.87–12.33) was higher than that of HE staining (DOR 1.89; 95% CI 1.13–3.16) (P = 0.01). Pooled κ values were 0.37 (95% CI 0.22–0.52) and 0.62 (95% CI 0.04–0.99) for HE and additional staining for LVI, respectively. Conclusion: Additional staining for LVI may increase the DOR for LNM and interobserver agreement for positive LVI among pathologists. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Differentiation grade as a risk factor for lymph node metastasis in T1 colorectal cancer

Author

Shiina, Osamu, primary, Kudo, Shin‐ei, additional, Ichimasa, Katsuro, additional, Takashina, Yuki, additional, Kouyama, Yuta, additional, Mochizuki, Kenichi, additional, Morita, Yuriko, additional, Kuroki, Takanori, additional, Kato, Shun, additional, Nakamura, Hiroki, additional, Matsudaira, Shingo, additional, Misawa, Masashi, additional, Ogata, Noriyuki, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Sawada, Naruhiko, additional, Baba, Toshiyuki, additional, Nemoto, Tetsuo, additional, Ishida, Fumio, additional, and Miyachi, Hideyuki, additional

Published
2023
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25. Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta‐analysis

Author

Watanabe, Jun, primary, Ichimasa, Katsuro, additional, Kataoka, Yuki, additional, Miki, Atsushi, additional, Someko, Hidehiro, additional, Honda, Munenori, additional, Tahara, Makiko, additional, Yamashina, Takeshi, additional, Yeoh, Khay Guan, additional, Kawai, Shigeo, additional, Kotani, Kazuhiko, additional, and Sata, Naohiro, additional

Published
2023
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27. Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience

Author

Misawa, Masashi, Kudo, Shin-ei, Mori, Yuichi, Cho, Tomonari, Kataoka, Shinichi, Yamauchi, Akihiro, Ogawa, Yushi, Maeda, Yasuharu, Takeda, Kenichi, Ichimasa, Katsuro, Nakamura, Hiroki, Yagawa, Yusuke, Toyoshima, Naoya, Ogata, Noriyuki, Kudo, Toyoki, Hisayuki, Tomokazu, Hayashi, Takemasa, Wakamura, Kunihiko, Baba, Toshiyuki, Ishida, Fumio, Itoh, Hayato, Roth, Holger, Oda, Masahiro, and Mori, Kensaku

Published
2018
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28. Impact of computer‐aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study.

Author

Kato, Shun, Kudo, Shin‐ei, Minegishi, Yosuke, Miyata, Yuki, Maeda, Yasuharu, Kuroki, Takanori, Takashina, Yuki, Mochizuki, Kenichi, Tamura, Eri, Abe, Masahiro, Sato, Yuta, Sakurai, Tatsuya, Kouyama, Yuta, Tanaka, Kenta, Ogawa, Yushi, Nakamura, Hiroki, Ichimasa, Katsuro, Ogata, Noriyuki, Hisayuki, Tomokazu, and Hayashi, Takemasa

Subjects
COLON polyps, POLYPS, COMPUTER-aided diagnosis, FORCEPS
Abstract

Objectives: Computer‐aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. Methods: This was a single‐center, multicase, multireader, image‐reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two‐tier classification (neoplastic or nonneoplastic) by analyzing narrow‐band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer‐based, image‐reading test. The test was conducted twice with a 4‐week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. Results: Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high‐confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). Conclusions: Computer‐aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high‐confidence diagnosis rate. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Performance evaluation of a computer‐aided polyp detection system with artificial intelligence for colonoscopy.

Author

Chino, Akiko, Ide, Daisuke, Abe, Seiichiro, Yoshinaga, Shigetaka, Ichimasa, Katsuro, Kudo, Toyoki, Ninomiya, Yuki, Oka, Shiro, Tanaka, Shinji, and Igarashi, Masahiro

Subjects
COMPUTER-aided diagnosis, DEEP learning, ARTIFICIAL intelligence, COLONOSCOPY, INFORMATION networks, INFORMED consent (Medical law)
Abstract

Objectives: A computer‐aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand‐alone performance of this device under blinded conditions. Methods: This multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance. Results: Of the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8–98.5%). The "successful detection sensitivity per colonoscopy" was 93% (95% CI 88.3–95.8%). For the frame‐based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8–88.4%), 84.7% (95% CI 83.8–85.6%), 34.9% (95% CI 32.3–37.4%), and 98.2% (95% CI 97.8–98.5%), respectively. Trial registration: University Hospital Medical Information Network (UMIN000044622). [ABSTRACT FROM AUTHOR]

Published
2024
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30. Depressed Colorectal Cancer: A New Paradigm in Early Colorectal Cancer

Author

Kudo, Shin-ei, Kouyama, Yuta, Ogawa, Yushi, Ichimasa, Katsuro, Hamada, Tsuyoshi, Kato, Kazuki, Kudo, Koki, Masuda, Takaaki, Otsu, Hajime, Misawa, Masashi, Mori, Yuichi, Kudo, Toyoki, Hayashi, Takemasa, Wakamura, Kunihiko, Miyachi, Hideyuki, Sawada, Naruhiko, Sato, Toshiro, Shibata, Tatsuhiro, Hamatani, Shigeharu, Nemoto, Tetsuo, Ishida, Fumio, Niida, Atsushi, Miyano, Satoru, Oshima, Masanobu, Ogino, Shuji, and Mimori, Koshi

Published
2020
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32. A NOVEL ENDOSCOPIC RESECTION APPROACH FOR T2 COLORECTAL CANCER -ANALYSIS OF RISK FACTORS FOR LYMPH NODE METASTASIS-

Author

Morita, Yuriko, primary, Kudo, Shin-ei, additional, Ichimasa, Katsuro, additional, Takashina, Yuki, additional, Kouyama, Yuta, additional, Mochizuki, Kenichi, additional, Shiina, Osamu, additional, Kato, Shun, additional, Kuroki, Takanori, additional, Nakahara, Kenta, additional, Sato, Yuta, additional, Sakurai, Tatsuya, additional, Ogawa, Yushi, additional, Yasuharu, Maeda, additional, Misawa, Masashi, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Enami, Yuta, additional, Sawada, Naruhiko, additional, Nemoto, Tetsuo, additional, Baba, Toshiyuki, additional, Ishida, Fumio, additional, and Miyachi, Hideyuki, additional

Published
2023
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33. DIAGNOSTIC PERFORMANCE OF ARTIFICIAL INTELLIGENCE AND MAGNIFYING ENDOSCOPY IN THE PREDICTION OF THE INVASION DEPTH OF EARLY COLORECTAL CANCER

Author

Tamura, Eri, primary, Kudo, Shin-ei, additional, Misawa, Masashi, additional, Shiina, Osamu, additional, Morita, Yuriko, additional, Kuroki, Takanori, additional, Kato, Shun, additional, Miyata, Yuki, additional, Takashina, Yuki, additional, Ishiyama, Misaki, additional, Mochizuki, Kenichi, additional, Ogura, Yohei, additional, Abe, Masahiro, additional, Sato, Yuta, additional, Sakurai, Tatsuya, additional, Kouyama, Yuta, additional, Igarashi, Kenta, additional, Ogawa, Yushi, additional, Maeda, Yasuharu, additional, Nakamura, Hiroki, additional, Ichimasa, Katsuro, additional, Matsudaira, Shingo, additional, Ogata, Noriyuki, additional, Hisayuki, Tomokazu, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Miyachi, Hideyuki, additional, Ishida, Fumio, additional, Baba, Toshiyuki, additional, and Nemoto, Tetsuo, additional

Published
2023
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34. CLINICAL AND PATHOLOGICAL FEATURES OF DEPRESSED-TYPE COLORECTAL NEOPLASM

Author

Kudo, Shin-ei, primary, Kawashima, Jiro, additional, Kouyama, Yuta, additional, Ichimasa, Katsuro, additional, Matsudaira, Shingo, additional, Nakamura, Hiroki, additional, Miyachi, Hideyuki, additional, Misawa, Masashi, additional, Ogata, Noriyuki, additional, Hisayuki, Tomokazu, additional, Wakamura, Kunihiko, additional, Hayashi, Takemasa, additional, Nemoto, Tetsuo, additional, Baba, Toshiyuki, additional, and Ishida, Fumio, additional

Published
2023
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35. EVALUATION OF THE IMPACT OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED CHARACTERIZATION FOR COLORECTAL LESIONS USING NARROW-BAND IMAGING FOR THE DIAGNOSTIC OF ENDOSCOPISTS -MULTI-READER, MULTI-CASE STUDY-

Author

Kato, Shun, primary, Kudo, Shin-ei, additional, Misawa, Masashi, additional, Niimura, Yu, additional, Sasabe, Keisuke, additional, Shibuya, Tomoya, additional, Ide, Yutaro, additional, Senba, Shigenori, additional, Iwasaki, Shunto, additional, Kawabata, Yurie, additional, Shiina, Osamu, additional, Morita, Yuriko, additional, Kuroki, Takanori, additional, Miyata, Yuki, additional, Tamura, Eri, additional, Abe, Masahiro, additional, Sato, Yuta, additional, Sakurai, Tatsuya, additional, Kouyama, Yuta, additional, Igarashi, Kenta, additional, Ogawa, Yushi, additional, Maeda, Yasuharu, additional, Nakamura, Hiroki, additional, Ichimasa, Katsuro, additional, Matsudaira, Shingo, additional, Ogata, Noriyuki, additional, Hisayuki, Tomokazu, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Miyachi, Hideyuki, additional, Baba, Toshiyuki, additional, Ishida, Fumio, additional, and Nemoto, Tetsuo, additional

Published
2023
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36. Diagnostic performance of endocytoscopy with normal pit‐like structure sign for colorectal low‐grade adenoma compared with conventional modalities

Author

Suzuki, Kenichi, primary, Kudo, Shin‐ei, additional, Kudo, Toyoki, additional, Misawa, Masashi, additional, Mori, Yuichi, additional, Ichimasa, Katsuro, additional, Maeda, Yasuharu, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Baba, Toshiyuki, additional, Ishda, Fumio, additional, Hamatani, Shigeharu, additional, Inoue, Haruhiro, additional, Yokoyama, Kazunori, additional, and Miyachi, Hideyuki, additional

Published
2023
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38. Whole slide image‐based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence.

Author

Takashina, Yuki, Kudo, Shin‐ei, Kouyama, Yuta, Ichimasa, Katsuro, Miyachi, Hideyuki, Mori, Yuichi, Kudo, Toyoki, Maeda, Yasuharu, Ogawa, Yushi, Hayashi, Takemasa, Wakamura, Kunihiko, Enami, Yuta, Sawada, Naruhiko, Baba, Toshiyuki, Nemoto, Tetsuo, Ishida, Fumio, and Misawa, Masashi

Subjects
LYMPHATIC metastasis, ARTIFICIAL intelligence, COLORECTAL cancer, RECEIVER operating characteristic curves, RANDOM forest algorithms
Abstract

Objectives: Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for determining the need for surgery after endoscopic resection because LNM occurs in 10%. We aimed to develop a novel artificial intelligence (AI) system using whole slide images (WSIs) to predict LNM. Methods: We conducted a retrospective single center study. To train and test the AI model, we included LNM status‐confirmed T1 and T2 CRC between April 2001 and October 2021. These lesions were divided into two cohorts: training (T1 and T2) and testing (T1). WSIs were cropped into small patches and clustered by unsupervised K‐means. The percentage of patches belonging to each cluster was calculated from each WSI. Each cluster's percentage, sex, and tumor location were extracted and learned using the random forest algorithm. We calculated the areas under the receiver operating characteristic curves (AUCs) to identify the LNM and the rate of over‐surgery of the AI model and the guidelines. Results: The training cohort contained 217 T1 and 268 T2 CRCs, while 100 T1 cases (LNM‐positivity 15%) were the test cohort. The AUC of the AI system for the test cohort was 0.74 (95% confidence interval [CI] 0.58–0.86), and 0.52 (95% CI 0.50–0.55) using the guidelines criteria (P = 0.0028). This AI model could reduce the 21% of over‐surgery compared to the guidelines. Conclusion: We developed a pathologist‐independent predictive model for LNM in T1 CRC using WSI for determination of the need for surgery after endoscopic resection. Trial registration: UMIN Clinical Trials Registry (UMIN000046992, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053590). [ABSTRACT FROM AUTHOR]

Published
2023
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40. Molecular and clinicopathological differences between depressed and protruded T2 colorectal cancer

Author

Mochizuki, Kenichi, primary, Kudo, Shin-ei, additional, Kato, Kazuki, additional, Kudo, Koki, additional, Ogawa, Yushi, additional, Kouyama, Yuta, additional, Takashina, Yuki, additional, Ichimasa, Katsuro, additional, Tobo, Taro, additional, Toshima, Takeo, additional, Hisamatsu, Yuichi, additional, Yonemura, Yusuke, additional, Masuda, Takaaki, additional, Miyachi, Hideyuki, additional, Ishida, Fumio, additional, Nemoto, Tetsuo, additional, and Mimori, Koshi, additional

Published
2022
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44. Comprehensive Diagnostic Performance of Real-Time Characterization of Colorectal Lesions Using an Artificial Intelligence–Assisted System: A Prospective Study

Author

Minegishi, Yosuke, primary, Kudo, Shin-Ei, additional, Miyata, Yuki, additional, Nemoto, Tetsuo, additional, Mori, Kensaku, additional, Misawa, Masashi, additional, Mori, Yuichi, additional, Mochida, Kentaro, additional, Akimoto, Yoshika, additional, Ishiyama, Misaki, additional, Ogura, Yohei, additional, Abe, Masahiro, additional, Sato, Yuta, additional, Ogawa, Yushi, additional, Yasuharu, Maeda, additional, Tanaka, Kenta, additional, Ichimasa, Katsuro, additional, Nakamura, Hiroki, additional, Ogata, Noriyuki, additional, Hisayuki, Tomokazu, additional, Kudo, Toyoki, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Miyachi, Hideyuki, additional, Baba, Toshiyuki, additional, Ishida, Fumio, additional, Itoh, Hayato, additional, and Oda, Masahiro, additional

Published
2022
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45. Deep Submucosal Invasion Is Not an Independent Risk Factor for Lymph Node Metastasis in T1 Colorectal Cancer: A Meta-Analysis

Author

MS MDL 1, Cancer, Zwager, Liselotte W, Bastiaansen, Barbara A J, Montazeri, Nahid S M, Hompes, Roel, Barresi, Valeria, Ichimasa, Katsuro, Kawachi, Hiroshi, Machado, Isidro, Masaki, Tadahiko, Sheng, Weiqi, Tanaka, Shinji, Togashi, Kazutomo, Yasue, Chihiro, Fockens, Paul, Moons, Leon M G, Dekker, Evelien, MS MDL 1, Cancer, Zwager, Liselotte W, Bastiaansen, Barbara A J, Montazeri, Nahid S M, Hompes, Roel, Barresi, Valeria, Ichimasa, Katsuro, Kawachi, Hiroshi, Machado, Isidro, Masaki, Tadahiko, Sheng, Weiqi, Tanaka, Shinji, Togashi, Kazutomo, Yasue, Chihiro, Fockens, Paul, Moons, Leon M G, and Dekker, Evelien

Published
2022

47. Use of advanced endoscopic technology for optical characterization of neoplasia in patients with ulcerative colitis: Systematic review

Author

Maeda, Yasuharu, primary, Kudo, Shin‐ei, additional, Ogata, Noriyuki, additional, Kuroki, Takanori, additional, Takashina, Yuki, additional, Takishima, Kazumi, additional, Ogawa, Yushi, additional, Ichimasa, Katsuro, additional, Mori, Yuichi, additional, Kudo, Toyoki, additional, Hayashi, Takemasa, additional, Miyachi, Hideyuki, additional, Ishida, Fumio, additional, Nemoto, Tetsuo, additional, Ohtsuka, Kazuo, additional, and Misawa, Masashi, additional

Published
2022
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48. THE INDICATION FOR ADDITIONAL SURGERY WITH LYMPH NODE DISECTION AMONG ELDERY PATIENTS WITH T1 COLORECTAL CANCERS TREATED ENDOSCOPICALLY

Author

Miyachi, Hideyuki, primary, Kudo, Shinei, additional, Kouyama, Yuta, additional, Ichimasa, Katsuro, additional, Matsudaira, Shingo, additional, Mochizuki, Kenichi, additional, Takashina, Yuki, additional, Ogawa, Yushi, additional, Yasuharu, Maeda, additional, Nakamura, Hiroki, additional, Misawa, Masashi, additional, Mori, Yuichi, additional, Ogata, Noriyuki, additional, Hisayuki, Tomokazu, additional, Kudo, Toyoki, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Baba, Toshiyuki, additional, Nemoto, Tetsuo, additional, and Ishida, Fumio, additional

Published
2022
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49. A PROSPECTIVE STUDY OF REAL-TIME COMPUTER-AIDED CHARACTERIZATION OF COLORECTAL LESIONS: DIAGNOSTIC PERFORMANCE AND IMPACT ON HUMAN DIAGNOSIS

Author

Misawa, Masashi, primary, Kudo, Shinei, additional, Miyata, Yuki, additional, Minegishi, Yosuke, additional, Mori, Yuichi, additional, Takashina, Yuki, additional, Yasuharu, Maeda, additional, Ichimasa, Katsuro, additional, Nakamura, Hiroki, additional, Kudo, Toyoki, additional, Hayashi, Takemasa, additional, Wakamura, Kunihiko, additional, Miyachi, Hideyuki, additional, Baba, Toshiyuki, additional, and Ishida, Fumio, additional

Published
2022
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50. CLINICAL AND PATHOLOGICAL FEATURE OF DEPRESSED-TYPE COLORECTAL NEOPLASM

Author

Kudo, Shinei, primary, Sato, Yuta, additional, Mochida, Kentaro, additional, Kouyama, Yuta, additional, Ichimasa, Katsuro, additional, Matsudaira, Shingo, additional, Miyachi, Hideyuki, additional, Mori, Yuichi, additional, Misawa, Masashi, additional, Kudo, Toyoki, additional, Ogata, Noriyuki, additional, Hisayuki, Tomokazu, additional, Wakamura, Kunihiko, additional, Hayashi, Takemasa, additional, Baba, Toshiyuki, additional, and Ishida, Fumio, additional

Published
2022
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