A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence

Mazaki, Junichi; Katsumata, Kenji; Ohno, Yuki; Udo, Ryutaro; Tago, Tomoya; Kasahara, Kenta; Kuwabara, Hiroshi; Enomoto, Masanobu; Ishizaki, Tetsuo; Nagakawa, Yuichi; Tsuchida, Akihiko

doi:10.21873/anticanres.15400

Cited by 22 publications

(15 citation statements)

References 6 publications

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“…Our reported anastomotic leakage rates under these conditions are in the single digits, which we consider acceptable. [26] Anastomotic failure depends on various factors, including tissue thickness, collagen content, blood flow, improper selection of staple cartridges, ischemia, and tension. [27,28] Anastomotic leakage appears to be closely related to the strength of a freshly created anastomosis.…”

Section: Discussionmentioning

confidence: 99%

Comparison of pressure resistance of double-rows and triple-rows circular stapler in rectal double stapling technique: In vitro study

Mazaki

Katsumata²,

Udo³

et al. 2022

Medicine

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Background: Anastomotic leak after gastrointestinal anastomosis is a serious complication. Anastomotic failure depends on various parameters. The aim of our study was to evaluate the pressure resistance of a new device, EEA™ circular stapler with Tri-Staple™ technology 28 mm Medium/Thick (Triple-rows circular stapler; TCS) compared with EEA™ circular stapler with DST series™ technology 28 mm, 4.8 mm staples (double-rows circular stapler; DCS). Patients and methods: We performed 30 anastomoses (DSC: 15, TCS: 15) of DST with porcine colon model in vitro. We performed following 3 comparative experiences; Experiment 1: observation of staple shape with a colonoscopy, Experiment 2: comparison of the pressure resistance, Experiment 3: comparison of leakage points. Results: There was no hypoplasia of staples and the shapes were well-formed by colonoscopy. The leakage pressure of DCS was 19.6 ± 4.4 mm Hg (mean ± standard deviation) and that of TCS was 38.6 ± 10.2 mm Hg (mean ± standard deviation). There was a significantly difference between 2 groups (P < .001). 12 cases of DCS (80%) and 10 cases of TCS (66.7%) had leakages from Circular stapler point. 2 cases of DCS (13.3%) and 5 cases of TCS (33.3%) had leakages from Crossing points. Only 1 case of DCS had leakages from Dog ear point (6.7%). There was no significantly difference in leakage site between 2 groups (P = .195). Conclusions: TSC showed high pressure resistance during DST compared with that of DCS. It was suggested that TCS may contribute to the reduction of anastomotic leakage rate.

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Section: Discussionmentioning

confidence: 99%

Comparison of pressure resistance of double-rows and triple-rows circular stapler in rectal double stapling technique: In vitro study

Mazaki

Katsumata²,

Udo³

et al. 2022

Medicine

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“…The value of the difference in the model output indicated how much the model depended on the variables. The value of the difference was computed for each covariate and then averaged over those in the dataset 30 .…”

Section: Discussionmentioning

confidence: 99%

AI model developed using machine learning for predicting sperm retrieval in micro TESE for non-obstructive azoospermia patients

Kobayashi

Uetani

Yamabe

et al. 2022

Preprint

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Azoospermia is a severe problem that prevents couples from having their own children through natural pregnancy. To obtain sperm from patients with azoospermia testicular sperm extraction (TESE) is required. In non-obstructive azoospermia (NOA), a particularly severe type of male infertility, patients need to undergo microdissection testicular sperm extraction (micro TESE) to collect sperm and, at 40 to 60%, the sperm retrieval success rate is not very high. Therefore, our objective in the present study was to make a model for predicting the possibility of sperm retrieval in patients with NOA before performing micro TESE, using machine learning. We retrospectively obtained data from the medical records of 430 patients who underwent micro TESE from 2011 to 2020, and used Prediction One, which does not require coding, to create the model. We successfully achieved our objective. The AUC for the AI model was 0.7246, which is acceptable. In addition, among the variables, we found that T/E2 ratios contributed most to predicting whether sperm retrieval was possible or not. T/E2 ratios have potential as a clinical predictor of sperm retrieval in NOA.

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“…Individualized treatment decisions may benefit from the use of an AI survival prediction system. Synthesized intelligence worked on a novel anastomotic leakage prediction model for colorectal double stapler (DST) anastomosis ( 57 ). To anticipate recurrence, the automatic artificial intelligence program “prediction one” (Sony Network Communications Inc.) is employed, which enhances accuracy over the old approach ( 58 ).…”

Section: Discussionmentioning

confidence: 99%

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

et al. 2022

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BackgroundArtificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC.MethodsScientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords.ResultsThe total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified.ConclusionAAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.

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A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence

Cited by 22 publications

References 6 publications

Comparison of pressure resistance of double-rows and triple-rows circular stapler in rectal double stapling technique: In vitro study

Comparison of pressure resistance of double-rows and triple-rows circular stapler in rectal double stapling technique: In vitro study

AI model developed using machine learning for predicting sperm retrieval in micro TESE for non-obstructive azoospermia patients

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

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