2.Personalized approaches to the treatment of hepatocellular carcinoma using immune checkpoint inhibitors: Editorial on “Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: Insights from the IMbrave150 trial”
Clinical and Molecular Hepatology 2025;31(1):311-315
4.Personalized approaches to the treatment of hepatocellular carcinoma using immune checkpoint inhibitors: Editorial on “Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: Insights from the IMbrave150 trial”
Clinical and Molecular Hepatology 2025;31(1):311-315
6.Personalized approaches to the treatment of hepatocellular carcinoma using immune checkpoint inhibitors: Editorial on “Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: Insights from the IMbrave150 trial”
Clinical and Molecular Hepatology 2025;31(1):311-315
7.Atezolizumab and bevacizumab for hepatocellular carcinoma: How to approach salvage therapy for non-responders?: Editorial on “Sorafenib vs. Lenvatinib in advanced hepatocellular carcinoma after atezolizumab/bevacizumab failure: A real-world study”
Clinical and Molecular Hepatology 2024;30(4):682-688
8.Artificial intelligence models for the diagnosis and management of liver diseases
Naoshi NISHIDA ; Masatoshi KUDO
Ultrasonography 2023;42(1):10-19
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
9.Bispectral index-guided propofol sedation during endoscopic ultrasonography
Ayana OKAMOTO ; Ken KAMATA ; Takeshi MIYATA ; Tomoe YOSHIKAWA ; Rei ISHIKAWA ; Tomohiro YAMAZAKI ; Atsushi NAKAI ; Shunsuke OMOTO ; Kosuke MINAGA ; Kentaro YAMAO ; Mamoru TAKENAKA ; Yasutaka CHIBA ; Toshiharu SAKURAI ; Naoshi NISHIDA ; Masayuki KITANO ; Masatoshi KUDO
Clinical Endoscopy 2022;55(4):558-563
Background/Aims:
Bispectral index (BIS) monitors process and display electroencephalographic data are used to assess the depth of anesthesia. This study retrospectively evaluated the usefulness of BIS monitoring during endoscopic ultrasonography (EUS).
Methods:
This study included 725 consecutive patients who underwent EUS under sedation with propofol. BIS monitoring was used in 364 patients and was not used in 361. The following parameters were evaluated: (1) median dose of propofol; (2) respiratory and circulatory depression; (3) occurrence of body movements; (4) awakening score >8 at the time; and (5) awakening score 2 hours after leaving the endoscopy room.
Results:
The BIS group received a significantly lower median dose of propofol than the non-BIS group (159.2 mg vs. 167.5 mg; p=0.015) in all age groups. For patients aged ≥75 years, the reduction in heart rate was significantly lower in the BIS group than in the non-BIS group (1.2% vs. 9.1%; p=0.023). Moreover, the occurrence of body movements was markedly lower in the BIS group than in the non-BIS group (8.5% vs. 39.4%; p<0.001).
Conclusions
During EUS examination, BIS monitoring is useful for maintaining a constant depth of anesthesia, especially in patients 75 years of age or older.
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