Progression Rates by Age, Sex, Treatment, and Disease Activity by AASLD and EASL Criteria: Data for Precision Medicine

Park, Jiyoon; Le, An K.; Tseng, Tai Chung; Yeh, Ming Lun; Jun, Dae Won; Trinh, Huy N.; Wong, Grace Lai–Hung; Chen, Chien Hung; Peng, Cheng Yuan; Kim, Sung Eun; Oh, Hongseob; Kwak, Min Sun; Cheung, Ka Shing; Toyoda, Hidenori; Hsu, Yao Chun; Jeong, Jae Yoon; Yoon, Eileen L.; Ungtrakul, Teerapat; Zhang, Jian; Xie, Qing; Ahn, Sang Bong; Enomoto, Masaru; Shim, Jae Jun; Cunningham, Chris; Jeong, Soung Won; Cho, Yong Kyun; Ogawa, Eiichi; Huang, Rui; Lee, Dong Hoon; Takahashi, Hirokazu; Tsai, Pei‐Chien; Huang, Chung Feng; Dai, Chia Yen; Tseng, Cheng Hao; Yasuda, Satoshi; Kozuka, Ritsuzo; Li, Jiayi; Wong, Christopher; Wong, Clifford; Zhao, Changqing; Hoang, Joseph; Eguchi, Yuichiro; Wu, Chao; Tanaka, Yasuhito; Gane, Ed; Tanwandee, Tawesak; Cheung, Ramsey; Yuen, Man Fung; Lee, Hyo Suk; Yu, Ming; Yang, Hwai I.; Nguyen, Mindie H.

doi:10.1016/j.cgh.2021.05.062

Cited by 6 publications

(8 citation statements)

References 29 publications

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“…The inclusion criteria for data collection are as follows: (1) Patients' liver tumors were detected by B-mode US. (2) The diagnosis of the liver tumor was confirmed via histology, contrast-enhanced computed tomography (CECT), contrast-enhanced ultrasonography using SonzoidÒ (CEUS), or gadoliniumethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI.…”

Section: Dataset For the Training Validation And Test Cohorts Of The ...mentioning

confidence: 99%

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Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts

et al. 2022

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Background Ultrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US. Methods We constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images. Results The diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%–69.1%) and 47.3% (45.5%–47.3%) by human experts and non-experts, respectively. Conclusion The performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.

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Section: Dataset For the Training Validation And Test Cohorts Of The ...mentioning

confidence: 99%

“…With the increase of the aged population, the prevalence of malignant liver tumors is expected to increase, so an efficient method for screening lesions is imperative [ 1 , 2 ]. Abdominal ultrasonography (US) is a non-invasive, highly convenient, and versatile imaging technique that is commonly used for liver tumor diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts

et al. 2022

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“…evaluated the annual incidence of HCC in patients classified by age, sex, and antiviral treatment. They found that noncirrhotic men with 30–39 years of age had HCC risk above 0.20%, the cut‐off perceived to be cost‐effective to recommend HCC surveillance 31 …”

Section: Prediction Of Hepatocellular Carcinoma Risk In Patients With...mentioning

confidence: 99%

“…Recent study on the interaction of alcohol consumption and HCC risk in patients treated with NAs is limited. In a multinational, large‐scale, retrospective study, transition probability of liver cirrhosis and HCC was studied based on the age, sex, treatment, and disease activity 31 . Further studies with this approach would be the basis for the precision medicine in patients with significant alcohol intake and CHB.…”

Section: Increasing Age and Rates Of Comorbidities In Patients With C...mentioning

confidence: 99%

“…They found that noncirrhotic men with 30-39 years of age had HCC risk above 0.20%, the cut-off perceived to be cost-effective to recommend HCC surveillance. 31 Increasing age and rates of comorbidities in patients with chronic hepatitis B Potent and acceptably safe NAs (e.g. tenofovir alafenamide and entecavir) have led us to admit the possibility of life-long treatment for CHB with NAs.…”

Section: Prediction Of Hepatocellular Carcinoma Risk In Patients With...mentioning

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Precision medicine in the era of potent antiviral therapy for chronic hepatitis B

Yoon¹,

Jun²

2022

J of Gastro and Hepatol

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With the wide use of potent and safe nucloes(t‐)ide analogues (NAs) treatment, patient‐centered care is getting important. Intensive care for comorbidity has gain utmost importance in care of aging chronic hepatitis B (CHB) patients with life‐long antiviral treatment. Linkage to care of patients with CHB is essential for the goal of hepatitis B virus (HBV) eradication. As long‐term suppression of HBV DNA replication does not prevent hepatocellular carcinoma (HCC), prevention of HCC is another challenge for NAs treatment. There is a possibility of hepatocarcinogenesis in the immune‐tolerant phase and risk of loss of patients during active monitoring seeking the time point for antiviral treatment initiation. Initiation of NAs treatment from the immune‐tolerant phase would improve the linkage to care. However, universal recommendation is premature and evidence for cost‐effectiveness needs to be accumulated. Early initiation of NAs in the evidence of significant disease progression, either HBV associated or comorbidity associated, would be a better strategy to reduce the risk of HCC in patients located in the gray zone.

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Sex and ethnic disparities in hepatitis B evaluation and treatment across the world

Kudaravalli,

Huang,

Yeh

et al. 2024

Journal of Hepatology

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Progression Rates by Age, Sex, Treatment, and Disease Activity by AASLD and EASL Criteria: Data for Precision Medicine

Cited by 6 publications

References 29 publications

Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts

Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts

Precision medicine in the era of potent antiviral therapy for chronic hepatitis B

Sex and ethnic disparities in hepatitis B evaluation and treatment across the world

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