aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis

Fan, Rong; Papatheodoridis, George; Sun, Jian; Innes, Hamish; Toyoda, Hidenori; Xie, Qing; Mo, Shuyuan; Sypsa, Vana; Guha, Indra Neil; Kumada, Takashi; Niu, Junqi; Dalekos, George Ν.; Yasuda, Satoshi; Barnes, Eleanor; Lian, Jiangshan; Suri, Vithika; İdilman, Ramazan; Barclay, Stephen T.; Dou, Xiaoguang; Berg, Thomas; Hayes, Peter; Flaherty, John F.; Zhou, Yuanping; Zhang, Zhengang; Buti, Marı́a; Hutchinson, Sharon; Guo, Yabing; Calleja, José Luís; Lin, Lanjia; Zhao, Lijing; Chen, Yongpeng; Janssen, Harry L.A.; Zhu, Chaonan; Shi, Lei; Tang, Xiaoping; Gaggar, Anuj; Wei, Lai; Jia, Jidong; Irving, William L.; Johnson, Philip J.; Lampertico, Pietro; Hou, Jilun

doi:10.1016/j.jhep.2020.07.025

Cited by 205 publications

(254 citation statements)

References 22 publications

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“…However, the 3-year cumulative incidence of HCC was 1.5% and 4.8%, and the 5-year cumulative incidence of HCC was 8.1% and 19.9% in patients classified into the intermediate-risk (50-60 points) and high-risk (60-100 points) groups, respectively. However, this study found that low-risk patients accounted for 45%, while the highrisk patients accounted for only 17.9% of all non-viral HCC [85] . Therefore, the role of the aMAP model in identifying high-risk patients for development of HCC in patients with non-viral liver disease needs further evaluation.…”

Section: Family History Of Liver Cancercontrasting

confidence: 61%

Guideline for stratified screening and surveillance in patients with high risk of primary liver cancer (2020)

Ding¹,

Tu²,

Qu³

et al. 2021

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Section: Family History Of Liver Cancercontrasting

confidence: 61%

Guideline for stratified screening and surveillance in patients with high risk of primary liver cancer (2020)

Ding¹,

Tu²,

Qu³

et al. 2021

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“…Fan R et al. built a prediction model which incorporates ALBI score to predicts HCC development in patients with chronic hepatitis ( 49 ). This model was developed based on more than 17,000 patients from 11 global prospective studies and yielded a C-index of 0.82–0.87.…”

Section: Discussionmentioning

confidence: 99%

“…Ding M et al found that ALBI grade is an independent risk factor for overall survival in patients with HCC after thermal ablation (48). Fan R et al built a prediction model which incorporates ALBI score to predicts HCC development in patients with chronic hepatitis (49). This model was developed based on more than 17,000 patients from 11 global prospective studies and yielded a C-index of 0.82-0.87.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Yuan

Song

et al. 2021

Front. Immunol.

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BackgroundThere is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.AimThe aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.MethodsA total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsEight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.ConclusionsThis study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.

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“…We retrospectively assessed the diagnostic performance of five validated HCC risk scores, namely: Guide with Age, Gender, HBV DNA, core promotor mutations and cirrhosis score (GAG HCC score), 10 Risk estimation for hepatocellular carcinoma in Chronic Hepatitis B (REACH B score), 11 Modified REACH B score (HBV DNA substituted for liver fibrosis measurements), 12 modified platelets, age, gender (mPAGE B score) 13 and the age‐Male‐ALBI‐Platelets (aMAP) score 14 …”

Section: Methodsmentioning

confidence: 99%

Assessing efficacy of hepatocellular carcinoma prediction scores to prioritise hepatitis B surveillance in the COVID‐19 era

et al. 2021

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Objective An estimated 250 million people worldwide are chronically infected with hepatitis B virus (HBV), the leading cause of hepatocellular carcinoma (HCC) globally. The novel Sars‐cov2 virus continues to spread at an alarming rate, and with guidance at the onset of the pandemic recommending the deferral of HCC surveillance, the implications on liver cancer care are now emerging and highlight the urgent need for reorganisation of services. Methods We analysed how five HCC risk prediction scores could aid stratification of patients with chronic HBV. We calculated scores using parameters measured from 3 years prior (where available, n = 17) and at the time of HCC diagnosis in all adult patients with chronic HBV diagnosed with HCC (n = 46), and controls (n = 100). We compared the number of patients requiring cancer surveillance according to each score and regional surveillance guidance. Results The aMAP score had the highest discriminatory performance in HCC risk prediction at 3 years (area under receiver‐operating characteristic curve (auROC) of 0.824), followed by the mREACH B score (auROC of 0.719), and mPAGE B score (auROC of 0.742). However, only the mREACH B score had a negative predictive value (NPV) >99%. Applying the mREACH B score to our HBV cohort identified 11 patients requiring HCC surveillance, compared with 62 under current guidelines. Conclusion The use of HCC risk prediction scores could streamline the surveillance of patients with chronic HBV at a time of extremely limited resources. Overall, the mREACH B score had both a strong discriminatory performance and a high NPV, thus safely identifying low risk patients not requiring surveillance.

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aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis

Cited by 205 publications

References 22 publications

Guideline for stratified screening and surveillance in patients with high risk of primary liver cancer (2020)

Guideline for stratified screening and surveillance in patients with high risk of primary liver cancer (2020)

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Assessing efficacy of hepatocellular carcinoma prediction scores to prioritise hepatitis B surveillance in the COVID‐19 era

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