scholarly journals Albumin platelet product as a novel score for liver fibrosis stage and prognosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Koji Fujita ◽  
Kazumi Yamasaki ◽  
Asahiro Morishita ◽  
Tingting Shi ◽  
Joji Tani ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Validation Cohort ◽  
Activity Grade ◽  
Fibrosis Stage ◽  
Strongly Correlated ◽  
The Novel ◽  
Training Cohort ◽  
Blood Examination ◽  
Retrospective Multicenter Study ◽  
Overall Mortality

AbstractFibrosis-4 index, a conventional biomarker for liver fibrosis stage, is confounded by age and hepatitis activity grade. The current retrospective multicenter study aimed to formulate the novel indices of liver fibrosis by mathematically combining items of peripheral blood examination and to evaluate ability of prognosis prediction. After a novel index was established in a training cohort, the index was tested in a validation cohort. Briefly, a total of 426 patients were enrolled in a training cohort. Albumin and platelet most strongly correlated to fibrosis stage among blood examination. Albumin platelet product (APP) = Albumin × platelet/1000 could differentiate the four stages of liver fibrosis (p < 0.05). APP indicated fibrosis stage independent from hepatitis activity grade. A cut-off value = 4.349 diagnosed cirrhosis with area under ROC more than 0.8. Multivariate analysis revealed that smaller APP independently contributed to HCC prevalence and overall mortality. The results were validated in another 707 patients with HCV infection. In conclusion, APP was not confounded by age or hepatitis activity grade contrary to Fibrosis-4 index. APP is as simple as physicians can calculate it by pen calculation. The product serves physicians in managing patients with chronic liver disease.

scholarly journals Albumin Platelet Product: A Novel Score for Liver Fibrosis Stage and Prognosis.

2020 ◽  
Author(s):  
Koji Fujita ◽  
Kazumi Yamasaki ◽  
Asahiro Morishita ◽  
Tingting Shi ◽  
Joji Tani ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Validation Cohort ◽  
Activity Grade ◽  
Fibrosis Stage ◽  
Strongly Correlated ◽  
The Novel ◽  
Training Cohort ◽  
Blood Examination ◽  
Retrospective Multicenter Study ◽  
Overall Mortality

Abstract Fibrosis-4 index, a conventional biomarker for liver fibrosis stage, is confounded by age and hepatitis activity grade . The current retrospective multicenter study aimed to formulate the novel indices of liver fibrosis by mathematically combining items of peripheral blood examination and to evaluate ability of prognosis prediction. After a novel index was established in a training cohort, the index was tested in a validation cohort. Briefly, a total of 426 patients were enrolled in a training cohort. Albumin and platelet most strongly correlated to fibrosis stage among blood examination. Albumin platelet product (APP) = Albumin × platelet / 1000 could differentiate the four stages of liver fibrosis (p < 0.05). APP indicated fibrosis stage independent from hepatitis activity grade. A cut-off value = 4.349 diagnosed cirrhosis with area under ROC more than 0.8. Multivariate analysis revealed that smaller APP independently contributed to HCC prevalence and overall mortality. The results were validated in another 707 patients with HCV infection. In conclusion, APP was not confounded by age or hepatitis activity grade contrary to Fibrosis-4 index. APP is as simple as physicians can calculate it by pen calculation. The product serves physicians in managing patients with chronic liver disease.

scholarly journals AGLR is a Novel Index for the Prognosis of Hepatocellular Carcinoma Patients: A Retrospective Study

2020 ◽  
Author(s):  
Yan Liao ◽  
Rongyu Wei ◽  
Renzhi Yao ◽  
Liling Qin ◽  
Jun Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Validation Cohort ◽  
Multivariate Analyses ◽  
The Novel ◽  
Gamma Glutamyl Transpeptidase ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Scoring Model ◽  
Kaplan Meier

Abstract Background: Most hepatocellular carcinoma (HCC) patients’ liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase) / lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. Methods: A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan-Meier method.Results: According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with α-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI, 1.21-2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI, 1.35-2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis.Conclusions: The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

scholarly journals AGLR is a novel index for the prognosis of hepatocellular carcinoma patients: a retrospective study

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yan Liao ◽  
Rongyu Wei ◽  
Renzhi Yao ◽  
Liling Qin ◽  
Jun Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Validation Cohort ◽  
Multivariate Analyses ◽  
The Novel ◽  
Gamma Glutamyl Transpeptidase ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Scoring Model ◽  
Kaplan Meier

Abstract Background Most hepatocellular carcinoma (HCC) patients’ liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase)/lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. Methods A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan–Meier method. Results According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with alpha-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI 1.21–2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI 1.35–2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis. Conclusions The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

Liver Fibrosis stage at baseline predicts outcome of metabolic surgery

2019 ◽  
Author(s):  
C Kienbacher ◽  
M Wakolbinger ◽  
S Traussnigg ◽  
R Kruschitz ◽  
T Würger ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Metabolic Surgery ◽  
Fibrosis Stage

scholarly journals Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model

2021 ◽  
Author(s):  
Yunchao Yin ◽  
Derya Yakar ◽  
Rudi A. J. O. Dierckx ◽  
Kim B. Mouridsen ◽  
Thomas C. Kwee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Fibrosis ◽  
Liver Parenchyma ◽  
Ct Images ◽  
Fibrosis Stage ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Liver Surface ◽  
Fibrosis Staging

Abstract Objectives Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. Methods The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. Results The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2–F4), advanced fibrosis (F3–F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). Conclusions Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning–based liver fibrosis staging algorithms. Key Points • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

scholarly journals Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sijia Cui ◽  
Tianyu Tang ◽  
Qiuming Su ◽  
Yajie Wang ◽  
Zhenyu Shu ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Main Pancreatic Duct ◽  
Validation Cohort ◽  
External Validation ◽  
High Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Independent Validation ◽  
Medical Centers ◽  
Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

scholarly journals 1358. Using natural language processing to optimize case ascertainment of acute otitis media in a large, state-wide pediatric practice network

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S690-S691
Author(s):  
Joshua C Herigon ◽  
Amir Kimia ◽  
Marvin Harper
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Otitis Media ◽  
Language Processing ◽  
Validation Cohort ◽  
Recursive Partitioning ◽  
Acute Otitis Media ◽  
Clinical Documentation ◽  
Training Cohort ◽  
Icd Codes

Abstract Background Antibiotics are the most commonly prescribed drugs for children and frequently inappropriately prescribed. Outpatient antimicrobial stewardship interventions aim to reduce inappropriate antibiotic use. Previous work has relied on diagnosis coding for case identification which may be inaccurate. In this study, we sought to develop automated methods for analyzing note text to identify cases of acute otitis media (AOM) based on clinical documentation. Methods We conducted a cross-sectional retrospective chart review and sampled encounters from 7/1/2018 – 6/30/2019 for patients &lt; 5 years old presenting for a problem-focused visit. Complete note text and limited structured data were extracted for 12 randomly selected weekdays (one from each month during the study period). An additional weekday was randomly selected for validation. The primary outcome was correctly identifying encounters where AOM was present. Human review was considered the “gold standard” and was compared to ICD codes, a natural language processing (NLP) model, and a recursive partitioning (RP) model. Results A total of 2,724 encounters were included in the training cohort and 793 in the validation cohort. ICD codes and NLP had good performance overall with sensitivity 91.2% and 93.1% respectively in the training cohort. However, NLP had a significant drop-off in performance in the validation cohort (sensitivity: 83.4%). The RP model had the highest sensitivity (97.2% training cohort; 94.1% validation cohort) out of the 3 methods. Figure 1. Details of encounters included in the training and validation cohorts. Table 1. Performance of ICD coding, a natural language processing (NLP) model, and a recursive partitioning (RP) model for identifying cases of acute otitis media (AOM) Conclusion Natural language processing of outpatient pediatric visit documentation can be used successfully to create models accurately identifying cases of AOM based on clinical documentation. Combining NLP and structured data can improve automated case detection, leading to more accurate assessment of antibiotic prescribing practices. These techniques may be valuable in optimizing outpatient antimicrobial stewardship efforts. Disclosures All Authors: No reported disclosures

Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15565-e15565
Author(s):  
Qiqi Zhu ◽  
Du Cai ◽  
Wei Wang ◽  
Min-Er Zhong ◽  
Dejun Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Training Cohort ◽  
Gene Pairs ◽  
Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6046-6046
Author(s):  
Sik-Kwan Chan ◽  
Cheng Lin ◽  
Shao Hui Huang ◽  
Tin Ching Chau ◽  
Qiaojuan Guo ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Bone Metastases ◽  
Validation Cohort ◽  
De Novo ◽  
Multivariable Analysis ◽  
Term Survival ◽  
Training Cohort ◽  
Study Results ◽  
Multivariable Analyses

6046 Background: The eighth edition TNM (TNM-8) classified de novo metastatic (metastatic disease at presentation) nasopharyngeal carcinoma (NPC) as M1 without further subdivision. However, survival heterogeneity exists and long-term survival has been observed in a subset of this population. We hypothesize that certain metastatic characteristics could further segregate survival for de novo M1 NPC. Methods: Patients with previously untreated de novo M1 NPC prospectively treated in two academic institutions (The University of Hong Kong [n = 69] and Provincial Clinical College of Fujian Medical University [n = 114] between 2007 and 2016 were recruited and re-staged based on TNM-8 in this study. They were randomized in 2:1 ratio to generate a training cohort (n = 120) and validation cohort (n = 63) respectively. Univariable and multivariable analyses (MVA) were performed for the training cohort to identify the anatomic prognostic factors of overall survival (OS). We then performed recursive partitioning analysis (RPA) which incorporated the anatomic prognostic factors identified in multivariable analyses and derived a new set of RPA stage groups (Anatomic-RPA groups) which predicted OS in the training cohort. The significance of Anatomic-RPA groups in the training cohort was then validated in the validation cohort. UVA and MVA were performed again on the validation cohorts to identify significant OS prognosticators. Results: The training and the validation cohorts had a median follow-up of 27.2 months and 30.2 months, respectively, with the 3-year OS of 51.6% and 51.1%, respectively. Univariable analysis (UVA) and multivariable analysis (MVA) revealed that co-existing liver and bone metastases was the only factor prognostic of OS. Anatomic-RPA groups based on the anatomic prognostic factors identified in UVA and MVA yielded good segregation (M1a: no co-existing liver and bone metastases and M1b: co-existing both liver and bone metastases; median OS 39.5 and 23.7 months respectively; P =.004). RPA for the validation set also confirmed good segregation with co-existing liver and bone metastases (M1a: no co-existing liver and bone metastases and M1b: co-existing liver and bone metastases), with median OS 47.7 and 16.0 months, respectively; P =.008). It was also the only prognostic factor in UVA and MVA in the validation cohort. Conclusions: Our Anatomic-RPA M1 stage groups with anatomical factors provided better subgroup segregation for de novo M1 NPC. The study results provide a robust justification to refine M1 categories in future editions of TNM staging classification.

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