Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.

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A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.633852 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xingyu Liu ◽

Xiaoyuan Liang ◽

Lingxiang Ruan ◽

Sheng Yan

Keyword(s):

Lymph Node ◽

Lymph Node Metastasis ◽

Gallbladder Cancer ◽

Prediction Models ◽

Validation Cohort ◽

External Validation ◽

Training Cohort ◽

Clinical Variables ◽

Node Metastasis ◽

Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

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P-L12 Exploring Pathological Signatures for Predicting Recurrence of Early-stage Hepatocellular Carcinoma Based on Deep Learning

British Journal of Surgery ◽

10.1093/bjs/znab430.105 ◽

2021 ◽

Vol 108 (Supplement_9) ◽

Author(s):

Yinghong Shi ◽

Weifeng Qu ◽

Mengxin Tian ◽

Jingtao Qiu ◽

Kun Qian ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Validation Cohort ◽

Early Stage ◽

External Validation ◽

Microvascular Invasion ◽

Risk Scores ◽

Forecast Performance ◽

Histological Score ◽

The Relationship

Abstract Background Early-stage hepatocellular carcinoma (HCC) is the ideal indication for liver resection. High recurrence rate limits the radical possibility. Current clinicopathological determinants are insufficient to reliably evaluate the recurrence risk after surgery. To address this global issue, we aimed to use deep learning to explore novel pathological signatures based on histological slides for predicting early-stage HCC recurrence and to evaluate the relationship between histological features and multi-omics information. Methods 576 pathological images collected from 547 patients with BCLC stage 0-A HCC who underwent hepatectomy from 2006 to 2015 were randomly divided into the training cohort and validation cohort. The external validation cohort was composed of 147 TNM I patients from TCGA database. Weakly supervised convolutional neural networks were used to identify six classes of HCC tissues. Pathological signatures were extracted and two novel risk scores were constructed by LASSO Cox to predict recurrence. The forecast performance of the scores and patients' prognosis were evaluated. The relationship between histological score (HS) and immune infiltrating cells was estimated by clustering analysis. Results The classification accuracy of HCC tissue was 94.17%. The C-indexes of histological score in the training, validation and TCGA cohorts were 0.804, 0.739 and 0.708, respectively. Multivariate analysis showed that microvascular invasion (HR = 1.46, 95% CI: 1.09-1.95) and HS (HR = 4.05, 95% CI: 3.40-4.84) were independent risk factors for recurrence-free survival. Patients in HS high-risk group had elevated alpha fetoprotein, worse tumor differentiation and higher proportion of microvascular invasion. HS was positively correlated with the expression of CD14 in adjacent normal liver tissue (P = 0.013), and negatively correlated with the expression of CD8 in tumor (P < 0.001). Conclusions This study established and validated two novel risk scores based on the histological slides using deep learning. HS performed well in recurrence prediction for early-stage HCC patients and indication of important clinicopathological features.

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Nomograms for Preoperative Prediction of Microvascular Invasion and Lymph Node Metastasis Risk in Hepatocellular Carcinoma

SSRN Electronic Journal ◽

10.2139/ssrn.3320121 ◽

2019 ◽

Author(s):

Yongcong Yan ◽

Qianlei Zhou ◽

Haohan Liu ◽

Kai Mao ◽

Chuanchao He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Microvascular Invasion ◽

Node Metastasis ◽

Preoperative Prediction

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Development and Validation of a Hematological Nomogram for Preoperative Prediction of Microvascular Invasion in Patients with Hepatocellular Carcinoma

SSRN Electronic Journal ◽

10.2139/ssrn.3361162 ◽

2019 ◽

Author(s):

Minjie Mao ◽

Yukun Song ◽

Zhonglian He ◽

Wenjuan Shi ◽

Yijun Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Microvascular Invasion ◽

Preoperative Prediction ◽

Development And Validation

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A Novel Hematological Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Patients with Hepatocellular Carcinoma

SSRN Electronic Journal ◽

10.2139/ssrn.3441818 ◽

2019 ◽

Author(s):

Minjie Mao ◽

Yukun Song ◽

Shuqi Chen ◽

Wenjuan Shi ◽

Yijun Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Microvascular Invasion ◽

Invasion Risk ◽

Preoperative Prediction

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Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

Acta Epileptologica ◽

10.1186/s42494-020-00035-9 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Shijun Yang ◽

Bin Wang ◽

Xiong Han

Keyword(s):

Antiepileptic Drugs ◽

Statistical Models ◽

Prediction Models ◽

External Validation ◽

Machine Learning Algorithms ◽

Treatment Withdrawal ◽

Patient Treatment ◽

Recent Developments ◽

Progression Of Disease ◽

Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

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The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

Acta Medica Philippina ◽

10.47895/amp.vi0.4195 ◽

2021 ◽

Author(s):

Patricia Pauline M. Remalante-Rayco ◽

Evelyn Osio-Salido

Keyword(s):

Systematic Review ◽

Emergency Department ◽

Early Warning ◽

Prediction Models ◽

External Validation ◽

Risk Of Bias ◽

Clinical Deterioration ◽

Early Warning Score ◽

Good Prediction ◽

Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

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Identification and validation of a potent multi-mRNA signature for the prediction of early relapse in hepatocellular carcinoma

Carcinogenesis ◽

10.1093/carcin/bgz018 ◽

2019 ◽

Vol 40 (7) ◽

pp. 840-852 ◽

Cited By ~ 3

Author(s):

Jie Cai ◽

Ying Tong ◽

Lifeng Huang ◽

Lei Xia ◽

Han Guo ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Messenger Rna ◽

Validation Cohort ◽

Expression Profiles ◽

External Validation ◽

Early Recurrence ◽

Discovery Cohort ◽

Early Relapse ◽

Staging Systems

Abstract Early recurrence of hepatocellular carcinoma (HCC) is implicated in poor patient survival and is the major obstacle to improving prognosis. The current staging systems are insufficient for accurate prediction of early recurrence, suggesting that additional indicators for early recurrence are needed. Here, by analyzing the gene expression profiles of 12 Gene Expression Omnibus data sets (n = 1533), we identified 257 differentially expressed genes between HCC and non-tumor tissues. Least absolute shrinkage and selection operator regression model was used to identify a 24-messenger RNA (mRNA)-based signature in discovery cohort GSE14520. With specific risk score formula, patients were divided into high- and low-risk groups. Recurrence-free survival within 2 years (early-RFS) was significantly different between these two groups in discovery cohort [hazard ratio (HR): 7.954, 95% confidence interval (CI): 4.596–13.767, P < 0.001], internal validation cohort (HR: 8.693, 95% CI: 4.029–18.754, P < 0.001) and external validation cohort (HR: 5.982, 95% CI: 3.414–10.480, P < 0.001). Multivariable and subgroup analyses revealed that the 24-mRNA-based classifier was an independent prognostic factor for predicting early relapse of patients with HCC. We further developed a nomogram integrating the 24-mRNA-based signature and clinicopathological risk factors to predict the early-RFS. The 24-mRNA-signature-integrated nomogram showed good discrimination (concordance index: 0.883, 95% CI: 0.836–0.929) and calibration. Decision curve analysis demonstrated that the 24-mRNA-signature-integrated nomogram was clinically useful. In conclusion, our 24-mRNA signature is a powerful tool for early-relapse prediction and will facilitate individual management of HCC patients.

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