Nomograms to predict survival outcomes after microwave ablation in elderly patients (&gt;65 years old) with early-stage hepatocellular carcinoma

Huang, Zijing; Gu, Yeunhwa; Zhang, Tianqi; Wu, Shaoyong; Wang, Xiuchen; An, Chao; Huang, Jinhua

doi:10.1080/02656736.2020.1785556

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…Previous studies revealed that younger patients had longer CSS ( 20 , 21 ), which is similar to our study. Our study found that whites had longer CSS than blacks while other races (Asian/Pacific Islander, American Indian/Alaskan Native) had longer CSS than blacks and whites, which was consistent with two previous studies ( 21 , 22 ), but different from others ( 23 , 24 ). Although married patients with HCC were reported to have better survival ( 13 , 20 , 25 ), our study found that no significant difference existed in CSS between patients with different marital statuses.…”

Section: Discussionsupporting

confidence: 89%

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

Chen

Liu

et al. 2022

Front. Public Health

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Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

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Section: Discussionsupporting

confidence: 89%

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

Chen

Liu

et al. 2022

Front. Public Health

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“…The radiomics model presented here was based on preoperative MRI examination, which could be more objective and highly reproducible. Huang Z and his colleagues (26) found that nomogram models can be useful in determining the risk of recurrence-free survival with a C-index of 0.733 (95%CI: 0.672, 0.774). Zhang X et al (27) constructed a CT radiomics-based models to predict microvascular infiltration (MVI) status and MVI risk of HCC.…”

Section: Discussionmentioning

confidence: 99%

A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation

Weng

Bai

et al. 2021

Front. Oncol.

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BackgroundPatients with small hepatocellular carcinoma (HCC) (≤3 cm) still have a poor prognosis. The purpose of this study was to develop a radiomics nomogram to preoperatively predict early recurrence (ER) (≤2 years) of small HCC.MethodsThe study population included 111 patients with small HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and September 2018 and were followed for at least 2 years. Radiomic features were extracted from the entire tumor by using the MaZda software. The least absolute shrinkage and selection operator (LASS0) method was applied for feature selection, and radiomics signature construction. A rad-score was then calculated. Multivariable logistic regression analysis was used to establish a prediction model including independent clinical risk factors, radiologic features and rad-score, which was ultimately presented as a radiomics nomogram. The predictive ability of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve and internal validation was performed via bootstrap resampling and 5-fold cross-validation method.ResultsA total of 53 (53/111, 47.7%) patients had confirmed ER according to the final clinical outcomes. In univariate logistic regression analysis, cirrhosis and hepatitis B infection (P=0.015 and 0.083, respectively), hepatobiliary phase hypointensity (P=0.089), Child-Pugh score (P=0.083), the preoperative platelet count (P=0.003), and rad-score (P<0.001) were correlated with ER. However, after multivariate logistic regression analysis, only the preoperative platelet count and rad-score were included as predictors in the final model. The area under ROC curve (AUC) of the radiomics nomogram to predict ER of small HCC was 0.981 (95% CI: 0.957, 1.00), while the AUC verified by bootstrap is 0.980 (95% CI: 0.962, 1.00), indicating the goodness-of-fit of the final model.ConclusionsThe radiomics nomogram containing the clinical risk factors and rad-score can be used as a quantitative tool to preoperatively predict individual probability of ER of small HCC.

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“…A nomogram has been considered to be evidence-based, individualized and accurate in risk estimation, which has been applied to various malignancies (29)(30)(31)(32). It has also been used to predict the clinical outcomes of patients with hepatocellular carcinoma who underwent thermal ablation (33)(34)(35)(36). However, to our best knowledge, a prognostic nomogram for benign thyroid nodules following RFA has not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

A Nomogram to Predict Regrowth After Ultrasound-Guided Radiofrequency Ablation for Benign Thyroid Nodules

Yan

Zhang

et al. 2022

Front. Endocrinol.

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ObjectiveTo develop and validate a nomogram to predict regrowth for patients with benign thyroid nodules undergoing radiofrequency ablation (RFA).MethodsA total of 200 patients with 220 benign thyroid nodules who underwent RFA were included in this respective study. After RFA, patients were followed up at 1, 3, 6, and 12 months, and every 12 months thereafter. Regrowth was defined as an increase in nodule volume 50% over the previously recorded smallest volume. A nomogram was developed based on the variables identified by multivariate logistic regression and the model performance was evaluated by discrimination(concordance index) and calibration curves.ResultsThe incidence of regrowth was 13.64% (30/220) after a mean follow-up period of 27.43 ± 17.99 months. Multivariate logistic regression revealed initial volume (OR = 1.047, 95%CI 1.020–1.075), vascularity (OR = 2.037, 95%CI 1.218–3.404), and location close to critical structure (OR = 4.713, 95%CI 1.817–12.223) were independent factors associated with regrowth. The prognostic nomogram incorporating these three factors achieved good calibration and discriminatory abilities with a concordance index of 0.779 (95%CI 0.686–0.872).ConclusionsA prognostic nomogram was successfully developed to predict nodule regrowth after RFA, which might guide physician in stratifying patients and provide precise guidance for individualized treatment protocols.

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Nomograms to predict survival outcomes after microwave ablation in elderly patients (>65 years old) with early-stage hepatocellular carcinoma

Abstract: View related articlesView Crossmark data Citing articles: 1 View citing articlesNomograms to predict survival outcomes after microwave ablation in elderly patients (>65 years old) with early-stage hepatocellular carcinoma

Cited by 5 publications

References 29 publications

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation

A Nomogram to Predict Regrowth After Ultrasound-Guided Radiofrequency Ablation for Benign Thyroid Nodules

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