Predictive factors for survival following stereotactic body radiotherapy for hepatocellular carcinoma with portal vein tumour thrombosis and construction of a nomogram

A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures

Aims

This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures.

Methods

504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses. Based on these parameters, a nomogram model was constructed and validated. The predicting performance of nomogram was evaluated by discrimination, calibration, and clinical usefulness.

Results

The predictors contained in the nomogram model included age, surgical approach, 1-day postoperative D-dimer value and admission ultrasound diagnosis of the lower limb vein. Furthermore, the area under the ROC curve (AUC) for the specific DVT risk-stratification nomogram model (0.815; 95% CI 0.746-0.884) was significantly higher than the current model (Caprini) (0.659; 95% CI 0.572-0.746, P < 0.05). According to the calibration plots, the prediction and actual observation were in good agreement. In the range of threshold probabilities of 0.2-0.8, the predictive performance of the model on DVT risk could be maximized.

Conclusions

The current predictive model could serve as a reliable tool to quantify the possibility of postoperative DVT in hip fractures patients.

Prognostic Value of a CT Radiomics-Based Nomogram for the Overall Survival of Patients with Nonmetastatic BCLC Stage C Hepatocellular Carcinoma after Stereotactic Body Radiotherapy

Purpose

This study aimed to investigatie the feasibility of pretherapeutic CT radiomics-based nomograms to predict the overall survival (OS) of patients with nondistant metastatic Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) undergoing stereotactic body radiotherapy (SBRT).

Methods

A retrospective review of 137 patients with nondistant metastatic BCLC-C HCC who underwent SBRT was made. Radiomics features distilled from pretherapeutic CT images were selected by the method of LASSO regression for radiomics signature construction. Then, the clinical model was constructed based on clinical characteristics. A radiomics nomogram was constructed using the radiomics score (Rad-score) and clinical characteristics to predict post-SBRT OS in BCLC-C HCC patients. An analysis of discriminatory ability and calibration was performed to confirm the efficacy of the radiomics nomogram.

Results

In order to construct the radiomic signature, seven significant features were selected. Patients were divided into low-risk (Rad-score < -0.03) and high-risk (Rad-score ≥ -0.03) groups based on the best Rad-score cutoff value. There were statistically significant differences in OS both in the training set (p < 0.0001) and the validation set (p=0.03) after stratification. The C-indexes of the radiomics nomogram were 0.77 (95% CI: 0.72-0.82) in the training set and 0.71 (95% CI: 0.61-0.81) in the validation set, which outperformed the clinical model and radiomics signature. An AUC of 0.76, 0.79, and 0.84 was reached for 6-, 12-, and 18-month survival predictions, respectively.

Conclusions

The predictive nomogram that combines radiomic features with clinical characteristics has great prospects for application in the prediction of post-SBRT OS in nondistant metastatic BCLC-C HCC patients.

A web-based nomogram model for predicting the overall survival of hepatocellular carcinoma patients with external beam radiation therapy: A population study based on SEER database and a Chinese cohort

BACKGROUND

External beam radiation therapy (EBRT) for hepatocellular carcinoma (HCC) is rarely used in clinical practice. This study aims to develop and validate a prognostic nomogram model to predict overall survival (OS) in HCC patients treated with EBRT.

METHOD

We extracted eligible data of HCC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Those patients were randomly divided into a training cohort (n=1004) and an internal validation cohort (n=429), and an external validation cohort composed of a Chinese cohort (n=95). A nomogram was established based on the independent prognostic variables identified from univariate and multivariate Cox regression analyses. The effective performance of the nomogram was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA).

RESULTS

T stage, N stage, M stage, AFP, tumor size, surgery, and chemotherapy were independent prognostic risk factors that were all included in the nomogram to predict OS in HCC patients with EBRT. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the prediction model was 0.728 (95% confidence interval (CI): 0.716-0.740), 0.725 (95% CI:0.701-0.750), and 0.696 (95% CI:0.629-0.763), respectively. The 6-, 12-,18- and 24- month areas under the curves (AUC) of ROC in the training cohort were 0.835 、0.823 、0.810, and 0.801, respectively; and 0.821 、0.809 、0.813 and 0.804 in the internal validation cohort, respectively; and 0.749 、0.754 、0.791 and 0.798 in the external validation cohort, respectively. The calibration curves indicated that the predicted value of the prediction model performed well. The DCA curves showed better clinical practicability. In addition, based on the nomogram, we established a web-based nomogram to predict the OS of these patients visually.

CONCLUSION

Based on the SEER database and an independent external cohort from China, we established and validated a nomogram to predict OS in HCC patients treated with EBRT. In addition, for the first time, a web-based nomogram model can help clinicians judge the prognoses of these patients and make better clinical decisions.

The α-RECIST (RECIST 1.1 Combined With Alpha Fetoprotein): A Novel Tool for Identifying Tumor Response of Conversion-Radiotherapy for Unresectable Hepatocellular Carcinoma Before Hepatectomy

Purpose

To develop a novel criterion based on the response evaluation criteria in solid tumors (RECIST) 1.1 and alpha fetoprotein (AFP) and evaluate its performance in tumor response for patients with unresectable hepatocellular carcinoma (uHCC) receiving conversion-radiotherapy before hepatectomy.

Method

From June 2012 to December 2020, a total of 39 patients with uHCC, who received intensity-modulated radiotherapy (IMRT) before hepatectomy, were retrospectively included in this study. Pre- and post-treatment contrast-enhanced magnetic resonance imaging (CE-MRI) scans were performed in all patients. Eight modified criteria were developed with the combination of RECIST 1.1, modified RECIST (mRECIST), and the percentage change of AFP, baseline AFP. The endpoint events were recurrence-free survival (RFS).

Results

The median RFS and OS was 26.5 (IQR, 15.7-43.1), 38.8 (IQR, 18.4-53.6) months. An optimal revised evaluation criterion named α-RECIST (alpha fetoprotein-RECIST 1.1) was developed by combining the RECIST 1.1 with the AFPΔ (cut-off value, 76%). Patients defined as responders by α-RECIST showed significantly better RFS and OS than those defined as non-responders (p = 0.035, 0.048). The other criteria (RECIST 1.1, mRECIST, αΔ-mRECIST, α&Δ-RECIST, α&Δ-mRECIST, αBL-RECIST, αBL-mRECIST, α&BL-RECIST, α&BL-mRECIST) all failed to identify responders from non-responders (p = 0.405, 0.201, 0.773, 0.424, 0.266, 0.060, 0.721, 0.644, 0.910, respectively) when correlated with RFS. Responders according to α-RECIST showed significant better RFS compared to non-responders [HR, 0.31 (95% CI: 0.10, 0.98); p=0.046], but no statistical significance was observed in terms of OS [HR, 0.33 (95% CI: 0.11, 1.05); p = 0.06].

Conclusions

Patients identified as responders by α-RECIST provided significant better RFS. The α-RECIST criteria might be a promising tool for identifying tumor response of conversion-radiotherapy for unresectable hepatocellular carcinoma before hepatectomy.