Development and validation of a nomogram combining hematological and imaging features for preoperative prediction of microvascular invasion in hepatocellular carcinoma patients

A novel nomogram for predicting microvascular invasion in hepatocellular carcinoma

INTRODUCTION AND OBJECTIVES

In hepatocellular carcinoma (HCC), the prognosis of patients with microvascular invasion (MVI) is poor. Therefore, in this study, we established and evaluated the performance of a novel nomogram to predict MVI in patients with HCC.

MATERIALS AND METHODS

We retrospectively obtained clinical data of 497 patients with HCC who underwent hepatectomy at Liaoning Cancer Hospital from November 1, 2018, to November 4, 2021. The patients (n = 497) were randomized in a 7:3 ratio into the training cohort (TC, n = 349) and the validation cohort (VC, n = 148). We performed Least Absolute Shrinkage and Selection Operator (LASSO) and univariate as well as multivariate logistic regression analyses (ULRA, MRLA) on patients in the TC to identify factors independently predicting MVI.

RESULTS

Preoperative FIB-4, AFU, AFP levels, liver cirrhosis, and non-smooth tumor margin were independent risk factors for preoperative MVI prediction. The C-index of the TC, VC, and the entire cohort was 0.846, 0.786, and 0.829, respectively. The calibration curves demonstrated the outstanding agreement between predicted MVI incidences by our model and the actual MVI risk. Decision curve analysis (DCA) confirmed the significance of our predictive model in clinical settings. The Kaplan-Meier (KM) survival curve showed that the recurrence-free survival (RFS) and overall survival (OS) of patients in the high-MVI risk group were poor compared to those in the low-MVI risk group.

CONCLUSIONS

We constructed and evaluated the performance of the novel nomogram for predicting MVI risk. Our predictive model could adequately predict MVI risk and aid clinicians in selecting appropriate therapeutic strategies for patients.

A Reliable and Repeatable Model for Predicting Microvascular Invasion in Patients With Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

The reproducibility of imaging models for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains questionable due to inconsistent interpretation of image signs. Our aim was to screen for high-consensus MRI features to develop a repeatable model for predicting MVI.

MATERIALS AND METHODS

We included 219 patients with HCC who underwent surgical resection, and patients were divided into a training cohort (n = 145) and a validation cohort (n = 74). Morphological characteristics, signal features on hepatobiliary phases, and dynamic enhancement patterns were qualitatively interobserver evaluated. Interobserver agreement was assessed using Cohen's κ for selecting features with high interobserver agreement. Risk factors that were significant in stepwise multivariate analysis and that could be measured with good interobserver agreement were used to construct a predictive model, which was assessed in the validation cohort. The diagnostic performance of the model was evaluated based on area under the receiver operating characteristic curve (AUC).

RESULTS

Multivariate analysis identified nonsmooth tumor margin, absence of radiologic capsule, and intratumoral artery as independent risk factors of MVI. These MRI-based features showed good or nearly perfect interobserver agreement between radiologists (κ > 0.6). The predictive model predicted MVI well in the training (AUC 0.734) and validation cohorts (AUC 0.759) and fitted well to calibration curves.

CONCLUSION

MRI features included nonsmooth tumor margin, absence of radiologic capsule, and intratumoral artery that can be assessed with high interobserver agreement can predict MVI in HCC patients. The predictive model described here may be useful to radiologists, regardless of experience level.

Contrast-enhanced CT findings-based model to predict MVI in patients with hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is important in early recurrence and leads to poor overall survival (OS) in hepatocellular carcinoma (HCC). A number of studies have reported independent risk factors for MVI. In this retrospective study, we designed to develop a preoperative model for predicting the presence of MVI in HCC patients to help surgeons in their surgical decision-making and improve patient management.

PATIENTS AND METHODS

We developed a predictive model based on a nomogram in a training cohort of 225 HCC patients. We analyzed patients' clinical information, laboratory examinations, and imaging features from contrast-enhanced CT. Mann-Whitney U test and multiple logistic regression analysis were used to confirm independent risk factors and develop the predictive model. Internal and external validation was performed on 75 and 77 HCC patients, respectively. Moreover, the diagnostic performance of our model was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

In the training cohort, maximum tumor diameter (> 50 mm), tumor margin, direct bilirubin (> 2.7 µmol/L), and AFP (> 360.7 ng/mL) were confirmed as independent risk factors for MVI. In the internal and external validation cohort, the developed nomogram model demonstrated good diagnostic ability for MVI with an area under the curve (AUC) of 0.723 and 0.829, respectively.

CONCLUSION

Based on routine clinical examinations, which may be helpful for clinical decision-making, we have developed a nomogram model that can successfully assess the risk of MVI in HCC patients preoperatively. When predicting HCC patients with a high risk of MVI, the surgeons may perform an anatomical or wide-margin hepatectomy on the patient.

Perioperative factors related to the prognosis of elderly patients with hepatocellular carcinoma

BACKGROUND

Hepatic resection is a potentially curative treatment for patients with hepatocellular carcinoma (HCC). The treatment of elderly patients with HCC has always been difficult. With the development of geriatrics and geriatric surgery, the number of elderly patients with HCC undergoing hepatectomy has gradually increased. To further improve the long-term prognosis of elderly patients with hepatocellular carcinoma undergoing surgery, it is necessary to study the related perioperative factors. Our aim was to study the impact of preoperative and intraoperative factors on the long-term survival of elderly patients with HCC who underwent hepatectomy.

METHODS

A total of 151 elderly patients with HCC who underwent hepatectomy were retrospectively studied. Univariate and multivariate Cox regression analyses were performed for preoperative- and intraoperative-related prognostic factors.

RESULTS

The 1-, 3-, 5- and 10-year overall survival rates of elderly patients with HCC who underwent resection were 79.5%, 60.8%, 46.6%, and 25.4%, respectively. Multivariate analyses identified four independent predictors of long-term prognosis: Child-Pugh grade (B/C versus A: HR[hazard ratio] = 2.318, P = 0.019), alpha-fetoprotein value (> 20 ng/ml versus ≤ 20 ng/ml: HR = 1.972, P = 0.005), resection style (anatomical versus no anatomical: HR = 1.976, P = 0.006), and intraoperative blood loss (> 400 ml versus ≤ 400 ml: HR = 2.008, P = 0.003).

CONCLUSIONS

Poor survival of elderly patients with HCC who underwent hepatectomy was correlated with the preoperative and intraoperative factors of Child-Pugh grade, Alpha-fetoprotein value, resection style, and intraoperative blood loss.

Construction and validation of a biochemical signature to predict the prognosis and the benefit of induction chemotherapy in patients with nasopharyngeal carcinoma

This study aimed to develop and validate a biochemical signature for predicting the prognosis of patients with nasopharyngeal carcinoma (NPC) and explore roles of the constructed signature for screening optimal candidates for induction chemotherapy (IC). The biochemical signature was constructed based on a retrospective cohort of 3742 patients from January 2008 to December 2010; 2078 patients from prospective studies from January 2011 to December 2012 and 2153 patients from January 2013 to December 2016 served as validation cohort A and validation cohort B. Overall survival (OS) was the primary endpoint. The least absolute shrinkage and selection operator coefficients on the Cox regression model were calculated to construct the prediction model with the data of 33 biochemical indicators. A total of six prognostic indicators, including sodium, alkaline phosphatase, lactate dehydrogenase, albumin, indirect bilirubin, and cystatin-C, were screened for constructing the biochemical signature. The patients were divided into low-risk and high-risk groups using an optimal cut-off value of 0.823. The patients in high-risk group had significantly lower OS and distant metastasis-free survival (DMFS) compared with patients in low-risk group in three cohorts (P < 0.05). Furthermore, among patients with high-risk scores in the combined cohort, the addition of IC to CCRT further improved their OS and DMFS, whereas patients with low-risk scores did not benefit from IC. Our study developed and validated a clinically useful biochemical signature that could predict the survival outcomes in NPC patients. This signature can help clinicians design personalized treatment strategies.

A Non-Invasive Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Hepatocellular Carcinoma

Background

Microvascular invasion (MVI) is a significant predictive factor for early recurrence, metastasis, and poor prognosis of hepatocellular carcinoma. The aim of the present study is to identify preoperative factors for predicting MVI, in addition to develop and validate non-invasive nomogram for predicting MVI.

Methods

A total of 381 patients with resected HCC were enrolled and divided into a training cohort (n = 267) and a validation cohort (n = 114). Serum VEGF-A level was examined by enzyme-linked immunosorbent assay (ELISA). Risk factors for MVI were assessed based on univariate and multivariate analyses in the training cohort. A nomogram incorporating independent risk predictors was established and validated.

Result

The serum VEGF-A levels in the MVI positive group (n = 198) and MVI negative group (n = 183) were 215.25 ± 105.68 pg/ml and 86.52 ± 62.45 pg/ml, respectively (P <0.05). Serum VEGF-A concentration ≥138.30 pg/ml was an independent risk factor of MVI (OR: 33.088; 95%CI: 12.871–85.057; P <0.001). Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery were identified as significant predictors for MVI. The nomogram indicated excellent predictive performance with an AUROC of 0.948 (95% CI: 0.923–0.973) and 0.881 (95% CI: 0.820–0.942) in the training and validation cohorts, respectively. The nomogram showed a good model fit and calibration.

Conclusions

Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery are promising markers for MVI prediction in HCC. A reliable non-invasive nomogram which incorporated blood biomarkers and imaging risk factors was established and validated. The nomogram achieved desirable effectiveness in preoperatively predicting MVI in HCC patients.