A Novel Nomogram to Predict Prolonged Survival After Hepatectomy in Repeat Recurrent Hepatocellular Carcinoma

Nomograms for postsurgical extrahepatic recurrence prediction of hepatocellular carcinoma based on presurgical circulating tumor cell status and clinicopathological factors

BACKGROUND AND AIMS

Extrahepatic recurrence (EHR) is one of the major reasons for the poor prognosis of hepatocellular carcinoma (HCC). The present study aimed to develop and assess the performance of predictive models by using a combination of presurgical circulating tumor cell (CTCs) data and clinicopathological features to screen patients at high risk of EHR to achieve precise decision-making.

PATIENTS AND METHODS

A total of 227 patients with recurrent HCC and preoperative CTC data from January 2014 to August 2019 were enrolled. All patients were randomly assigned to one of two cohorts: development or validation. Two preoperative and postoperative nomogram models for EHR prediction were developed and multi-dimensionally validated.

RESULTS

Patients with EHR had generally lower recurrence-free survival (p < 0.001), and overall survival (p < 0.001), and significantly higher CTC counts (epithelial CTCs, epithelial/mesenchymal hybrid CTCs, and mesenchymal CTCs count, all p < 0.05) than those without EHR. Univariate and multivariate analyses revealed that EHR was associated with four risk factors in the development cohort: total CTC count (p = 0.014), tumor size (p = 0.028), node number (p = 0.045), and microvascular invasion (p = 0.035). These factors were incorporated into two nomogram models (preoperative and postoperative), which reliably predicted EHR through multidimensional verification (e.g., calibration plot, receiver operating characteristic analysis, decision curve analysis, and clinical impact curve analysis) in the development and validation cohorts, respectively. With threshold of scores of 100.3 and 176.8 before and after surgery respectively, both nomograms were able to stratify patients into two distinct prognostic subgroups (all p < 0.05).

CONCLUSION

The present study proposed two nomogram models integrating presurgical CTC counts and clinicopathological risks and showed relatively good predictive performance of EHR, which may be beneficial to the clinical practice of HCC recurrence. Further multicenter studies are needed to assess its general applicability.

A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer

Background

Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.

Method

We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.

Result

A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.

Conclusion

We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02544-y.

Clinical implications and biological features of a novel postoperative recurrent HCC classification: A multi-centre study

BACKGROUND & AIMS

The multiplicity of hepatocellular carcinoma (HCC) recurrence patterns is the most important determinant of patients' postsurgical survival. A systematic HCC recurrence classification is needed to help prevent and treat postoperative HCC recurrence in the era of precision medicine.

METHODS

A total of 1319 patients with recurrent HCC from four hospitals were enrolled and divided into a development cohort (n = 916), internal validation cohort (n = 225) and external validation cohort (n = 178). A comprehensive study of patients' clinicopathological factors and biological features was conducted.

RESULTS

Four subtypes of recurrence were identified, which integrated recurrence features, survival, effects on systemic and liver function and potential therapeutics after recurrence: type I (solitary-intrahepatic oligorecurrence); type II (multi-intrahepatic oligorecurrence); type III (progression recurrence) and type IV (hyper-progression recurrence). Type III~IV recurrence indicated exceptionally poor prognosis. Subsequently, two nomogram models were established for type III~IV recurrence prediction, and both demonstrated excellent predictive performance and applicability of pre and postoperative strategy formulation. Multiple biological analyses revealed that HCC cases with type III~IV recurrence were characterized by enrichment in p53 mutations, CCND1 amplification, high proliferation/metastasis potential, inactive metabolism and immune exhaustion features. Over-expression of high mobility group protein 2 (HMGA2) enhanced the highly malignant behaviour of HCC through multiple molecular pathways, making it a potential prognostic predictor and therapeutic target.

CONCLUSIONS

This 'recurrent HCC classification' has important potential value in identifying patients with surgical benefit, predicting postsurgical survival and guiding treatment strategies. Multidimensional biological insights also increased knowledge of factors associated with HCC recurrence.

A Nomogram Model to Predict Post-Progression Survival in Esophageal Squamous Cell Carcinoma Patients With Recurrence After Radical Resection

Background

Esophageal squamous cell carcinoma (ESCC) is characterized clinically by frequent recurrence, leading to a poor prognosis after radical surgery. The aim of this study was to identify a prognostic nomogram to predict the post-progression survival (PPS) of ESCC patients based on the features of primary tumor and recurrence.

Methods

A total of 234 ESCC patients who underwent recurrence after radical surgery were enrolled in this study. The independent prognostic factors screened by the univariate and multivariate Cox regression analysis were subsequently used to construct a nomogram. The predictive performance of the nomogram was evaluated with the concordance index (C-index), decision curve, and the area under the receiver operating characteristic curve (AUC) and validated in two validation cohorts. The Kaplan-Meier curves of different recurrence patterns were analyzed.

Results

The prognostic nomogram of PPS was established by integrating independent prognostic factors, including age, body mass index, number of lymph node dissection, recurrence pattern, and recurrence treatment. The nomogram demonstrated good performance, with C-index values of 0.756, 0.817, and 0.730 for the training and two validation cohorts. The 1-year AUC values were 0.773, 0.798, and 0.735 and 3-year AUC values were 0.832, 0.871, and 0.791, respectively. Furthermore, we found that patients with bone metastasis displayed the worst PPS compared to other isolated recurrence patterns.

Conclusion

We constructed a nomogram to reliably predict PPS, which would be valuable to provide individual managements for ESCC patients after radical surgery.