Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

The Role of Lymphadenectomy in the Surgical Treatment of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Background: Lymphadenectomy in the operative management of hepatocellular carcinoma (HCC) remains controversial, with no recommendation for routine practice. Our study aimed to assess the effects of lymphadenectomy in addition to hepatic resection (HR) compared to HR alone for adults with HCC. Methods: This systematic review was conducted according to PRISMA guidelines until March 2023, searching and selecting the relevant literature comparing lymph node dissection or sampling, combined with HR, and with no lymph node removal. Critical appraisal of the included studies was performed using the ROBINS-I tool. Fixed- or random-effect meta-analysis models were carried out, and inter-studies were assessed for heterogeneity. Results: Fourteen studies were selected during the screening process. Data from eight studies containing 32,041 HCC patients were included in the quantitative synthesis. In total, 12,694 patients underwent lymph node dissection (LND), either selectively for preoperatively diagnosed or intraoperatively suspected lymph node metastasis (LNM) or unselectively (i.e., regardless of suspected LNM). According to LN status, 1-, 3- and 5-year mortality rates were higher in the LNM group with respect to both clinically negative LN (OR 3.25, 95% CI 2.52-4.21; p < 0.001; OR 3.79, 95% CI 2.74-5.24; p < 0.001; OR 3.92, 95% CI 2.61-5.88; p < 0.001) and proven LN0 (OR 1.75, 95% CI 1.0-3.04; p = 0.05; OR 2.88, 95% CI 1.79-4.63; p < 0.001; OR 2.54, 95% CI 1.33-4.84; p < 0.001). Moreover, the summary estimates of two controlled trials showed no significant difference in overall survival between LND groups and those without LND for negative LN patients. Conclusions: Lymph node dissection does not appear to improve overall survival, according to the available literature; thus, this does not support its routine adoption as part of standard liver resection for HCC. A case-by-case decision remains advisable.

Nomogram predicting survival in patients with lymph node-negative hepatocellular carcinoma based on the SEER database and external validation

BACKGROUND

The relationship between lymph node (LN) status and survival outcome in hepatocellular carcinoma (HCC) is a highly controversial topic. The aim of this study was to investigate the prognostic factors in patients without LN metastasis (LNM) and to construct a nomogram to predict cancer-specific survival (CSS) in this group of patients.

METHODS

We screened 6840 eligible HCC patients in the Surveillance, Epidemiology and End Results（SEER）database between 2010 and 2019 and randomized them into a training cohort and an internal validation cohort, and recruited 160 patients from Zhongnan Hospital of Wuhan University as an external validation cohort. Independent prognostic factors obtained from univariate and multivariate analysis were used to construct a nomogram prediction model. The concordance index (C-index), area under curve (AUC), calibration plots and decision curve analysis (DCA) were used to assess the predictive power and clinical application of the model.

RESULTS

Univariate and multivariate analysis revealed age, gender, bone metastasis, lung metastasis, AFP, T stage, surgery and chemotherapy as independent prognostic factors. The C-index of the constructed nomogram for the training cohort, internal validation cohort and external validation cohort are 0.746, 0.740, and 0.777, respectively. In the training cohort, the AUC at 1-, 3-, and 5-year were 0.81, 0.800, and 0.800, respectively. Calibration curves showed great agreement between the actual observations and predictions for the three cohorts. The DCA results suggest that the nomogram model has more clinical application potential.

CONCLUSION

We constructed a nomogram to predict CSS in HCC patients without LNM. The model has been internally and externally validated to have excellent predictive performance and can help clinicians determine prognosis and make treatment decisions.

Head-to-head comparison of 18F-FAPI and 18F-FDG PET/CT in staging and therapeutic management of hepatocellular carcinoma

BACKGROUND

Fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) has limitations in staging hepatocellular carcinoma (HCC). The recently introduced 18F-labeled fibroblast-activation protein inhibitor (FAPI) has shown promising prospects in detection of HCC lesions. This study aimed to investigate the initial staging and restaging performance of 18F-FAPI PET/CT compared to 18F-FDG PET/CT in HCC.

METHODS

This prospective study enrolled histologically confirmed HCC patients from March 2021 to September 2022. All patients were examined with 18F-FDG PET/CT and 18F-FAPI PET/CT within 1 week. The maximum standard uptake value (SUVmax), tumor-to-background ratio (TBR), and diagnostic accuracy were compared between the two modalities.

RESULTS

A total of 67 patients (57 men; median age, 57 [range, 32-83] years old) were included. 18F-FAPI PET showed higher SUVmax and TBR values than 18F-FDG PET in the intrahepatic lesions (SUVmax: 6.7 vs. 4.3, P < 0.0001; TBR: 3.9 vs. 1.7, P < 0.0001). In diagnostic performance, 18F-FAPI PET/CT had higher detection rate than 18F-FDG PET/CT in intrahepatic lesions [92.2% (238/258) vs 41.1% (106/258), P < 0.0001] and lymph node metastases [97.9% (126/129) vs 89.1% (115/129), P = 0.01], comparable in distant metastases [63.6% (42/66) vs 69.7% (46/66), P > 0.05]. 18F-FAPI PET/CT detected primary tumors in 16 patients with negative 18F-FDG, upgraded T-stages in 12 patients and identified 4 true positive findings for local recurrence than 18F-FDG PET, leading to planning therapy changes in 47.8% (32/67) of patients.

CONCLUSIONS

18F-FAPI PET/CT identified more primary lesions, lymph node metastases than 18F-FDG PET/CT in HCC, which is helpful to improve the clinical management of HCC patients.

TRIAL REGISTRATION

Clinical Trials, NCT05485792 . Registered 1 August 2022, Retrospectively registered.

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.

An easy-to-use artificial intelligence preoperative lymph node metastasis predictor (LN-MASTER) in rectal cancer based on a privacy-preserving computing platform: multicenter retrospective cohort study

BACKGROUND

Although the surgical treatment strategy for rectal cancer (RC) is usually based on the preoperative diagnosis of lymph node metastasis (LNM), the accurate diagnosis of LNM has been a clinical challenge. In this study, we developed machine learning (ML) models to predict the LNM status before surgery based on a privacy-preserving computing platform (PPCP) and created a web tool to help clinicians with treatment-based decision-making in RC patients.

PATIENTS AND METHODS

A total of 6578 RC patients were enrolled in this study. ML models, including logistic regression, support vector machine, extreme gradient boosting (XGB), and random forest, were used to establish the prediction models. The areas under the receiver operating characteristic curves (AUCs) were calculated to compare the accuracy of the ML models with the US guidelines and clinical diagnosis of LNM. Last, model establishment and validation were performed in the PPCP without the exchange of raw data among different institutions.

RESULTS

LNM was detected in 1006 (35.3%), 252 (35.3%), 581 (32.9%), and 342 (27.4%) RC patients in the training, test, and external validation sets 1 and 2, respectively. The XGB model identified the optimal model with an AUC of 0.84 [95% confidence interval (CI), 0.83-0.86] compared with the logistic regression model (AUC, 0.76; 95% CI, 0.74-0.78), random forest model (AUC, 0.82; 95% CI, 0.81-0.84), and support vector machine model (AUC, 0.79; 95% CI, 0.78-0.81). Furthermore, the XGB model showed higher accuracy than the predictive factors of the US guidelines and clinical diagnosis. The predictive XGB model was embedded in a web tool (named LN-MASTER) to predict the LNM status for RC.

CONCLUSION

The proposed easy-to-use model showed good performance for LNM prediction, and the web tool can help clinicians make treatment-based decisions for patients with RC. Furthermore, PPCP enables state-of-the-art model development despite the limited local data availability.

Diagnosis of Metastatic Lymph Nodes in Patients With Hepatocellular Carcinoma Using Dual-Energy Computed Tomography

OBJECTIVE

Our study aimed to investigate the role of quantitative parameters derived from dual-energy computed tomography (DECT) in discriminating metastatic from nonmetastatic lymph nodes in hepatocellular carcinoma (HCC).

METHODS

Forty-two patients (34 males; mean age, 53.7 years) with HCC underwent unenhanced computed tomography scans and triple-phase DECT scans of the upper abdomen. A total of 72 suspected lymph nodes were resected, including 43 nonmetastatic and 29 metastatic lymph nodes. The maximum short-axis diameter of the lymph nodes, iodine concentration, normalized iodine concentration (NIC), and slope of the spectral curve were analyzed for the HCC primary lesions and the suspected lymph nodes. Lymph node metastasis was confirmed by pathologic examination.

RESULTS

A maximum short-axis diameter of >10 mm had a sensitivity and a specificity of 75.9% (22/29) and 53.5% (23/43) in diagnosing metastatic lymph nodes. The iodine concentration, NIC, and slope of the spectral curve of the nonmetastatic lymph nodes were significantly higher than those of the primary HCC lesions and the metastatic lymph nodes (all P < 0.05). Among all the analyzed spectral parameters, the NIC in the arterial phase had the highest sensitivity and specificity of 88.4% and 86.2% in diagnosing metastatic lymph nodes.

CONCLUSIONS

The arterial phase NIC of DECT has superior diagnostic performance than the traditional lymph node size in diagnosing metastatic lymph nodes in HCC.

Risk factors and predictive nomograms for early death of patients with advanced hepatocellular carcinoma: a large retrospective study based on the SEER database

BACKGROUND

Hepatocellular carcinoma (HCC) is a kind of tumor with high invasiveness, and patients with advanced HCC have a higher risk of early death. The aim of the present study was to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms.

METHODS

Death that occurred within 3 months of initial diagnosis is defined as early death. Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and verification. Univariable and multivariable logistic regression analyses were used to identify the risk factors. Predictive nomograms were constructed and an internal validation was performed. Decision curve analysis (DCA) was used to verify the true clinical application value of the models.

RESULTS

Of 6603 patients (57% age > 60, 81% male, 70% white, 46% married), 21% and 79% had stage IVA and IVB, respectively. On the multivariable analyses, risk factors for early deaths in patients with stage IVA were age, tumor size, histological grade, alpha-fetoprotein (AFP), fibrosis score, tumor stage (T stage), surgery, radiotherapy, and chemotherapy, and that in stage IVB were age, histological grade, AFP, T stage, node stage (N stage), bone metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy. The areas under the curves (AUCs) were 0.830 (95% CI 0.809-0.851) and 0.789 (95% CI 0.768-0.810) in stage IVA and IVB, respectively. Nomograms comprising risk factors with the concordance indexes (C-indexes) were 0.820 (95% CI 0.799-0.841) in stage IVA and 0.785 (95% CI 0.764-0.0.806) in stage IVB for internal validation (Bootstrapping, 1000re-samplings). The calibration plots of the nomograms show that the predicted early death was consistent with the actual value. The results of the DCA analysis show that the nomograms had a good clinical application.

CONCLUSION

The nomograms can be beneficial for clinicians in identifying the risk factors for early death of patients with advanced HCC and predicting the probability of early death, so as to allow for individualized treatment plans to be accurately selected.

Epidemiological and Clinical Characteristics of Five Rare Pathological Subtypes of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor with several rare pathological subtypes and which is still poorly understood. This study aimed to describe the epidemiological and clinical spectrum of five rare HCC subtypes and develop a competing risk nomogram for cancer-specific survival prediction.

Methods

The study cohort was recruited from the Surveillance, Epidemiology, and End Results database. The clinicopathological data of 50,218 patients histologically diagnosed with classic HCC and five rare subtypes (ICD-O-3 Histology Code = 8170/3-8175/3) between 2004 and 2018 were reviewed. The annual percent change (APC) was calculated utilizing Joinpoint regression. The nomogram was developed based on multivariable competing risk survival analyses. Akaike information criterion, Bayesian information criterion, C-index, calibration curve, and area under the receiver operating characteristic curve were obtained to evaluate the prognostic performance. A decision curve analysis was introduced to examine the clinical value of the models.

Results

Despite scirrhous carcinoma, which showed a decreasing trend (APC = -6.8%, P = 0.025), the morbidity of other rare subtypes remained stable from 2004 to 2018. The incidence-based mortality was plateau in all subtypes during the period. Clear cell carcinoma is the most common subtype (n = 551, 1.1%), followed by subtypes of fibrolamellar (n = 241, 0.5%), scirrhous (n = 82, 0.2%), spindle cell (n = 61, 0.1%), and pleomorphic (n = 17, ~0%). The patients with fibrolamellar carcinoma were younger and more likely to have a non-cirrhotic liver and better prognoses. Scirrhous carcinoma shared almost the same macro-clinical characteristics and outcomes as the classic HCC. Clear cell carcinoma tended to occur in the Asia-Pacific elderly male population, and more than half of them were large HCC (Size&gt;5cm). Sarcomatoid (including spindle cell and pleomorphic) carcinoma was associated with a larger tumor size, poorer differentiation, and more dismal prognoses. The pathological subtype, T stage, M stage, surgery, alpha-fetoprotein, and cancer history were confirmed as the independent predictors in patients with rare subtypes. The nomogram showed good calibration, discrimination, and net benefits in clinical practice.

Conclusion

The rare subtypes had unique clinicopathological features and biological behaviors compared with the classic HCC. Our findings could provide a valuable reference for clinicians. The constructed nomogram could predict the prognoses with good performance, which is meaningful to individualized management.