Novel Models Predict Postsurgical Recurrence and Overall Survival for Patients with Hepatitis B Virus-Related Solitary Hepatocellular Carcinoma ≤10 cm and Without Portal Venous Tumor Thrombus

Development and validation of nomograms for predicting prognosis in patients with solitary HCC: A TRIPOD-Compliant study

Objective

To develop and validate nomograms for predicting the OS and CSS of patients with Solitary Hepatocellular Carcinoma (HCC).

Methods

Using the TRIPOD guidelines, this study identified 5206 patients in the Surveillance, Epidemiology, and End Results (SEER) 17 registry database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 3646) and a validation cohort (n = 1560), and the Chinese independent cohort (n = 307) constituted the external validation group. The prognosis-related risk factors were selected using univariate Cox regression analysis, and the independent prognostic factors of OS and CSS were identified using the Lasso-Cox regression model. The nomograms for predicting the OS and CSS of the patients were constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve in both the training and validation cohorts.

Results

We identified factors that predict OS and CSS and constructed two nomograms based on the data. The ROC analysis, C-index analysis, and calibration analysis indicated that the two nomograms performed well over the 1, 3, and 5-year OS and CSS periods in both the training and validation cohorts. Additionally, these results were confirmed in the external validation group. Decision curve analysis (DCA) demonstrated that the two nomograms were clinically valuable and superior to the TNM stage system.

Conclusion

We established and validated nomograms to predict 1,3, and 5-year OS and CSS in solitary HCC patients, and our results may also be helpful for clinical decision-making.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND

Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.

AIM

To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.

METHODS

The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice.

RESULTS

Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.

CONCLUSION

The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.

Advances in postoperative adjuvant therapy for primary liver cancer

Hepatocellular carcinoma (HCC) is a highly heterogeneous, invasive, and conventional chemotherapy-insensitive tumor with unique biological characteristics. The main methods for the radical treatment of HCC are surgical resection or liver transplantation. However, recurrence rates are as high as 50% and 70% at 3 and 5 years after liver resection, respectively, and even in Milan-eligible recipients, the recurrence rate is approximately 20% at 5 years after liver transplantation. Therefore, reducing the postoperative recurrence rate is key to improving the overall outcome of liver cancer. This review discusses the risk factors for recurrence in patients with HCC radical surgical resection and adjuvant treatment options that may reduce the risk of recurrence and improve overall survival, including local adjuvant therapy (e.g., transcatheter arterial chemoembolization), adjuvant systemic therapy (e.g., molecular targeted agents and immunotherapy), and other adjuvant therapies (e.g., antiviral and herbal therapy). Finally, potential research directions that may change the paradigm of adjuvant therapy for HCC are analyzed.

Preoperative neutrophil-to-lymphocyte ratio (preNLR) for the assessment of tumor characteristics in lung adenocarcinoma patients with brain metastasis

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A relationship between preoperative Neutrophil-to-Lymphocyte ratio (preNLR) and brain metastasis characteristics such as tumor location and peritumoral brain edema is proposed.

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The corresponding spearman correlations of peritumoral brain edema and preoperative NLR between different tumor location was performed.

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A prognostic nomogram, that provide survival predictions for brain metastasis on lung adenocarcinoma (LUAD) patients has been established.

Objectives

Brain metastases from lung adenocarcinoma cause significant patient mortality. This study aims to evaluate the role of preoperative Neutrophil-to-Lymphocyte ratio (preNLR) in predicting the survival and prognosis of Lung adenocarcinoma (LUAD) patients with brain metastasis (BM) and provide more references for predicting peritumoral edema.

Methods

We retrospectively reviewed 125 LUAD-BM patients who had undergone surgical resection from December 2015 to December 2020. The clinical characteristic, demographic, MRI data, and preNLR within 24–48 h before craniotomy were collected. Patients were divided into two groups based on preNLR (high NLR and low NLR), with cutoff values determined by receiver operating characteristic (ROC) analysis. Association between preoperative NLR and clinical features was determined by using Pearson chi-squared tests. Uni- and multivariate analyzes were performed to compare the overall survival (OS) of clinical features.

Results

The patients were divided into NLR-low (64 patients) and NLR-high (61 patients) groups based on receiver operating characteristic analysis of NLR area. According to correlation analysis, a high preNLR (NLR≥2.8) is associated with the both supra- and infratentorial location involved (P = 0.017) and a greater incidence of severe peritumoral edema (P = 0.038). By multivariable analysis, age ≥ 65 years (P = 0.011), KPS < 70 (P = 0.043), elevated preNLR (P = 0.013), extracerebral metastases (P = 0.003), EGFR/ALK+ (P = 0.037), postoperative radiotherapy (P = 0.017) and targeted therapy (P = 0.007) were independent prognostic factors. OS nomogram was constructed based on cox model and model performance was examined (AUC = 0.935).

Conclusions

PreNLR may serve as a prognosis indicator in LUAD patients with brain metastasis, and high preNLR tends to be positively associate with multiple locations and severe peritumoral edema.

A New Prognostic Algorithm Predicting HCC Recurrence in Patients With Barcelona Clinic Liver Cancer Stage B Who Received PA-TACE

Background

Postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) is effective in preventing the recurrence of hepatocellular carcinoma (HCC) in patients treated with surgery. However, there is a lack of reports studying the risk factors associated with recurrence in HCC patients who received PA-TACE. In this study, we identified the independent risk factors for recurrence of HCC patients who received PA-TACE. We also developed a novel, effective, and valid nomogram to predict the individual probability of recurrence, 1, 3, and 5 years after PA-TACE.

Methods

A retrospective study was performed to identify the independent risk factors for recurrence of HCC in a group of 502 patients diagnosed in stage B based on the Barcelona Clinic Liver Cancer (BCLC) evaluation system for HCC that underwent curative resections. Then, subgroup analysis was performed for 184 patients who received PA-TACE, who were included in the training cohort. The other 147 HCC patients were included in a validation cohort. A recurrence-free survival (RFS)-predicting nomogram was constructed, and results were assessed using calibration and decision curves and a time-dependent AUC diagram.

Results

PA-TACE was shown to be a significant independent prognostic value for patients with BCLC stage B [p < 0.001, hazard ratio (HR) = 0.508, 95% CI = 0.375–0.689 for OS, p = 0.002; HR = 0.670, 95%CI = 0.517–0.868 for RFS]. Alpha fetoprotein (AFP), tumor number, tumor size, microvascular invasion (MVI), and differentiation were considered as independent risk factors for RFS in the training cohort, and these were further confirmed in the validation cohort. Next, a nomogram was constructed to predict RFS. The C-index for RFS in the nomogram was 0.721 (95% CI = 0.718–0.724), which was higher than SNACOR, HAP, and CHIP scores (0.587, 0.573, and 0.607, respectively). Calibration and decision curve analyses and a time-dependent AUC diagram were used. Our nomogram showed stronger performance than these other nomograms in both the training and validation cohorts.

Conclusions

HCC patients diagnosed as stage B according to BCLC may benefit from PA-TACE after surgery. The RFS nomogram presented here provides an accurate and reliable prognostic model to monitor recurrence. Patients with a high recurrence score based on the nomogram should receive additional high-end imaging exams and shorter timeframes in between follow-up visits.

Multiple-level copy number variations in cell-free DNA for prognostic prediction of HCC with radical treatments

Copy number variations (CNVs) in cell-free DNA (cfDNA) are emerging as noninvasive biomarkers for various cancers. However, multiple-level analysis of cfDNA CNVs for hepatocellular carcinoma (HCC) patients with radical treatments remains uninvestigated. Here, CNVs at genome-wide, chromosomal-arm, and bin levels were analyzed in cfDNA from 117 HCC patients receiving radical treatments. Then, the relationship between cfDNA CNVs and clinical outcomes was explored. Our results showed that a concordant profile of CNVs was observed between cfDNA and tumor tissue DNA. Three genome-wide CNV indicators including tumor fraction (TFx), prediction score (P-score), and stability score (S-score) were calculated and demonstrated to exhibit significant correlation with poorer overall survival (OS) and recurrence-free survival (RFS). Furthermore, the high-frequency cfDNA CNVs at chromosomal-arm level including the loss of 4q, 17p, and 19p and the gain of 8q and 1q clearly predicted HCC prognosis. Finally, a bin-level risk score was constructed to improve the ability of CNVs in predicting prognosis. Altogether, our study indicates that the multiple-level cfDNA CNVs are significantly associated with OS and RFS in HCC patients with radical treatments, suggesting that cfDNA CNVs detected by low-coverage whole-genome sequencing (WGS) may be used as potential prognostic biomarkers of HCC patients.

Development and validation of prognostic nomograms for single large and huge hepatocellular carcinoma after curative resection

AIM

The prediction model of postoperative survival for single large and huge hepatocellular carcinoma (SLH-HCC, diameter > 5.0 cm) without portal vein tumour thrombus has not been well established. This study aimed to develop novel nomograms to predict postoperative recurrence and survival of these patients.

METHODS

Data from 2469 patients with SLH-HCC who underwent curative resection from January 2005 to December 2015 in China were retrospectively collected. Specifically, nomograms of recurrence-free survival (RFS) and overall survival (OS) using data from a training cohort were developed with the Cox regression model (n = 1012). The modes were verified in an internal validation cohort (n = 338) and an external cohort comprising four tertiary institutions (n = 1119).

RESULTS

The nomograms of RFS and OS based on tumour clinicopathologic features (diameter, differentiation, microvascular invasion, α-fetoprotein), operative factors (preoperative transcatheter arterial chemoembolisation therapy, scope of liver resection and intraoperative blood transfusion), underlying liver function (albumin-bilirubin grade) and systemic inflammatory or immune status (neutrophil-to-lymphocyte ratio) achieved high C-indexes of 0.85 (95% confidence interval [CI], 0.79-0.91) and 0.86 (95% CI, 0.79-0.93) in the training cohort, respectively, which were significantly higher than those of the five conventional HCC staging systems (0.62-0.73 for RFS, 0.63-0.75 for OS). The nomograms were validated in the internal cohort (0.83 for RFS, 0.84 for OS) and external cohort (0.87 for RFS, 0.88 for OS) and had well-fitted calibration curves. Our nomograms accurately stratified patients with SLH-HCC into low-, intermediate- and high-risk groups of postsurgical recurrence and mortality.

CONCLUSIONS

The two nomograms achieved optimal prediction for postsurgical recurrence and OS for patients with SLH-HCC after curative resection.

Biomarkers for hepatocellular carcinoma based on body fluids and feces

Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma (HCC). Overwhelming evidence from published studies on HCC has documented that multiple molecular biomarkers detected in body fluids and feces can be utilized in early-diagnosis, predicting responses to specific therapies, evaluating prognosis before or after therapy, as well as serving as novel therapeutic targets. Detection and analysis of proteins, metabolites, circulating nucleic acids, circulating tumor cells, and extracellular vesicles in body fluids (e.g., blood and urine) and gut microbiota (e.g., in feces) have excellent capabilities to improve different aspects of management of HCC. Numerous studies have been devoted in identifying more promising candidate biomarkers and therapeutic targets for diagnosis, treatment, and monitoring responses of HCC to conventional therapies, most of which may improve diagnosis and management of HCC in the future. This review aimed to summarize recent advances in utilizing these biomarkers in HCC and discuss their clinical significance.

A Nomogram to Predict Lifestyle Factors for Recurrence of Large-Vessel Ischemic Stroke

Background

Stroke is the leading cause of morbidity and mortality in China. Recurrent stroke (RS) could occur in a significant portion of patients with ischemic stroke with devastating consequence.

Methods

To investigate the association between lifestyle and the risk of RS in Chinese patients with acute large-vessel ischemic stroke (ALVIS). A total of 258 patients with ALVIS were recruited in the study (median age 63 years, 30.6% female), and followed for a median of 366 days. The primary outcomes were first RS. Cox Regression and Akaike information criterion were used to establish the best-fit nomograms.

Results

During follow-up, 38 of 258 (14.7%) participants had the primary endpoint event. After adjusting for confounding factors in multivariate Cox regression analysis, healthy lifestyles, including bland diet (hazard ratio [HR], 0.365; 95% CI, 0.138–0.965), daily fruit consumption (HR, 0.474; 95% CI, 0.238–0.945), good sleep (HR, 0.364; 95% CI, 0.180–0.739), housework: HR (0.461; 95% CI, 0.200–1.065), and HDL (HR, 0.329; 95% CI, 0.130–0.831) were associated with significantly decreased risk for RS after ALVIS, while smoking was associated with a substantial increase in RS risk (HR, 2.590; 95% CI, 1.340–5.005) and included into the nomogram. A weighted point (from 0 to 100) was given to each risk factor, and the total points could be used to predict the probability of RS for the patient.

Conclusion

The nomogram shows that healthy lifestyles (bland diet, daily fruit consumption, good sleep, cigarette cessation, and housework) were important for reducing RS in patients with ALVIS.