A nomogram predicting pulmonary metastasis of hepatocellular carcinoma following partial hepatectomy.
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ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC3950869 | biostudies-other | 2014 Mar
REPOSITORIES: biostudies-other
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Project description:BackgroundPredicting the long-term prognosis of individuals who experienced sorafenib treatment following partial hepatectomy due to hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is difficult. This work aims to create an effective prognostic nomogram for HBV related HCC patients who are receiving sorafenib treatment as adjuvant therapy after surgery.MethodsA total of 233 HBV-related HCC patients treated with or without sorafenib following partial hepatectomy at the Eastern Hepatobiliary Surgery Hospital from 2008 to 2013 were matched with propensity score matching analysis. The optimal cut-off point of the overall survival (OS) factor level was determined by x-tile. The selection of indicators was based on clinical findings. The Cox regression model with an interaction term was employed for evaluating the predictive value. Using a multivariate Cox proportional hazards model, a nomogram was subsequently formulated to analyze 111 patients treated with sorafenib. The nomogram's discriminative ability and predictive accuracy were determined using the concordance index (C-index), calibration, and ROC curve.ResultsThe matched sorafenib cohort of 111 patients and control cohort of 118 patients were analyzed. Subgroup analysis revealed that low GPC3, pERK, pAKT, serum AFP levels, without MVI, under 50 years old, male, TNM stage I/II and BCLC stage 0/A were significantly associated with a better OS in patients subjected to sorafenib treatment compared to those without sorafenib treatment after surgery. Multivariate analysis of the sorafenib cohort revealed GPC3, pERK, pAKT, serum AST, and BCLC stage as independent factors for OS, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.73(95% CI, 0.67-0.78). The area under the ROC curve (AUC) for the nomogram to predict the survival for 1, 3, and 5-year was 0.726, 0.816, and 0.823, respectively.ConclusionThis proposed nomogram shows the potential to make a precise prediction regarding the prognosis of HBV-related HCC patients and may help to stratify patients for personalized therapy following partial hepatectomy.
| S-EPMC7906076 | biostudies-literature
Project description:Nearly 50 % of colorectal cancer (CRC) patients develop liver metastases with liver resection being the only option to cure patients. Residual micrometastases or circulating tumor cells are considered a cause of tumor relapse. This work investigates the influence of partial hepatectomy (PH) on the growth and molecular composition of CRC liver metastasis in a syngeneic rat model. One million CC531 colorectal tumor cells were implanted via the portal vein in WAG/Rij rats followed by a 30 % PH a day later. Control groups either received tumor cells followed by a sham-operation or were injected with a buffer solution followed by PH. Animals were examined with magnetic resonance imaging (MRI) and liver tissues were processed for immunolabeling and PCR analysis. One-third PH was associated with an almost threefold increase in relative tumor mass (MRI volumetry: 2.8-fold and transcript levels of CD44: 2.3-fold). Expression of molecular markers for invasiveness and aggressiveness (CD49f, CXCR4, Axin2 and c-met) was increased following PH, however with no significant differences when referring to the relative expression levels (relating to tumor mass). Liver metastases demonstrated a significantly higher proliferation rate (Ki67) 2 weeks following PH and cell divisions also increased in the surrounding liver tissue. Following PH, the stimulated growth of metastases clearly exceeded the compensation in liver volume with long-lasting proliferative effects. However, the distinct tumor composition was not influenced by liver regeneration. Future investigations should focus on the inhibition of cell cycle (i.e. systemic therapy strategies, irradiation) to hinder liver regeneration and therefore restrain tumor growth.
| S-EPMC3663204 | biostudies-literature
Project description:Background and aimExtrahepatic metastasis (EHM) of hepatocellular carcinoma (HCC) leads to a worse prognosis. We aimed to develop a nomogram based on noninvasive pretreatment clinical data to predict EHM of HCC sooner.MethodsThree cohorts containing 1820, 479, and 988 HCC patients were enrolled from three hospitals in different regions in Taiwan and served as the training and validation cohorts. Pretreatment clinical data were analyzed by Cox regression modeling for independent risk factors of EHM.ResultsPlatelet count ≥ 200 × 103/μL, serum alfa-fetoprotein ≥ 100 ng/dL, tumor size ≥ 3 cm, tumor number > 1, and macrovascular invasion were independent risk factors for EHM and were used to develop a nomogram. This nomogram had concordance indices of 0.733 (95% confidence interval [CI]: 0.688-0.778) and 0.739 (95% CI: 0.692-0.787) for the prediction of EHM during a 5-year follow-up duration in the training and validation cohorts, respectively. A nomogram score > 61 implied a high risk of EHM (hazard ratio [HR] = 3.83; 95% CI: 2.77-5.31, P < 0.001).ConclusionWe have developed a nomogram that could accurately predict EHM of HCC and be readily available for formulating individualized treatment for all individual HCC patients to improve therapeutic efficacy.
| S-EPMC6386739 | biostudies-literature
Project description:ObjectivesThe accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence.MethodsIn this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction.ResultsMVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram.ConclusionThe proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy.Key points• Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.
| S-EPMC10289921 | biostudies-literature
Project description:Extrahepatic recurrence (EHR) after curative hepatectomy for hepatocellular carcinoma (HCC) is associated with a poor prognosis. We investigated the features of EHR and identified its predictive factors. This retrospective study included 398 treatment-naive patients who underwent curative hepatectomy for HCC at two tertiary hospitals. Multivariate Cox-regression analysis was performed to identify the variables associated with EHR. EHR was diagnosed in 94 patients (23.6%) over a median follow-up period of 5.92 years, most commonly in the lungs (42.6%). The 5-/10-year cumulative rates of HCC recurrence and EHR were 63.0%/75.6% and 18.1%/35.0%, respectively. The median time to EHR was 2.06 years. Intrahepatic HCC recurrence was not observed in 38.3% of patients on EHR diagnosis. On multivariate analysis, pathologic modified Union for International Cancer Control stage (III, IVa), surgical margin involvement, tumor necrosis, sum of tumor size > 7 cm, and macrovascular invasion were predictive factors of EHR. Four risk levels and their respective EHR rates were defined as follows: very low risk, 1-/5-year, 3.1%/11.6%; low risk, 1-/5-year, 12.0%/27.7%; intermediate risk, 1-/5-year, 36.3%/60.9%; and high risk, 1-year, 100.0%. Our predictive model clarifies the clinical course of EHR and could improve the follow-up strategy to improve outcomes.
| S-EPMC8217564 | biostudies-literature
Project description:Background: Repeat hepatectomy is an important treatment for patients with repeat recurrent hepatocellular carcinoma (HCC). Methods: This study was a multicenter retrospective analysis of 1,135 patients who underwent primary curative liver resection for HCC. One hundred recurrent patients with second hepatectomy were included to develop a nomogram to predict the risk of post-recurrence survival (PRS). Thirty-eight patients in another institution were used to externally validate the nomogram. Univariate and multivariate Cox regression analyses were used to identify independent risk factors of PRS. Discrimination, calibration, and the Kaplan-Meier curves were used to evaluate the model performance. Results: The nomogram was based on variables associated with PRS after HCC recurrence, including the tumor, node, and metastasis (TNM) stage; albumin and aspartate aminotransferase levels at recurrence; tumor size, site, differentiation of recurrences; and time to recurrence (TTR). The discriminative ability of the nomogram, as indicated by the C statistics (0.758 and 0.811 for training cohort and external validation cohorts, respectively), was shown, which was better than that of the TNM staging system (0.609 and 0.609, respectively). The calibration curves showed ideal agreement between the prediction and the real observations. The area under the curves (AUCs) of the training cohort and external validation cohorts were 0.843 and 0.890, respectively. The Kaplan-Meier curve of the established nomogram also performed better than those of both the TNM and the BCLC staging systems. Conclusions: We constructed a nomogram to predict PRS in patients with repeat hepatectomy (RH) after repeat recurrence of HCC.
| S-EPMC8027067 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is one of the most deadly tumors. Prognosis of patients with HCC is generally poor due to the high recurrence rate. In the present study, TaqMan Real-time PCR microRNA Array was used to identify differentially expressed miRNAs from 10 tumor tissue samples (5 from recurrence group vs. 5 from non-recurrence group) and the matched serum samples. Four differentially expressed miRNAs (miR-486-5p, miR-422a, miR-125b and miR-139-5p) were further quantified in 20 tumor tissues and 116 HCC patients' serum before they received hepatectomy. Univariate analysis revealed that miR-486-5p, miR-422a and miR-125b were significantly associated with patients' relapse free survival (RFS). Multivariate analysis demonstrated that miR-486-5p, AFP and microvascular invasion (MVI) were the independent prognostic factors associated with RFS in this cohort (p = 0.000, 0.043, 0.000, respectively). Besides, the expression levels of miR-486-5p were positively correlated in tumor tissues and the paired serum samples, so was miR-422a. The probability of the prognostic accuracy of miR-486-5p in predicting postoperative recurrence of HCC within the first year was 76.79% (65.38% specificity and 81.58% sensitivity), which was almost equal to the classifier established by combination of AFP and MVI (75.98% probability, 63.13% specificity and 85.90% sensitivity). Furthermore, the combination of AFP, MVI and miR-486-5p yielded a ROC curve area of 88.02% (69.20% specificity and 92.10% sensitivity). Our study was the first to identify that serum miR-486-5p could be used to stratify the patients with higher recurrence risk before hepatic resection and potentially guide more effective surveillance strategies for them.
| S-EPMC4846138 | biostudies-literature
Project description:BACKGROUND:We have previously reported on the cDNA microarray gene expression profiles of hepatocellular carcinomas (HCCs). Among the genes that show prognostic significance and are overexpressed in tumor compared with adjacent nontumorous liver, transcript AA454543 may have potential for practical use. Our aim is to validate the prognostic significance of transcript AA454543 by alternative research methods and in a separate group of HCC patients. METHODS AND RESULTS:The data of transcript AA454543 derived from microarray analysis of 48 patients having curative partial hepatectomy (group 1) were verified by quantitative reverse transcription polymerase chain reaction (r = 0.618, P < .001). A separate sample set of HCCs obtained from 53 patients (group 2) was examined and the association of AA454543 expression level with overall survival was again validated (P = .027). By Cox regression analysis, transcript AA454543 [hazard ratio (HR) = 3.0, P = .017] and pathologic tumor node metastasis (pTNM) stage (HR = 3.3, P = .010) were independent prognostic factors for overall survival. The accuracy of prediction for 3-year overall survival for transcript AA454543 (74.2%, P = .001) and pTNM stage (76.4%, P = .001) was comparable as measured by the area under the receiver operating characteristic curve. CONCLUSION:Transcript AA454543 is potentially useful molecular prognostic marker for overall survival after curative partial hepatectomy for HCC.
| S-EPMC1501123 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients.MethodsInternational Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn.ResultsSix mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients.ConclusionsWe established an independent prognostic model of predicting OS for 1-3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures.
| S-EPMC7709450 | biostudies-literature
Project description:BackgroundWhether obesity affects surgical outcomes in patients with hepatocellular carcinoma (HCC) is controversial. Here we retrospectively evaluated the impact of obesity on outcomes in HCC patients after curative hepatectomy.MethodsPatients with Child-Pugh A liver function who underwent curative hepatectomy between 2006 and 2010 were categorized as obese (BMI ?25 kg/m2, n = 68) and non-obese (<25 kg/m2, n = 242). To reduce interference from baseline differences between the two groups, propensity score-matched analysis was performed in the ratio 1:2 using a caliper width of 0.1. Surgical outcomes were compared for 61 obese and 115 non-obese patients.ResultsObese patients had higher levels of albumin and aspartate aminotransferase, and more solitary tumors compared to the non-obese patients (all P<0.05). In the propensity-matched cohort, baseline characteristics did not differ between the two groups (all P>0.05). Obese and non-obese patients had comparable 30-day mortality (1.6% vs. 2.6%, P = 1.000), 90-day mortality (3.3% vs. 4.3%, P = 1.000), and incidence of postoperative complications (19.7% vs. 18.3%, P = 0.819). Overall survival at 1, 3, and 5 years was similar for obese patients (83.6%, 63.6%, 41.6%) as for non-obese patients (80.9%, 65.9%, 49.1%; P = 0.358). Disease-free survival at 1, 3, and 5 years was also similar for obese patients (71.5%, 36.3%, 24.3%) as for non-obese ones (60.2%, 43.7%, 27.7%; P = 0.969).ConclusionOur propensity score-matched analysis strengthens the case that obesity does not adversely affect surgical outcomes of HCC patients undergoing curative hepatectomy.
| S-EPMC4428764 | biostudies-literature