Advanced hepatocellular carcinoma: which staging systems best predict prognosis?

Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study

OBJECTIVES

To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC).

METHODS

Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index).

RESULTS

Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients.

CONCLUSIONS

The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.

Prognostic impact of the high-sensitivity modified glasgow prognostic score on patients undergoing radical surgery for hepatocellular carcinoma : Authorship

OBJECTIVE

Our study aimed to assess the ability of high-sensitivity modified Glasgow prognostic Score (HS-mGPS) predicting survival in patients undergoing radical surgery for hepatocellular carcinoma (HCC) and to compare the impact with other Inflammation-Based prognostic scoring systems including Glasgow prognostic Score (GPS) and modified GPS (mGPS).

METHODS

Our study evaluated 293 patients with HCC who had undergone hepatectomy at the Third Affiliated Hospital of Soochow University between 2010 and 2018. The HS-mGPS, mGPS, and GPS were calculated based on particular cut-off values of preoperative C-reactive protein and albumin, and the correlations between HS-mGPS and clinicopathological parameters were evaluated. Univariate and multivariate survival analyses were conducted by Kaplan-Meier method and Cox proportional hazards model. To evaluate the discrimination ability of each prognostic score, the receiver operating characteristic (ROC) curve were generated and the areas under the curve (AUC) were measured and compared.

RESULT

The study results indicated a correlation between elevated HS-mGPS scores and adverse clinical factors, including higher BCLC stage, C-P grade, multiple tumors, and larger tumor diameter. Kaplan-Meier and univariate survival analyses revealed that higher scores of HS-mGPS, GPS, and mGPS were all associated with significantly reduced overall survival (OS) (all p < 0.001). In multivariate survival analysis, HS-mGPS emerged as an independent risk factor for poor OS in patients undergoing hepatectomy for HCC (p = 0.010), along with factors including maximal tumor diameter (p < 0.001), microvascular invasion (MVI) (p = 0.008), and BCLC stage (p = 0.001). The analysis of ROC curves and the AUC values indicated that HS-mGPS outperforms GPS and mGPS in predicting the long-term prognosis of patients with resectable HCC.

CONCLUSION

Preoperative HS-mGPS proves superior in predicting adverse long-term outcomes in HCC patients undergoing radical surgery.

Development and validation of nomograms to predict survival and recurrence after hepatectomy for intermediate/advanced (BCLC stage B/C) hepatocellular carcinoma

BACKGROUND

Despite the Barcelona Clinic Liver Cancer system discouraging hepatectomy for intermediate/advanced hepatocellular carcinoma, the procedure is still performed worldwide, particularly in Asia. This study aimed to develop and validate nomograms for predicting survival and recurrence for these patients.

METHODS

We analyzed patients who underwent curative-intent hepatectomy for intermediate/advanced hepatocellular carcinoma between 2010 and 2020 across 3 Chinese hospitals. The Eastern Hepatobiliary Surgery Hospital cohort was used as the training cohort for the nomogram construction, and the Jilin First Hospital and Fujian Mengchao Hepatobiliary Hospital cohorts served as the external validation cohorts. Independent preoperative predictors for survival and recurrence were identified through univariable and multivariable Cox regression analyses. Predictive accuracy was measured using the concordance index and calibration curves. The predictive performance between nomograms and conventional hepatocellular carcinoma staging systems was compared.

RESULTS

A total of 1,328 patients met the inclusion criteria. The nomograms for predicting survival and recurrence were developed using 10 and 6 independent variables, respectively. Nomograms' concordance indices in the training cohort were 0.777 (95% confidence interval 0.759-0.800) and 0.719 (95% confidence interval 0.697-0.742) for survival and recurrence, outperforming 4 conventional staging systems (P < .001). Nomograms accurately stratified risk into low, intermediate, and high subgroups. These results were validated well by 2 external validation cohorts.

CONCLUSION

We developed and validated nomograms predicting survival and recurrence for patients with intermediate/advanced hepatocellular carcinoma, contradicting Barcelona Clinic Liver Cancer surgical guidelines. These nomograms may facilitate clinicians to formulate personalized surgical decisions, estimate long-term prognosis, and strategize neoadjuvant/adjuvant anti-recurrence therapy.

Prognostic nomogram based on pre-treatment HALP score for patients with advanced non-small cell lung cancer

BACKGROUND

To explore the correlation of pre-treatment Hemoglobin-Albumin-Lymphocyte-Platelet (HALP) score with the prognosis of patients with advanced Non-Small Cell Lung Cancer (NSCLC) undergoing first-line conventional platinum-based chemotherapy.

METHODS

In this retrospective cohort study, 203 patients with advanced NSCLC were recruited from January 2017 to December 2021. The cut-off value for the HALP score was determined by Receiver Operating Characteristic (ROC) curve analysis. The baseline characteristics and blood parameters were recorded, and the Log-rank test and Kaplan-Meier curves were applied for the survival analysis. In the univariate and multivariate analyses, the Cox regression analysis was carried out. The predictive accuracy and discriminative ability of the nomogram were determined by the Concordance index (C-index) and calibration curve and compared with a single HALP score by ROC curve analysis.

RESULTS

The optimal cut-off value for the HALP score was 28.02. The lower HALP score was closely associated with poorer Progression-Free Survival (PFS) and Overall Survival (OS). The male gender and other pathological types were associated with shorter OS. Disease progression and low HALP were correlated with shorter OS and PFS. In addition, nomograms were established based on HALP scores, gender, pathology type and efficacy rating, and used to predict OS. The C-index for OS prediction was 0.7036 (95% CI 0.643 to 0.7643), which was significantly higher than the C-index of HALP at 6-, 12-, and 24-months.

CONCLUSION

The HALP score is associated with the prognosis of advanced NSCLC patients receiving conventional platinum-based chemotherapy, and the nomogram established based on the HALP score has a better predictive capability for OS.

A prognostic model for hepatocellular carcinoma patients based on polyunsaturated fatty acid-related genes

OBJECTIVE

Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics.

METHOD

Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs.

RESULTS

We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development.

CONCLUSION

Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

Development and validation of a nomogram for predicting lymph node metastasis in ductal carcinoma in situ with microinvasion: A SEER population-based study

BACKGROUND

Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making.

METHODS

A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation.

RESULTS

This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607-0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility.

CONCLUSION

In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.

Establishment and external validation of prognosis prediction nomogram for patients with distant metastatic intrahepatic cholangiocarcinoma: based on a large population

BACKGROUND

Most patients with intrahepatic cholangiocarcinoma (ICC) have developed distant metastasis at the time of diagnosis, while there is rear related nomogram to predict the prognosis.

METHODS

Clinical data of patients pathologically diagnosed of ICC with distant metastasis were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database during 2005 to 2019. Finally, patients diagnosed as ICC in the Second Affiliated Hospital of Nanchang University from 2014 to 2019 were collected for external verification. All data were divided into training cohort and validation cohort in a ratio of 7:3. The nomogram was established based on independent prognostic factors using Cox univariate and multivariate analyses. The area under the receiver operating characteristic (ROC) curves (AUC), the calibration curve and the decision curve analysis (DCA) were used to determine the prediction accuracy of the nomogram.

RESULTS

This study finally included 572 ICC with distant metastasis patients, another 32 patients collected by the author's hospital were used as external verification. Results showed that age, surgery, radiotherapy and chemotherapy were independent prognostic factors, and nomogram was established. The AUC of predicting 3, 6, 9-month overall survival were 0.866, 0.841 and 0.786. The ROC curves and calibration curves showed that the nomogram had good predictive accuracy, and DCA showed that the nomogram had good clinical applicability.

CONCLUSIONS

The nomogram has good accuracy in predicting prognosis of DM-ICC patients, which would be of good significance to improve the prognosis of these patients.

Development of a predictive nomogram for postembolization syndrome after transcatheter arterial chemoembolization of hepatocellular carcinoma

Post-embolization syndrome (PES) is a frequent complication after receiving transcatheter arterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC), but only a few studies have focused on the factors influencing PES in those patients. In this study, the impact factors of PES were explored and a nomogram was constructed to predict the occurrence of PES in HCC patients with TACE. This was a retrospective cohort study of HCC patients who underwent TACE obtained from the third affiliated Hospital of Kunming Medical University between January 1, 2020, and September 1, 2022. T‑test and Chi‑square test were used to search for factors influencing PES occurrence, and then the nomogram was further established based on multivariable logistic regression analysis. Validation of the predictive nomogram was also evaluated by calibration curve, concordance index (C-index), and receiver operating characteristic (ROC) curves. The enrolled patients (n = 258) were randomly assigned to the primary cohort (n = 180) and validation cohort (n = 78) in a 7:3 ratio. Among 180 patients in the primary cohort, 106 (58.89%) experienced PES. TACE types (P = 0.015), embolization degree (P = 0.008), and tumor number (P = 0.026) were identified as predictors by the logistic regression analysis and were used to develop the predictive nomogram. The internally validated and externally validated C-indexes were 0.713 and 0.703, respectively. The calibration curves presented good consistency between actual and predictive survival. Types of embolic agents, embolization degree, and tumor number were found to be the predictors of PES after TACE. The nomogram could reliably predict PES in HCC patients with TACE. This predictive model might be considered for clinical practice.

Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma

To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57-0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39-0.83]; highest C-index [CI] 0.71 [0.49-0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30-0.73]; highest C-index [CI] 0.66 [0.48-0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.

Hepatocellular Carcinoma: Surgical Management and Evolving Therapies

Hepatocellular carcinoma (HCC) is the fifth most common cancer in men and the eighth most common cancer in women worldwide. It is also the second leading cause of cancer death worldwide, with 780,000 deaths in 2018. Seventy-two percent of HCC cases occur in Asia, 10% in Europe, 8% in Africa, 5% in North America, and 5% in Latin America (Singal et al. in J Hepatol 72(2):250-261, 2020 [1]).

Two lncRNA signatures with cuproptosis as a novel prognostic model and clinicopathological value for endometrioid endometrial adenocarcinoma

OBJECTIVE

Cuproptosis may contribute to tumorigenesis. However, the predictive value and therapeutic significance of cuproptosis-related lncRNAs (CRLs) in endometrioid endometrial adenocarcinoma (EEA) remains unknown.

METHODS

We obtained RNA-seq data from TCGA database and searched the Literature to identify cuproptosis-related genes. Using machine learning models, we identified prognostic lncRNAs for cuproptosis. Immune properties and drug sensitivity were investigated based on these signatures. Further, a ceRNA network was constructed by bioinformatics and in vitro experiments were performed.

RESULTS

We determined two cuproptosis-related signatures to build the prognostic model in EEA. Afterward, the risk scores of two cuproptosis-related signatures were associated with clinicopathological molecular typing and as independent prognostic factors for EEA. In addition, we observed significant differences in immune function, checkpoints, and CD8+ T lymphocyte infiltration between the two risk groups. Furthermore, chemotherapy drugs such as AKT inhibitors exhibited lower IC50 values in the high-risk group. We speculate that ACOXL-AS1 can be served as an endogenous 'sponge' to regulate the expression of MTF1 by miR-421. Through in vitro experiments, we preliminarily validated the ceRNA network relationship in the cellular model.

CONCLUSION

In EEAs, this study proposed a broad molecular signature of CRLs are promising biomarkers for predicting clinical outcomes and therapeutic responses.

Identification of a Novel CD8+ T cell exhaustion-related gene signature for predicting survival in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a major health concern, necessitating a deeper understanding of its prognosis and underlying mechanisms. This study aimed to investigate the mechanism and prognostic value of CD8+ T Cell exhaustion (CD8+ TEX)-related genes in HCC and construct a survival prognosis prediction model for patients with HCC.

METHODS

CD8+ TEX-related genes associated with HCC prognosis were analysed and identified, and a prognostic prediction model was constructed using the 'least absolute shrinkage and selection operator' Cox regression model. Immunohistochemistry was used to verify the expression of the model genes in HCC tissues. A nomogram was constructed based on risk scores and clinical features, and its predictive efficacy was verified. The expression of STAM, ANXA5, and MAD2L2 in HCC cell lines was detected by western blotting; subsequently, these genes were knocked down in HCC cell lines by small interfering RNA, and their effects on the proliferation and migration of HCC cell lines were detected by colony formation assay, cck8, wound healing, and transwell assays.

RESULTS

Six genes related to CD8+ TEX were included in the risk-prediction model. The prognosis of patients with HCC in the low-risk group was significantly better than that of those in the high-risk group. Cox regression analysis revealed that the risk score was an independent risk factor for the prognosis of patients with HCC. The differentially expressed genes in patients with high-risk HCC were mainly enriched in the nucleotide-binding oligomerization domain-containing protein-like receptor, hypoxia-inducible factor-1, and tumour programmed cell death protein (PD)-1/PD-L1 immune checkpoint pathways. The CD8+ TEX-related genes STAM, ANXA5, and MAD2L2 were knocked down in HCC cell lines to significantly inhibit cell proliferation and migration. The prediction results of the nomogram based on the risk score showed a good fit and application value.

CONCLUSION

The prediction model based on CD8+ TEX-related genes can predict the prognosis of HCC and provide a theoretical basis for the early identification of patients with poor HCC prognosis.

Prognostic factors of sarcomas occurring in bone and joint: A SEER based study

To clarify the epidemiology, treatment, and prognosis of sarcomas occurring in the bones and joints. The surveillance, epidemiology, and end results (SEER) 18 registries, comprising sarcoma diagnoses made between 2008 and 2014, were queried for sarcomas arising in bones or joints. Kaplan-Meier analysis, multivariate logistic regression analysis, Cox proportional hazards model, and nomograms were used to identify prognostic factors. 2794 patients aged from 1 to 99 (55.8% male) with microscopically confirmed diagnosed as sarcomas (including osteosarcoma, chondrosarcoma, Ewing sarcoma, and soft tissue sarcomas) which primary site limited to bone and joint were identified. Eight independent factors, including age, race, sex, tumor site, histology, pathology grade, tumor size, and total number of malignant tumors (TNOMT), were associated with tumor metastasis. Nine independent prognostic factors, including age (>=60 year, hazard ratio [HR] = 4.145, 95% confidence interval [CI], P < .001), sex (female, HR = 0.814, 95%CI, P = .007), tumor site (spine, HR = 2.527, 95%CI, P < .001), histology, pathology grade (undifferentiated, HR = 5.816, 95%CI, P < .001), tumor size (>=20 cm, HR = 3.043, 95%CI, P < .001), tumor extent (distant, HR = 4.145, 95%CI, P < .001), surgery (no performed, HR = 2.436, 95%CI, P < .001), and TNOMT (1, HR = 0.679, 95%CI, P < .001, were identified and incorporated to construct a nomogram for 2- and 5-year overall survival (OS). The calibration curve for the probability of survival showed good agreement between prediction by the nomogram and actual observation. The C-index of the nomogram for survival prediction was 0.814. Patients who received chemotherapy had a significantly decreased risk of death only for Ewing sarcoma, poorly differentiated tumors, undifferentiated tumors, and distant tumor invasion (P < .05). However, radiotherapy did not show significant differences in OS. This study presents population-based estimates of prognosis for patients with bone sarcomas and demonstrates the impact of age, race, sex, tumor site, histology, pathology grade, tumor size, tumor extent, surgery, radiotherapy, chemotherapy, and the TNOMT on OS. Moreover, the nomogram resulted in a more accurate prognostic prediction. However, in our study, radiotherapy showed no survival benefit, perhaps because detailed data on treatment factors were unavailable and which may have influenced the results.

Developing a nomogram model and prognostic analysis of nasopharyngeal squamous cell carcinoma patients: a population-based study

BACKGROUND

Nasopharyngeal squamous cell carcinoma (NPSCC) is a common histo-logical subtype of nasopharyngeal cancer with a generally poor prognosis. The aim of this study is to identify factors affecting the survival prognosis of NPSCC patients and develop a specialized nomogram model.

METHODS

We extracted clinical data of 1235 diagnosed cases of NPSCC from the SEER database using SEER*Stat software. Univariate and multivariate Cox proportional hazards regression analyses were conducted to explore clinical factors that impact the prognosis of NPSCC patients. Based on significant independent factors, we developed a nomogram to predict the 1, 3, and 5 years overall survival rates. The discriminative and predictive abilities of the nomogram were evaluated using C-index, calibration curve, area under the curve (AUC), and receiver operating characteristic curve. We evaluated the clinical value of the nomogram using decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

We performed a cohort analysis on 846 patients with nasopharyngeal cancer in the training cohort. Multivariate Cox regression analysis revealed age, race, marital status, primary tumor, radiation therapy, chemotherapy, SJCC stage, primary tumor size, Lung metastasis and brain metastasis as independent prognostic factors for NPSCC patients, which we used to construct the nomogram prediction model. The C-index of the training cohort was 0.737. The ROC curve analysis indicated that the AUC of the OS rate at 1, 3, and 5 years in the training cohort was > 0.75. The calibration curves of the two cohorts showed good consistency between the predicted and observed results. DCA and CIC demonstrated that the nomogram prediction model had good clinical benefits.

CONCLUSIONS

The nomogram risk prediction model for NPSCC patient survival prognosis, constructed in this study, has exhibited excellent predictive capability. This model can be employed for swift and precise assessment of individualized survival prognosis. It can offer valuable guidance to clinical physicians in diagnosing and treating NPSCC patients.

Combined low levels of H4K16ac and H4K20me3 predicts poor prognosis in breast cancer

BACKGROUND

Results of previous studies about the prognostic roles of histone H4 lysine 16 acetylation (H4K16ac) and histone H4 lysine 20 trimethylation (H4K20me3) in breast cancer were inconsistent. Cellular experiments revealed the interplays between H4K16ac and H4K20me3, but no population study explored the interaction between them on the prognosis.

METHODS

H4K16ac and H4K20me3 levels in tumors were evaluated by immunohistochemistry for 958 breast cancer patients. Hazard ratios for overall survival (OS) and progression-free survival (PFS) were estimated using Cox regression models. Interaction was assessed on multiplicative scale. Concordance index (C-index) was calculated to verify the predictive performance.

RESULTS

The prognostic roles of the low level of H4K16ac or H4K20me3 were significant only in patients with the low level of another marker and their interactions were significant. Moreover, compared with joint high levels of both them, only the combined low levels of both them was associated with a poor prognosis but not the low level of single one. The C-index of the clinicopathological model combined the joint expression of H4K16ac and H4K20me3 [0.739 for OS; 0.672 for PFS] was significantly larger than that of the single clinicopathological model [0.699 for OS, P < 0.001; 0.642 for PFS, P = 0.003] or the model combined with the single H4K16ac [0.712 for OS, P < 0.001; 0.646 for PFS, P < 0.001] or H4K20me3 [0.724 for OS, P = 0.031; 0.662 for PFS, P = 0.006].

CONCLUSIONS

There was an interaction between H4K16ac and H4K20me3 on the prognosis of breast cancer and the combination of them was a superior prognostic marker compared to the single one.

Systemic treatment in patients with Child-Pugh B liver dysfunction and advanced hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major cause of death among patients with liver cirrhosis. The rise of immuno-oncology has revolutionized treatment for advanced HCC. However, most pivotal randomized controlled trials have excluded patients with moderate liver dysfunction (Child-Pugh-Turcotte B), despite the high incidence of liver disease in patients with HCC at the time of diagnosis. Overall survival in patients with HCC and moderate liver dysfunction treated with sorafenib has been found to be only approximately 3-5 months, underlining the need for improved treatment algorithms for this increasingly important subgroup of patients. In this review, we summarize available data on the treatment of patients with HCC and moderate liver dysfunction. Opportunities, as well as clinical challenges, are discussed in detail, highlighting potential changes to the therapeutic landscape.

Treatment of hepatocellular carcinoma with macroscopic vascular invasion: A systematic review and network meta-analysis

BACKGROUND

This study aimed to evaluate the outcomes of different treatments for patients with hepatocellular carcinoma (HCC) and macroscopic vascular invasion.

METHODS

A systematic review and meta-analysis of comparative studies was performed to evaluate various treatment modalities for HCC with macroscopic vascular invasion, including liver resection (LR), liver transplantation (LT), transarterial chemoembolization (TACE), transarterial radioembolization (TARE), radiotherapy (RT), radiofrequency ablation (RFA), and antineoplastic systemic therapy (AnST).

RESULTS

After applying the selection criteria, 31 studies were included. The surgical resection (SR) group (including LR and LT) had a similar mortality rate to the non-surgical resection (NS) group (RD = -0.01; 95% CI -0.05 to 0.03). The SR group had a higher rate of complications (RD = 0.06; 95% CI 0.00 to 0.12) but a higher 3-year overall survival (OS) rate than the NS group (RD = 0.12; 95% CI 0.05 to 0.20). The network analysis revealed that the overall survival was lower in the AnST group. LT and LR had similar survival benefits. The meta-regression suggested that SR has a greater impact on the survival of patients with impaired liver function.

DISCUSSION

Most likely, LT has a significant impact on long-term survival and consequently would be a better option for HCC with macroscopic vascular invasion in patients with impaired liver function. LT and LR offer a higher chance of long-term survival than NS alternatives, although LR and LR are associated with a higher risk of procedure-related complications.

Machine learning integrations develop an antigen-presenting-cells and T-Cells-Infiltration derived LncRNA signature for improving clinical outcomes in hepatocellular carcinoma

As a highly heterogeneous cancer, the prognostic stratification and personalized management of hepatocellular carcinoma (HCC) are still challenging. Recently, Antigen-presenting-cells (APCs) and T-cells-infiltration (TCI) have been reported to be implicated in modifying immunology in HCC. Nevertheless, the clinical value of APCs and TCI-related long non-coding RNAs (LncRNAs) in the clinical outcomes and precision treatment of HCC is still obscure. In this study, a total of 805 HCC patients were enrolled from three public datasets and an external clinical cohort. 5 machine learning (ML) algorithms were transformed into 15 kinds of ML integrations, which was used to construct the preliminary APC-TCI related LncRNA signature (ATLS). According to the criterion with the largest average C-index in the validation sets, the optimal ML integration was selected to construct the optimal ATLS. By incorporating several vital clinical characteristics and molecular features for comparison, ATLS was demonstrated to have a relatively more significantly superior predictive capacity. Additionally, it was found that the patients with high ATLS score had dismal prognosis, relatively high frequency of tumor mutation, remarkable immune activation, high expression levels of T cell proliferation regulators and anti-PD-L1 response as well as extraordinary sensitivity to Oxaliplatin/Fluorouracil/Lenvatinib. In conclusion, ATLS may serve as a robust and powerful biomarker for improving the clinical outcomes and precision treatment of HCC.

Over-expression of RRM2 predicts adverse prognosis correlated with immune infiltrates: A potential biomarker for hepatocellular carcinoma

Background

Ribonucleotide reductase regulatory subunit M2 (RRM2) has been reported to be an oncogene in some malignant tumors, such as lung adenocarcinoma, oral squamous cell carcinoma, glioblastoma, and breast cancer. However, the clinical significance of RRM2 in hepatocellular carcinoma has been less studied. The aim of this study was to assess the importance of RRM2 in hepatocellular carcinoma (HCC) based on the Cancer Genome Atlas (TCGA) database.

Methods

The RRM2 expression levels and clinical features were downloaded from the TCGA database. Immunohistochemistry results between tumor tissues and normal tissues were downloaded from the Proteinatlas database. Meanwhile, the expression levels of RRM2 in tumor and paraneoplastic tissues were further verified by qRT-PCR and Western Blotting. Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein-interactions (PPI) network were constructed to analyze RRM2-related downstream molecules. In addition, RRM2 expression-related pathways performed by gene set enrichment analysis (GSEA). Association analysis of RRM2 gene expression and immune infiltration was performed by single-sample GSEA (ssGSEA).

Results

The RRM2 expression level in tumor tissues was higher than normal tissues (P &lt;0.001). The elevated expression of RRM2 in HCC was significantly correlated with T stage (P &lt;0.05), pathologic stage (P &lt;0.05), tumor status (P &lt;0.05), histologic grade (P&lt;0.001), and AFP (P &lt;0.001). HCC with higher RRM2 expression was positively associated with worse OS (overall survival), PFS (progression-free survival), and DSS (disease-specific survival). In the univariate analysis, the expression of RRM2, T stage, M stage, pathologic stage, and tumor status were negatively correlated with OS (P &lt;0.05). Further analysis using multivariate Cox regression showed that tumor status (P&lt;0.01) and RRM2 expression (P&lt;0.05) were independent prognostic factors of OS in HCC. GO/KEGG analysis showed that the critical biological process (chromosome condensation and p53 signaling pathway) might be the possible function mechanism in promoting HCC. Moreover, GSEA showed that several pathways were enriched in RRM2 high-expression samples, including PD-1 signaling, cell cycle, P27 pathway, and T cell receptor signaling pathway. RRM2 was significantly correlated with the infiltration level of CD8 T cells, Cytotoxic cells, DCs, Neutrophils, NK cells, and T helper cells (P &lt;0.05).

Conclusion

Over-expression of RRM2 predict adverse prognosis and is correlated with immune infiltrates in HCC. RRM2 may be a significant molecular biomarker for HCC diagnosis and prognosis.

A Novel Systematic Oxidative Stress Score Predicts the Survival of Patients with Early-Stage Lung Adenocarcinoma

This study aimed to construct an effective nomogram based on the clinical and oxidative stress-related characteristics to predict the prognosis of stage I lung adenocarcinoma (LUAD). A retrospective study was performed on 955 eligible patients with stage I LUAD after surgery at our hospital. The relationship between systematic-oxidative-stress biomarkers and the prognosis was analyzed. The systematic oxidative stress score (SOS) was established based on three biochemical indicators, including serum creatinine (CRE), lactate dehydrogenase (LDH), and uric acid (UA). SOS was an independent prognostic factor for stage I LUADs, and the nomogram based on SOS and clinical characteristics could accurately predict the prognosis of these patients. The nomogram had a high concordance index (C-index) (0.684, 95% CI, 0.656–0.712), and the calibration curves for recurrence-free survival (RFS) probabilities showed a strong agreement between the nomogram prediction and actual observation. Additionally, the patients were divided into two groups according to the cut-off value of risk points based on the nomogram, and a significant difference in RFS was observed between the high-risk and low-risk groups (p < 0.0001). SOS is an independent prognostic indicator for stage I LUAD. These things considered, the constructed nomogram based on SOS could accurately predict the survival of those patients.

Adjuvant transarterial chemoembolization timing after radical resection is an independent prognostic factor for patients with hepatocellular carcinoma

Background

It has been reported that postoperative adjuvant TACE (PA-TACE) treatment decreases recurrence and significantly improves the survival of patients who undergo radical resection of hepatocellular carcinoma (HCC) with high-risk recurrence factors. However, when to perform PA-TACE has not been fully studied.

Methods

We retrospectively collected the clinicopathologic characteristics of the patients with HCC between October 2013 and June 2020. The optimal cutoff value for PA-TACE time was determined based on the R package “maxstat”. Logistic regression and Cox regression analysis were used to determine the effect of the choice of PA-TACE timing on prognosis.

Results

The analysis was performed on 789 patients with HCC, and 484 patients were finally involved and were divided into training cohort (378) and validation cohort (106). The PA-TACE timing was found to be associated with survival outcomes. Multivariate logistic analysis found independent predictors of the PA-TACE timing, including gender and history of HBV. Multivariate Cox analysis showed that Ki-67, tumor size, MVI and the PA-TACE timing were independent prognostic factors for RFS in HCC patients.

Conclusions

Based on this study, HCC patients with high-risk recurrence factors can receive personalized assistance in undergoing PA-TACE treatment and improve their survival outcomes.

A survival nomogram model constructed with common clinical characteristics to assist clinical decisions for diffuse low-grade gliomas: A population analysis based on SEER database

Background

The prognosis of diffuse low-grade gliomas (DLGGs, WHO grade 2) is highly variable, making it difficult to evaluate individual patient outcomes. In this study, we used common clinical characteristics to construct a predictive model with multiple indicators.

Methods

We identified 2459 patients diagnosed with astrocytoma and oligodendroglioma from 2000 to 2018 in the SEER database. After removing invalid information, we randomly divided the cleaned patient data into training and validation groups. We performed univariate and multivariate Cox regression analyses and constructed a nomogram. Receiver operating characteristic (ROC) curve, c-index, calibration curve, and subgroup analyses were used to assess the accuracy of the nomogram by internal and external validation.

Results

After univariate and multivariate Cox regression analyses, we identified seven independent prognostic factors, namely, age (P<0.001), sex (P<0.05), histological type (P<0.001), surgery (P<0.01), radiotherapy (P<0.001), chemotherapy (P<0.05) and tumor size (P<0.001). The ROC curve, c-index, calibration curve, and subgroup analyses of the training group and the validation group showed that the model had good predictive value. The nomogram for DLGGs predicted patients' 3-, 5- and 10-year survival rates based on these seven variables.

Conclusions

The nomogram constructed with common clinical characteristics has good prognostic value for patients with DLGGs and can help physicians make clinical decisions.

Novel Nomograms Based on Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio Predict Prognosis of Hepatocellular Carcinoma Patients After Hepatectomy

Background

The prediction of prognosis of hepatocellular carcinoma (HCC) is of great significance in improving disease outcome and optimizing clinical management, while reliable prognostic indicators are lacking. This study was conducted to develop readily-to-use nomograms for prognosis prediction of HCC after hepatectomy.

Materials and Methods

Data of eligible patients were collected and analyzed retrospectively. Independent prognostic factors were identified by Cox regression, and nomograms for the prediction of disease-free survival (DFS) and overall survival (OS) were developed. The performance of the nomograms was evaluated by receiver operating characteristics (ROC) curves, C-indexes and calibration curves and was verified by the validation cohort. The predictive value of the nomograms was also compared with the 8th edition of American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) and the Barcelona Clinic Liver Cancer (BCLC) staging systems.

Results

In total, 599 patients were enrolled in the analysis: 420 in the training cohort and 179 in the validation cohort. The optimal cut-off value of Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio (GLR) was 19.5. GLR contributed significantly to the nomograms with good predictive power. In ROC analyses, the areas under curve (AUCs) of the nomograms for 1-, 3- and 5-year DFS and OS prediction were 0.758, 0.756, 0.734 and 0.810, 0.799, 0.758, respectively. The C-indexes of the DFS nomogram were 0.697 (95% CI 0.665-0.729) in the training cohort and 0.710 (95% CI 0.664-0.756) in the validation cohort. For OS prediction, the C-indexes were 0.741 (95% CI 0.704-0.778) and 0.758 (95% CI 0.705-0.811) in the training and validation cohorts, respectively. The calibration curves demonstrated satisfactory agreement between nomogram predictions and actual observations. The nomograms demonstrated superior predictive performance to the TNM and the BCLC staging systems.

Conclusion

Our novel nomograms showed adequate performance in the prediction of HCC prognosis after hepatectomy, which may facilitate the risk stratification and individualized management of HCC patients.

A Novel Method for Dynamically Assessing the Prognosis of Patients with pT1 Gastric Cancer: A Large Population-Based Dynamic Prognostic Analysis

Background

While early gastric cancer (EGC) patients are likely to experience relatively long postoperative survival, certain disease-related findings are associated with a poorer prognosis. This study sought to develop and validate a novel predictive model capable of estimating conditional disease-specific survival (CDSS) in EGC patients.

Methods

A total of 3016 patients diagnosed with pT1NxM0 GC after gastrectomy between 1998 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database and were separated into training and validation cohorts. Kaplan‒Meier curves and log-rank tests were employed to evaluate DSS, after which univariate and multivariate Cox regression analyses were used to construct a predictive nomogram and to estimate CDSS at 1, 2, and 3 years postoperatively in these patients.

Results

In the training cohort, the 3-year CDSS rose from 89.1% to 94.6% from 0 to 5 years postoperatively, while the 5-year CDSS rose from 84.5% to 92.0%. Cox regression analyses led to the construction of a nomogram that was able to reliably predict 3- and 5-year CDSS at 1, 2, and 3 years postoperatively (all P < 0.05) based upon patient age, tumor size, pT stage, pN stage, and the number of retrieved lymph nodes. This model exhibited good discriminative power in the training and validation cohorts (concordance index: 0.791 and 0.813, respectively), and nomogram calibration curves confirmed that actual and predicted survival outcomes were close to one another.

Conclusions

We herein developed a nomogram capable of accurately predicting the CDSS of EGC patients that had survived for multiple years after undergoing surgery.

Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the prognosis, and was significantly correlated with clinical indicators and molecular features. Compared with 77 published gene signatures, the TCTS exhibited superior predictive performance. In the external clinical cohort, a novel nomogram (comprising TNM stage, Hepatitis B, Vascular invasion, Perineural invasion, AFP and TCTS) was constructed to test the clinical performance of TCTS. The results showed that the high TCTS scoring group showed dismal prognosis, improved sensitivity to oxaliplatin and good response to anti-PD-1/PD-L1 immunotherapy. Moreover, the low TCTS score group had few genomic alterations, low immune activation and low PD-1/PD-L1 expression levels. In conclusion, TCTS is an ideal biomarker for predicting the clinical outcomes and improving precision treatment of HCC.

A pyroptosis-related gene signature provides an alternative for predicting the prognosis of patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular Carcinoma (HCC) is a common malignant neoplasm with limited treatment options and poor outcomes. Thus, there is an urgent need to find sensitive biomarkers for HCC.

METHODS

Gene expression and clinicopathological information were obtained from public databases, based on which a pyroptosis-related gene signature was constructed by the least absolute shrinkage and selection operator Cox regression. The applicability of the signature was evaluated via Kaplan-Meier curve and time-dependent ROC curve. TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT, ssGSEA, and ESTIMATE were employed to assess the immune status. Comparisons between groups were analyzed with Wilcoxon test. Pearson and Spearman correlation analyses were adopted for linear correlation analysis. Genetic knockdown was conducted using siRNA transfection and the mRNA expression levels of interest genes were measured using quantitative reverse transcription PCR. Finally, protein levels in 10 paired tumor tissues and adjacent non-tumor tissues from HCC patients were measured using immunohistochemistry.

RESULTS

A pyroptosis-related gene signature was established successfully to calculate independent prognostic risk scores. It was found that survival outcomes varied significantly between different risk groups. In addition, an attenuated antitumor immune response was found in the high-risk group. Meanwhile, multiple immune checkpoints were up-regulated in high-risk score patients. Cell cycle-related genes, angiogenesis-related genes and tumor drug resistance genes were also markedly elevated. Knockdown of prognostic genes in the signature significantly inhibited the expression of immune checkpoint genes and angiogenesis-related genes. Besides, each prognostic gene was expressed at a higher level in HCC tissues than in adjacent normal tissues.

CONCLUSIONS

We successfully established a novel pyroptosis-related gene signature which could help predict the overall survival and assess the immune status of HCC patients.

Correlation between systemic immune inflammatory index and prognosis of patients with hepatic alveolar hydatid disease and establishment of a nomogram prediction model

Background

To explore the evaluation value of systemic immune inflammation index (SII) in the prognosis of patients with alveolar hydatid disease, and establish a nomogram prediction model.

Methods

Collect the clinical data of 351 patients undergoing hepatic alveolar hydatid surgery admitted to the Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qinghai University from January 2015 to December 2020, calculate the SII value, and use the receiver operating characteristic curve (ROC curve) to determine According to the optimal clinical cut-off value of SII, patients were divided into two groups with high SII and low SII, and the relationship between SII and clinicopathological factors and prognosis of patients with alveolar echinococcosis was analyzed. Establish a nomogram prediction model based on independent risk factors for patient prognosis, and evaluate the prediction accuracy and discrimination ability of the nomogram through the consistency index (C-index) and calibration curve. The result is through the use of bootstrapping validation with 1,000 re-sampling Method for internal verification.

Results

The ROC curve was used to determine the optimal cut-off value of SII before operation 761.192, and patients were divided into low SII group (n = 184) cases and high SII group (n = 167) cases. The 1, 3, and 5-year survival rates of patients with hepatic alveolar hydatid in the low SII group and the high SII group were 98.90%, 96.90%, 86.50% and 98.20%, 72.50%, 40.30%, respectively. The survival rate of worm disease patients was significantly better than that of the high SII group, and the overall survival rate difference between the two groups was statistically significant (P < 0.001). Multivariate Cox regression model analysis results showed that intraoperative blood loss (HR = 1.810, 95%CI: 1.227-2.668, P = 0.003), SII (HR = 5.011, 95%CI: 3.052-8.228, P < 0.001), Complications (HR = 1.720, 95%CI: 1.162-2.545, P = 0.007) are independent risk factors for the prognosis of patients with alveolar hydatid disease. Draw a nomogram and include statistically significant factors in the multivariate Cox regression model to predict the overall survival rate of patients with alveolar hydatid disease at 1, 3, and 5 years. The survival probability calibration curve is displayed. The nomogram is compared with The actual results have a high degree of agreement. The concordance index (C-index) of the nomogram model in the modeling sample is 0.777, and the C-index in the verification sample is 0.797, indicating that the nomogram model of this study has good accuracy and discrimination.

Conclusions

SII has a clear correlation to the prognosis of patients with alveolar echinococcosis. The nomogram prediction model constructed on this basis is beneficial to the clinically individualized analysis of the patient's prognosis.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Combining Both Clinicopathological and Imaging Indicators

To construct a nomogram for early prediction of pathological complete response (pCR) in patients with breast cancer (BC) after neoadjuvant chemotherapy (NAC). A total of 257 patients with BC from the fourth hospital of Hebei Medical University were included in the study. The patients were divided into training (n = 128) and validation groups (n = 129). Variables were screened using univariate and multivariate logistic regression analyses, and the nomogram model was set up based on the training group. The training and validation groups were validated using the receiver operating characteristic (ROC) curves and calibration plots. The diagnostic value of the nomogram was evaluated using decision curve analysis (DCA). Indicators such as hormone receptor status, clinical TNM stage, and change rate in apparent diffusion coefficient of breast magnetic resonance imaging after two NAC cycles were used for nomogram construction. The calibration plots showed high consistency between nomogram-predicted and actual pCR probabilities in the training and validation groups. The areas under the curve of the ROC curve with discrimination ability were 0.942 and 0.921 in the training and validation groups, respectively. This showed an excellent discrimination ability of our nomogram for pCR prediction. Further, DCA showed favorable diagnostic value in our model. The nomogram may be instructive to clinicians for early prediction of pCR and helpful to adjust the treatment program on time in neoadjuvant management.

Blockade of growth hormone receptor signaling by using pegvisomant: A functional therapeutic strategy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive neoplasm with poor clinical outcome because most patients present at an advanced stage, at which point curative surgical options, such as tumor excision or liver transplantation, are not feasible. Therefore, the majority of HCC patients require systemic therapy. Nonetheless, the currently approved systemic therapies have limited effects, particularly in patients with advanced and resistant disease. Hence, there is a critical need to identify new molecular targets and effective systemic therapies to improve HCC outcome. The liver is a major target of the growth hormone receptor (GHR) signaling, and accumulating evidence suggests that GHR signaling plays an important role in HCC pathogenesis. We tested the hypothesis that GHR could represent a potential therapeutic target in this aggressive neoplasm. We measured GH levels in 767 HCC patients and 200 healthy controls, and then carried out clinicopathological correlation analyses. Moreover, specific inhibition of GHR was performed in vitro using siRNA and pegvisomant (a small peptide that blocks GHR signaling and is currently approved by the FDA to treat acromegaly) and in vivo, also using pegvisomant. GH was significantly elevated in 49.5% of HCC patients, and these patients had a more aggressive disease and poorer clinical outcome (P<0.0001). Blockade of GHR signaling with siRNA or pegvisomant induced substantial inhibitory cellular effects in vitro. In addition, pegvisomant potentiated the effects of sorafenib (P<0.01) and overcame sorafenib resistance (P<0.0001) in vivo. Mechanistically, pegvisomant decreased the phosphorylation of GHR downstream survival proteins including JAK2, STAT3, STAT5, IRS-1, AKT, ERK, and IGF-IR. In two patients with advanced-stage HCC and high GH who developed sorafenib resistance, pegvisomant caused tumor stability. Our data show that GHR signaling represents a novel “druggable” target, and pegvisomant may function as an effective systemic therapy in HCC. Our findings could also lead to testing GHR inhibition in other aggressive cancers.

Real-life multi-center retrospective analysis on nivolumab in difficult-to-treat patients with advanced hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. The landscape of the systemic treatment for advanced HCC is changing quickly, and recently, the standard of care became either atezolizumab plus bevacizumab or tremelimumab plus durvalumab in the single tremelimumab regular interval durvalumab regimen. Nivolumab monotherapy has proven to be effective sometimes for advanced HCC and could be a valuable treatment option for patients outside current treatment indications and reimbursement criteria for the standard of care. This is a particular population of interest.

AIM

To evaluate the real-world effectiveness of nivolumab monotherapy in patients with advanced HCC who are not eligible for other treatment.

METHODS

We conducted a retrospective, multicentric study including 29 patients with advanced HCC from 3 Belgian tertiary hospitals. All patients had had prior chemotherapy or were intolerant or ineligible for treatments. All study subjects received nivolumab 3 mg/kg in monotherapy, administered once every two weeks intravenously. Treatment continued until disease progression, severe adverse events or death. Data were retrieved from patients' medical records. The outcome parameters such as radiological response according to response evaluation criteria in solid tumors (RECIST) criteria, the biological response through the evolution of the alpha-fetoprotein (AFP) level, and clinical response considering both the Child-Pugh (CP) score and the World Health Organization (WHO) performance status (PS) were reported. A safety profile was also reported. Statistical analysis was performed using the SPSS Statistics 27 statistical software package.

RESULTS

The radiological overall response rate (defined as complete or partial response according to the immune RECIST and modified RECIST criteria) to nivolumab monotherapy was 24.1%. The biological overall response rate (defined as a decrease of ≥ 25% in AFP blood level) was 20.7%. Radiological and biological responses were significantly associated both with each other (P < 0.001) and with overall survival (P < 0.005 for radiological response and P < 0.001 for biological response). Overall survival was 14.5 mo (+/- 2.1), and progression-free survival was 10.9 mo (+/- 2.3). After 4 mo of treatment, 78.3% of patients remained clinically stable or even showed improvement in WHO PS. Grade 3 adverse events occurred in 17.2% of patients, none had grade 4 adverse events, and no patients ceased nivolumab due to adverse events.

CONCLUSION

Nivolumab monotherapy is a good treatment choice in frail patients with HCC who are ineligible for the standard of care or other validated systemic treatments.

Disruption of Growth Hormone Receptor Signaling Abrogates Hepatocellular Carcinoma Development

Introduction

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancers. It is an aggressive neoplasm with dismal outcome because most of the patients present with an advanced-stage disease, which precludes curative surgical options. Therefore, these patients require systemic therapies that typically induce small improvements in overall survival. Hence, it is crucial to identify new and promising therapeutic targets for HCC to improve the current outcome. The liver is a key organ in the signaling cascade triggered by the growth hormone receptor (GHR). Previous studies have shown that GHR signaling stimulates the proliferation and regeneration of liver cells and tissues; however, a definitive role of GHR signaling in HCC pathogenesis has not been identified.

Methods

In this study, we used a direct and specific approach to analyze the role of GHR in HCC development. This approach encompasses mice with global (Ghr-/- ) or liver-specific (LiGhr-/- ) disruption of GHR expression, and the injection of diethylnitrosamine (DEN) to develop HCC in these mice.

Results

Our data show that DEN induced HCC in a substantial majority of the Ghr+/+ (93.5%) and Ghr +/- (87.1%) mice but not in the Ghr-/- (5.6%) mice (P < 0.0001). Although 57.7% of LiGhr-/- mice developed HCC after injection of DEN, these mice had significantly fewer tumors than LiGhr+/+ (P < 0.001), which implies that the expression of GHR in the liver cells might increase tumor burden. Notably, the pathologic, histologic, and biochemical characteristics of DEN-induced HCC in mice resembled to a great extent human HCC, despite the fact that etiologically this model does not mimic this cancer in humans. Our data also show that the effects of DEN on mice livers were primarily related to its carcinogenic effects and ability to induce HCC, with minimal effects related to toxic effects.

Conclusion

Collectively, our data support an important role of GHR in HCC development, and suggest that exploiting GHR signaling may represent a promising approach to treat HCC.

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.

Hepatitis C virus eradication ameliorates the prognosis of advanced hepatocellular carcinoma treated with sorafenib

The risk of hepatocellular carcinoma (HCC) occurrence following hepatitis C virus (HCV) eradication has been previously reported, but the impact of HCV eradication on advanced HCC patient survival remains unclear. Therefore, the present study aimed to evaluate the effect of HCV eradication on the survival outcome of patients with advanced HCC treated with sorafenib. One hundred and three HCV-related advanced HCC patients who were treated with sorafenib were enrolled in this study. Of these, 43 patients were administered antiviral therapy before sorafenib treatment (HCV eradication group), while 60 patients remained HCV-infected (HCV non-eradication group). We analysed the impact of HCV eradication on survival in advanced HCC treated with sorafenib. Median overall survival (OS) was significantly longer in the HCV eradication group than in the HCV non-eradication group (24.0 months vs. 14.1 months; p = 0.001). Although there was no significant difference in the albumin-bilirubin (ALBI) score at the start of treatment between the HCV eradication group and the non-eradication group (p = 0.065), the ALBI score at 2 months after initiation of sorafenib treatment was significantly decreased in the HCV non-eradication group (p < 0.001), but not in the HCV eradication group (p = 0.121). Multivariate logistic analysis revealed HCV eradication (hazard ratio [HR], 0.5; p = 0.006) and ALBI score at the start of treatment (HR, 2.47; p = 0.002) as factors that may contribute to OS. HCV eradication may serve an important role in the survival outcome of advanced HCC patients treated with sorafenib.

Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI

Background Patients with intermediate- and advanced-stage hepatocellular carcinoma (HCC) represent a highly heterogeneous patient collective with substantial differences in overall survival. Purpose To evaluate enhancing tumor volume (ETV) and enhancing tumor burden (ETB) as new criteria within the Barcelona Clinic Liver Cancer (BCLC) staging system for optimized allocation of patients with intermediate- and advanced-stage HCC to undergo transarterial chemoembolization (TACE). Materials and Methods In this retrospective study, 682 patients with HCC who underwent conventional TACE or TACE with drug-eluting beads from January 2000 to December 2014 were evaluated. Quantitative three-dimensional analysis of contrast-enhanced MRI was performed to determine thresholds of ETV and ETB (ratio of ETV to normal liver volume). Patients with ETV below 65 cm³ or ETB below 4% were reassigned to BCLC B_n, whereas patients with ETV or ETB above the determined cutoffs were restratified or remained in BCLC C_n by means of stepwise verification of the median overall survival (mOS). Results This study included 494 patients (median age, 62 years [IQR, 56-71 years]; 401 men). Originally, 123 patients were classified as BCLC B with mOS of 24.3 months (95% CI: 21.4, 32.9) and 371 patients as BCLC C with mOS of 11.9 months (95% CI: 10.5, 14.8). The mOS of all included patients (including the BCLC B and C groups) was 15 months (95% CI: 12.3, 17.2). A total of 152 patients with BCLC C tumors were restratified into a new BCLC B_n class, in which the mOS was then 25.1 months (95% CI: 21.8, 29.7; P < .001). The mOS of the remaining patients (ie, BCLC C_n group) (n = 222; ETV ≥65 cm³ or ETB ≥4%) was 8.4 months (95% CI: 6.1, 11.2). Conclusion Substratification of patients with intermediate- and advanced-stage hepatocellular carcinoma according to three-dimensional quantitative tumor burden identified patients with a survival benefit from transarterial chemoembolization before therapy. © RSNA, 2022 Online supplemental material is available for this article.

Prognostic Nutritional Index in Hepatocellular Carcinoma Patients With Hepatitis B Following US-Guided Percutaneous Microwave Ablation: A Retrospective Study With 1,047 Patients

Objective

Several studies have revealed that the prognostic nutritional index (PNI) was associated with survival in several cancers. However, the prognostic value of PNI in hepatocellular carcinoma (HCC) patients following ultrasound-guided percutaneous microwave ablation (US-PMWA) remains unknown, especially in patients with hepatitis B virus (HBV) infection. Therefore, the present study aimed to evaluate the potential prognostic value of PNI in these patients.

Materials

The medical records of 1,047 HCC patients with HBV infection following US-PMWA were retrospectively reviewed. The association between preoperative PNI and overall survival (OS), as well as other clinical characteristics of HCC, were analyzed using the Kaplan–Meier plot, log-rank test, multi-parameter Cox proportional hazards model, restricted cubic spline (RCS), and time-dependent receiver operating characteristic (ROC) curve analyses.

Results

Patients with a preoperative PNI more than 45 were verified to have better OS than patients with a PNI less than 45. In the multi-parameter Cox proportional hazards models, the log-transformed PNI was verified as an independent prognostic factor for OS. The result of the RCS analysis revealed that there was a nearly linear relationship between PNI and OS. The area under the time-dependent ROC curve for PNI in predicting OS was 0.56, which is relatively stable.

Conclusion

Preoperative PNI represents a convenient, noninvasive, and independent prognostic indicator in HCC patients with HBV infection following US-PMWA.

The Development of Predictive Nomogram of Recurrence for Patients With Endometrioma After Cystectomy Who Were Younger Than 45 Years Old and Received Postoperative Therapy

Objective:

This study aimed to establish an effective prognostic nomogram for the postoperative recurrence of endometrioma or endometriosis-related pain for patients with endometrioma after long-term follow-up, who were younger than 45 years old and received postoperative therapy.

Methods

The predictive nomogram was based on 323 patients who underwent cystectomy for endometrioma at Perking Union Medical College Hospital from January 2009 to April 2013, and the last follow-up occurred in September 2018. We collected information on all included patients, including preoperative data, intraoperative data, and long-term follow-up data after surgery. The Cox proportional hazards regression model was used to evaluate the prognostic effects of multiple clinical parameters on recurrence. The survival curve was depicted based on Kaplan-Meier method and compared by log-rank method. The Index of concordance (C-index) and calibration curves were used to access the discrimination ability and predictive accuracy of the nomogram respectively, and the results were further validated via bootstrap resampling. In addition, calculating the area under the curve (AUC) via risk scores of patients aimed to further access the prediction ability of the model.

Results

On multivariate analysis of derivation cohort, independent factors for recurrence such as dysmenorrhea degree, sum of both cyst diameters, presence of adenomyosis, and other essential factors for recurrence such as age at surgery, presence of uterine fibroids were all selected into the nomogram. The C-index of the nomogram for predicting recurrence was 0.683 (95% CI, 0.610- 0.755). The calibration curve for probability of recurrence for 7 years and 9 years showed great agreement between prediction by nomogram and actual observation. Furthermore, the AUCs of risk score for 7-year and 9-year were 0.680 and 0.790 respectively.

Conclusion

This research tried to develop the predictive nomogram of recurrence for patients with endometrioma after cystectomy. The C-index and calibration curve of nomogram, as well as the AUC of the nomogram was potential to predict the recurrence probability. In addition, this predictive nomogram needs external data sets to further validate its prognostic accuracy in the future.

Preoperative Survival Prediction in Intrahepatic Cholangiocarcinoma Using an Ultrasound-Based Radiographic-Radiomics Signature

OBJECTIVES

To construct a preoperative model for survival prediction in intrahepatic cholangiocarcinoma (ICC) patients using ultrasound (US) based radiographic-radiomics signatures.

METHODS

Between April 2010 and September 2015, 170 patients with ICC who underwent curative resection were retrospectively recruited. Overall survival (OS)-related radiographic signatures and radiomics signatures based on preoperative US were built and assessed through a time-dependent receiver operating characteristic curve analysis. A nomogram was developed based on the selected predictors from the radiographic-radiomics signatures and clinical and laboratory results of the training cohort (n = 127), validated in an independent testing cohort (n = 43) by the concordance index (C-index), and compared with the Tumor Node Metastasis (TNM) cancer staging system as well as the radiographic and radiomics nomograms.

RESULTS

The median areas under the curve of the radiomics signature and radiographic signature were higher than that of the TNM staging system in the testing cohort, although the values were not significantly different (0.76-0.82 versus 0.62, P = .485 and .264). The preoperative nomogram with CA 19-9, sex, ascites, radiomics signature, and radiographic signature had C-indexes of 0.72 and 0.75 in the training and testing cohorts, respectively, and it had significantly higher predictive performance than the 8th TNM staging system in the testing cohort (C-index: 0.75 versus 0.67, P = .004) and a higher C-index than the radiomics nomograms (0.75 versus 0.68, P = .044).

CONCLUSIONS

The preoperative nomogram integrated with the radiographic-radiomics signature demonstrated good predictive performance for OS in ICC and was superior to the 8th TNM staging system.

Prognostic Nomograms Based on the Cirrhotic Severity Scoring for Preoperative Prediction of Long-Term Outcomes in Patients with HBV-Related Hepatocellular Carcinoma and Child-Pugh Grade A Liver Function

Background

Cirrhotic severity scoring (CSS) is a noninvasive method that can predict histological severity of cirrhosis. This study is aimed at assessing the predictive value of CSS on long-term outcomes after curative hepatectomy for patients with hepatitis B virus- (HBV-) related hepatocellular carcinoma (HCC) and Child-Pugh grade A liver function and further developing novel nomograms to preoperatively predict posthepatectomy recurrence and survival.

Methods

Consecutive patients who underwent curative hepatectomy for HCC between 2008 and 2014 were retrospectively studied. According to the CSS, patients were subclassified into 3 groups: no/mild, moderate, and severe cirrhosis. The impact of CSS on recurrence-free survival (RFS) and overall survival (OS) was assessed. Furthermore, RFS and OS nomograms were developed.

Results

The 5-year RFS and OS rates were 36.1% and 62.8% in the no/mild cirrhosis group, compared with 28.4% and 56.2% in the moderate cirrhosis group, and 16.2% and 33.0% in the severe cirrhosis group. Long-term survival outcomes were significantly worse with the increment of cirrhotic severity. CSS, alpha-fetoprotein level, tumor size, tumor number, and macrovascular invasion were identified as independent predictors of both RFS and OS. Besides, albumin-bilirubin grade was an independent risk factor of OS not RFS. RFS- and OS-predictive nomograms based on these preoperative variables were built. For these 2 nomograms, the C-indexes were 0.696 and 0.732, respectively. Calibration curves exhibited good agreement between actual observation and nomogram prediction.

Conclusions

CSS was a predictor for long-term outcomes in HCC patients after curative hepatectomy. The novel nomograms exhibited accurate preoperative prediction of posthepatectomy recurrence and OS.

Development of a prognostic nomogram for metastatic pancreatic ductal adenocarcinoma integrating marital status

Previous studies have shown that marital status can affect the overall survival (OS) of cancer patients yet its role in metastatic pancreatic ductal adenocarcinoma (mPDAC) remains unclear. This study aimed to explore the impact of marital status on the OS of mPDAC patients and to construct a prognostic nomogram to predict OS outcomes. Data from patients diagnosed with mPDAC were obtained from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. The patients were randomized into primary and validation cohorts. Kaplan-Meier survival analysis was performed to compare differences in survival depending on marital status. Univariate and multivariate analyses were conducted to identify independent prognostic factors and a nomogram was established based using Cox regression analyses. Validation of the prognostic nomogram was evaluated with a calibration curve and concordance index (C-index). Our data showed significant differences in the OS of mPDAC patients with different marital status by Kaplan-Meier analysis (P < 0.05). Univariate and multivariate analyses confirmed that marital status was an independent OS-related factor in mPDAC patients. Based on the multivariate models of the primary cohort, a nomogram was developed that combined marital status, age, grade, tumor size, surgery of primary site, surgery of lymph node and metastatic. The nomogram showed that marital status had a moderate influence on predicting the OS of mPDAC patients. Moreover, the internally and externally validated C-indexes were 0.633 and 0.619, respectively. A calibration curve confirmed favorable consistency between the observed and predicted outcomes. Marital status was identified as an independent prognostic factor for OS of mPDAC patients and is a reliable and valid parameter to predict the survival of patients with mPDAC. This prognostic model has value and may be integrated as a tool to inform decision-making in the clinic.

Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

Background

This study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.

Methods

We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes.

Results

Seventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.

Conclusion

We constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Background

To develop a precise prognostic model of overall survival in patients with terminating cervical cancer based on surveillance, epidemiology, and end results (SEER) program.

Methods

The patients were retrieved from SEER data who are diagnosed with terminating cervical cancer from 2004 to 2016. The data were performed using univariate and multivariate analyses and constructed nomograms for predicting survival. Use C-index to validate the model accuracy.

Results

Totally 15839 patients diagnosed with cervical cancer were independently allocated into the training set (n = 11088) and validation set (n = 4751). The multivariate analysis results indicated that age, race, stage_T, stage_M, and stage_N were confirmed as independent risk predictors, and those factors are applied to construct this clinical model. The C-index of overall survival in the training set was 0.6816 (95% confidence intervene (CI), 0.694-0.763) and that in the validation set was 0.6931(95% CI, 0.613-0.779). All calibration curves of various factors were consistent with predicted and actual survival.

Conclusion

The nomogram provides a novel method for predicting the survival of patients with terminating cervical cancer, assisting in accurate therapeutic methods for patients with primary terminating cervical cancer.

Scoring systems for the management of oncological hepato-pancreato-biliary patients

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

The clinical prognostic factors of patients with stage IB lung adenocarcinoma

Background

Lung adenocarcinoma (ADC) at stage IB has its own prognostic characteristics. This study aimed to investigate the clinical factors that may affect the prognosis of patients with stage IB ADC.

Methods

The data of ADC cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2016) and patients in Zhongshan Hospital, Fudan University (Department of Thoracic Surgery, 2015–2016). Kaplan-Meier method was used to obtain the overall survival (OS). Factors that significantly related to the prognosis were evaluated by univariate and multivariate analysis (UVA, MVA) using the Cox model. A nomogram was developed and validated to predict the 3-year OSs of those patients.

Results

7,605 patients with stage IB ADC were included ultimately and were divided into two groups, a training cohort (n=5,324) and a test cohort (n=2,281). Besides, there was a validation cohort (n=272) for the verification of the nomogram model. Those with significantly older age, male, the white race, lower grades of tumor differentiation, larger tumor size (31–40 mm) without pleural layer (PL) invasion as well as receiving sublobectomy suffered from poorer survival (P<0.001), which were identified as independent factors for stage IB ADC (P<0.001), and according to which, a nomogram model was created.

Conclusions

Age, sex, race, histological grade, surgery to the primary site, and tumor size combined with PL invasion were independent risk factors for stage IB ADC, based on which a nomogram was constructed to predict the prognosis.

Development and Validation of Nomograms to Predict Overall Survival Outcomes in Serous Ovarian Cancer Patients with Satisfactory Cytoreductive Surgery and Chemotherapy

Objective

Nomograms are statistics-based predictive tools that integrate predictive factors. Herein, a nomogram was developed and validated to predict the overall survival (OS) in serous ovarian cancer (SOC).

Methods

Primary SOC patients with satisfactory cytoreductive surgery, chemotherapy, and OS ≥1 month were included in this study. A total of 6957 patients from the Surveillance, Epidemiology, and End Results (SEER) database comprised the training group and 1244 patients comprised the external validation group. The nomogram was structured on Cox models and evaluated in both the training and validation groups using consistency index, area under the receiver operating characteristics curve, calibration plots, and risk subgroup classification. Kaplan–Meier curves were plotted to compare the survival outcomes between subgroups. A decision-curve analysis was used to test the clinical value of the nomogram.

Results

Independent factors, including age, tumor grade, and Federation of Gynecology and Obstetrics (FIGO) stage, identified by multivariate analysis in the training cohort, were selected for the nomogram. The consistency indexes for OS were 0.689 in the training cohort and 0.639 in the validation cohort. The calibration curves showed good consistency between predicted and actual 3- and 5-year OS. Significant differences were observed in the survival curves of different risk subgroups. The decision-curve analysis indicated that our nomogram was superior to the American Joint Committee on Cancer (AJCC) staging system.

Conclusion

A nomogram was constructed to predict the long-term OS in SOC and verified in Asians. The accurate predictions facilitated personalized treatments and follow-up strategies.

Evaluation of staging systems to predict prognosis in hepatocellular carcinoma patients treated with radioembolization

Purpose

To compare the prognostic accuracy of nine staging systems, some of which are well-known and some of which have only been more recently described, for patients with unresectable HCC treated with radioembolization (RE).

Materials and methods

Individual scores or classes for the following staging systems were recorded or calculated for patients (n = 89) with unresectable HCC who underwent RE at a single tertiary care center from January 2008 to October 2016: Eastern Cooperative Oncology Group, Barcelona Clinic Liver Cancer, Hong Kong Liver Cancer, Okuda, Cancer of the Liver Italian Program (CLIP), Model for End Stage Liver Disease, Child-Pugh (CP) Categorical and Numeric, and Albumin-Bilirubin. For each staging system, a cox proportional hazards regression model was fit to the data and log-rank test statistics, concordance indices, Akaike Information Criteria (AIC) and other diagnostic statistics were calculated.

Results

Of the nine staging systems analyzed, the basic discriminatory ability assessed with the log-rank test (rejected at the α = .05-level) was significant for two of the systems: CP Numeric (p < .001) and CLIP (p < .05). Out of these two systems, CP Numeric system had a higher prognostic accuracy than CLIP with the lowest AIC (464.90), the highest optimism-corrected pseudo R² (0.16), and the highest estimated concordance index (0.64).

Conclusion

As applied to our patient population, the CP Numeric system contained the most predictive prognostic information for patients with HCC undergoing radioembolization. However, all evaluated staging systems performed suboptimally, and the relative superiority of any of the systems remains unclear when ranking them according to common practice. Further evaluation of current ranking methodologies is recommended.

•

There is no consensus on utility of staging systems to predict prognosis of HCC patients.

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Data for HCC patients undergoing RE is only available from a single center.

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Basic discriminatory ability (log-rank) was significant only for Child-Pugh Numeric and CLIP.

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Further analyses were performed to rank the staging systems.

•

The results suggest that Child-Pugh Numeric has an overall greater prognostic value.

Predictive factors for long-term survival in patients with advanced hepatocellular carcinoma treated with sorafenib

BACKGROUND AND AIMS

Sorafenib, used for advanced-stage hepatocellular carcinoma (HCC), has an overall survival (OS) of 10 months. However, some patients have better response and long-term survival (LTS). Aims to assess predictive factors for LTS.

METHODS

Retrospectively reviewed 77 advanced HCC patients, starting sorafenib treatment between 2007 and 2016, with LTS (OS ≥24 months) as primary endpoint. Univariate and multivariable analysis of clinical variables were performed in order to identify predictive factors for LTS.

RESULTS

Patients: seventy (90.9%) males; median age: 65 years (39-82). All had cirrhosis mostly HCV infection (n = 32, 41.6%). Majority were Child-Pugh class A (n = 50, 64.9%); median MELD-Na: 11 (6-30). Multinodular HCC: 74% (n = 57); portal vein invasion (PVI): 50.6% (n = 39); extrahepatic spread: 18.2% (n = 14). Median time between HCC diagnosis and sorafenib start: 3.3 months (0-37.6). Median OS: 13 months [95% confidence interval (CI) 8.2-17.8]. Twenty-five (32.5%) patients were considered LTS, with amedian OS: 52.3 months (95% CI 17.1-87.4). Multivariable analysis identified Child-Pugh class A [odds ratio (OR) 11.1, 95% CI 1.78-69.54] and absence of PVI (OR 7.88, 95% CI 1.56-39.8) as independent predictors of LTS. Sub-analysis of Child-Pugh class A: absence of PVI (OR 7.13, 95% CI 1.69-30.2) and alpha-fetoprotein <400 ng/ml (OR 5.82, 95% CI 1.18-28.75) independently related to LTS.

CONCLUSION

Despite global short median OS, sorafenib treatment is associated with longer than 2-year survival in a sub-group, more likely in compensated liver disease and absence of PVI.

Nonmetastatic breast cancer patients subsequently developing second primary malignancy: A population-based study

Background

With life span extending, breast cancer (BC) survivors may face the possibility of developing second primary cancer (SPC) and considerably shorten survivorship. However, little is known about multiple primary cancer (MPC) patients with nonmetastatic breast cancer as a first primary malignancy (BCFPM).

Methods

Here, we retrospectively analyzed data on cancer survivors with BCFPM diagnosed between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic factors for breast cancer‐specific survival (BCSS) were ascertained by the stepwise regression analysis and a competing risk model, and were integrated to the establishment of prognostic nomogram, of which the accuracy was measured by the calibration curve and the concordance index (C‐index).

Results

In total, 8616 patients were identified with 4.6% of 3‐year breast cancer‐ specific death (BCSD) and 8.6% of 5‐year BCSD. The most common SPC among BCFPM patients were female BC and lung cancer. Besides, the median latency time between BC and SPC was 22 months. At a ratio of 7:3, all patients were randomly categorized into a training cohort (n = 6032) and a validation cohort (n = 2584). By a proportional subdistribution hazards regression analysis, the following factors were considered to own independent prognostic abilities of BCSS: subtypes, grade, T classification, N classification, radiation, and sites of SPC. The nomogram could accurately predict 3‐year and 5‐year breast cancer‐associated survival of BCFPM patients with high internal and external validated C‐index, 0.715 (95% CI, 0.691–0.739), and 0.683 (95% CI, 0.642–0.724), respectively.

Conclusions

BC survivors remained a high risk of developing SPC and considerably shortened survival time. In this study, a favorable nomogram was constructed to as a prediction model for 3‐year and 5‐year BCSS of BCFPM patients, largely intending to prolong the life of these patients by assisting clinicians to make individualized follow‐up plans.

The decreased platelet-to-lymphocyte ratio could predict a good prognosis in patients with oligometastatic colorectal cancer: a single-center cohort retrospective study

BACKGROUND

Inflammation markers have an important effect on tumor proliferation, invasion, and metastasis. Oligometastatic disease (OMD) is an intermediate state between widespread metastases and locally confined disease, where curative strategies may be effective for some patients. We aimed to explore the predictive value of inflammatory markers in patients with oligometastatic colorectal cancer (OMCC) and build a nomogram to predict the prognosis of these patients.

METHODS

Two hundred nine patients with OMCC were retrospectively collected in this study. The Kaplan-Meier survival curves and Cox regression analysis were used to estimate overall survival (OS) and progression-free survival (PFS). A multivariate Cox analysis model was utilized to establish the nomogram. The concordance index (C-index), calibration curve, and receiver operating characteristics (ROC) were established to verify the validity and accuracy of the prediction model.

RESULTS

According to the multivariate analysis, decreased platelet-to-lymphocyte ratio (PLR) might independently improve OS in patients with OMCC (HR = 2.396, 95% CI 1.391-4.126, P = 0.002). Metastases of extra-regional lymph nodes indicated poor OS (HR = 2.472, 95% CI 1.247-4.903, P = 0.010). While the patients with early N stage had better OS (HR = 4.602, 95% CI 2.055-10.305, P = 0.001) and PFS (HR = 2.100, 95% CI 1.364-3.231, P = 0.007). Primary tumor resection (HR = 0.367, 95% CI 0.148-0.908, P = 0.030) and lower fibrinogen (HR = 2.254, 95% CI 1.246-4.078, P = 0.007) could significantly prolong the OS in patients with OMCC. PLR, metastases of extra-regional lymph nodes, N stage, primary tumor resection, and fibrinogen were used to make up the nomogram. The C-index and area under the curve (AUC) of the ROC in nomogram were 0.721 and 0.772 respectively for OS, showed good consistency between predictive probability of OS and actual survival.

CONCLUSIONS

Decreased PLR could predict a good prognosis in patients with OMCC. The nomogram including inflammatory factors and clinicopathological markers was credible and accurate to predict survivals in patients with OMCC.