Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PurposeTo develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.MethodsT1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell''s concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).ResultsThe nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.ConclusionsUnlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.     

A predictive model with a risk-classification system for cancer-specific survival in patients with primary osteosarcoma of long bone

BackgroundOsteosarcoma (OS), most commonly occurring in long bone, is a group of malignant tumors with high incidence in adolescents. No individualized model has been developed to predict the prognosis of primary long bone osteosarcoma (PLBOS) and the current AJCC TNM staging system lacks accuracy in prognosis prediction. We aimed to develop a nomogram based on the clinicopathological factors affecting the prognosis of PLBOS patients to help clinicians predict the cancer-specific survival (CSS) of PLBOS patients.MethodWe studied 1199 PLBOS patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 and randomly divided the dataset into training and validation cohorts at a proportion of 7:3. Independent prognostic factors determined by stepwise multivariate Cox analysis were included in the nomogram and risk-stratification system. C-index, calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsAge, Histological type, Surgery of primary site, Tumor size, Local extension, Regional lymph node (LN) invasion, and Distant metastasis were identified as independent prognostic factors. C-indexes, calibration curves and DCAs of the nomogram indicating that the nomogram had good discrimination and validity. The risk-stratification system based on the nomogram showed significant differences (P < 0.05) in CSS among different risk groups.ConclusionWe established a nomogram with risk-stratification system to predict CSS in PLBOS patients and demonstrated that the nomogram had good performance. This model can help clinicians evaluate prognoses, identify high-risk individuals, and give individualized treatment recommendation of PLBOS patients.     

Development and Validation of Nomograms for Predicting Overall and Breast Cancer–Specific Survival in Young Women with Breast Cancer: A Population-Based Study

INTRODUCTION: The objective of current study was to develop and validate comprehensive nomograms for predicting the survival of young women with breast cancer. METHODS: Women aged <40 years diagnosed with invasive breast cancer between 1990 and 2010 were selected from the Surveillance, Epidemiology, and End Results database and randomly divided into training (n = 12,465) and validation (n = 12,424) cohorts. A competing-risks model was used to estimate the probability of breast cancer–specific survival (BCSS). We identified and integrated significant prognostic factors for overall survival (OS) and BCSS to construct nomograms. The performance of the nomograms was assessed with respect to calibration, discrimination, and risk group stratification. RESULTS: The entire cohort comprised 24,889 patients. The 5- and 10-year probabilities of breast cancer–specific mortality were 11.6% and 20.5%, respectively. Eight independent prognostic factors for both OS and BCSS were identified and integrated for the construction of the nomograms. The calibration curves showed optimal agreement between the predicted and observed probabilities. The C-indexes of the nomograms in the training cohort were higher than those of the TNM staging system for predicting OS (0.724 vs 0.694; P < .001) and BCSS (0.733 vs 0.702; P < .001). Additionally, significant differences in survival were observed in patients stratified into different risk groups within respective TNM categories. CONCLUSIONS: We developed and validated novel nomograms that can accurately predict OS and BCSS in young women with breast cancer. These nomograms may help clinicians in making decisions on an individualized basis.     

Lymph node ratio-based nomogram for prognosis evaluation and treatment optimization of non-metastatic oral cavity squamous cell carcinoma

BackgroundLymph node ratio (LNR) has been increasingly reported as a prognostic factor in oral cavity squamous cell carcinoma (OCSCC). This study aimed to develop and validate a prognostic nomogram integrating LNR and to further assess its role in guiding adjuvant therapy for OCSCC.MethodsA total of 8703 OCSCC patients treated primarily with surgery in the Surveillance, Epidemiology and End Results (SEER) database were retrieved and randomly divided into training and validation cohorts. The nomogram was created based on the factors identified by Cox model. The value of PORT and chemotherapy was respectively evaluated in each prognostic group according to nomogram-deduced individualized score.ResultsThe final nomogram included tumor site, grade, T stage, number of positive lymph nodes and LNR. Calibration plots demonstrated a good match between predicted and observed rates of overall survival (OS). The concordance indexes for training and validation cohorts were 0.720 (95% confidence interval (CI): 0.708, 0.732) and 0.711 (95% CI: 0.687, 0.735), both significantly higher than did TNM stage (p< 0.001). According to individualized nomogram score, patients were stratified into three subgroups with significantly distinct outcome. PORT presented survival benefit among medium- and high-risk groups whereas a near-detrimental effect in low-risk group. Chemotherapy was found to be beneficial only in high-risk group.ConclusionThis LNR-incorporated nomogram surpassed the conventional TNM stage in predicting prognosis of patients with non-metastatic OCSCC and identified sub-settings that could gain survival benefit from adjuvant thearpy.     

A Novel Nomogram for Predicting the Overall Survival in Patients with Unresectable HCC after TACE plus Hepatic Arterial Infusion Chemotherapy

Background and aimTransarterial chemoembolization combined with hepatic arterial infusion chemotherapy (TACE-HAIC) has shown encouraging efficacy in the treatment of unresectable hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict overall survival (OS) of patients with unresectable HCC treated with TACE-HAIC.MethodsA total of 591 patients with unresectable HCC treated with TACE-HAIC between May 2009 and September 2020 were enrolled. These patients were randomly divided into training and validation cohorts. The independent prognostic factors were identified with Cox proportional hazards model. The model's discriminative ability and accuracy were validated using concordance index (C-index), calibration plots, the area under the time-dependent receiver operating characteristic curve (AUC) and decision curve analyses (DCAs).ResultsThe median OS was 15.6 months. A nomogram was established based on these factors, including tumor size, vein invasion, extrahepatic metastasis, tumor number, alpha fetoprotein (AFP), and albumin-bilirubin (ALBI), to predict OS for patients with unresectable HCC treated with TACE-HAIC. The C-index of the nomogram were 0.717 in the training cohort and 0.724 in validation cohort. The calibration plots demonstrated good agreement between the predicted outcomes and the actual observations. The AUC values were better than those of three conventional staging systems. The results of DCA indicated that the nomogram may have clinical usefulness. The patients in the low-risk group had a longer OS than those in intermediate-risk and high-risk groups (P<0.001).ConclusionA prognostic nomogram was developed and validated to assist clinicians in accurately predicting the OS of patients with unresectable HCC after TACE-HAIC.     

Prognostic Value of E-cadherin-, CD44-, and MSH2-associated Nomograms in Patients With Stage II and III Colorectal Cancer

BACKGROUND: To evaluate the prognostic value of E-cadherin, CD44, and MSH2 expression for colorectal cancer (CRC) and construct nomograms that can predict prognosis. METHODS: We retrospectively analyzed the expression of E-cadherin, CD44, and MSH2 in 223 paraffin-embedded stage II and III CRC specimens using immunohistochemistry in the training cohort. Their prognostic values were assessed using Kaplan–Meier curves and univariate and multivariate COX regression models. Moreover, a number of risk factors were used to form nomograms to evaluate survival, and Harrell's concordance index (C-index) was used to evaluate the predictive accuracy. Further validation of the nomograms was performed in an independent cohort of 115 cases. RESULTS: Low E-cadherin expression and low CD44 expression were significantly associated with diminished overall survival (OS) and disease-free survival (DFS) in stage II and III CRC patients and patients with negative MSH2 expression had better clinical outcomes. Moreover, the multivariate COX analysis identified E-cadherin, CD44 and MSH2 expression as independent prognostic factors for DFS and OS. Using these three markers and three clinicopathological risk variables, two nomograms were constructed and externally validated for predicting OS and DFS (C-index: training cohort, 0.779 (95% CI 0.722–0.835) and 0.771 (0.720–0.822), respectively; validation cohort, 0.773 (0.709–0.837) and 0.670 (0.594–0.747), respectively). CONCLUSION: The expression levels of E-cadherin, CD44 and MSH2 were independent prognostic factors for stage II and III CRC patients. By incorporating clinicopathological features and these biomarkers, we have established two nomograms that could be used to make individualized predictions for OS and DFS.     

Development of novel DNAJB6-KIAA1522-p-mTOR three-protein prognostic prediction models for CRC

BackgroundTo evaluate the prognostic value of DNAJB6, KIAA1522, and p-mTOR expression for colorectal cancer (CRC) and to develop effective prognostic models for CRC patients.MethodsThe expression of DNAJB6, KIAA1522, and p-mTOR (Ser2448) was detected using immunohistochemistry in 329 CRC specimens. The prognostic values of the three proteins in the training cohort were assessed using Kaplan-Meier curves and univariate and multivariate Cox proportional hazards models. Prediction nomogram models integrating the three proteins and TNM stage were constructed. Subsequently, calibration curves, receiver operating characteristic (ROC) curves, the concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of the nomograms in the training and validation cohorts.ResultsThe three proteins DNAJB6, KIAA1522, and p-mTOR were significantly overexpressed in CRC tissues (each P < 0.01), and their expression was an independent prognostic factor for overall survival (OS) and disease-free survival (DFS) (each P < 0.05). The area under the ROC curves (AUC) and C-index values were approximately 0.7. Additionally, the calibration curves showed that the predicted values and the actual values fit well. Furthermore, DCA curves indicated that the clinical value of the nomogram models was higher than that of TNM stage. Overall, the novel prediction models have good discriminability, sensitivity, specificity and clinical utility.ConclusionThe nomograms containing DNAJB6, KIAA1522, and p-mTOR may be promising models for predicting postoperative survival in CRC.     

Increased CYFRA 21-1, CEA and NSE are Prognostic of Poor Outcome for Locally Advanced Squamous Cell Carcinoma in Lung: A Nomogram and Recursive Partitioning Risk Stratification Analysis

OBJECTIVES: This study aimed to: (1) assess the prognostic significance of serum tumor markers in locally advanced squamous cell carcinoma in lung (LA-SCCL); (2) generate a nomogram to predict the overall survival (OS) and (3) identify a prognostic stratification to assist the therapeutic decision-making. METHODS: LA-SCCL patients receiving definitive radiotherapy and baseline tumor marker measurement were eligible for this retrospective study. Cox proportional hazards regression was used to determine independent factors associated with various survival indexes and a nomogram was created to estimate the 5-year OS probability for individual patient. The identified prognostic factors were recruited into a recursive partitioning analysis (RPA) for OS to stratify patients with distinct outcome. RESULTS: A total of 224 patients were eligible for analysis. Increased cytokeratin-19 fragment (CYFRA 21-1) was independently associated with inferior OS, progression free survival (PFS) and a borderline decreased local-regional progression free survival (LRPFS). Elevated carcino-embryonic antigen (CEA) served as an unfavorable determinant for OS and increased neuron-specific enolase (NSE) was predictive of poor distant metastasis free survival (DMFS). A nomogram integrating KPS, TNM stage, CEA and CYFRA 21-1 was created, resulting in a c-index of 0.62. RPA identified 4 prognostic classifications, with median OS of 27.6, 19.9, 17.3 and 10.9?months for low, intermediate, high and very-high risk groups, respectively. CONCLUSIONS: Baseline tumor marker panel including CYFRA 21-1, CEA and NSE can be prognostic of outcome for LA-SCCL receiving definitive radiotherapy. The RPA identified four prognostic subgroups, which could assist personalized therapy and clinical trial design in LA-SCCL.     

Prognostic Significance of CSN2, CD8, and MMR Status-Associated Nomograms in Patients with Colorectal Cancer

BACKGROUND: COP9 signalosome subunit 2 (CSN2) is believed to be involved in human cancer, but its prognostic significance in colorectal cancer (CRC) has not been elucidated. PATIENTS AND METHODS: We retrospectively analyzed the expression of CSN2 andCD8+ tumor-infiltrating lymphocytes (TILs), and mismatch repair (MMR) status in 267 paraffin-embedded specimens using immunohistochemistry in a training cohort. A number of risk factors were used to form nomograms to evaluate survival, and Harrell's concordance index (C-index) was used to evaluate the predictive accuracy. Further validation was performed in an independent cohort of 238cases. RESULTS: Low CSN2 expression and a low number of CD8 + TILs were significantly associated with diminished disease-free survival (DFS) and overall survival (OS) in CRC patients, and patients with MMR-deficient CRC had enhanced DFS and OS. Moreover, the multivariate Cox analysis identified CSN2, CD8 + TILs, and MMR status as independent prognostic factors for DFS and OS. Using these three markers and four clinicopathological risk variables, two nomograms were constructed and validated for predicting DFS and OS (C-index: training cohort, 0.836 (95% CI:0.804–0.868) and 0.841 (0.808–0.874), respectively; validation cohort, 0.801 (0.760–843) and 0.843 (0.806–0.881), respectively). CONCLUSIONS: CSN2, CD8+ TILs, and MMR status were independent prognostic factors. The nomograms could be used to generate individualized predictions for DFS and OS.     

Prognostic Nomogram for Thoracic Esophageal Squamous Cell Carcinoma after Radical Esophagectomy

Nomogram has demonstrated its capability in individualized estimates of survival in diverse cancers. Here we retrospectively investigated 1195 patients with esophageal squamous-cell carcinoma (ESCC) who underwent radical esophagectomy at Zhejiang Cancer Hospital in Hangzhou, China. We randomly assigned two-thirds of the patients to a training cohort (n = 797) and one-third to a validation cohort (n = 398). Cox proportional hazards regression analyses were performed using the training cohort, and a nomogram was developed for predicting 3-year and 5-year overall survival rates. Multivariate analysis identified tumor length, surgical approach, number of examined lymph node, number of positive lymph node, extent of positive lymph node, grade, and depth of invasion as independent risk factors for survival. The discriminative ability of the nomogram was externally determined using the validation cohort, showing that the nomogram exhibited a sufficient level of discrimination according to the C-index (0.715, 95% CI 0.671–0.759). The C-index of the nomogram was significantly higher than that of the sixth edition (0.664, P-value<0.0001) and the seventh edition (0.696, P-value<0.0003) of the TNM classification. This study developed the first nomogram for ESCC, which can be applied in daily clinical practice for individualized survival prediction.     

Development and validation of two nomograms for predicting overall survival and cancer-specific survival in gastric cancer patients with liver metastases: A retrospective cohort study from SEER database

BackgroundGastric cancer is heterogeneous and aggressive, especially with liver metastasis. This study aims to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of gastric cancer with liver metastasis (GCLM) patients.MethodsFrom January 2000 to December 2018, a total of 1936 GCLM patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. They were further divided into a training cohort and a validation cohort, with the OS and CSS serving as the study's endpoints. The correlation analyses were used to determine the relationship between the variables. The univariate and multivariate Cox analyses were used to confirm the independent prognostic factors. To discriminate and calibrate the nomogram, calibration curves and the area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used. DCA curves were used to examine the accuracy and clinical benefits. The clinical utility of the nomogram and the AJCC Stage System was compared using net reclassification improvement (NRI) and integrated differentiation improvement (IDI) (IDI). Finally, the nomogram and the AJCC Stage System risk stratifications were compared.ResultsThere was no collinearity among the variables that were screened. The results of multivariate Cox regression analysis showed that six variables (bone metastasis, lung metastasis, surgery, chemotherapy, grade, age) and five variables (lung metastasis, surgery, chemotherapy, grade, N stage) were identified to establish the nomogram for OS and CSS, respectively. The calibration curves, time-dependent AUC curves, and DCA revealed that both nomograms had pleasant predictive power. Furthermore, NRI and IDI confirmed that the nomogram outperformed the AJCC Stage System.ConclusionBoth nomograms had satisfactory accuracy and were validated to assist clinicians in evaluating the prognosis of GCLM patients.     

A high-quality model for predicting the prognosis of breast neuroendocrine carcinoma to help clinicians decide on appropriate treatment methods: A population-based analysis

BackgroundBreast neuroendocrine carcinoma (NEC) is a rare malignancy with unclear treatment options and prognoses. This study aimed to construct a high-quality model to predict overall survival (OS) and breast cancer-specific survival (BCSS) and help clinicians choose appropriate breast NEC treatments.Patients and methodsA total of 378 patients with breast NEC and 349,736 patients with breast invasive ductal carcinoma (IDC) were enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2018. Propensity score matching (PSM) was performed to balance the clinical baseline. Prognostic factors determined by multivariate Cox analysis were included in the nomogram. C-index and calibration curves were used to verify the performance of the nomogram.ResultsNomograms were constructed for the breast NEC and breast IDC groups after PSM. The C–index of the nomograms ranged from 0.834 to 0.880 in the internal validation and 0.818–0.876 in the external validation, indicating that the nomogram had good discrimination. The risk stratification system showed that patients with breast NEC had worse prognoses than those with breast IDC in the low-risk and intermediate-risk groups but had a similar prognosis that those in the high-risk group. Moreover, patients with breast NEC may have a better prognosis when undergoing surgery plus chemotherapy than when undergoing surgery alone or chemotherapy alone.ConclusionsWe established nomograms with a risk stratification system to predict OS and BCSS in patients with breast NEC. This model could help clinicians evaluate prognosis and provide individualized treatment recommendations for patients with breast NEC.     

Survival Nomogram for Curatively Resected Korean Gastric Cancer Patients: Multicenter Retrospective Analysis with External Validation

BackgroundA small number of nomograms have been previously developed to predict the individual survival of patients who undergo curative resection for gastric cancer. However, all were derived from single high-volume centers. The aim of this study was to develop and validate a nomogram for gastric cancer patients using a multicenter database.MethodsWe reviewed the clinicopathological and survival data of 2012 patients who underwent curative resection for gastric cancer between 2001 and 2006 at eight centers. Among these centers, six institutions were randomly assigned to the development set, and the other two centers were assigned to the validation set. Multivariate analysis using the Cox proportional hazard regression model was performed, and discrimination and calibration were evaluated by external validation.ResultsMultivariate analyses revealed that age, tumor size, lymphovascular invasion, depth of invasion, and metastatic lymph nodes were significant prognostic factors for overall survival. In the external validation, the concordance index was 0.831 (95% confidence interval, 0.784–0.878), and Hosmer-Lemeshow chi-square statistic was 3.92 (P = 0.917).ConclusionsWe developed and validated a nomogram to predict 5-year overall survival after curative resection for gastric cancer based on a multicenter database. This nomogram can be broadly applied even in general hospitals and is useful for counseling patients, and scheduling follow-up.     

Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.     

Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction

PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.     

Prognostic Nomogram for Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy

This study was aimed to define possible predictors of overall survival in nasopharyngeal carcinoma (NPC). Patients were treated with intensity-modulated radiation therapy (IMRT), to establish an effective prognostic nomogram that could provide individualized predictions of treatment outcome in this setting. We reviewed the records of 533 patients with non-metastatic NPC who underwent IMRT with or without concurrent chemotherapy at the Department of Radiation Oncology of Sun Yat-Sen University from 2002 to 2009; none of these patients received induction or adjuvant chemotherapy. These data sets were used to construct a nomogram based on Cox regression. Nomogram performance was determined via a concordance index (C-index) and a calibration curve which was compared with the TNM staging system for NPC. The results were validated in an external cohort of 442 patients from the Department of Radiation Oncology of Wenzhou Medical College who were treated during the same period. Results showed that the greatest influence on survival were primary gross tumor volume, age, tumor stage and nodal stage (2002 Union for International Cancer Control [UICC] staging system), which were selected into the nomogram. The C-index of the nomogram for predicting survival was 0.748 (95%CI, 0.704–0.785), which was statistically higher than that of TNM staging system (0.684, P<0.001). The calibration curve exhibited agreement between nomogram-predicted and the actual observed probabilities for overall survival. In the validation cohort, the nomogram discrimination was superior to the TNM staging system (C-index: 0.768 vs 0.721; P = 0.026). In conclusion, the nomogram proposed in this study resulted in more-accurate prognostic prediction for patients with NPC after IMRT and compared favorably to the TNM staging system; this individualized information will aid in patient counseling and may be used for de-escalation trials in the future.     

Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

This study built and tested two effective nomograms for the purpose of predicting cancer-specific survival and overall survival of chromophobe renal cell carcinoma (chRCC) patients. Multivariate Cox regression analysis was employed to filter independent prognostic factors predictive of cancer-specific survival and overall survival, and the nomograms were built based on a training set incorporating 2901 chRCC patients in a retrospective study (from 2004 to 2015) downloaded from the surveillance, epidemiology, and end results (SEER) database. The nomograms were verified on a validation cohort of 1934 patients, subsequently the performances of the nomograms were examined according to the receiver operating characteristic curve, calibration curves, the concordance (C-index), and decision curve analysis. The results showed that tumor grade, AJCC and N stages, race, marital status, age, histories of chemotherapy, radiotherapy and surgery were the individual prognostic factors for overall survival, and that AJCC, N and SEER stages, histories of surgery, radiotherapy and chemotherapy, age, tumor grade were individual prognostic factors for cancer-specific survival. According to C-indexes, receiver operating characteristic curves, and decision curve analysis outcomes, the nomograms showed a higher accuracy in predicting overall survival and OSS when compared with TNM stage and SEER stage. All the calibration curves were significantly consistent between predictive and validation sets. In this study, the nomograms, which were validated to be highly accurate and applicable, were built to facilitate individualized predictions of the cancer-specific survival and overall survival to patients diagnosed with chRCC between 2004 and 2015.     

Lymphocyte-Related Inflammation and Immune-Based Scores Predict Prognosis of Chordoma Patients After Radical Resection

The inflammatory microenvironment plays a critical role in the development and progression of malignancies. In the present study, we aimed to evaluate the prognostic value of lymphocyte-related inflammation and immune-based prognostic scores in patients with chordoma after radical resection, including the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII). A total of 172 consecutive patients with chordoma who underwent radical resection were reviewed. R software was used to randomly select 86 chordoma patients as a training set and 86 chordoma patients as a validation set. Potential prognostic factors were also identified, including age, sex, tumor localization, KPS, Enneking stage, tumor size, and tumor metastasis. Overall survival (OS) was calculated using the Kaplan–Meier method and multivariate Cox regression analyses. NLR, PLR, SII, Enneking stage, tumor differentiation and tumor metastasis were identified as significant factors from the univariate analysis in both the training and validation sets and were subjected to multivariate Cox proportional hazards analysis. The univariate analysis showed that NLR ≥1.65, PLR ≥121, and SII ≥370×10⁹/L were significantly associated with poor OS. In the multivariate Cox proportional hazard analysis, SII, Enneking stage and tumor metastasis were significantly associated with OS. As noninvasive, low-cost, reproducible prognostic biomarkers, NLR, PLR and SII could help predict poor prognosis in patients with chordoma after radical resection. This finding may contribute to the development of more effective tailored therapy according to the characteristics of individual tumors.     

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PurposeTo establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.MethodsThis study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.ResultsA total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1–3 years AUC:0.774–0.837; validation cohort 1–3 years AUC:0.754–0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.ConclusionsThe CR model could predict the OS of the HCC patients after hepatectomy.