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.     

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 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.     

Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BackgroundIntravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.MethodsEighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.ResultsThe radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).ConclusionsThe IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.     

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 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 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.     

Preoperative prediction of microvascular invasion in non-metastatic hepatocellular carcinoma based on nomogram analysis

PurposeThe presence of microvascular invasion (MVI) is an unfavorable prognostic factor for hepatocellular carcinoma (HCC). This study aimed to construct a nomogram-based preoperative prediction model of MVI, thereby assisting to preoperatively select proper surgical procedures.MethodsA total of 714 non-metastatic HCC patients undergoing radical hepatectomy were retrospectively selected from Zhongshan Hospital between 2010 and 2018, followed by random assignment into training (N = 520) and validation cohorts (N = 194). Nomogram-based prediction model for MVI risk was constructed by incorporating independent risk factors of MVI presence identified from multivariate backward logistic regression analysis in the training cohort. The performance of nomogram was evaluated by calibration curve and ROC curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.ResultsIn total, 503 (70.4%) patients presented MVI. Multivariate analysis in the training cohort revealed that age (OR: 0.98), alpha-fetoprotein (≥400 ng/mL) (OR: 2.34), tumor size (>5 cm) (OR: 3.15), cirrhosis (OR: 2.03) and γ-glutamyl transpeptidase (OR: 1.61) were significantly associated with MVI presence. The incorporation of five risk factors into a nomogram-based preoperative estimation of MVI risk demonstrated satisfactory discriminative capacity, with C-index of 0.702 and 0.690 in training and validation cohorts, respectively. Calibration curve showed good agreement between actual and predicted MVI risks. Finally, DCA revealed the clinical utility of the nomogram.ConclusionThe nomogram showed a satisfactory discriminative capacity of MVI risk in HCC patients, and could be used to preoperatively estimate MVI risk, thereby establishing more rational therapeutic strategies.     

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.     

Low prevalence and independent prognostic role of del(11q) in Chinese patients with chronic lymphocytic leukemia

The 11q deletion (del(11q)) is a conventional cytogenetic aberration observed in chronic lymphocytic leukemia (CLL) patients. However, the prevalence and the prognostic value of del(11q) are still controversial. In this research, we retrospectively explored the prevalence, association, and prognostic significance of del(11q) in 352 untreated and 99 relapsed/refractory Chinese CLL patients. Totally 11.4% of untreated and 19.2% of relapsed/refractory patients harbored del(11q). Del(11q) was more common in patients with β2-microglobulin > 3.5 mg/L, positive CD38, positive zeta-chain associated protein kinase 70, unmutated immunoglobulin heavy variable-region gene and ataxia telangiectasia mutated mutation. Kaplan-Meier method and univariate Cox regression indicated that del(11q) was an independent prognostic factor for overall survival (OS). Based on the results of univariate Cox regression analysis, two nomograms that included del(11q) were established to predict survival. Desirable area under curve of receiver operating characteristic curves was obtained in the training and validation cohorts. In addition, the calibration curves for the probability of survival showed good agreement between the prediction by nomogram and actual observation. In summary, the prevalence of del(11q) is relatively low in our cohort and del(11q) is an unfavorable prognostic factor for untreated CLL patients. Besides, these two nomograms could be used to accurately predict the prognosis of untreated CLL patients.     

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria

PurposeTo establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria.MethodsIn total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves.ResultsThe risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78–0.92) and 0.85 (95% CI: 0.74–0.95), and those for the Rad-score were 0.82 (95% CI: 0.73–0.90) and 0.80 (95% CI: 0.67–0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81–0.94) in the training group and 0.89 (95% CI: 0.81–0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits.ConclusionsThe nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.     

MCM6、MCM7、KIAA1522蛋白联合检测对非小细胞肺癌诊断及预后预测的临床价值研究

摘要 目的：筛选肺癌蛋白分子标志物,寻找可诊断及预测肺癌预后的蛋白标志物。方法：选择2014年8月~2019年7月于西安市第四医院确诊并进行肺部切除手术的非小细胞肺癌(non-small-cell lung Cancer,NSCLC)患者80例,采用免疫组织化学(immunohistochemistry,IHC)检测NSCLC患者肺癌组织标本和癌旁MCM2(Minichromosome maintenance protein2, 微小染色体维持蛋白2)、MCM5(Minichromosome maintenance protein5,微小染色体维持蛋白5)、MCM6(Minichromosome maintenance protein6,微小染色体维持蛋白6)、MCM7(Minichromosome maintenance protein7,微小染色体维持蛋白7)、KIAA1522和KIAA0317蛋白表达阳性率,探讨多蛋白联合检测对NSCLC诊断及预后预测的临床应用价值。结果：肺癌组织中MCM2、MCM5、MCM6、MCM7、KIAA1522和KIAA0317的阳性表达率均显著高于癌旁正常肺组织(P＜0.05),其中MCM6、MCM7和KIAA1522在50 %以上;以MCM6、MCM7、KIAA15223蛋白联合检测肺癌组织,不同性别、不同年龄、类型和分期的NSCLC患者的联合蛋白阳性率无统计学差异(P＞0.05),且蛋白阳性率均大于80 %;MCM7高表达较之低表达或不表达的病例,显著增加患者的死亡风险(P=0.000)。男性(P=0.031)、III~IV期患者(P＜0.001)、以及低分化程度(P=0.012)也是患者的不良预后因素,多因素回归分析显示,MCM7是一个独立的预测指标(P=0.000), 与患者生存具有显著相关性,对预后有一定的预测作用。结论：NSCLC患者肺癌组织中MCM6、MCM7和KIAA1522呈高表达,三者联合检测对NSCLC的检测具有较高的准确性、敏感性和特异性,高水平的MCM7表达提示肺癌患者的不良预后。     

DNA methylation analysis reveals the effect of arsenic on gestational diabetes mellitus

BackgroundArsenic (As) exposure is one of the risk factors for gestational diabetes mellitus (GDM). This study aimed to explore the effect of As-exposure on DNA methylation in GDM and to establish a risk assessment model of GDM in As exposed pregnant women.MethodWe collected elbow vein blood of pregnant women before delivery to measure As concentration and DNA methylation data. Then compared the DNA methylation data and established a nomogram.ResultWe identified a total of 10 key differentially methylated CpGs (DMCs) and found 6 corresponding genes. Functions were enriched in Hippo signaling pathway, cell tight junction, prophetic acid metabolism, ketone body metabolic process, and antigen processing and presentation. A nomogram was established that can predict GDM risks (c-index = 0.595, s:p = 0.973).ConclusionWe found 6 genes associated with GDM with high As exposure. The prediction of the nomograms has been proven to be effective.     

A panel of differentially methylated regions enable prognosis prediction for colorectal cancer

We aim to identify a panel of differentially methylated regions (DMRs) for predicting survival outcomes for patients with CRC from the TCGA (n = 393). Four DMRs (MUC12, TBX20, CHN2, and B3GNT7) were selected as candidate prognostic markers for CRC. The prediction potential of selected DMRs was validated by the targeted bisulfite sequencing method in an independent cohort with 251 Chinese CRC patients. DMR methylation scores (DMSs) were constructed to evaluate the prognosis of CRC. Results of the validation cohort confirmed that higher DMSs were associated with poor overall survival (OS) of CRC, with hazard ratio (HR) value ranged from 1.445 to 2.698 in multivariable Cox models. Patients in the high prognostic index (high-PI) group showed a markedly unfavorable prognosis compared to the low-PI group in both TCGA discovery cohort (HR = 3.508, 95%CI: 2.196–5.604, P < 0.001) and independent validation cohort (HR = 1.912, 95%CI: 1.258–2.907, P = 0.002).     

Prognostic implications of ENE and LODDS in relation to lymph node-positive colorectal cancer location

BackgroundExtranodal extension (ENE) and log odds of positive lymph nodes (LODDS) are associated with the aggressiveness of both colon and rectal cancers. The current study evaluated the clinicopathological significance and the prognostic impact of ENE and LODDS in the colon and rectal patients independently.MethodsThe clinical and histological records of 389 colorectal cancer (CRC) patients who underwent curative surgery were reviewed.ResultsFor the ENE system, 244 patients were in the ENE1 group and 145 in the ENE2 system. Compared with the ENE1 system, the patients included in the ENE2 system were prone to nerve invasion (P < 0.001) and vessel invasion (P < 0.001) with higher TNM (P = 0.009), higher T category (P = 0.003), higher N category (P < 0.001), advanced differentiation (P = 0.013), more number of positive lymph nodes (NPLN) (P < 0.001), more lymph node ratio (LNR) (P < 0.001), and a higher value of LODDS (P < 0.001). ENE was more frequent in patients with left and rectal than right cancer. For the LODDS system, 280 patients were in the LODDS1 group, and 109 in the LODDS2 group. Compared to the LODDS1 group, the patients included in the LODDS2 group were more prone to nerve invasion (P = 0.0351) and vessel invasion (P < 0.001) with a higher rate of N2 stage, less NDLN (P < 0.001), more NPLN (P < 0.001), more LNR (P < 0.001), and a higher value of ENE (P < 0.001). Based on the results in the univariable analysis, the N, NPLN, LNR, LODDS, and ENE were separately incorporated into five different Cox regression models combined with the same confounders. The multivariable Cox regression analysis demonstrated that all the five staging systems were independent prognostic factors for overall survival.ConclusionThe current study confirmed that the LODDS stage is an independent influence on the prognosis of both CRC and CC patients. ENE is an independent influencing factor on the prognosis of both CRC and CC patients, and the prognostic impact of extracapsular lymph node was observed in both CRC and CC. The frequency of ENE increases from the proximal (right) to the distal (left) colon as well as the rectum. Therefore, combining ENE and LODDS into the current TNM system to compensate for the inadequacy of pN staging needs further investigation.     

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.     

A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria

PurposeThe prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients.MethodsIn total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets.ResultsUnivariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC.ConclusionsThe nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.     

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.     

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

ObjectivesThe subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images.MethodsA dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms.ResultsThis study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set.ConclusionsThe automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies.