A modified TNM staging system for non-metastatic colorectal cancer based on nomogram analysis of SEER database

Prognostic scoring system based on indicators reflecting the tumor glandular differentiation and microenvironment for patients with colorectal cancer

Prognostic stratification is an urgent concern for patients with colorectal cancer (CRC). The desmoplastic reaction (DR) is speculated to mirror the tumor microenvironment. DR types are considered independent prognostic indicators in CRC, but have not been incorporated in previous prognostic nomograms. We aimed to assess the prognostic significance of a novel approach incorporating histopathological indicators reflecting tumor glandular differentiation and microenvironment. We evaluated 329 consecutive patients with CRC who underwent surgical resection at Kansai Medical University. Histological glandular differentiation was scored as 2 (0 point), 3 (1 point), or 4 (2 points). Tumor buddings (TBs) were classified as TB1 (0 point), TB2 (1 point), or TB3 (2 points). pT1 or 2 was considered as 0 point, pT3 or 4 + DR non-immature type as 1 point, and pT3 or 4 + DR immature type as 2 points. Lymph node metastasis was classified as pN0 (0 point), pN1 (1 point), or pN2 (2 points). The preoperative carcinoembryonic antigen levels were categorized as < 5.0 ng/mL (0 point) and ≧5.0 (1 point). Considering these factors, the following D&M (tumor differentiation and microenvironment) scoring system was applied: I (0-2 points), II (3-4 points), III (5-6 points), and IV (7-9 points). Kaplan-Meier curves showed significant differences in disease-specific survival and recurrence-free survival among the assigned scores, highlighting their enhanced utility compared with the American Joint Committee on Cancer 8th edition staging system. The D&M scoring system was valuable as the initial prognostic nomogram, including DR.

Prognostic nomogram for colorectal cancer patients with multi-organ metastases: a Surveillance, Epidemiology, and End Results program database analysis

BACKGROUND

A nomogram that integrates risk models and clinical characteristics can accurately predict the prognosis of individual patients. We aimed to identify the prognostic factors and establish nomograms for predicting overall survival (OS) and cause-specific survival (CSS) in patients with multi-organ metastatic colorectal cancer (CRC).

METHODS

Demographic and clinical information on multi-organ metastases from 2010 to 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) Program. Univariate and multivariate Cox analyses were used to identify independent prognostic factors that were used to develop nomograms to predict CSS and OS, and to assess the concordance index (C-index), area under the curve (AUC), and calibration curve.

RESULTS

The patients were randomly assigned to the training and validation groups at a 7:3 ratio. A Cox proportional hazards model was conducted for CRC patients to identify independent prognostic factors, including age, sex, tumor size, metastases, degree of differentiation, stage T, stage N, primary and metastasis surgery. The competing risk models employed by Fine and Gray were used to identify the risk factors for CRC. Death from other causes was treated as a competing event, and Cox models were used to identify the factors for death to identify the independent factors of CSS. By incorporating the corresponding independent prognostic factors, we established prognostic nomograms for OS and CSS. Finally, we used the C-index, ROC curve, and calibration plots to assess the utility of the nomogram.

CONCLUSIONS

Using the SEER database, we constructed a predictive model for CRC patients with multi-organ metastases. Nomograms provide clinicians with 1-, 3-, and 5-year OS and CSS predictions for CRC, allowing them to formulate appropriate treatment plans.

Development and validation of a nomogram for predicting the overall survival of patients with testicular cancer

BACKGROUND

The purpose of this study was to develop and validate a nomogram to predict survival in testicular cancer patients.

METHODS

Testicular cancer patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were selected for this study. A random sampling method was used to divide patients into training and validation cohorts, which accounted for 30% and 70% of the total sample, respectively. The nomogram was developed using the training cohort and evaluated using the C index, calibration chart, and area under the receiver operating characteristic curve (AUC).

RESULTS

Seven risk factors that affect the survival of testicular cancer patients (AJCC stage, marital status, age at diagnosis, race, SEER historic stage A, surgery status, and origin) were identified using Cox proportional hazard regression analysis. The nomogram has a higher C index (0.897) and AUC when compared with the AJCC staging system. The results of the calibration chart of the nomogram show that the predicted survival of testicular cancer patients at 3, 5, and 10 years after diagnosis is very close to their actual survival.

CONCLUSIONS

We developed and validated a nomogram for predicting the survival rate of testicular cancer patients at 3, 5, and 10 years after diagnosis. This nomogram has better discrimination, calibration, and clinical validity than the AJCC staging system. This indicates that the nomogram can be used to predict the survival of testicular cancer patients effectively, and provide a reference for patient treatment strategies.

Prognostic modeling of overall survival and analysis of K-M survival curves in patients with primary colon cancer: A SEER-based study

This study aimed to establish a validated prognostic survival column line chart by analyzing data from patients with colon cancer (CC) in the SEER database. The nomogram proposed in this study was based on the retrospective data of patients diagnosed with CC in the SEER database from 1975 to 2015. Randomly divided into training and validation sets, the nomogram was constructed using the Cox model, and the discriminatory power of the nomogram and its predictive accuracy were determined using the consistency index and associated calibration curves. In a multifactorial analysis of the main cohort, the independent factors for survival were age, sex, race, tumor stage, and tumor grade, all of which were included in the nomogram and were prognostic factors for patients with CC (P < .05). The calibration curve of the survival probability showed good agreement between the prediction of the nomogram and the actual observation. The validation calibration curve showed good correlation and agreement between predicted and observed values. Multifactorial analysis showed that the factors affecting the prognosis of patients with CC included age, sex, race, tumor-node-metastasis stage, and tumor pathological stage. The nomogram prediction model proposed in this study has high accuracy and can provide more accurate prognostic prediction and relevant reference values for assessing the postoperative survival of CC patients and guiding clinical decision-making.

Establishment and validation of a prognostic nomogram for patients with early-onset stage I–II colon cancer

Background

The aims of this study were to establish and validate a nomogram model for predicting the survival of patients with early-onset stage I–II colon cancer (CC).

Methods

Data of eligible patients enrolled from 2012 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly allocated to training and validation groups in a 7:3 ratio. Significant prognostic factors were identified by univariate and multivariate analysis and a nomogram model constructed. The predictive performance of the nomogram was evaluated by the concordance index (C-index), calibration plots, and decision curve analysis.

Results

Our study cohort comprised 3528 early-onset CC patients with stage I–II disease, 2469 of whom were allocated to the training cohort and 1059 to the validation cohort. Race, age, marital status, tumor grade, tumor size, tumor stage (T stage), and chemotherapy were considered the significant predictor by univariate analysis. Race, marital status, and T stage were found to be independent prognostic factors by multivariate analysis. The C-indexes of the nomogram were 0.724 and 0.692 in the training and validation cohorts, respectively. Likewise, the calibration plots showed good agreement regarding the probability of 3- and 5-year observed and nomogram-predicted overall survival in the training group. Decision curve analysis showed that the nomogram model was clinically practical and effective. Moreover, applying the nomogram enabled dividing of the patients into two cohorts with different risk scores. The low-risk group thus created had a better survival than the high-risk group.

Conclusions

We developed and validated a meaningful prognostic nomogram model for patients with early-onset stage I–II CC that clinicians can use to make better decisions for individual patients.

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

BACKGROUND

To construct prognostic model of colorectal cancer (CRC) recurrence and metastasis (R&M) with traditional Chinese medicine (TCM) factors based on different machine learning (ML) methods. Aiming to offset the defects in the existing model lacking TCM factors.

METHODS

Patients with stage I-III CRC after radical resection were included as the model data set. The training set and the internal verification set were randomly divided at a ratio of 7: 3 by the "set aside method". The average performance index and 95% confidence interval of the model were calculated by repeating 100 tests. Eight factors were used as predictors of Western medicine. Two types of models were constructed by taking "whether to accept TCM intervention" and "different TCM syndrome types" as TCM predictors. The model was constructed by four ML methods: logistic regression, random forest, Extreme Gradient Boosting (XGBoost) and support vector machine (SVM). The predicted target was whether R&M would occur within 3 years and 5 years after radical surgery. The area under curve (AUC) value and decision curve analysis (DCA) curve were used to evaluate accuracy and utility of the model.

RESULTS

The model data set consisted of 558 patients, of which 317 received TCM intervention after radical resection. The model based on the four ML methods with the TCM factor of "whether to accept TCM intervention" showed good ability in predicting R&M within 3 years and 5 years (AUC value > 0.75), and XGBoost was the best method. The DCA indicated that when the R&M probability in patients was at a certain threshold, the models provided additional clinical benefits. When predicting the R&M probability within 3 years and 5 years in the model with TCM factors of "different TCM syndrome types", the four methods all showed certain predictive ability (AUC value > 0.70). With the exception of the model constructed by SVM, the other methods provided additional clinical benefits within a certain probability threshold.

CONCLUSION

The prognostic model based on ML methods shows good accuracy and clinical utility. It can quantify the influence degree of TCM factors on R&M, and provide certain values for clinical decision-making.

Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort

Purpose

The present study aimed to develop prognostic prediction models based on machine learning (ML) for non-metastatic colon cancer (CRC), which can provide a precise quantitative risk assessment and serve as an assistive method for treatment strategy development. The possibility of improving prediction accuracy using nonlinear methods compared to linear methods was investigated.

Patients and Methods

A cancer-specific survival (CSS) model constructed using logistic regression, extreme gradient boosting (XGBoost), and random forest algorithms was trained on the Surveillance, Epidemiology, and End Results datasets for 15,254 patients with non-metastatic CRC (split into training [70%] and internal validation [30%] datasets) and externally validated with an outpatient cohort of 311 cases from Xiyuan Hospital in China. A Chinese cohort was also used to develop recurrence and metastasis (R&M) models for CRC patients. The experiments for each model were performed 100 times to obtain average scores and 95% confidence intervals. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) values.

Results

The XGBoost approach showed the highest AUC values of 0.86 (0.84-0.88), 0.82 (0.81-0.83), and 0.81 (0.79-0.82) for one-, three-, and five-year CSS cohorts, respectively, along with a relatively high generalization ability. The XGBoost approach also performed best for the R&M model, with the AUC values of 0.71 (0.64-0.79), 0.79 (0.74-0.86), and 0.89 (0.82-0.95) for one-, three-, and five-year R&M cohorts, respectively. The rankings of predictor importance for the CSS and R&M models were different, and the higher model accuracy was associated with more prognostic predictors.

Conclusion

Three different ML algorithms for developing prognostic prediction models for non-metastatic CRC were compared. The predictive performance results showed that the nonlinear XGBoost approach performed best, suggesting that it can be used for quantifying the prognostic risk. It was also demonstrated that the model performance can be improved when more prognostic predictors are considered.

Nomogram for Predicting the Prognoses of Patients With Pancreatic Head Cancer After Pancreaticoduodenectomy: A Population-Based Study on SEER Data

Objective

In this study, we retrieved the data available in the Surveillance, Epidemiology, and End Results database to identify the prognostic factors for patients with pancreatic head cancer who had undergone pancreaticoduodenectomy and developed a prediction model for clinical reference.

Methods

We screened the data between 1973 and 2015. Propensity score matching (PSM) was used to control for the confounding factors. Kaplan-Meier (log-rank test) curves were used to compare the survival rates. A nomogram was established using multifactorial Cox regression.

Results

In total, 4099 patients were identified. Their median survival was 22 months, with 74.2%, 36.5%, and 26.2% survival after 1, 3, and 5 years, respectively. The median cancer-specific survival was 24.0 months, with 71.1%, 32.6%, and 21.9% survival after 1, 3, and 5 years, respectively. The results of the Cox proportional risk regression showed that age, insurance status, gender, histological type, degree of tissue differentiation, T and N stages, tumor size, extent of regional lymph node dissection, and postoperative radiotherapy or chemotherapy are independent factors affecting prognosis. PSM was used twice to eliminate any bias from the unbalanced covariates in the raw data. After PSM, the patients who had received postoperative radiotherapy were found to have a better survival prognosis and disease-specific survival prognosis than those who had not received radiotherapy [HR = 0.809, 95% CI (0.731–0.894), P < 0.001 and HR = 0.814, 95% CI (0.732–0.904), P < 0.001; respectively]. A similar result was observed for the patients who had received postoperative chemotherapy versus those who had not [HR = 0.703, 95% CI (0.633–0.78), P < 0.001 and HR = 0.736, 95% CI (0.658–0.822), P < 0.001, for survival and disease-specific survival prognoses, respectively]. Finally, the β coefficients of the Cox proportional risk regression were used to establish a nomogram.

Conclusion

Age, insurance status, gender, histological type, degree of differentiation, T and N stages, tumor size, regional lymph node dissection, and postoperative radiotherapy or chemotherapy are factors affecting the prognosis in pancreatic head cancer after pancreaticoduodenectomy. Postoperative radiotherapy and chemotherapy can improve patient survival. These still need to be further validated in the future.

Re-evaluation of controversial issues in the treatment of cT3N0-2 rectal cancer: a 10-year cohort analysis using propensity-score matching

BACKGROUND

Although neoadjuvant treatment is thought to provide optimal local control for stage II and III rectal cancers, many patients have been reported cured by total mesorectal excision (TME), alone or with additional chemotherapy (CTX).

METHODS

This study retrospectively evaluated outcomes in 2643 patients with cT3N0-2 rectal cancers undergoing curative TME during 2005-2015. Recurrence and survival outcomes were measured in three propensity-score matched groups, consisting of patients who underwent preoperative chemoradiotherapy (CRT) with postoperative CTX (NAPOC), postoperative CRT (POCRT), and exclusively postoperative CTX (EPOCT).

RESULTS

Near-complete or complete TME was conducted in more than 95.9% of patients and 80% of scheduled dose of postoperative CTX was completed in 99%. Except for higher SR rate in the POCRT group than the NAPOC group (p = 0.008), 5-year cumulative local and systemic recurrence (LR and SR) rates were 4.9% and 15.2% for cT3N0, and 4.2% and 21% for cT3N1-2 patients (LR, p = 0.703; SR, 0.065), respectively, with no significant differences associated with treatment exposure (p = 0.11-1). The 5-year cumulative disease-free (75.6% vs 65.7%, p = 0.018) and overall survival (87.1% vs 79.4%, p = 0.018 each) rates were higher in the NAPOC group than the POCRT group with cT3N1-2. However, any significant survival differences were not identified between the NAPOC and EPOCT groups according to tumor sub-stages or locations (p = 0.395-0.971).

CONCLUSIONS

We found any treatment modalities including competent TME and postoperative adjuvant CTX efficiently reducing LR generating robust survival outcome in the propensity-matched cohorts, demanding further randomized controlled trials by clinical sub-stages II-III.

Prognostic nomograms for predicting overall survival and cause-specific survival of signet ring cell carcinoma in colorectal cancer patients

BACKGROUND

Signet ring cell carcinoma (SRCC) is an uncommon subtype in colorectal cancer (CRC), with a short survival time. Therefore, it is imperative to establish a useful prognostic model. As a simple visual predictive tool, nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years. Until now, there has been no nomogram to predict the outcome of CRC patients with SRCC.

AIM

To build effective nomograms for predicting overall survival (OS) and cause-specific survival (CSS) of CRC patients with SRCC.

METHODS

Data were extracted from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms. Performance of the nomograms was assessed by concordance index, calibration curves, and receiver operating characteristic (ROC) curves. ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis (TNM) staging system. Patients were classified as high-risk, moderate-risk, and low-risk groups using the novel nomograms. Kaplan-Meier curves were plotted to compare survival differences.

RESULTS

In total, 1230 patients were included. The concordance index of the nomograms for OS and CSS were 0.737 (95% confidence interval: 0.728-0.747) and 0.758 (95% confidence interval: 0.738-0.778), respectively. The calibration curves and ROC curves demonstrated good predictive accuracy. The 1-, 3-, and 5-year area under the curve values of the nomogram for predicting OS were 0.796, 0.825 and 0.819, in comparison to 0.743, 0.798, and 0.803 for the TNM staging system. In addition, the 1-, 3-, and 5-year area under the curve values of the nomogram for predicting CSS were 0.805, 0.847 and 0.863, in comparison to 0.740, 0.794, and 0.800 for the TNM staging system. Based on the novel nomograms, stratified analysis showed that the 5-year probability of survival in the high-risk, moderate-risk, and low-risk groups was 6.8%, 37.7%, and 67.0% for OS (P < 0.001), as well as 9.6%, 38.5%, and 67.6% for CSS (P < 0.001), respectively.

CONCLUSION

Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC, which are superior to the conventional TNM staging system.

Novel Nomograms-Based Prediction Models for Patients with Primary Undifferentiated Pleomorphic Sarcomas Resections

Simple Summary

Undifferentiated pleomorphic sarcomas (UPS) are one of the most common soft tissue sarcomas which have relatively high potentials of recurrence and metastasis. Surgery remains the mainstream treatment for UPS patients. However, in modern medicine, doctors nowadays lack proper models to tell patients the exact prognosis of individuals after they have undergone primary surgery. In this work, we for the first time develop two nomograms that are able to predict 3- and 5-year overall survival (OS) and time to recurrence (TTR) for UPS patients. These nomograms show relatively good accuracy and practicability which may contribute a lot to the modern medical decision-making process.

Abstract

Background: Undifferentiated pleomorphic sarcomas (UPS) were one of the most common soft tissue sarcomas. As UPS had relatively high potentials of recurrence and metastasis, we designed two nomograms to better predict the overall survival (OS) and time to recurrence (TTR) for patients who underwent primary surgery. Methods: The data of UPS patients who underwent primary surgery were extracted from Shanghai Cancer Center, Fudan University. Multivariate analyses were performed using Cox proportional hazards regression to identify independent prognostic factors. Kaplan–Meier analysis was used to compare differences for patients who underwent primary surgery in OS and TTR. Nomograms were designed with the help of R software and validated using calibration curves and receiver operating characteristic curves (ROC). Results: Kaplan–Meier curves showed that patients with older ages (p = 0.0024), deeper locations (p = 0.0422), necrosis (p < 0.0001), G3 French Federation Nationale des Centres de Lutte Contre le Cancer (FNCLCC) classification (p < 0.0001), higher Ki-67 (p < 0.0001), higher mitotic index (p < 0.0001), R1/R2 resections (p = 0.0002) and higher invasive depth (p = 0.0099) had shorter OS than the other patients while patients with older ages (p = 0.0108), necrosis (p = 0.0001), G3 FNCLCC classification (p < 0.0001), higher Ki-67 (p = 0.0006), higher mitotic index (p < 0.0001) and R1/R2 resections (p < 0.0001) had shorter TTR compared with those without. Multivariate analyses demonstrated that mitotic rates and surgical margin were independent factors for TTR while age and invasive depth were independent factors for OS. Three parameters were adopted to build the nomograms for 3- and 5-year OS and TTR. The Area Under Curve (AUC) of this nomogram at 3- and 5-year TTR reached 0.802, 0.814, respectively, while OS reached 0.718, 0.802, respectively. Calibration curves for the prediction of 3- and 5-year OS and TTR showed excellent agreement between the predicted and the actual survival outcomes. Conclusions: Some important parameters could be used to predict the outcome of individual UPS patients such as mitotic age, rates, surgical margin, and invasive depth. We developed two accurate and practicable nomograms that could predict 3- and 5-year OS and TTR for UPS patients, which could be involved in the modern medical decision-making process.

Improving the Prognosis of Colon Cancer through Knowledge-Based Clinical-Molecular Integrated Analysis

Background

Colon cancer has high morbidity and mortality rates among cancers. Existing clinical staging systems cannot accurately assess the prognostic risk of colon cancer patients. This study was aimed at improving the prognostic performance of the colon cancer clinical staging system through knowledge-based clinical-molecular integrated analysis.

Methods

374 samples from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) dataset were used as the discovery set. 98 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset were used as the validation set. After converting gene expression data into pathway dysregulation scores (PDSs), the random survival forest and Cox model were used to identify the best prognostic supplementary factors. The corresponding clinical-molecular integrated prognostic model was built, and the improvement of prognostic performance was assessed by comparing with the clinical prognostic model.

Results

The PDS of 14 pathways played important roles in prognostic prediction together with clinical prognostic factors through the random survival forest. Further screening with the Cox model revealed that the PDS of the pathway hsa00532 was the best clinical prognostic supplementary factor. The integrated prognostic model constructed with clinical factors and the identified molecular factor was superior to the clinical prognostic model in discriminative performance. Kaplan-Meier (KM) curves of patients grouped by PDS suggested that patients with a higher PDS had a poorer prognosis, and stage II patients could be distinctly distinguished.

Conclusions

Based on the knowledge-based clinical-molecular integrated analysis, a clinical-molecular integrated prognostic model and corresponding nomogram for colon cancer overall survival prognosis was built, which showed better prognostic performance than the clinical prognostic model. The PDS of the pathway hsa00532 is a considerable clinical prognostic supplementary factor for colon cancer and may represent a potential prognostic marker for stage II colon cancer. The PDS calculation involves only 16 genes, which supports its potential for clinical application.

Survival in stage IIB/C compared to stage IIIA rectal cancer: an Australian experience affirming that size does matter

BACKGROUND

Colorectal cancer (CRC) is one of the most commonly diagnosed malignancies globally; however, a survival paradox has been observed unique to this malignancy. The aim of this study was to review survival outcomes of patients diagnosed with stage II and stage III rectal cancer, to determine whether a survival paradox is present in our centre and assess for patient-related factors that can explain the observed paradox or were predictors of prognosis.

METHODS

A retrospective review of data collected from 2006 to 2018 of patients diagnosed with rectal cancer in three separate centres was conducted. Percentages pertaining to patient and tumour characteristics, presentation, management and subsequent recurrence were reported. Preoperative and postoperative factors associated with survival were determined using univariable and multivariable logistic regression analysis.

RESULTS

Stage IIB/C patients had significantly higher carcinoembryonic antigen (CEA) levels compared to stage IIA and stage IIIA patients (P < 0.001). Stage IIB/C patients had significantly larger primary rectal tumour and were more symptomatic (i.e. rectal bleeding, altered bowel habits and obstruction) at the time of diagnosis (P = 0.007). Preoperative CEA was an independent prognostic factor for cancer-specific survival in patients diagnosed with stage IIB/C and stage IIIA disease (P = 0.008) on multivariable analysis. Overall survival was greatest in stage IIIA disease, which was significantly greater than stage IIB/C disease.

CONCLUSION

This study confirms the existence of a survival paradox in patients diagnosed with CRC in an Australian tertiary centre and adds further weight to the revision of the TNM staging to provide more emphasis on the T stage.

Development and Validation of the Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients With Synovial Sarcoma

BACKGROUND

The study aimed to build and validate practical nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with synovial sarcoma (SyS).

METHODS

A total of 893 eligible patients confirmed to have SyS between 2007 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n = 448) and validation cohort (n = 445). Clinically independent prognostic and important factors were determined according to the Akaike information criterion in multivariate Cox regression models when developing the nomograms with the training cohort. The predictive accuracy of nomograms was bootstrapped validated internally and externally with the concordance index (C-index) and calibration curve. Decision curve analysis (DCA) was performed to compare the clinical usefulness between nomograms and American Joint Commission on Cancer (AJCC) staging system.

RESULTS

Two nomograms shared common indicators including age, insurance status, tumor site, tumor size, SEER stage, surgery, and radiation, while marital status and tumor site were only included into the OS nomogram. The C-index of nomograms for predicting OS and CSS was 0.819 (0.873-0.764) and 0.821 (0.876-0.766), respectively, suggesting satisfactory predictive performance. Internal and external calibration curves exhibited optimal agreement between the nomogram prediction and the actual survival. Additionally, DCA demonstrated that our nomograms had obvious superiority over the AJCC staging system with more clinical net benefits.

CONCLUSIONS

Two nomograms predicting 3- and 5-year OS and CSS of SyS patients were successfully constructed and validated for the first time, with higher predictive accuracy and clinical values than the AJCC staging system regarding OS and CSS.

Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis

Background

This study aimed to establish nomogram models of overall survival (OS) and cancer-specific survival (CSS) in elderly colorectal cancer (ECRC) patients (Age ≥ 70).

Methods

The clinical variables of patients confirmed as ECRC between 2004 and 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate analysis were performed, followed by the construction of nomograms in OS and CSS.

Results

A total of 44,761 cases were finally included in this study. Both C-index and calibration plots indicated noticeable performance of newly established nomograms. Moreover, nomograms also showed higher outcomes of decision curve analysis (DCA) and the area under the curve (AUC) compared to American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage and SEER stage.

Conclusions

This study established nomograms of elderly colorectal cancer patients with distinct clinical values compared to AJCC TNM and SEER stages regarding both OS and CSS.

Development and validation of a nomogram to predict survival after curative resection of nonmetastatic colorectal cancer

Background

We aimed to develop a clinical applicable nomogram to predict overall survival (OS) for patients with curatively resected nonmetastatic colorectal cancer.

Methods

Records from a retrospective cohort of 846 patients with complete information were used to construct the nomogram. The nomogram was validated in a prospective cohort of 379 patients. The performance of the nomogram was evaluated with concordance index (c‐index), time‐dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits respectively, and further compared with AJCC 8th TNM staging and the MSKCC nomogram. Risk stratification based on nomogram scores was performed with recursive partitioning analysis.

Results

The nomogram incorporated age, Glasgow prognostic score, pretreatment carcinoembryonic antigen levels, T staging, N staging, number of harvested lymph nodes, and histological grade. Compared with the 8th AJCC staging and MSKCC model, the nomogram had a statistically higher c‐index (0.77, 95% CI: 0.73‐0.80), bigger areas under the time‐dependent ROC curves (AUC at 3 years: 79; at 5 years: 79), and improved clinical net benefits. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort. Nomogram‐based risk stratification successfully discriminated patients within each AJCC stage (all log‐rank P < .05).

Conclusion

We established an accurate, reliable, and easy‐to‐use nomogram to predict OS after curative resection for nonmetastatic colorectal cancer (CRC). The nomogram outperformed the 8th AJCC staging and the MSKCC model and could aid in personalized treatment and follow‐up strategy for CRC patients.

Development and validation of nomograms to accurately predict risk of recurrence for patients with laryngeal squamous cell carcinoma: Cohort study

BACKGROUND

Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC.

METHODS

A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCC patients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram.

RESULTS

Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort.

CONCLUSION

A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.

Outcomes after venous thromboembolism in patients with gastric cancer: Analysis of the RIETE Registry

Gastric cancer is the fifth most common malignancy worldwide. Venous thromboembolism is an independent predictor of death among patients with gastric cancer. We aimed to describe the factors associated with mortality, thrombosis recurrence, and bleeding complications in patients with gastric cancer who develop venous thromboembolism. We included 612 patients with gastric cancer and venous thromboembolism in the Registro Informatizado de la Enfermedad TromboEmbólica (RIETE) registry from 2001 to 2018. We used Cox proportional hazard ratios and a Fine-Gray model to define factors associated with outcomes. The overall mortality at 6 months was 44.4%. Factors associated with increased 6-month mortality included immobility (HR 1.8, 95% CI 1.3-2.4; p < 0.001), anemia (HR 1.4, 95% CI 1.1-1.8; p < 0.02), and leukocytosis (HR 1.8, 95% CI 1.4-2.3; p < 0.001). Recurrent thrombosis occurred in 6.5% of patients and major bleeding complications in 8.5% of the cohort. Male sex was the main factor associated with thrombosis recurrence (HR 2.1, 95% CI 1.1-4.0; p < 0.02) and hemoglobin below 10 g/dL (HR 1.6, 95% CI 1.05-2.50; p = 0.03) the main factor associated with bleeding. In conclusion, patients with gastric cancer who develop venous thrombosis have a very high likelihood of death. Low hemoglobin in this population is associated with poor outcomes.

COX-2/C-MET/KRAS status-based prognostic nomogram for colorectal cancer: A multicenter cohort study

BACKGROUND/AIM

To construct quantitative prognostic models for colorectal cancer (CRC) based on COX-2/C-MET/KRAS expression status in clinical practice.

PATIENTS AND METHODS

Clinical factors and COX-2/C-MET/KRAS expression status of 578 eligible patients from two Chinese hospitals were included. The patients were randomly allocated into training and validation datasets. We created several models using Cox proportional hazard models: Signature^C contained clinical factors, Signature^G contained COX-2/C-MET/KRAS expression status, and Signature^CG contained both. After comparing their accuracy, nomograms for progression-free survival (PFS) and overall survival (OS) were built for the best signatures, with their concordance index and calibration tested. Further, patients were subgrouped by the median of the best signatures, and survival differences between the subgroups were compared.

RESULTS

For PFS, among the three signatures, Signature^PFS-CG had the best area under the curve (AUC), with the 1-, 2- and 3-year AUCs being 0.70, 0.73 and 0.89 in the training dataset, respectively and 0.67, 0.73 and 0.87 in the validation dataset, respectively. For OS, the AUCs of Signature^OS-CG for 1-, 2- and 3-years were 0.63, 0.71 and 0.81 in the training dataset, respectively and 0.68, 0.71 and 0.76 in validation dataset, respectively. The nomograms based on Signature^PFS-CG and Signature^OS-CG had good calibrations. Subsequent stratification analysis demonstrated that the subgroups were significantly different for both PFS (training:P < 0.001; validation:P< 0.001) and OS (training:P < 0.001; validation:P < 0.001).

CONCLUSIONS

Combining clinical factors and COX-2/C-MET/KRAS expression status, our models provided accurate prognostic information in CRC. They can be used to aid treatment decisions in clinical practice.

Challenges and solutions in patient treatment strategies for stage II colon cancer

Colorectal cancer remains one of the most common cancers worldwide and, despite improvements in treatment options for late-stage metastatic cancer, there are still questions surrounding how best to treat early-stage disease patients. Some recent advances have been made in the staging of cancer and improving the risk assessment of strategies for patient treatment. A number of high-risk features have been proposed that may help to stratify stage II cancer patients into groups that will truly benefit from adjuvant chemotherapy. Diagnostic tests are becoming available to measure these biomarkers, utilizing both currently available and novel technologies. This review will describe the challenges in treatment decisions for early-stage colon cancer and how personalized medicine can assist clinicians in making the best treatment choices for patients with stage II colon cancer in particular.

Novel nomograms individually predict the survival of patients with soft tissue sarcomas after surgery

Background: The aim of the study was to build and validate practical nomograms to better predict the overall survival (OS) and cancer-specific survival (CSS) of the patients with soft tissue sarcomas (STS) who underwent surgery.

Methods: Patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified 8804 patients who underwent surgery with STS between 2007 and 2015, and randomly divided them into the training (n=6164) and validation (n=2640) cohorts. The Cox regression analysis and cumulative incidence function were performed to identify the independent prognostic factors associated with OS and CSS, respectively. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index) and the calibration curves. Decision curve analysis (DCA) was introduced to compare the clinical practicality between the nomograms and the AJCC staging system.

Results: Eight independent prognostic factors for OS and seven for CSS were determined and then used to build the nomograms for 3- and 5-year OS and CSS, respectively. The C-indexes of the nomograms for predicting OS were 0.788 in the internal validation and 0.823 in external validation, significantly higher than C-index of the AJCC staging system (P<0.001). The similar results were obtained in the validation cohort. Internal and external calibration curves for the predicting 3- and 5-year OS and CSS showed excellent agreement between the prediction and the actual survival outcomes. In addition, DCA demonstrated that our nomograms were superior over the AJCC staging system with obtaining more clinical net benefits.

Conclusions: We established and validated the nomograms that could accurately predict the 3- and 5-year OS and CSS for STS patients who underwent surgery. The nomograms showed more robust and applicable performance than the AJCC staging system for predicting OS and CSS.

Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer

Background

An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients.

Methods

Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions.

Results

Based on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]).

Conclusions

Based on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4985-2) contains supplementary material, which is available to authorized users.

A Prognostic Nomogram for Disease-Specific Survival in Patients with Pancreatic Ductal Adenocarcinoma of the Head of the Pancreas Following Pancreaticoduodenectomy

BACKGROUND This study developed and validated a nomogram to predict patient prognosis for pancreatic ductal adenocarcinoma (PDAC) of the head of the pancreas following pancreaticoduodenectomy. MATERIAL AND METHODS Retrospective data were obtained from 4,383 patients with PDAC of the head of the pancreas who underwent pancreaticoduodenectomy between 2004-2013 from 11 Registries Research Data of the Surveillance, Epidemiology,and End Results (SEER) database. Cox proportional hazards model was used to identify independent risk factors. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curve. The results were externally validated by comparison with data from 1,743 patients from 7 other Registries Research Data. RESULTS Of the 4,383 patients in the training dataset, median disease-specific survival (DSS) was 17.0 months (range, 1.0-131 months), and postoperative 1-year, 3-year, and 5-year DSS rates were 70.3%, 26.1%, and 16.8%, respectively. Multivariate analysis showed that patient sex, age, tumor grade, regional lymph nodes examined, positive regional lymph nodes, tumor size, extent of local invasion, and tumor metastases were independent risk factors for DSS. The C-index of the internal validation dataset for prediction of DSS was 0.64 (95% CI, 0.63-0.65), which was superior to the American Joint Committee on Cancer (AJCC) staging, 0.57 (95% CI, 0.56-0.58) (P<0.001). The 5-year DSS rates and median DSS time for patients in the low-risk group were significantly greater compared with high-risk group (P<0.001). CONCLUSIONS A validated prognostic disease-specific nomogram for patient survival in PDAC of the head of the pancreas following pancreaticoduodenectomy was developed.

The expression profile and clinical application potential of hsa_circ_0000711 in colorectal cancer

Introduction

Circular RNAs, as a class of long-time-ignored non-coding RNA, have been revealed as multifunctional RNAs in recent years, especially in the cancer research. However, the mechanism of most circular RNAs and their clinical application values in human cancers remain unknown, including in colorectal cancer (CRC).

Methods

In this study, we focused on the expression pattern and clinical values of hsa_circ_0000711 in CRC. The expression level of hsa_circ_0000711 in 101 paired CRC tissues and 3 CRC cell lines (HCT116, COLO205, and H-T29), as well as human normal colon epithelial cell line NCM460, were measured by quantitative real-time polymerase chain reaction.

Results

Our results revealed that the expression level of hsa_circ_0000711 was significantly downregulated in CRC tissues (P=9.35E-16) and CRC cell lines (P<0.01). In addition, the area under the receiver characteristic curves was 0.81. The sensitivity and specificity were 0.91 and 0.58, respectively. Meanwhile, our study showed that low expression of hsa_circ_0000711 could serve as an independent prediction biomarker associated with poor overall survival of CRC patients (hazard ratio =2.409; 95% CI: 1.276–4.547; P=0.004).

Conclusion

All of these results indicated that hsa_circ_0000711 may play a crucial role in CRC carcinogenesis and could be a potential effective biomarker for the diagnosis and prognosis of CRC.