Nomograms for colorectal cancer: A systematic review

Development, assessment and validation of a novel prediction nomogram model for risk identification of tracheobronchial tuberculosis in patients with pulmonary tuberculosis

OBJECTIVE

Tracheobronchial tuberculosis (TBTB), a specific subtype of pulmonary tuberculosis (PTB), can lead to bronchial stenosis or bronchial occlusion if not identified early. However, there is currently no available means for predicting the risk of associated TBTB in PTB patients. The objective of this study was to establish a risk prediction nomogram model for estimating the associated TBTB risk in every PTB patient.

METHODS

A retrospective cohort study was conducted with 2153 PTB patients. Optimised characteristics were selected using least absolute shrinkage and selection operator regression. Multivariate logistic regression was applied to build a predictive nomogram model. Discrimination, calibration and clinical usefulness of the prediction model were assessed using C-statistics, receiver operator characteristic curves, calibration plots and decision analysis. The developed model was validated both internally and externally.

RESULTS

Among all PTB patients who underwent bronchoscopies (n=2153), 40.36% (n=869) were diagnosed with TBTB. A nomogram model incorporating 11 predictors was developed and displayed good discrimination with a C-statistics of 0.782, a sensitivity of 0.661 and a specificity of 0.762 and good calibration with a calibration-in-the-large of 0.052 and a calibration slope of 0.957. Model's discrimination was favourable in both internal (C-statistics, 0.782) and external (C-statistics, 0.806) validation. External validation showed satisfactory accuracy (sensitivity, 0.690; specificity, 0.804) in independent cohort. Decision curve analysis showed that the model was clinically useful when intervention was decided on at the exacerbation possibility threshold of 2.3%-99.2%. A clinical impact curve demonstrated that our model predicted high-risk estimates and true positives.

CONCLUSION

We developed a novel and convenient risk prediction nomogram model that enhances the risk assessment of associated TBTB in PTB patients. This nomogram can help identify high-risk PTB patients who may benefit from early bronchoscopy and aggressive treatment to prevent disease progression.

Development and Validation of a Nomogram to Predict the Risk of Tinnitus Severity in Patients With Unilateral Subjective Tinnitus

Purpose: To develop and validate a nomogram for predicting the risk of tinnitus severity in patients with unilateral subjective tinnitus. Methods: The objective of this study was to establish and validate a nomogram specifically designed for patients with unilateral subjective tinnitus. We collected data on unilateral subjective tinnitus from the Air Force Medical Center, including 146 participants between January 2021 and June 2022. Risk factors for unilateral subjective tinnitus severity were evaluated by least absolute shrinkage and selection operator (LASSO) and binary logistic regression analysis. Internal verification was used to evaluate the performance of the nomogram. The discriminative ability was measured by the consistency index (C-indices) and the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. Results: All included patients were randomized according to a 7:3 ratio into the training cohort (104 patients) and the validation cohort (42 patients). The LASSO regression model identified sex, tinnitus loudness, and hearing loss as candidate variables. Binary logistic regression analysis showed that gender (OR: 0.76; 95% CI: 0.6-0.95; P = 0.021) and tinnitus loudness (OR: 1.37; 95% CI: 1.09-1.72; P = 0.009) were significant predictors of unilateral subjective tinnitus severity, while age, tinnitus matching frequency, and tinnitus duration were not. The significant predictors were included in the nomogram. Hearing loss was included in the nomogram based on prior clinical experience and previous studies. The training and validation cohorts C-indexes were 0.707 (95% CI: 0.607-0.806) and 0.706 (95% CI: 0.548-0.863), respectively. The training and validation cohort's AUC of the ROC curves were 0.692 and 0.705, respectively. Conclusion: We have developed and validated a nomogram based on gender, hearing loss, and tinnitus loudness, which can effectively predict the risk of tinnitus severity in patients with unilateral subjective tinnitus. The nomogram provides personalized prediction results for patients with unilateral subjective tinnitus, which is beneficial for clinical decision-making and treatment plan development.

Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy, accounting for approximately 90% of all primary liver cancers, with high mortality and a poor prognosis. A large number of predictive models have been applied that integrate multiple clinical factors and biomarkers to predict the prognosis of HCC. Nomograms, as easy-to-use prognostic predictive models, are widely used to predict the probability of clinical outcomes. We searched PubMed with the keywords "hepatocellular carcinoma" and "nomogram", and 974 relative literatures were retrieved. According to the construction methodology and the real validity of the nomograms, in this study, 97 nomograms for HCC were selected in 77 publications. These 97 nomograms were established based on more than 100,000 patients, covering seven main prognostic outcomes. The research data of 56 articles are from hospital-based HCC patients, and 13 articles provided external validation results of the nomogram. In addition to AFP, tumor size, tumor number, stage, vascular invasion, age, and other common prognostic risk factors are included in the HCC-related nomogram, more and more biomarkers, including gene mRNA expression, gene polymorphisms, and gene signature, etc. were also included in the nomograms. The establishment, assessment and validation of these nomograms are also discussed in depth. This study would help clinicians construct and select appropriate nomograms to guide precise judgment and appropriate treatments.

Prediction models for overall and cancer-specific survival in patients with metastatic early-onset colorectal cancer: a population-based study

PURPOSE

Metastatic early-onset colorectal cancer (EO-CRC) is on the rise, yet there is a dearth of predictive models for this disease. Therefore, it is crucial to develop a nomogram to aid in the early detection and management of metastatic colorectal cancer in young patients.

METHODS

We retrieved data from the SEER database on patients with metastatic colorectal cancer aged 50 or younger between 2010 and 2017. The data were randomly allocated in a 7:3 ratio to training and validation cohorts, and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) at 1, 3, and 5 years. The nomograms were developed based on these factors, and their discriminatory and calibration capabilities were validated. Using the nomogram risk scores, patients were stratified into low-risk and high-risk groups.

RESULTS

The study included 2470 patients with metastatic EO-CRC. Univariate and multivariate Cox regression analysis identified 12 independent risk factors that were included in the nomogram. The training cohort had a consistency index (C-index) of 0.71, while the validation cohort had a C-index of 0.70, demonstrating good predictive accuracy. Calibration plots showed a high level of consistency between the observed and predicted values, with overlapping plots along the diagonal. The decision curve analysis (DCA) revealed that the nomogram had a high clinical application value.

CONCLUSIONS

The novel nomograms were created to predict the prognosis of patients with metastatic EO-CRC, which can aid clinicians in developing more effective treatment strategies and contribute to more accurate prognostic assessments.

A Novel Clinical Nomogram for Predicting Overall Survival in Patients with Emergency Surgery for Colorectal Cancer

BACKGROUND

Long-term survival after emergency colorectal cancer surgery is low, and its estimation is most frequently neglected, with priority given to the immediate prognosis. This study aimed to propose an effective nomogram to predict overall survival in these patients.

MATERIALS AND METHODS

We retrospectively studied 437 patients who underwent emergency surgery for colorectal cancer between 2008 and 2019, in whom we analyzed the clinical, paraclinical, and surgical parameters.

RESULTS

Only 30 patients (6.86%) survived until the end of the study. We identified the risk factors through the univariate Cox regression analysis and a multivariate Cox regression model. The model included the following eight independent prognostic factors: age > 63 years, Charlson score > 4, revised cardiac risk index (RCRI), LMR (lymphocytes/neutrophils ratio), tumor site, macroscopic tumoral invasion, surgery type, and lymph node dissection (p < 0.05 for all), with an AUC (area under the curve) of 0.831, with an ideal agreement between the predicted and observed probabilities. On this basis, we constructed a nomogram for prediction of overall survival.

CONCLUSIONS

The nomogram created, on the basis of a multivariate logistic regression model, has a good individual prediction of overall survival for patients with emergency surgery for colon cancer and may support clinicians when informing patients about prognosis.

A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study

BACKGROUND

Precisely predicting the short- and long-term survival of patients with cancer is important. The tumor-node-metastasis (TNM) stage can accurately predict the long-term, but not short-term, survival of cancer. Nutritional status can affect the individual status and short-term outcomes of patients with cancer. Our hypothesis was that incorporating TNM stage and nutrition-related factors into one nomogram improves the survival prediction for patients with colorectal cancer (CRC).

METHOD

This multicenter prospective primary cohort included 1373 patients with CRC, and the internal validation cohort enrolled 409 patients with CRC. Least absolute shrinkage and selection operator regression analyses were used to select prognostic indicators and develop a nomogram. The concordance (C)-index, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the prognostic discriminative ability of the nomogram, TNM stage, Patient-Generated Subjective Global Assessment (PGSGA), and TNM stage + PGSGA models. The overall survival (OS) curve of risk group stratification was calculated based on the nomogram risk score.

RESULTS

TNM stage, radical resection, reduced food intake, activities and function declined, and albumin were selected to develop the nomogram. The C-index and calibration plots of the nomogram showed good discrimination and consistency for CRC. Additionally, the ROC curves and DCA of the nomogram showed better survival prediction abilities in CRC than the other models. The stratification curves of the different risk groups of the different TNM categories were significantly different.

CONCLUSION

The novel nomogram showed good short- and long-term outcomes of OS in patients with CRC. This model provides a personalized and convenient prognostic prediction tool for clinical applications.

A nomogram prediction model for lymph node metastasis risk after neoadjuvant chemoradiotherapy in rectal cancer patients based on SEER database

Background

Rectal cancer patients who received neoadjuvant chemoradiotherapy (CRT) may have a lower cancer stage and a better prognosis. Some patients may be able to avoid invasive surgery. It is critical to accurately assess lymph node metastases (LNM) after neoadjuvant chemoradiotherapy. The goal of this study is to identify clinical variables associated with LNM and to develop a nomogram for LNM prediction in rectal cancer patients following nCRT.

Methods

From 2010 to 2015, patients were drawn from the Surveillance, Epidemiology, and End Results (SEER) database. To identify clinical factors associated with LNM, the least absolute shrinkage and selection operator (LASSO) aggression and multivariate logistic regression analyses were used. To predict the likelihood of LNM, a nomogram based on multivariate logistic regression was created using decision curve analyses.

Reslut

The total number of patients included in this study was 6,388. The proportion of patients with pCR was 17.50% (n=1118), and the proportion of patients with primary tumor pCR was 20.84% (n = 1,331). The primary tumor was pCR in 16.00% (n=213) of the patients. Age, clinical T stage, clinical N stage, and histology were found to be significant independent clinical predictors of LNM using LASSO and multivariate logistic regression analysis. The nomogram was developed based on four clinical factors. The 5-year overall survival rate was 78.9 percent for those with ypN- and 66.3 percent for those with ypN+, respectively (P<0.001).

Conclusion

Patients over 60 years old, with clinical T1-2, clinical N0, and adenocarcinoma may be more likely to achieve ypN0. The watch-and-wait (WW) strategy may be considered. Patients who had ypN0 or pCR had a better prognosis.

External validation and updating of prediction models for estimating the 1-year risk of low health-related quality of life in colorectal cancer survivors

OBJECTIVES

Timely identification of colorectal cancer (CRC) survivors at risk of experiencing low health-related quality of life (HRQoL) in the near future is important for enabling appropriately tailored preventive actions. We previously developed and internally validated risk prediction models to estimate the 1-year risk of low HRQoL in long-term CRC survivors. In this article, we aim to externally validate and update these models in a population of short-term CRC survivors.

STUDY DESIGN AND SETTING

In a pooled cohort of 1,596 CRC survivors, seven HRQoL domains (global QoL, cognitive/emotional/physical/role/social functioning, and fatigue) were measured prospectively at approximately 5 months postdiagnosis (baseline for prediction) and approximately 1 year later by a validated patient-reported outcome measure (European Organization for Research and Treatment of Cancer Quality of life Questionnaire-Core 30). For each HRQoL domain, 1-year scores were dichotomized into low vs. normal/high HRQoL. Performance of the previously developed multivariable logistic prediction models was evaluated (calibration and discrimination). Models were updated to create a more parsimonious predictor set for all HRQoL domains.

RESULTS

Updated models showed good calibration and discrimination (AUC ≥0.75), containing a single set of 15 predictors, including nonmodifiable (age, sex, education, time since diagnosis, chemotherapy, radiotherapy, stoma, and comorbidities) and modifiable predictors (body mass index, physical activity, smoking, anxiety/depression, and baseline fatigue and HRQoL domain scores).

CONCLUSION

Externally validated and updated prediction models performed well for estimating the 1-year risk of low HRQoL in CRC survivors within 6 months postdiagnosis. The impact of implementing the models in oncology practice to improve HRQoL outcomes in CRC survivors needs to be evaluated.

Mutational Status of SMAD4 and FBXW7 Affects Clinical Outcome in TP53-Mutated Metastatic Colorectal Cancer

Next-generation sequencing (NGS) provides a molecular rationale to inform prognostic stratification and to guide personalized treatment in cancer patients. Here, we determined the prognostic and predictive value of actionable mutated genes in metastatic colorectal cancer (mCRC). Among a total of 294 mCRC tumors examined by targeted NGS, 200 of them derived from patients treated with first-line chemotherapy plus/minus monoclonal antibodies were included in prognostic analyses. Discriminative performance was assessed by time-dependent estimates of the area under the curve (AUC). The most recurrently mutated genes were TP53 (64%), KRAS or NRAS (49%), PIK3CA (15%), SMAD4 (14%), BRAF (13%), and FBXW7 (9.5%). Mutations in FBXW7 correlated with worse OS rates (p = 0.036; HR, 2.24) independently of clinical factors. Concurrent mutations in TP53 and FBXW7 were associated with increased risk of death (p = 0.02; HR, 3.31) as well as double-mutated TP53 and SMAD4 (p = 0.03; HR, 2.91). Analysis of the MSK-IMPACT mCRC cohort (N = 1095 patients) confirmed the same prognostic trend for the previously identified mutated genes. Addition of the mutational status of these genes upon clinical factors resulted in a time-dependent AUC of 87%. Gene set enrichment analysis revealed specific molecular pathways associated with SMAD4 and FBXW7 mutations in TP53-defficient tumors. Conclusively, SMAD4 and FBXW7 mutations in TP53-altered tumors were predictive of a negative prognostic outcome in mCRC patients treated with first-line regimens.

A CT-Based Radiomics Nomogram in Predicting the Postoperative Prognosis of Colorectal Cancer: A Two-center Study

RATIONALE AND OBJECTIVES

This retrospective study aimed to develop a practical model to determine overall survival after surgery in patients with colorectal cancer according to radiomics signatures based on computed tomography (CT) images and clinical predictors.

MATERIALS AND METHODS

A total of 121 colorectal cancer (CRC) patients were selected to construct the model, and 51 patients and 114 patients were selected for internal validation and external testing. The radiomics features were extracted from each patient's CT images. Univariable Cox regression and least absolute shrinkage and selection operator regression were used to select radiomics features. The performance of the nomogram was evaluated by calibration curves and the c-index. Kaplan-Meier analysis was used to compare the overall survival between these subgroups.

RESULTS

The radiomics features of the CRC patients were significantly correlated with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of CRC patients' overall survival. The calibration curves showed that the predicted survival time was close to the actual survival time. According to Kaplan-Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group were higher than those of the high-risk group.

CONCLUSION

The nomogram combining the optimal radiomics signature and clinical predictors further improved the predicted accuracy of survival prognosis for CRC patients. These findings might affect treatment strategies and enable a step forward for precise medicine.

A Nomogram for Predicting Survival in Patients With Colorectal Cancer Incorporating Cardiovascular Comorbidities

Background

Cardiovascular comorbidities (CVCs) affect the overall survival (OS) of patients with colorectal cancer (CRC). However, a prognostic evaluation system for these patients is currently lacking.

Objectives

This study aimed to develop and validate a nomogram, which takes CVCs into account, for predicting the survival of patients with CRC.

Methods

In total, 21,432 patients with CRC were recruited from four centers in China between January 2011 and December 2017. The nomogram was constructed, based on Cox regression, using a training cohort (19,102 patients), and validated using a validation cohort (2,330 patients). The discrimination and calibration of the model were assessed by the concordance index and calibration curve. The clinical utility of the model was measured by decision curve analysis (DCA). Based on the nomogram, we divided patients into three groups: low, middle, and high risk.

Results

Independent risk factors selected into our nomogram for OS included age, metastasis, malignant ascites, heart failure, and venous thromboembolism, whereas dyslipidemia was found to be a protective factor. The c-index of our nomogram was 0.714 (95% CI: 0.708–0.720) in the training cohort and 0.742 (95% CI: 0.725–0.759) in the validation cohort. The calibration curve and DCA showed the reliability of the model. The cutoff values of the three groups were 68.19 and 145.44, which were also significant in the validation cohort (p < 0.001).

Conclusion

Taking CVCs into account, an easy-to-use nomogram was provided to estimate OS for patients with CRC, improving the prognostic evaluation ability.

Establishment and validation of a nomogram for predicting potential lateral pelvic lymph node metastasis in low rectal cancer

BACKGROUND

Identifying lateral pelvic lymph node (LPN) metastasis in low rectal cancer is crucial before treatment. Several risk factors and prediction models for LPN metastasis have been reported. However, there is no useful tool to accurately predict LPN metastasis. Therefore, we aimed to construct a nomogram for predicting LPN metastasis in rectal cancer.

METHODS

We analyzed the risk factors for potential LPN metastasis by logistic regression analysis in 705 patients who underwent primary resection of low rectal cancer. We included patients at 49 institutes of the Japan Society of Laparoscopic Colorectal Surgery between June 2010 and February 2012. Clinicopathological factors and magnetic resonance imaging findings were evaluated. The nomogram performance was assessed using the c-index and calibration plots, and the nomogram was validated using an external cohort.

RESULTS

In the univariable logistic regression analysis, age, sex, carcinoembryonic antigen, tumor location, clinical T stage, tumor size, circumferential resection margin (CRM), extramural vascular invasion (EMVI), and the short and long axes of LPN and perirectal lymph node (PRLN) were nominated as risk factors for potential LPN metastasis. We identified a combination of the short axis of LPN, tumor location, EMVI, and short axis of PRLN as optimal for predicting potential LPN metastasis and developed a nomogram using these factors. This model had a c-index of 0.74 and was moderately calibrated and well-validated.

CONCLUSIONS

This is the first study to construct a well-validated nomogram for predicting potential LPN metastasis in rectal cancer, and its performance was high.

Risk Nomogram Does Not Predict Anastomotic Leakage After Colon Surgery Accurately: Results of the Multi-center LekCheck Study

PURPOSE

Anastomotic leakage (AL) is a dreaded complication after colorectal surgery. Preoperatively identifying high-risk patients can help to reduce the incidence of this complication. For this reason, AL risk nomograms have been developed. The objective of this study was to test the AL risk nomogram developed by Frasson, et al. for validity and to identify risk-factors for AL.

METHODS

From the international multi-center LekCheck study database, patients who underwent colonic surgery with the formation of an anastomosis were included. Data were prospectively collected between 2016 and 2019 at 14 hospitals. Univariate and multivariable regression analyses, and area under receiver operating characteristic curve analysis (AUROC) were performed.

RESULTS

A total of 643 patients were included. The median age was 70 years and 51% were male. The majority underwent surgery for malignancies (80.7%). The overall AL rate was 9.2%. The risk nomogram was not predictive for AL in the population tested (AUROC 0.572). Low preoperative haemoglobin (p = 0.006), intraoperative hypothermia (p = 0.02), contamination of the operative field (p = 0.004), and use of epidural analgesia (p = 0.02) were independent risk-factors for AL.

CONCLUSION

The AL risk nomogram could not be validated using the international LekCheck study database. In the future, intraoperative predictive factors for AL, as identified in this study, should also be included in AL risk predictors.

A Prognostic Nomogram for Predicting Overall Survival in Pediatric Wilms Tumor Based on an Autophagy-related Gene Signature

BACKGROUND

Wilms tumor (WT) is the most common primary renal malignancy in children. Autophagy plays dual roles in the promotion and suppression of various cancers.

OBJECTIVE

The goal of our study was to develop a novel autophagy-related gene (ARG) prognostic nomogram for WT.

METHODS

The Cancer Genome Atlas (TCGA) database was used. We screened the expression profiles of ARGs in 136 WT patients. The differentially expressed prognostic ARGs were evaluated by multivariate Cox regression analysis and survival analysis. A novel prognostic nomogram based on the ARGs and clinical characteristics was established using multivariate Cox regression analysis.

RESULTS

First, 69 differentially expressed ARGs were identified in WT patients. Then, multivariate Cox regression analysis was used to determine 4 key prognostic ARGs (CC3CL1, ERBB2, HIF-α and CXCR4) in WT. According to their ARG expression levels, the patients were clustered into high- and low-risk groups. Next, survival analysis indicated that high-risk patients had significantly poorer overall survival than low-risk patients. The results of functional enrichment analysis suggested that autophagy may play a tumor-suppressive role in the initiation of WT. Finally, a prognostic nomogram with a Harrell's concordance index (C-index) of 0.841 was used to predict the survival probability of WT patients by integrating clinical characteristics and the 4-ARG signature. The calibration curve indicated its excellent predictive performance.

CONCLUSION

In summary, the ARG signature could be a promising biomarker for monitoring the outcomes of WT. We established a novel nomogram based on the ARG signature, which accurately predicts the overall survival of WT patients.

The concept and use of the neoadjuvant rectal score as a composite endpoint in rectal cancer

There is no universally accepted instrument to use as a validated surrogate endpoint for overall survival in phase 2 and phase 3 multimodal rectal cancer trials using chemoradiotherapy. Efforts are hampered by the inaccuracy of clinical TNM staging, the variability of indications for neoadjuvant treatment, and diverse definitions of tumour regression grade. Pathological complete response is commonly used, but fails to capture information from the majority of patients. The neoadjuvant rectal score categorises response and downstaging from the entire trial population to identify whether or not a novel treatment group in a chemoradiation trial is superior by predicting overall survival outcomes. Additionally, the neoadjuvant rectal score assesses the difference between initial clinical and pathological T stage and the presence or absence of nodal involvement after treatment. The neoadjuvant rectal score has been conceptually, but incompletely, statistically validated by two independent trial datasets. However, a fundamental weakness of the score is that no preoperative phase 3 trials in locally advanced rectal cancer in the past 20 years have provided a significant benefit in overall survival to statistically validate the neoadjuvant rectal score as a surrogate endpoint for overall survival. We review the robustness, practical value, applicability, generalisability, advantages, and disadvantages of the neoadjuvant rectal score as a surrogate endpoint for overall survival and recommend how this score could be improved and be acceptable as a standard endpoint in studies investigating neoadjuvant chemotherapy and chemoradiation in patients with rectal cancer.

Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine

OBJECTIVE

The success of treatment planning relies critically on our ability to predict the potential benefit of a therapy. In colorectal cancer (CRC), several nomograms are available to predict different outcomes based on the use of tumour specific features. Our objective is to provide an accurate and explainable prediction of the risk to die within 10 years after CRC diagnosis, by incorporating the tumour features and the patient medical and demographic information.

DESIGN

In the prostate, lung, colorectal and ovarian cancer screening (PLCO) Trial, participants (n=154 900) were randomised to screening with flexible sigmoidoscopy, with a repeat screening at 3 or 5 years, or to usual care. We selected patients who were diagnosed with CRC during the follow-up to train a gradient-boosted model to predict the risk to die within 10 years after CRC diagnosis. Using Shapley values, we determined the 20 most relevant features and provided explanation to prediction.

RESULTS

During the follow-up, 2359 patients were diagnosed with CRC. Median follow-up was 16.8 years (14.4-18.9) for mortality. In total, 686 patients (29%) died from CRC during the follow-up. The dataset was randomly split into a training (n=1887) and a testing (n=472) dataset. The area under the receiver operating characteristic was 0.84 (±0.04) and accuracy was 0.83 (±0.04) with a 0.5 classification threshold. The model is available online for research use.

CONCLUSIONS

We trained and validated a model with prospective data from a large multicentre cohort of patients. The model has high predictive performances at the individual scale. It could be used to discuss treatment strategies.

Magnitude of the Age-Advancement Effect of Comorbidities in Colorectal Cancer Prognosis

BACKGROUND

Comorbidities and old age independently compromise prognosis of patients with colorectal cancer (CRC). The impact of comorbidities could thus be considered as conveying worse prognosis already at younger ages, but evidence is lacking on how much worsening of prognosis with age is advanced to younger ages in comorbid versus noncomorbid patients. We aimed to quantify, for the first time, the impact of comorbidities on CRC prognosis in "age advancement" of worse prognosis.

METHODS

A total of 4,602 patients aged ≥30 years who were diagnosed with CRC in 2003 through 2014 were recruited into a population-based study in the Rhine-Neckar region of Germany and observed over a median period of 5.1 years. Overall comorbidity was quantified using the Charlson comorbidity index (CCI). Hazard ratios and age advancement periods (AAPs) for comorbidities were calculated from multivariable Cox proportional hazards models for relevant survival outcomes.

RESULTS

Hazard ratios for CCI scores 1, 2, and ≥3 compared with CCI 0 were 1.25, 1.53, and 2.30 (P<.001) for overall survival and 1.20, 1.48, and 2.03 (P<.001) for disease-free survival, respectively. Corresponding AAP estimates for CCI scores 1, 2, and ≥3 were 5.0 (95% CI, 1.9-8.1), 9.7 (95% CI, 6.1-13.3), and 18.9 years (95% CI, 14.4-23.3) for overall survival and 5.5 (95% CI, 1.5-9.5), 11.7 (95% CI, 7.0-16.4), and 21.0 years (95% CI, 15.1-26.9) for disease-free survival. Particularly pronounced effects of comorbidity on CRC prognosis were observed in patients with stage I-III CRC.

CONCLUSIONS

Comorbidities advance the commonly observed deterioration of prognosis with age by many years, meaning that at substantially younger ages, comorbid patients with CRC experience survival rates comparable to those of older patients without comorbidity. This first derivation of AAPs may enhance the empirical basis for treatment decisions in patients with comorbidities and highlight the need to incorporate comorbidities into prognostic nomograms for CRC.

Analysis of clinical characteristics and prognosis with cervical adenosquamous carcinoma: a large population-based study

Objective: The target of this work was to analyze the clinical characteristics and construct nomograms to predict prognosis in patients with cervical adenosquamous carcinoma (ASC). Methods: A total of 788 ASC patients were tracked in the Surveillance, Epidemiology and End Results database. We compared the clinical characteristics and prognostic factors of ASC. Cox regression models were established, and nomograms were constructed and verified. Results: ASC patients have lower age levels and higher histological grades than patients with squamous cell carcinoma. Nomograms were constructed with good consistency and feasibility in clinical practice. The C-indices for overall survival and cancer-specific survival were 0.783 and 0.787, respectively. Conclusion: ASC patients have unique clinicopathological and prognostic characteristics. Nomograms were successfully constructed and verified.

Log Odds of Positive Lymph Node- (LODDS-) Based Competing-Risk Nomogram for Predicting Prognosis of Resected Rectal Cancer: A Development and Validation Study

Background and Aims. Cancer-specific survival (CSS) of rectal cancer (RC) is associated with several factors. We aimed to build an efficient competing-risk nomogram based on log odds of positive lymph nodes (LODDS) to predict RC survival. Methods. Medical records of 8754 patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database, of 4895 patients from SEER during 2011–2014 and of 478 patients from an Eastern center as a development cohort, validation cohort, and test cohort, respectively. Univariate and multivariate competing-risk analyses were performed to build competing-risk nomogram for predicting the CSS of RC patients. Prediction efficacy was evaluated and compared with reference to the 8th TNM classification using the factor areas under the receiver operating characteristic curve (AUC) and Brier score. Results. The competing-risk nomogram was based on 6 variables: size, M stage, LODDS, T stage, grade, and age. The competing-risk nomogram showed a higher AUC value in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.81 vs. 0.76), validation cohort (0.85 vs. 0.82), and test cohort (0.71 vs. 0.66). The competing-risk nomogram also showed a higher Brier score in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.120 vs. 0.127), validation cohort (0.123 vs. 0.128), and test cohort (0.202 vs. 0.226). Conclusion. We developed and validated a competing-risk nomogram for RC death, which could provide the probability of survival averting competing risk to facilitate clinical decision-making.

Nomogram Predicting Overall Survival of Resected Locally Advanced Rectal Cancer Patients with Neoadjuvant Chemoradiotherapy

PURPOSE

The overall survival (OS) of resected locally advanced rectal cancer patients who underwent neoadjuvant chemoradiotherapy (nCRT) was significantly different, even among patients with the same tumor stage. The nomogram was designed to predict OS of rectal cancer with nCRT and divide the patients into different risk groups.

MATERIALS AND METHODS

Based on materials from 911 rectal cancer patients with nCRT, the multivariable Cox regression model was carried out to select the significant prognostic factors for overall survival. And then, the nomogram was formulated using these independent prognostic factors. The discrimination of the nomogram was assessed by concordance index (C-index), calibration curves and time-dependent area under curve (AUC). The patients respective risk scores were calculated through the nomogram. The best cut-off risk score was calculated to stratify the patients. The survival curves of the two different risk cohorts were performed, which assessed the predictive ability of the nomogram.

RESULTS

Age, cT stage, pretreatment CEA, pretreatment CA19-9, surgery, posttreatment CEA, posttreatment CA19-9, pT stage, pN stage and adjuvant chemotherapy were selected for the construction of the nomogram. And then the nomogram was constructed with independent prognostic factors. The C-index of the nomogram was 0.724, which showed the nomogram provided good discernment. The acceptable agreement between the predictions of nomogram and actual observations was illustrated by calibration plots for 3-, 5- and 10-year OS in the cohort. Time-dependent AUC with 6-fold cross-validation also showed consistent results of the nomogram. Risk group stratification confirmed that the nomogram had great capacity for distinguishing the prognosis.

CONCLUSION

The nomogram was developed and validated to predict overall survival of resected locally advanced rectal cancer patients with nCRT. The proposed nomogram might help clinicians to develop individualized treatment strategies. However, further studies are warranted to optimize the nomogram by finding out other unknown prognostic factors, and more external validation is still required.

Predicting Risk of Recurrence After Colorectal Cancer Surgery in the United States: An Analysis of a Special Commission on Cancer National Study

BACKGROUND

Several factors can affect the risk of recurrence after curative resection of colorectal cancer (CRC). We aimed to develop a risk model for recurrence after definitive treatment of Stage I-III CRC using data from a nationally representative database and to develop an individualized web-based risk calculator.

METHODS

A random sample of patients who underwent resection for Stage I-III CRC between 2006 and 2007 at Commission on Cancer (CoC) accredited centers were included. Primary data regarding first recurrence was abstracted from medical records and merged with the National Cancer Database. Multivariable cox regression analysis was used to test for factors associated with cancer recurrence, stratified by stage. Model performance was tested by c statistic and calibration plots. Hazard Ratios were utilized to develop an individualized web-based recurrence prediction tool.

RESULTS

A total of 8249 patients from 1175 CoC centers were included. Of these, 1656 (20.1%) patients had a recurrence during 5 years of follow-up. Median time to recurrence was 16 months. The final predictive models displayed excellent discrimination and calibration with concordance indexes of 0.7. The online calculator included 12 variables, including tumor site, stage, time since surgery, and surveillance intensity. Output is displayed numerically and graphically with an icon array.

CONCLUSIONS

Using primarily abstracted recurrence data from a random sample of patients treated for CRC at CoC accredited centers across the United States, we successfully created an individualized CRC recurrence risk assessment tool. This web-based calculator can be used by physicians and patients in shared decision making to guide management discussions.

TRIAL REGISTRATION

ClinicalTrials.gov Registration Number: NCT02217865.

Nomograms for predicting overall and cancer-specific survival in patients with papillary renal cell carcinoma: a population-based study using SEER database

BACKGROUND

To establish and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with papillary renal cell carcinoma (pRCC).

METHODS

Patients diagnosed with pRCC between 2010 and 2014 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively included in this study and divided into training and validation groups randomly. Uni- and multivariate Cox regression analyses were used to identify significant variables related to OS and CSS in the training group. Based on results of multivariate Cox regression analysis, nomograms for 3- and 5-year CSS and OS were established, respectively. Additionally, Kaplan-Meier (KM) survival curves were produced to learn the actual effects of different variables. Finally, the nomograms were evaluated both in the training group and the validation group using the area under the receiver operating characteristic (ROC) curve, the concordance index (C-index) and calibration curves.

RESULTS

A total of 4,859 eligible patients were enrolled, with 3,403 categorized into the training group and 1,456 into the validation group. Seven factors [age, T stage, N stage, M stage, use of surgery/lymph node removal (LNR) and insurance status] were significantly related to OS and seven factors (age, T stage, N stage, M stage and use of surgery/chemotherapy/LNR) were significantly associated with CSS. These factors were eventually included in the predictive nomograms. The C-indexes for OS in the training and validation groups were 0.764 and 0.723 respectively, and 0.859 and 0.824 for CSS. The 3- and 5-year AUCs for OS were 0.779 and 0.752 in the training cohort, and 0.749 and 0.722 in the validation cohort. Similarly, 3- and 5-year AUCs for OS were 0.871 and 0.844 in the training cohort, and 0.853 and 0.822 in the validation group. Finally, the calibration curves suggested that the predictive nomograms had a good consistency between the observed and the predicted survival.

CONCLUSIONS

It was the first time to develop nomograms to predict the survival outcomes of pRCC patients. The prognostic nomograms were reliable with high accuracy, which might have guiding significance for clinical practice.

Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach

BACKGROUND

A significant proportion of patients with rectal cancer will present with synchronous metastasis at the time of diagnosis. Overall survival (OS) for these patients are highly variable and previous attempts to build predictive models often have low predictive power, with concordance indexes (c-index) less than 0.70.

METHODS

Using the National Cancer Database (2010-2014), we identified patients with synchronous metastatic rectal cancer. The data was split into a training dataset (diagnosis years 2010-2012), which was used to build the machine learning model, and a testing dataset (diagnosis years 2013-2014), which was used to externally validate the model. A nomogram predicting 3-year OS was created using Cox proportional hazard regression with lasso penalization. Predictors were selected based on clinical significance and availability in NCDB. Performance of the machine learning model was assessed by c-index.

RESULTS

A total of 4098 and 3107 patients were used to construct and validate the nomogram, respectively. Internally validated c-indexes at 1, 2, and 3 years were 0.816 (95% CI 0.813-0.818), 0.789 (95% CI 0.786-0.790), and 0.778 (95% CI 0.775-0.780), respectively. External validated c-indexes at 1, 2, and 3 years were 0.811, 0.779, and 0.778, respectively.

CONCLUSIONS

There is wide variability in the OS for patients with metastatic rectal cancer, making accurate predictions difficult. However, using machine learning techniques, more accurate models can be built. This will aid patients and clinicians in setting expectations and making clinical decisions in this group of challenging patients.

Development and internal validation of prediction models for colorectal cancer survivors to estimate the 1-year risk of low health-related quality of life in multiple domains

Background

Many colorectal cancer (CRC) survivors experience persisting health problems post-treatment that compromise their health-related quality of life (HRQoL). Prediction models are useful tools for identifying survivors at risk of low HRQoL in the future and for taking preventive action. Therefore, we developed prediction models for CRC survivors to estimate the 1-year risk of low HRQoL in multiple domains.

Methods

In 1458 CRC survivors, seven HRQoL domains (EORTC QLQ-C30: global QoL; cognitive, emotional, physical, role, social functioning; fatigue) were measured prospectively at study baseline and 1 year later. For each HRQoL domain, scores at 1-year follow-up were dichotomized into low versus normal/high. Separate multivariable logistic prediction models including biopsychosocial predictors measured at baseline were developed for the seven HRQoL domains, and internally validated using bootstrapping.

Results

Average time since diagnosis was 5 years at study baseline. Prediction models included both non-modifiable predictors (age, sex, socio-economic status, time since diagnosis, tumor stage, chemotherapy, radiotherapy, stoma, micturition, chemotherapy-related, stoma-related and gastrointestinal complaints, comorbidities, social inhibition/negative affectivity, and working status) and modifiable predictors (body mass index, physical activity, smoking, meat consumption, anxiety/depression, pain, and baseline fatigue and HRQoL scores). Internally validated models showed good calibration and discrimination (AUCs: 0.83–0.93).

Conclusions

The prediction models performed well for estimating 1-year risk of low HRQoL in seven domains. External validation is needed before models can be applied in practice.

Nomogram for predicting overall survival in stage II-III colorectal cancer

PURPOSE

The overall survival (OS) of patients diagnosed with stage II-III colorectal cancer (CRC) can vary greatly, even between patients with the same tumor stage. We aimed to design a nomogram to predict OS in resected, stage II-III CRC and stratify patients with CRC into different risk groups.

PATIENTS AND METHODS

Based on data from 873 patients with CRC, we used univariate Cox regression analysis to select the significant prognostic features, which were subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. Cross-validation was used to confirm suitable tuning parameters (λ) for LASSO logistic regression. Then, the nomogram was used to estimate 3- and 5-year OS based on the multivariable Cox regression model. The survival curves of the two groups were produced using the Kaplan-Meier method. Risk group stratification was performed to assess the predictive capacity of the nomogram.

RESULTS

Preoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy were identified as independent prognostic factors by LASSO regression and integrated for the construction of the nomogram. The nomogram provided good discrimination, with C-indices of 0.67 and 0.69 for the training and validation sets, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 3- and 5-year OS. Moreover, a significant difference in OS was shown between patients stratified into different risk groups (P < .001).

CONCLUSION

We constructed and validated an original predictive nomogram for OS in patients with CRC after surgery, facilitating physicians to appraise the individual survival of postoperative patients accurately and identify high-risk patients who need more aggressive treatment and follow-up strategies.

A competing-risk nomogram to predict cause-specific death in elderly patients with colorectal cancer after surgery (especially for colon cancer)

Background

Clinically, when the diagnosis of colorectal cancer is clear, patients are more concerned about their own prognosis survival. Special population with high risk of accidental death, such as elderly patients, is more likely to die due to causes other than tumors. The main purpose of this study is to construct a prediction model of cause-specific death (CSD) in elderly patients using competing-risk approach, so as to help clinicians to predict the probability of CSD in elderly patients with colorectal cancer.

Methods

The data were extracted from Surveillance, Epidemiology, and End Results (SEER) database to include ≥ 65-year-old patients with colorectal cancer who had undergone surgical treatment from 2010 to 2016. Using competing-risk methodology, the cumulative incidence function (CIF) of CSD was calculated to select the predictors among 13 variables, and the selected variables were subsequently refined and used for the construction of the proportional subdistribution hazard model. The model was presented in the form of nomogram, and the performance of nomogram was bootstrap validated internally and externally using the concordance index (C-index).

Results

Dataset of 19,789 patients who met the inclusion criteria were eventually selected for analysis. The five-year cumulative incidence of CSD was 31.405% (95% confidence interval [CI] 31.402–31.408%). The identified clinically relevant variables in nomogram included marital status, pathological grade, AJCC TNM stage, CEA, perineural invasion, and chemotherapy. The nomogram was shown to have good discrimination after internal validation with a C-index of 0.801 (95% CI 0.795–0.807) as well as external validation with a C-index of 0.759 (95% CI 0.716–0.802). Both the internal and external validation calibration curve indicated good concordance between the predicted and actual outcomes.

Conclusion

Using the large sample database and competing-risk analysis, a postoperative prediction model for elderly patients with colorectal cancer was established with satisfactory accuracy. The individualized estimates of CSD outcome for the elderly patients were realized.

A nomogram for preoperative prediction of lymphatic infiltration in colorectal cancer: A personalized approach to clinical decision making

Lymphatic infiltration (LI) is a key factor affecting the treatment of patients with colorectal cancer (CRC). Thus, the aim of this study was to develop and validate a nomogram for individual preoperative prediction of LI in patients with CRC.We conducted a retrospective analysis of 664 patients who received their initial diagnosis of CRC at our center. Those patients were allocated to a training dataset (n = 468) and a validation dataset (n = 196). The least absolute shrinkage and selection operator regression model was used for data dimension reduction and feature selection. The nomogram was constructed from the training dataset and internally verified using the concordance index (C-index), calibration, area under the receiver operating characteristic curve and decision curve analysis (DCA).The enhancement computed tomography reported N1/N2 classification, preoperative tumor differentiation, elevated carcinoembryonic antigen, and carbohydrate antigen19-9 level were selected as variables for the prediction nomogram. Encouragingly, the nomogram showed favorable calibration with C-index 0.757 in the training cohort and 0.725 in validation cohort. The DCA signified that the nomogram was clinically useful. The Kaplan-Meier survival curve showed that patients with LI had a worse prognosis and could benefit from postoperative adjuvant chemotherapy.Use common clinicopathologic factors, a non-invasive scale for individualized preoperative forecasting of LI was established conveniently. LI prediction has great significance for risk stratification of prognosis and treatment of resectable CRC.

Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients with Stage IV colorectal cancer

BACKGROUND

Surgical resection of patients with resectable Stage IV colorectal cancer (CRC) is regarded as first choice if possible. However, its influence on overall survival (OS) has not been thoroughly explored. In this study, we aimed to construct nomograms to help predict 1-, 3- and 5-year OS rate and colorectal cancer-specific survival (CCSS) rate.

METHODS

A total of 2996 cases who underwent primary and metastatic resection were selected in the study from surveillance, epidemiology and end results (SEER) database. About 48 Stage IV CRC patients after resection from the Fudan University Shanghai Cancer Center (FUSCC) were assigned as an independent external validation group. Log-rank and multivariate Cox regression analysis were used. The competing-risks model was used to estimate the cumulative incidence of death. Nomograms were built for prediction of OS and CCSS after surgical resection in patients with Stage IV CRC.

RESULTS

The 1-, 3- and 5-year probabilities of OS were 76.6%, 41.4% and 23.2%, respectively. The 1-, 3- and 5-year colorectal cumulative incidence of death were 23.0%, 54.9% and 71.3%, respectively. The calibration curves for probability of 1-, 3- and 5-year OS and CCSS showed optimal agreement between nomogram prediction and actual observation, and the Harrell's C-indexes for the nomograms to predict OS and CCSS were 0.662 and 0.650, respectively. For FUSCC validation set, the C-index for this model to predict OS was 0.657.

CONCLUSION

Nomograms for prediction of OS and CCSS of patients with Stage IV CRC who underwent primary and metastatic resection were built. Performance of the model was excellent. These nomograms may be helpful for patients and physicians when making a decision.

Prognosis Prediction of Colorectal Cancer Using Gene Expression Profiles

Background: Investigation on prognostic markers for colorectal cancer (CRC) deserves efforts, but data from China are scarce. This study aimed to build a prognostic algorithm using differentially expressed gene (DEG) profiles and to compare it with the TNM staging system in their predictive accuracy for CRC prognosis in Chinese patients. Methods: DEGs in six paired tumor and corresponding normal tissues were determined using RNA-Sequencing. Subsequently, matched tumor and normal tissues from 127 Chinese patients were assayed for further validation. Univariate and multivariate Cox regressions were used to identify informative DEGs. A predictive index (PI) was derived as a linear combination of the products of the DEGs and their Cox regression coefficients. The combined predictive accuracy of the DEGs-based PI and tumors' TNM stages was also examined by a logistic regression model including the two predictors. The predictive performance was evaluated with the area under the receiver operating characteristics (AUCs). Results: Out of 75 candidate DEGs, we identified 10 DEGs showing statistically significant associations with CRC survival. A PI based on these 10 DEGs (PI-10) predicted CRC survival probability more accurately than the TNM staging system [AUCs for 3-year survival probability 0.73 (95% confidence interval: 0.64, 0.81) vs. 0.68 (0.59, 0.76)] but comparable to a simplified PI (PI-5) using five DEGs (LOC646627, BEST4, KLF9, ATP6V1A, and DNMT3B). The predictive accuracy was improved further by combining PI-5 and the TNM staging system [AUC for 3-year survival probability: 0.72 (0.63, 0.80)]. Conclusion: Prognosis prediction based on informative DEGs might yield a higher predictive accuracy in CRC prognosis than the TNM staging system does.

Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-Analysis of TCGA database

Hypoxia-related gene (HRG) expression is associated with survival outcomes of colorectal cancer (CRC). Our aim was developing a nomogram predicting CRC overall survival (OS) with HRGs and clinicopathological factors. The Cancer Genome Atlas (TCGA) database was used as discovery cohort and two Gene Expression Omnibus databases (GSE39582 and GSE41258) served as validation cohorts. A genetic risk score model prognosticating OS was developed using mRNA expression level of HRGs. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables. The genetic risk score model included four HRGs (HSPA1L, PUM1, UBE2D2, and HSP27) and successfully prognosticated OS of discovery and two validation cohorts (p < 0.001 for TCGA discovery set, p < 0.003 for the GSE39582 and p = 0.042 for the GSE41258 datasets). Nomogram included genetic risk score, age, and TNM stage. Harrell’s concordance indexes of the nomogram were higher than those of TNM stage alone in the discovery set (0.77 vs. 0.69, p < 0.001), GSE39582 (0.65 vs. 0.63, p < 0.001), and GSE41258 datasets (0.78 vs. 0.77, p < 0.001). Our nomogram successfully predicted OS of CRC patients. The mRNA expression level of the HRGs might be useful as an ancillary marker for prognosticating CRC outcome.

National cohort study on postoperative risks after surgery for submucosal invasive colorectal cancer

Background

The decision to perform surgery for patients with T1 colorectal cancer hinges on the estimated risk of lymph node metastasis, residual tumour and risks of surgery. The aim of this observational study was to compare surgical outcomes for T1 colorectal cancer with those for more advanced colorectal cancer.

Methods

This was a population‐based cohort study of patients treated surgically for pT1–3 colorectal cancer between 2009 and 2016, using data from the Dutch ColoRectal Audit. Postoperative complications (overall, surgical, severe complications and mortality) were compared using multivariable logistic regression. A risk stratification table was developed based on factors independently associated with severe complications (reintervention and/or mortality) after elective surgery.

Results

Of 39 813 patients, 5170 had pT1 colorectal cancer. No statistically significant differences were observed between patients with pT1 and pT2–3 disease in the rate of severe complications (8·3 versus 9·5 per cent respectively; odds ratio (OR) 0·89, 95 per cent c.i. 0·80 to 1·01, P = 0·061), surgical complications (12·6 versus 13·5 per cent; OR 0·93, 0·84 to 1·02, P = 0·119) or mortality (1·7 versus 2·5 per cent; OR 0·94, 0·74 to 1·19, P = 0·604). Male sex, higher ASA grade, previous abdominal surgery, open approach and type of procedure were associated with a higher severe complication rate in patients with pT1 colorectal cancer.

Conclusion

Elective bowel resection was associated with similar morbidity and mortality rates in patients with pT1 and those with pT2–3 colorectal carcinoma.

A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database

Background: To develop and validate a nomogram based on the conventional measurements and log of odds between the number of positive lymph node and the number of negative lymph node (LODDS) in predicting prognosis for cervical cancer patients after surgery. Methods: A total of 8202 cervical cancer patients with pathologically confirmed between 2004 and 2014 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n=3603) and validation (n=4599) cohorts based on consecutive age of diagnosis. Demographic and clinical pathological factors were evaluated the association with overall survival (OS). Parameters significantly correlating with OS were used to create a nomogram. An independent external validation cohort was subsequently used to assess the predictive performance of the model. Results: In the training set, age at diagnosis, race, marital status, tumor grade, FIGO stage, histology, size and LODDS were correlated significantly with outcome and used to develop a nomogram. The calibration curve for probability of survival showed excellent agreement between prediction by nomogram and actual observation in the training cohort, with a bootstrap-corrected concordance index of 0.749(95% CI, 0.731-0.767). Importantly, our nomogram performed favorably compared to the currently utilized FIGO model, with concordance indices of 0.786 (95% CI, 0.764 to 0.808) vs 0.685 (95%CI, 0.660 to 0.710) for OS in the validation cohort, respectively. Conclusions: By incorporating LODDS, our nomogram may be superior to the currently utilized FIGO staging system in predicting OS in cervical cancer patients after surgery.

A Refined Staging Model for Resectable Pancreatic Ductal Adenocarcinoma Incorporating Examined Lymph Nodes, Location of Tumor and Positive Lymph Nodes Ratio

Background: Nodal status and tumor site are prognostic factors for resectable pancreatic ductal adenocarcinoma (PDAC). Parameters for nodal status are diverse, and the number of examined lymph nodes (eNs) needed for good prognosis are uncertain. We try to modify staging system of resectable PDAC with parameters mentioned above by recursive partitioning analysis.

Methods: Patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into training cohort and internal validation cohort, randomly. PDAC patients from Sun Yat-sen University Cancer Center were regarded as external validation cohort. The training cohort was used to refine staging model by recursive partitioning analysis, while the internal validation cohort and the external validation cohort were applied to assess discriminatory capacity of staging model. For parameters included in the modified model, their effects were studied.

Results: The number of eNs, tumor site and tumor size were risk factors for positive nodal status. Lymph nodes ratio (LNR), tumor site, eNs and T stages of 8^th the American Joint Committee on Cancer (AJCC) were selected to develop a refined model, dividing patients into 5 groups of different outcomes, preceding 8^th AJCC classification. Besides, we found that (1) for small PDAC (diameter < 1cm), lymph node metastasis was rarely found; (2) enough eNs were needed to ensure better prognosis of node-negative patients; (3) tumors in the head of pancreas were prone to lymph nodes metastasis; (4) for node-positive patients, LNR was a better nodal parameter compared to positive lymph nodes (pNs).

Conclusion: Our improved staging system helps to illuminate the interactions among tumor site, size and eNs.

Genome-scale analysis to identify prognostic markers and predict the survival of lung adenocarcinoma

Lung cancer is one of the most malignant cancers worldwide, and lung adenocarcinoma (LAC) remains the most common histologic subtype. However, the functional significance of RNA expression-based prognosis prediction in LAC is still unclear and needs to be further studied. By utilizing the Cox multivariate regression, we established a risk score staging system to predict the outcome of patients with LAC and subsequently identified 10 genes, including PTPRH, OGFRP1, LDHA, AL365203.1, LINC02178, AL512488.1, LINC01312, AL353746.1, DRAXINP1, and LINC02310, which were closely related to the prognosis of patients with LAC. The identified genes allowed us to classify patients into high-risk group with poor outcome and low-risk group with better outcome. Compared with other clinical factors, the risk score performs better in predicting the outcome of LAC patients. We used Gene-Set Enrichment Analysis to identify the differences between the 2 groups in biological pathways. In conclusion, we identified 10 genes by utilizing Cox regression model and developed a risk staging model for LAC, which might prove significant for the clinical management of LAC patients.

Nomogram Integrating Genomics with Clinicopathologic Features Improves Prognosis Prediction for Colorectal Cancer

The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic-genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183-5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218-4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic-genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255-0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469-0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373-84. ©2018 AACR.

Development and validation of nomograms for predicting survival in patients with non-metastatic colorectal cancer

Background

This study aimed to develop nomograms for predicting survival in patients with non-metastatic colorectal cancer (CRC).

Results

On multivariate analyses of the derivation set, the nomograms for OS and CSS shared common significant prognostic factors: age, first-degree relative cancer history, differentiation grade, vessels/nerves invasion, TNM stage, CEA, CA19-9 and PNI. The nomograms displayed good accuracy in predicting OS and CSS, with C-indexes of 0.75 and 0.76, respectively. The calibration plots also showed an excellent agreement between the predicted and observed survival probabilities. Furthermore, the predictive accuracy of the nomograms was confirmed in the validation set, with C-indexes of 0.79 and 0.83 for OS and CSS, respectively.

Materials and Methods

On the basis of data from 822 patients with resected non-metastatic CRC, nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) were established using Cox regression model. The predictive performance of the nomograms was assessed by concordance index (C-index) and calibration plot. An independent external cohort of 171 patients was used to validate the nomograms.

Conclusions

We developed and validated two nomograms for patients with non-metastatic CRC, which could provide individual prediction of OS and CSS with high accuracy.

Preoperative combined hemoglobin, albumin, lymphocyte and platelet levels predict survival in patients with locally advanced colorectal cancer

More than 50% of patients with colorectal cancer (CRC) are initially diagnosed with locally advanced CRC (LACRC), and half of those patients develop recurrence or metastasis after resection. Here, we investigated whether the novel index HALP, which is a combination of preoperative hemoglobin, albumin, lymphocyte and platelet levels, correlates with survival in LACRC patients. A total of 820 patients with LACRC from two independent hospitals were included in our study. The correlations between HALP and overall and cancer-specific survival were calculated using training and validation sets. Lower HALP values correlated with an increased risk of death and cancer-related death in both sets. Moreover, the risk score based on HALP allowed stratification of patients into distinct prognostic groups with greater accuracy than previously proposed indexes. These results suggest that HALP may be useful as a clinical prognostic factor for patients with LACRC.

Integrating Downstaging in the Risk Assessment of Patients With Locally Advanced Rectal Cancer Treated With Neoadjuvant Chemoradiotherapy: Validation of Valentini's Nomograms and the Neoadjuvant Rectal Score

BACKGROUND

Adjuvant chemotherapy is controversial in patients with locally advanced rectal cancer after preoperative chemoradiation. Valentini et al developed 3 nomograms (VN) to predict outcomes in these patients. The neoadjuvant rectal score (NAR) was developed after VN to predict survival. We aimed to validate these tools in a retrospective cohort at an academic institution.

PATIENTS AND METHODS

VN and the NAR were applied to 158 consecutive patients with locally advanced rectal cancer treated with chemoradiation followed by surgery. According to the score, they were divided into low, intermediate, or high risk of relapse or death. For statistical analysis, we performed Kaplan-Meier curves, log-rank tests, and Cox regression analysis.

RESULTS

Five-year overall survival was 83%, 77%, and 67% for low-, intermediate-, and high-risk groups, respectively (P = .023), according to VN, and 84%, 71%, and 59% for low-, intermediate-, and high-risk groups, respectively (P = .004), according to NAR. When the score was considered as a continuous variable, a significant association with the risk of death was observed (NAR: hazard ratio, 1.04; P < .001; VN: hazard ratio, 1.10; P < .001).

CONCLUSION

We confirmed the value of these scores to stratify patients according to their individual risk when designing new trials.

Development and validation of a prognostic nomogram for colorectal cancer after radical resection based on individual patient data from three large-scale phase III trials

Background

Few prediction models have so far been developed and assessed for the prognosis of patients who undergo curative resection for colorectal cancer (CRC).

Materials and Methods

We prepared a clinical dataset including 5,530 patients who participated in three major randomized controlled trials as a training dataset and 2,263 consecutive patients who were treated at a cancer-specialized hospital as a validation dataset. All subjects underwent radical resection for CRC which was histologically diagnosed to be adenocarcinoma. The main outcomes that were predicted were the overall survival (OS) and disease free survival (DFS). The identification of the variables in this nomogram was based on a Cox regression analysis and the model performance was evaluated by Harrell's c-index. The calibration plot and its slope were also studied. For the external validation assessment, risk group stratification was employed.

Results

The multivariate Cox model identified variables; sex, age, pathological T and N factor, tumor location, size, lymphnode dissection, postoperative complications and adjuvant chemotherapy. The c-index was 0.72 (95% confidence interval [CI] 0.66-0.77) for the OS and 0.74 (95% CI 0.69-0.78) for the DFS. The proposed stratification in the risk groups demonstrated a significant distinction between the Kaplan–Meier curves for OS and DFS in the external validation dataset.

Conclusions

We established a clinically reliable nomogram to predict the OS and DFS in patients with CRC using large scale and reliable independent patient data from phase III randomized controlled trials. The external validity was also confirmed on the practical dataset.

The American Society of Colon and Rectal Surgeons Clinical Practice Guidelines for the Treatment of Colon Cancer

The American Society of Colon and Rectal Surgeons is dedicated to ensuring high-quality patient care by advancing the science, prevention, and management of disorders and diseases of the colon, rectum, and anus. The Clinical Practice Guidelines Committee is composed of society members who are chosen because they have demonstrated expertise in the specialty of colon and rectal surgery. This committee was created to lead international efforts in defining quality care for conditions related to the colon, rectum, and anus. This is accompanied by developing Clinical Practice Guidelines based on the best available evidence. These guidelines are inclusive and not prescriptive. Their purpose is to provide information on which decisions can be made, rather than to dictate a specific form of treatment. These guidelines are intended for the use of all practitioners, health care workers, and patients who desire information about the management of the conditions addressed by the topics covered in these guidelines. It should be recognized that these guidelines should not be deemed inclusive of all proper methods of care or exclusive of methods of care reasonably directed to obtaining the same results. The ultimate judgment regarding the propriety of any specific procedure must be made by the physician in light of all the circumstances presented by the individual patient.

A nomogram improves AJCC stages for colorectal cancers by introducing CEA, modified lymph node ratio and negative lymph node count

Lymph node stages (pN stages) are primary contributors to survival heterogeneity of the 7^th AJCC staging system for colorectal cancer (CRC), indicating spaces for modifications. To implement the modifications, we selected eligible CRC patients from the Surveillance Epidemiology and End Results (SEER) database as participants in a training (n = 6675) and a test cohort (n = 6760), and verified tumor deposits to be metastatic lymph nodes to derive modified lymph node count (mLNC), lymph node ratio (mLNR), and positive lymph node count (mPLNC). After multivariate Cox regression analyses with forward stepwise elimination of the mLNC and mPLNC for the training cohort, a nomogram was constructed to predict overall survival (OS) via incorporating preoperative carcinoembryonic antigen, pT stages, negative lymph node count, mLNR and metastasis. Internal validations of the nomogram showed concordance indexes (c-index) of 0.750 (95% CI, 0.736-0.764) and 0.749 before and after corrections for overfitting. Serial performance evaluations indicated that the nomogram outperformed the AJCC stages (c-index = 0.725) with increased accuracy, net benefits, risk assessment ability, but comparable complexity and clinical validity. All the results were reproducible in the test cohort. In summary, the proposed nomogram may serve as an alternative to the AJCC stages. However, validations with longer follow-up periods are required.

Employing dynamical computational models for personalizing cancer immunotherapy

INTRODUCTION

Recently, cancer immunotherapy has shown considerable success, but due to the complexity of the immune-cancer interactions, clinical outcomes vary largely between patients. A possible approach to overcome this difficulty may be to develop new methodologies for personal predictions of therapy outcomes, by the integration of patient data with dynamical mathematical models of the drug-affected pathophysiological processes.

AREAS COVERED

This review unfolds the story of mathematical modeling in cancer immunotherapy, and examines the feasibility of using these models for immunotherapy personalization. The reviewed studies suggest that response to immunotherapy can be improved by patient-specific regimens, which can be worked out by personalized mathematical models. The studies further indicate that personalized models can be constructed and validated relatively early in treatment.

EXPERT OPINION

The suggested methodology has the potential to raise the overall efficacy of the developed immunotherapy. If implemented already during drug development it may increase the prospects of the technology being approved for clinical use. However, schedule personalization, per se, does not comply with the current, 'one size fits all,' paradigm of clinical trials. It is worthwhile considering adjustment of the current paradigm to involve personally tailored immunotherapy regimens.

Nomograms to predict survival after colorectal cancer resection without preoperative therapy

Background

The predictive accuracy of the American Joint Committee on Cancer (AJCC) stages of colorectal cancer (CRC) is mediocre. This study aimed to develop postoperative nomograms to predict cancer-specific survival (CSS) and overall survival (OS) after CRC resection without preoperative therapy.

Methods

Eligible patients with stage I to IV CRC (n = 56072) diagnosed from 2004 to 2010 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were allocated into training (n = 27,700), contemporary (n = 3158), and prospective (n = 25,214) validation cohorts. Clinically important variables were incorporated and selected using the Akaike information criterion in multivariate Cox regressions to derive nomograms with the training cohort. The performance of the nomograms was assessed and externally testified using the concordance index (c-index), bootstrap validation, calibration, time-dependent receiver-operating characteristic curves, Kaplan–Meier curves, mosaic plots, and decision curve analysis (DCA). Performance of the conventional AJCC stages was also compared with the nomograms using similar statistics.

Results

The nomograms for CSS and OS shared common predictors: sex, age, race, marital status, preoperative carcinoembryonic antigen status, surgical extent, tumor size, location, histology, differentiation, infiltration depth, lymph node count, lymph node ratio, and metastasis. The c-indexes of the nomograms for CSS and OS were 0.816 (95 % CI 0.810–0.822) and 0.777 (95 % CI 0.772–0.782), respectively. Performance evaluations showed that the nomograms achieved considerable predictive accuracy, appreciable reliability, and significant clinical validity with wide practical threshold probabilities, while the results remained reproducible when applied to the validation cohorts. Additionally, model comparisons and DCA proved that the nomograms excelled in stratifying each AJCC stage into three significant prognostic subgroups, allowing for more robust risk classification with an improved net benefit.

Conclusions

We propose two prognostic nomograms that exhibit improved predictive accuracy and net benefit for patients who have undergone CRC resection. The established nomograms are intended for risk assessment and selection of suitable patients who may benefit from adjuvant therapy and intensified follow-up after surgery. Independent external validations may still be required.