A Novel Prognostic Model and Practical Nomogram for Predicting the Outcomes of Colorectal Cancer: Based on Tumor Biomarkers and Log Odds of Positive Lymph Node Scheme

Development and validation of a preoperative systemic inflammation-based nomogram for predicting surgical site infection in patients with colorectal cancer

BACKGROUND

Surgical site infection (SSI) represents a significant postoperative complication in colorectal cancer (CRC). Identifying associated factors is therefore critical. We evaluated the predictive value of clinicopathological features and inflammation-based prognostic scores (IBPSs) for SSI occurrence in CRC patients.

METHODS

We retrospectively analyzed data from 1445 CRC patients who underwent resection surgery at Wuhan Union Hospital between January 2015 and December 2018. We applied two algorithms, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), to identify key predictors. Participants were randomly divided into training (n = 1043) and validation (n = 402) cohorts. A nomogram was constructed to estimate SSI risk, and its performance was assessed by calibration, discrimination, and clinical utility.

RESULTS

Combining the 30 clinicopathological features identified by LASSO and SVM-RFE, we pinpointed seven variables as optimal predictors for a pathology-based nomogram: obstruction, dNLR, ALB, HGB, ALT, CA199, and CA125. The model demonstrated strong calibration and discrimination, with an area under the curve (AUC) of 0.838 (95% CI 0.799-0.876) in the training cohort and 0.793 (95% CI 0.732-0.865) in the validation cohort. Decision curve analysis (DCA) showed that our models provided greater predictive benefit than individual clinical markers.

CONCLUSION

The model based on simplified clinicopathological features in combination with IBPSs is useful in predicting SSI for CRC patients.

A log odds of positive lymph nodes-based predictive model effectively forecasts prognosis and guides postoperative adjuvant chemotherapy duration in stage III colon cancer: a multi-center retrospective cohort study

BACKGROUND

The log odds of positive lymph nodes (LODDS) was considered a superior staging system to N stage in colon cancer, yet its value in determining the optimal duration of adjuvant chemotherapy for stage III colon cancer patients has not been evaluated. This study aims to assess the prognostic value of a model that combines LODDS with clinicopathological information for stage III colon cancer patients and aims to stratify these patients using the model, identifying individuals who could benefit from varying durations of adjuvant chemotherapy.

METHOD

A total of 663 consecutive patients diagnosed with stage III colon cancer, who underwent colon tumor resection between November 2007 and June 2020 at Sun Yat-sen University Cancer Center and Longyan First Affiliated Hospital of Fujian Medical University, were enrolled in this study. Survival outcomes were analyzed using Kaplan-Meier, Cox regression. Nomograms were developed to forecast patient DFS, with the Area Under the Curve (AUC) values of time-dependent Receiver Operating Characteristic (timeROC) and calibration plots utilized to assess the accuracy and reliability of the nomograms.

RESULTS

Multivariate analysis revealed that perineural invasion (HR = 1.776, 95% CI: 1.052-3.003, P = 0.032), poor tumor differentiation (HR = 1.638, 95% CI: 1.084-2.475, P = 0.019), and LODDS groupings of 2 and 1 (HR = 1.920, 95% CI: 1.297-2.842, P = 0.001) were independent predictors of disease-free survival (DFS) in the training cohort. Nomograms constructed from LODDS, perineural invasion, and poor tumor differentiation demonstrated robust predictive performance for 3-year and 5-year DFS in both training (3-year AUC = 0.706, 5-year AUC = 0.678) and validation cohorts (3-year AUC = 0.744, 5-year AUC = 0.762). Stratification according to this model showed that patients in the high-risk group derived significant benefit from completing 8 cycles of chemotherapy (training cohort, 82.97% vs 67.17%, P = 0.013; validation cohort, 89.49% vs 63.97%, P = 0.030).

CONCLUSION

The prognostic model, integrating LODDS, pathological differentiation, and neural invasion, demonstrates strong predictive accuracy for stage III colon cancer prognosis. Moreover, stratification via this model offers valuable insights into optimal durations of postoperative adjuvant chemotherapy.

Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study

BACKGROUND

Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models.

METHODS

Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed.

RESULTS

Eight hundred forty-six of 10 578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively).

CONCLUSION

Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Finding the minimum number of retrieved lymph nodes in node-negative colorectal cancer using Real-world Data and the SEER database

BACKGROUND

Current clinical guidelines recommend the removal of at least 12 lymph nodes (LNs) in resectable colorectal cancer (CRC). With advancements in lymphadenectomy technologies, the number of retrieved lymph nodes (rLNs) has markedly increased. This study aimed to investigate the lowest number of rLNs in node-negative patients.

MATERIALS AND METHODS

A total of 1103 N0 and 208 N1a stage patients were enrolled in our cohort, while 8503 N0 and 1276 N1a patients from the Surveillance, Epidemiology, and End Results CRC database were included. Propensity score matching and multivariate Cox regression analyses were performed to mitigate the influence of selection bias and control for potential confounding variables.

RESULTS

The median number of rLNs in N0 patients increased from 13.5 (interquartile range [IQR]: 9-18) in 2013 to 17 (IQR: 15-20) in 2019. The restrictive cubic spline illustrated a nonlinear relationship between rLNs and prognosis (nonlinearity, P =0.009), with a threshold ( N =16) influencing clinical outcomes. Patients at either N0 or N1a stage with sufficient rLNs (≥16) demonstrated superior prognoses to those with a limited rLNs (<16). After adjusting for clinical confounders, similar prognoses were observed in N0 limited and N1a adequate populations. Furthermore, Kaplan-Meier curves revealed that N0 limited patients who received chemotherapy exhibited better outcomes than those who did not.

CONCLUSIONS

Among patients with node-negative CRC, it is crucial to remove 16 or more LNs effectively. Fewer than 16 rLNs should be regarded as an independent risk factor, implying the need for adjuvant chemotherapy.

Log odds of positive lymph nodes show better predictive performance on the prognosis of early-onset colorectal cancer

BACKGROUND

As the incidence of colorectal cancer tends to be younger, early-onset colorectal cancer (EOCRC) has attracted more attention in recent years. We aimed to assess the optimal lymph node staging system among EOCRC patients, and then, establish informative assessment models for prognosis prediction.

METHODS

Data of EOCRC were retrieved from the Surveillance, Epidemiology, and End Results database. Survival prediction ability of three lymph node staging systems including N stage of the tumor node metastasis (TNM) staging system, lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) was assessed and compared using Akaike information criterion (AIC), Harrell's concordance index (C-index), and likelihood ratio (LR) test. Univariate and multivariate Cox regression analyses were conducted to identify the prognostic predictors for overall survival (OS) and cancer-specific survival (CSS). Effectiveness of the model was demonstrated by receiver operative curve and decision curve analysis.

RESULTS

A total of 17,535 cases were finally included in this study. All three lymph node staging systems showed significant performance in survival prediction (p < 0.001). Comparatively, LODDS presented a better ability of prognosis prediction with lower AIC (OS: 70,510.99; CSS: 60,925.34), higher C-index (OS: 0.6617; CSS: 0.6799), and higher LR test score (OS: 998.65; CSS: 1103.09). Based on independent factors identified from Cox regression analysis, OS and CSS nomograms for EOCRC were established and validated.

CONCLUSIONS

LODDS shows better predictive performance than N stage or LNR among patients with EOCRC. Novel validated nomograms based on LODDS could effectively provide more prognostic information than the TNM staging system.

Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study

BACKGROUND

Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction.

METHODS

The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements.

RESULTS

In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery.

CONCLUSIONS

Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Real-world survival of colon cancer after radical surgery: A single-institutional retrospective analysis

The survival rate for colon cancer after radical surgery has been the focus of extensive debate. To assess the postoperative survival and prognostic factors for overall survival (OS), we collected clinicopathological information for 2,655 patients. The survival time and potential risk factors for OS were analyzed by using Kaplan–Meier curves, Cox proportional hazards models, best subset regression (BSR), and least absolute shrinkage and selection operator (LASSO). The 5-year survival rates of stage I–IV colon cancer were 96.6%, 88.7%, 69.9%, and 34.3%, respectively. Adjuvant chemotherapy improved the survival rate (90.4% vs. 82.4%, with versus without adjuvant chemotherapy, respectively) in stage II patients with high-risk factors. Elevated preoperative carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were significantly associated with worse OS compared with patients without these elevations. Less than 12 versus more than 12 harvested lymph nodes (LNs) affected prognosis (84.6% vs. 89.7%, respectively). Regarding the lymph node ratio (LNR), the 5-year OS rate was 89.2%, 71.5%, 55.8%, and 34.5% in patients with LNR values of 0, 0.3, 0.3–0.7, and &gt;0.7, respectively. We constructed a nomogram comprising the independent factors associated with survival to better predict prognosis. On the basis of these findings, we propose that stage II colon cancer patients without high-risk factors and with both elevated preoperative CEA and CA199 should receive adjuvant therapy. Furthermore, the LNR could complement TNM staging in patients with &lt;12 harvested LNs. Our nomogram might be useful as a new prognosis prediction system for colon cancer patients.

N7-methylguanosine-related lncRNAs: Distinction between hot and cold tumors and construction of predictive models in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a prevalent malignant tumor that severely threatens human health across the globe. Immunotherapy is an essential need for patients with COAD. N7-methylguanosine (m7G) has been associated with human diseases, and non-coding RNAs (lncRNAs) regulate various tumor-related biological processes. Nonetheless, the m7G-related lncRNAs involved in COAD regulation are limited. This study aims to construct the clustering features and prognostic model of m7G-related lncRNAs in COAD. First, The Cancer Genome Atlas (TCGA) database was used to identify m7G-related differentially expressed lncRNAs (DELs), based on which COAD cases could be classified into two subtypes. Subsequently, univariate Cox analysis was used to identify 9 prognostic m7G-related lncRNAs. Further, Five candidates were screened by LASSO-Cox regression to develop new models. The patients were divided into high-risk and low-risk groups based on the median risk score. Consequently, the Kaplan-Meier survival curve demonstrated a statistically significant overall survival (OS) between the high- and low-risk groups (P&lt;0.001). Multivariate Cox regression analysis revealed that risk score is an independent prognostic factor in COAD patients (P&lt;0.001). This confirms the clinical applicability of the model. Additionally, we performed Gene Set Enrichment Analysis (GSEA), which uncovered the biological and functional differences between risk subgroups, i.e., enrichment of immune-related diseases in the high-risk group and enrichment of metabolic-related pathways in the low-risk group. In a drug sensitivity analysis, high-risk group were more sensitive to some chemotherapeutics and targeted drugs than low-risk group. Eventually, the stability of the model was confirmed by qRT-PCR. Our study unraveled the features of different immune states of COAD and established a prognostic model, including five m7G-related lncRNAs for COAD patients. These results will bolster clinical treatment and survival prediction of COAD.

Log odds of positive lymph nodes as a novel prognostic predictor for colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent cancer in the world, which remains one of the leading causes of cancer-related deaths. Accurate prognosis prediction of CRC is pivotal to reduce the mortality and disease burden. Lymph node (LN) metastasis is one of the most commonly used criteria to predict prognosis in CRC patients. However, inaccurate surgical dissection and pathological evaluation may lead to inaccurate nodal staging, affecting the effectiveness of pathological N (pN) classification in survival prediction among patients with CRC. In this meta-analysis, we aimed to estimate the prognostic value of the log odds of positive lymph nodes (LODDS) in patients with CRC.

METHODS

PubMed, Medline, Embase, Web of Science and the Cochrane Library were systematically searched for relevant studies from inception to July 3, 2021. Statistical analyses were performed on Stata statistical software Version 16.0 software. To statistically assess the prognostic effects of LODDS, we extracted the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS) and disease-free survival (DFS) from the included studies.

RESULTS

Ten eligible articles published in English involving 3523 cases were analyzed in this study. The results showed that LODDS1 and LODDS2 in CRC patients was correlated with poor OS compared with LODDS0 (LODDS1 vs. LODDS0: HR = 1.77, 95% CI (1.38, 2.28); LODDS2 vs. LODDS0: HR = 3.49, 95% CI (2.88, 4.23)). Meanwhile, LODDS1 and LODDS2 in CRC patients was correlated with poor DFS compared with LODDS0 (LODDS1 vs. LODDS0: HR = 1.82, 95% CI (1.23, 2.68); LODDS2 vs. LODDS0: HR =3.30, 95% CI (1.74, 6.27)).

CONCLUSIONS

The results demonstrated that the LODDS stage was associated with prognosis of CRC patients and could accurately predict the prognosis of patients with CRC.

Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

Background

Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically.

Methods

We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model.

Results

A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956).

Conclusion

We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02424-7.

A Postsurgical Prognostic Nomogram for Locally Advanced Rectosigmoid Cancer to Assist in Patient Selection for Adjuvant Chemotherapy

Background

The perioperative treatment model for locally advanced rectosigmoid junction cancer (LARSC) has not been finalized; whether this model should refer to the treatment model for rectal cancer remains controversial.

Methods

We screened 10,188 patients with stage II/III rectosigmoid junction adenocarcinoma who underwent surgery between 2004 and 2016 from the National Cancer Institute Surveillance, Epidemiology, and End Results database. Among them, 4,960 did not receive adjuvant chemotherapy, while 5,228 did receive adjuvant chemotherapy. Propensity score matching was used to balance the two groups for confounding factors, and the Kaplan-Meier method and log-rank test were used for survival analysis. Cox proportional hazards regression analysis was used to identify independent prognostic factors and build a predictive nomogram of survival for LARSC. X-tile software was used to divide the patients into three groups (low, medium, and high) according to their risk scores. 726 patients in our hospital were included for external validation.

Results

LARSC patients did not show a benefit from neoadjuvant radiotherapy (P>0.05). After further excluding patients who received neoadjuvant radiotherapy, multivariate analysis found that age, grade, tumor size, T stage, and log odds of positive lymph nodes were independent prognostic factors for patients without adjuvant chemotherapy and were included in the nomogram. The C-index of the model was 0.690 (95% confidence interval: 0.668–0.712). We divided the patients into low, moderate, and high risk subgroups based on prediction scores of the nomogram. We found that adjuvant chemotherapy did not improve the prognosis of low risk patients, while moderate and high risk patients benefited from adjuvant therapy. External validation data found that moderate, and high risk patients also benefited from AT.

Conclusion

Direct surgery plus adjuvant chemotherapy may be the best perioperative treatment for LARSC. Moreover, adjuvant chemotherapy is only recommended for moderate and high risk patients as it did not benefit low risk patients.

Development of a Prognostic Application to Predict Survival for Chinese Women with Breast Cancer (Preprint)

Background

Objective

Methods

Results

Conclusions

Predictive Nomogram for the Prediction of Early Recurrence of Colorectal Cancer

Aim

The prognosis of colorectal cancer (CRC) individuals after curative resection is not satisfactory due to the early recurrence. We sought to identify the affecting features of early recurrence in CRC patients.

Methods

A total of 3500 CRC patients underwent curative resection were retrospectively incorporated into our study. Among them, 246 patients exhibited tumor recurrence: 121 had early recurrence (≤1 year after operation) and 125 had late recurrence (>1 year after operation). A total of 246 CRC patients with recurrence were randomly assigned into the training group (N=177) or validation group (N=69) based on the ratio of 7:3. LASSO COX regression and support vector machine (SVM) were utilized to screen for the significant clinical indexes associated with the presence of early recurrence. Recurrent nomogram was created based on the above informative parameters to predict the probability of early recurrence.

Results

Proportion of advanced TNM stage, platelet count, systemic immune-inflammation index (SII), mean corpuscular hemoglobin concentration (MCHC), CA-199, CA-125, lactate dehydrogenase, total bile acid (TBA), urea nitrogen were significantly higher in early recurrence group compared with that in late recurrence group. Results from LASSO COX regression and support vector machine (SVM) revealed that TNM stage, CA-199, CA125, SII and TBA were strong predictors for the presence of early recurrence among postoperative CRC patients in the training group. The recurrent nomogram based on the five predictors exhibited good predictive performance as calculated by C-index (0.846, 95% CI 0.789-0.902 in the training group and 0.799, 95% CI 0.697-0.902 in the validation group) for the prediction of early recurrence. Moreover, the recurrent nomogram exhibited not only encouraging calibration ability, but also great clinical utility both in the training group and validation group.

Conclusion

TNM stage, CA-199, CA125, SII and TBA were closely correlated with the presence of early recurrence of CRC patients. The recurrent nomogram held well predictive ability for the identification of CRC patients with early recurrence.