Therapeutic potential of surgery for metastatic colorectal cancer

Development and validation of prediction models for the prognosis of colon cancer with lung metastases: a population-based cohort study

Background

Current studies on the establishment of prognostic models for colon cancer with lung metastasis (CCLM) were lacking. This study aimed to construct and validate prediction models of overall survival (OS) and cancer-specific survival (CSS) probability in CCLM patients.

Method

Data on 1,284 patients with CCLM were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly assigned with 7:3 (stratified by survival time) to a development set and a validation set on the basis of computer-calculated random numbers. After screening the predictors by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, the suitable predictors were entered into Cox proportional hazard models to build prediction models. Calibration curves, concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to perform the validation of models. Based on model-predicted risk scores, patients were divided into low-risk and high-risk groups. The Kaplan-Meier (K-M) plots and log-rank test were applied to perform survival analysis between the two groups.

Results

Building upon the LASSO and multivariate Cox regression, six variables were significantly associated with OS and CSS (i.e., tumor grade, AJCC T stage, AJCC N stage, chemotherapy, CEA, liver metastasis). In development, validation, and expanded testing sets, AUCs and C-indexes of the OS and CSS prediction models were all greater than or near 0.7, which indicated excellent predictability of models. On the whole, the calibration curves coincided with the diagonal in two models. DCA indicated that the models had higher clinical benefit than any single risk factor. Survival analysis results showed that the prognosis was worse in the high-risk group than in the low-risk group, which suggested that the models had significant discrimination for patients with different prognoses.

Conclusion

After verification, our prediction models of CCLM are reliable and can predict the OS and CSS of CCLM patients in the next 1, 3, and 5 years, providing valuable guidance for clinical prognosis estimation and individualized administration of patients with CCLM.

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Purpose

The purpose of this study was to investigate the clinical and non-clinical characteristics that may affect the early death rate of patients with metastatic colorectal carcinoma (mCRC) and develop accurate prognostic predictive models for mCRC.

Method

Medical records of 35,639 patients with mCRC diagnosed from 2010 to 2019 were obtained from the SEER database. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. X-tile software was utilized to identify the optimal cutoff point for age and tumor size. Univariate and multivariate logistic regression models were used to determine the independent predictors associated with overall early death and cancer-specific early death caused by mCRC. Simultaneously, predictive and dynamic nomograms were constructed. Moreover, logistic regression, random forest, CatBoost, LightGBM, and XGBoost were used to establish machine learning (ML) models. In addition, receiver operating characteristic curves (ROCs) and calibration plots were obtained to estimate the accuracy of the models. Decision curve analysis (DCA) was employed to determine the clinical benefits of ML models.

Results

The optimal cutoff points for age were 58 and 77 years and those for tumor size of 45 and 76. A total of 15 independent risk factors, namely, age, marital status, race, tumor localization, histologic type, grade, N-stage, tumor size, surgery, radiation, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis, were significantly associated with the overall early death rate of patients with mCRC and the cancer-specific early death rate of patients with mCRC, following which nomograms were constructed. The ML models revealed that the random forest model accurately predicted outcomes, followed by logistic regression, CatBoost, XGBoost, and LightGBM models. Compared with other algorithms, the random forest model provided more clinical benefits than other models and can be used to make clinical decisions in overall early death and specific early death caused by mCRC.

Conclusion

ML algorithms combined with nomograms may play an important role in distinguishing early deaths owing to mCRC and potentially help clinicians make clinical decisions and follow-up strategies.

Survival Nomogram for Metastasis Colon Cancer Patients Based on SEER Database

Introduction: A prediction model for the 1-, 3-, and 5-year survival rates of metastatic colon cancer (mCC) patients was developed by analyzing important risk factors for the prognosis of mCC patients based on the SEER database.

Method: The characteristic of 10,946 patients diagnosed with mCC between 2010 and 2015 was obtained from the SEER database. The population was randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio. Univariate and multivariate cox for independent predictors of mCC prognosis were performed, and nomogram was constructed. The accuracy of the model was verified by calibration curves, ROC curves, and C-index, and the clinical utility of the model was analyzed using decision analysis curves.

Result: Age, primary site, grade, surgery, and other eight factors were significantly associated with the prognosis of mCC patients, and these predictors were included in the construction of the nomogram. The C-index was 0.731 (95% CI 0.725–0.737) and 0.736 (95% CI 0.726–0.746) for the training cohort and the validation set, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) exceeded 0.7 for both the training cohort and the validation set at 1, 3, and 5 years.

Conclusion: The constructed prediction model had an excellent predictive accuracy, which will help clinical decision-making of mCC patients after surgery and individualized treatment.

Is metastasectomy a worthy option?-the role of surgery in metastatic colon cancer to liver and lungs

Background

The role of surgery and metastasectomy is controversial in the treatment of stage IV colon cancer (CC). The aim of this study was to investigate the relationship between primary tumor resection (PTR) with metastasectomy and survival in patients diagnosed with metastatic CC.

Methods

The National Cancer Data Base (NCDB) was retrospectively queried for patients diagnosed with colon adenocarcinoma from 2004 to 2013. Patient demographics, clinical characteristics, and short-term outcomes were collected. Groups were generated based on if surgery was performed and, if so, was metastasectomy involved. Associations between groups were evaluated using Kruskal-Wallis and Pearson Chi-square tests. Overall survival (OS) was summarized using standard Kaplan-Meier methods. The association between surgical group and OS was evaluated using the log-rank test.

Results

Of 31,172 patients, 13,214 (42.4%) had surgery while 17,958 (57.6%) did not. Among these, 81.3% of patients had liver metastases only, while 18.7% of patients had both liver and lung metastases. Median OS was 15.1 months (95% CI: 14.8 to 15.5 months) for the entire cohort. However, median OS was significantly better for those who had surgery (either PTR alone or PTR with metastasectomy) compared to those who did not (21.8 vs. 7.5 months, P<0.001). Patients who received PTR with metastasectomy had worse median OS (20.5 vs. 21.8 months, P=0.035) compared to those who only received PTR (P=0.211).

Conclusions

PTR in select patients diagnosed with metastatic CC provides a remarkable improvement to survival rate. The role of metastasectomy remains controversial as no difference in survival outcomes exists between patients who received it and who did not.

Early detection of recurrence after curative resection for colorectal cancer - obstacles when using soluble biomarkers?

OBJECTIVE

Results from monitoring studies using biomarkers in blood samples aiming at early detection of recurrent colorectal cancer (CRC) are presently evaluated. However, some serological biomarker levels are influenced by the surgical trauma, which may complicate translation of the levels in relation to recurrence. The primary purpose of the present study was to evaluate the frequency of postoperative surgical interventions during a follow-up period of patients who have undergone surgery for primary CRC.

METHODS

In a prospective multicenter, clinical study, 634 patients resected for primary CRC were followed in the outpatient clinic every third month. Blood samples were drawn at each visit. A subgroup of 165 stage II and III patients, who had been followed for at least 3 years, was selected. Any recent surgical intervention associated with the primary disease and/or other diseases were recorded at each visit to the outpatient clinic.

RESULTS

Among the 165 patients, 49 developed recurrence (R+), 107 did not (R-) and 11 developed a new primary cancer, including 2 in the R+ group. Within the 3 years of observation, 78 (47.3%) of the 165 patients underwent 117 (range 1-5) postoperative surgical interventions. Seventy-five operations were related to CRC and 42 to benign diseases, while none were related to a new primary, malignant disease.

CONCLUSION

Patients resected for CRC are frequently undergoing surgical procedures in the postoperative follow-up period. Therefore, postoperative monitoring using soluble biomarker levels, which may be influenced by the surgical trauma, must be adjusted in relation to postoperative surgical interventions.