Associations of advanced age with comorbidity, stage and primary subsite as contributors to mortality from colorectal cancer

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Background

We attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.

Methods

Data from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.

Results

Eventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.

Conclusions

We constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.

Variations in colorectal cancer pattern of care by age and comorbidity in South Australia

BACKGROUND

Advanced age is associated with decreased likelihood of colorectal cancer treatment. Here, we investigated the extent to which comorbidities are accountable for this lesser treatment.

METHODS

Using population-based datasets, the pattern of care among CRC cases in South Australia during 2004-2013 was investigated. Models were used to investigate associations of age with each treatment type, and differences in these associations were explored by comorbidity and cancer site.

RESULTS

The presence of comorbidity was associated with a significantly weaker relationship of age with surgery and chemotherapy. The association of age with surgery also varied for colon and rectal primary cancer sites. Individual comorbidity types varied in their associations with each treatment category. For example, dementia was associated with less chemotherapy provision, however, it was not significantly related to the likelihood of surgery.

CONCLUSION

This study indicates that the association of age with surgical treatment differed significantly by the CRC subsite. Comorbidity moderated the negative association of age with chemotherapy, and less so, with extent of surgery. Results were novel in indicating associations of multiple individual comorbidity types with CRC treatment modalities. The data suggest that different individual comorbidity types may have different effects on treatment and should be studied separately.