The value of tumor deposits in evaluating colorectal cancer survival and metastasis: a population-based retrospective cohort study

Constructing a prognostic model for colorectal cancer with synchronous liver metastases after preoperative chemotherapy: a study based on SEER and an external validation cohort

BACKGROUND

The combination of preoperative chemotherapy and surgical treatment has been shown to significantly enhance the prognosis of colorectal cancer with liver metastases (CRLM) patients. Nevertheless, as a result of variations in clinicopathological parameters, the prognosis of this particular group of patients differs considerably. This study aimed to develop and evaluate Cox proportional risk regression model and competing risk regression model using two patient cohorts. The goal was to provide a more precise and personalized prognostic evaluation system.

METHODS

We collected information on individuals who had a pathological diagnosis of colorectal cancer between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) Database. We obtained data from patients who underwent pathological diagnosis of colorectal cancer and got comprehensive therapy at the hospital between January 1, 2010, and June 1, 2022. The SEER data collected after screening according to the inclusion and exclusion criteria were separated into two cohorts: a training cohort (training cohort) and an internal validation cohort (internal validation cohort), using a random 1:1 split. Subgroup Kaplan-Meier (K-M) survival analyses were conducted on each of the three groups. The data that received following screening from the hospital were designated as the external validation cohort. The subsequent variables were chosen for additional examination: age, gender, marital status, race, tumor site, pretreatment carcinoembryonic antigen level, tumor size, T stage, N stage, pathological grade, number of tumor deposits, perineural invasion, number of regional lymph nodes examined, and number of positive regional lymph nodes. The primary endpoint was median overall survival (mOS). In the training cohort, we conducted univariate Cox regression analysis and utilized a stepwise regression approach, employing the Akaike information criterion (AIC) to select variables and create Cox proportional risk regression models. We evaluated the accuracy of the model using calibration curve, receiver operating characteristic curve (ROC), and area under curve (AUC). The effectiveness of the models was assessed using decision curve analysis (DCA). To evaluate the non-cancer-related outcomes, we analyzed variables that had significant impacts using subgroup cumulative incidence function (CIF) and Gray's test. These analyses were used to create competing risk regression models. Nomograms of the two models were constructed separately and prognostic predictions were made for the same patients in SEER database.

RESULTS

This study comprised a total of 735 individuals. The mOS of the training cohort, internal validation cohort, and QDU cohort was 55.00 months (95%CI 46.97-63.03), 48.00 months (95%CI 40.65-55.35), and 68.00 months (95%CI 54.91-81.08), respectively. The multivariate Cox regression analysis revealed that age, N stage, presence of perineural infiltration, number of tumor deposits and number of positive regional lymph nodes were identified as independent prognostic risk variables (p < 0.05). In comparison to the conventional TNM staging model, the Cox proportional risk regression model exhibited a higher C-index. After controlling for competing risk events, age, N stage, presence of perineural infiltration, number of tumor deposits, number of regional lymph nodes examined, and number of positive regional lymph nodes were independent predictors of the risk of cancer-specific mortality (p < 0.05).

CONCLUSION

We have developed a prognostic model to predict the survival of patients with synchronous CRLM who undergo preoperative chemotherapy and surgery. This model has been tested internally and externally, confirming its accuracy and reliability.

Impact of mesorectal extranodal tumor deposits in magnetic resonance imaging on outcome of rectal cancer patients

AIM

Mesorectal extranodal tumor deposits (TDs) are identified in many rectal cancers. Their radiological features differ from metastatic lymph nodes, and they can be detected with magnetic resonance imaging (MRI). The purpose of this study was to determine the prevalence of rectal cancer TDs detected with MRI and their impact on overall (OS), cancer-specific (CSS), and disease-free survival (DFS) and the local recurrence rate.

METHOD

In this retrospective cohort study, we screened all 525 consecutive rectal cancer patients who underwent surgery during 2017-2018 in a tertiary center. Patients with synchronous metastases or who had not undergone MRI were excluded. We analyzed the OS, CSS, and DFS as well as local recurrences.

RESULTS

Of the 480 included patients, TDs were detected in the images of 81 (16.9 %). Extramural venous invasion (EMVI) and TDs were frequently found together (n = 50, 61.7 % of all cases with TDs). The presence of TDs alone [hazard ratio (HR) 1.66 (1.03-2.68)] or TDs and/or EMVI [HR 1.63 (1.01-2.62)] were risk factors for adverse DFS in multivariate Cox regression analysis. The OS and CSS rates were poorer among patients with TDs compared to those without, p = 0.009 and p < 0.001, respectively. TDs were also a risk factor for local recurrence in the univariate analysis.

CONCLUSIONS

TDs detected with imaging are a risk factor for impaired DFS and associated with impaired CSS and OS of rectal cancer patients and should be taken into consideration in clinical decision-making.

Performance of CT in the locoregional staging of colon cancer: detailed radiology-pathology correlation with special emphasis on tumor deposits, extramural venous invasion and T staging

PURPOSE

To investigate the performance of computed tomography (CT) in the local staging of colon cancer in different segments, with emphasis on parameters that have been found to be significant for rectal cancer, namely, extramural venous invasion (EMVI) and tumor deposits (TDs).

METHODS

CT and pathology data from 137 patients were independently reviewed by radiology and pathology teams. The performance of CT in categorizing a given patient into good, versus poor prognostic groups was assessed for each segment, as well as the presence of lymph nodes (LNs), TDs and EMVIs. Discordant cases were re-evaluated to determine potential sources of error. Elastic stain was applied for EMVI discordance.

RESULTS

The T staging accuracy was 80.2%. For T stage stratification, CT performed slightly better in the left colon, and the lowest accuracy was in the transverse colon. Under-staging was more common (in 12.4%), and most of the mis-staged cases were in sigmoid colon. According to the first comprehensive correlative analysis, the sensitivity, specificity, and accuracy of CT for detecting TDs were found to be 57.9%, 92.4%, 87.6%, respectively. These figures were 44.7%, 72.7%, and 63.5% for LN, and 58.5%, 82.1% and 73% for EMVI. The detection rate was better for multifocal EMVI. The detection rate was also comparable (although substantially underestimated) for LNs, with the half of the LNs missed by CT being < 5 mm. Four patients that were classified as TD by CT, disclosed to be LNs by pathology. Correlative analysis led to refinement of the pathology criteria, with subsequent modifications of the initial reports in 13 (9.5%) patients.

CONCLUSION

Overall, CT performed well in the evaluation of colon cancer, as did TD and EMVI. It is advisable to include these parameters in CT-based staging. Radiologists should be aware of the pitfalls that occur more commonly in different segments.

Prediction of liver metastasis and recommended optimal follow-up nursing in rectal cancer

We aimed to analyze and investigate the clinical factors that influence the occurrence of liver metastasis in locally advanced rectal cancer patients, with an attempt to assist patients in devising the optimal imaging-based follow-up nursing. Between June 2011 and May 2021, patients with rectal cancer at our hospital were retrospectively analyzed. A random survival forest model was developed to predict the probability of liver metastasis and provide a practical risk-based approach to surveillance. The results indicated that age, perineural invasion, and tumor deposit were significant factors associated with the liver metastasis and survival. The liver metastasis risk of the low-risk group was higher at 6-21 months, with a peak occurrence time in the 15th month. The liver metastasis risk of the high-risk group was higher at 0-24 months, with a peak occurrence time in the 8th month. In general, our clinical model could predict liver metastasis in rectal cancer patients. It provides a visualization tool that can aid physicians and nurses in making clinical decisions, by detecting the probability of liver metastasis.

A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer

Objective: To establish a nomogram prediction model (based on clinicopathological and radiological features) for the development of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). Methods: This retrospective study included patients with CRC who underwent surgery at Changshu No.1 People's Hospital and the Second Affiliated Hospital of Soochow University between January 2016 and December 2018. The clinical, pathological, and radiological features of each patient were investigated. Risk factors for MLM were identified by univariable and multivariable analyses. The predictive nomogram for MLM development was constructed. The predictive performance of the nomogram was estimated by the receiver operating characteristics curve, calibration curve, and decision curve analysis. Results: This study included 161 patients with CRC [median age: 66 (range, 33-87) years]. Fifty-nine developed MLM after a median of 12 (range, 2-52) months after surgery. The multivariable logistic regression analysis showed that age >66 years (OR=3.471, 95% CI: 1.272-9.473, P=0.015), N2 stage (OR=6.534, 95% CI: 1.456-29.317, P=0.014), positive vascular invasion (OR=2.995, 95% CI: 1.132-7.926, P=0.027), positive tumor deposit (OR=4.451, 95% CI: 1.153-17.179, P=0.030), and linear (OR=6.774, 95% CI: 1.306-35.135, P=0.023) and nodal pericolic fat infiltration patterns (OR=8.762, 95% CI: 1.521-50.457, P=0.015) were independently associated with MLM. These five factors were used to create a nomogram. The area under the receiver operating characteristics curve of the nomogram was 0.866 (95% CI: 0.803-0.914), indicating favorable prediction performance. The calibration curve of the nomogram showed a satisfactory agreement between the predicted and actual probabilities. Conclusions: A nomogram prediction model based on five clinicopathological and radiological features might have favorable prediction performance for MLM in patients who underwent surgery for CRC. Hence, the present study proposes a nomogram that can easily be used to predict MLM after CRC surgery based on readily available features.

Development and validation of a nomogram for preoperative prediction of tumor deposits in colorectal cancer

BACKGROUND

Based on the clinical data of colorectal cancer (CRC) patients who underwent surgery at our institution, a model for predicting the formation of tumor deposits (TDs) in this patient population was established.

AIM

To establish an effective model for predicting TD formation, thus enabling clinicians to identify CRC patients at high risk for TDs and implement personalized treatment strategies.

METHODS

CRC patients (n = 645) who met the inclusion criteria were randomly divided into training (n = 452) and validation (n = 193) cohorts using a 7:3 ratio in this retrospective analysis. Least absolute shrinkage and selection operator regression was employed to screen potential risk factors, and multivariable logistic regression analysis was used to identify independent risk factors. Subsequently, a predictive model for TD formation in CRC patients was constructed based on the independent risk factors. The discrimination ability of the model, its consistency with actual results, and its clinical applicability were evaluated using receiver-operating characteristic curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

Thirty-four (7.5%) patients with TDs were identified in the training cohort based on postoperative pathological specimens. Multivariate logistic regression analysis identified female sex, preoperative intestinal obstruction, left-sided CRC, and lymph node metastasis as independent risk factors for TD formation. The AUCs of the nomogram models constructed using these variables were 0.839 and 0.853 in the training and validation cohorts, respectively. The calibration curve demonstrated good consistency, and the training cohort DCA yielded a threshold probability of 7%-78%.

CONCLUSION

This study developed and validated a nomogram with good predictive performance for identifying TDs in CRC patients. Our predictive model can assist surgeons in making optimal treatment decisions.