Predictive and Prognostic Assessment Models for Tumor Deposit in Colorectal Cancer Patients With No Distant Metastasis

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.

Magnetic resonance imaging radiomics modeling predicts tumor deposits and prognosis in stage T3 lymph node positive rectal cancer

PURPOSE

To develop a magnetic resonance imaging radiomics model to predict tumor deposits (TDs) and prognosis in stage T3 lymph node positive (T3N+) rectal cancer (RC).

METHODS

This retrospective study included 163 patients with pathologically confirmed T3N + RC from December 2013 to December 2015. The patients were divided into two groups for training and testing. Extracting radiomic features from MR images and selecting features using principal component analysis (PCA), then radiomic scores (rad-scores) were obtained by logistic regression analysis. Finally, a combined TDs prediction model containing rad-scores and clinical features was developed. A receiver operating characteristic (ROC) curve was used to assess the prediction performance. The overall survival (OS) rate in patients with high-risk and low-risk TDs predicted by rad-scores was validated by Kaplan-Meier survival curves.

RESULTS

Of the 163 patients included, histological TDs was diagnosed in 45 patients. The area under the curve (AUC) of the final model was 0.833 (training) and 0.844 (testing). The patients with rad-scores predicted high-risk were associated with OS. In addition, postoperative adjuvant therapy improved the OS of the high-risk TDs group (P < 0.05).

CONCLUSION

MRI-based radiomics modeling helps in the preoperative prediction of patients with TDs+ in T3N + RC and provides risk stratification for neoadjuvant therapy. In addition, the rad-scores of TDs could suggest different survival benefits of postoperative adjuvant therapy for T3N + RC patients and guide clinical treatment.

Role of Traditional Chinese Medicine Syndrome Type, Gut Microbiome, and Host Immunity in Predicting Early and Advanced Stage Colorectal Cancer

OBJECTIVE

To investigate the role of Traditional Chinese Medicine (TCM) syndrome type, gut microbiome distribution, and host immunity function in predicting the early and advanced clinical stages of colorectal cancer (CRC).

METHODS

A cross-sectional case-control study was performed which included 48 early stage and 48 advanced patients with CRC enrolled from March 2018 to December 2020. 16S rRNA gene sequencing was performed to analyze the gut microbiomes of the patients, while T and B lymphocyte subsets in peripheral blood were assessed using flow cytometry. TCM syndrome type was measured using the spleen deficiency syndrome (SDS) scale.

RESULTS

The abundance levels of Prevotella, Escherichia-Shigella, and Faecalibacterium in the gut microbiota were significantly increased in the advanced group, while Bacteroides was significantly decreased. Phascolarctobacterium was detectable only in the early metaphase group, whereas Alistipes was detectable only in the advanced group. The lymphocyte (P = .006), T helper cell (TH) (P = .002), cytotoxic T cell (TC) (P = .003), double positive T cell (DPT) (P = .02), and total T counts (P = .001) were significantly higher in the early metaphase group than in the advanced metaphase group. Compared with patients with early stage CRC, the advanced group had a higher SDS score. After adjusting for clinical stage, Spearman's correlation analysis showed interactions among gut microbiome abundance, T cell level, and SDS score. Multivariate logistic analysis showed that after controlling for the SDS score, abundance of Alistipes and Faecalibacterium, and double negative T cell (DNT) level, DPT was significantly associated with a lower risk of advanced-stage disease (hazard ratio, 0.918; P = .022).

CONCLUSION

Our study suggested associations between clinical stage, SDS, gut microbiota, and T lymphocytes, which provided insights for a potential prediction model for the disease progression of CRC.

The Prognostic Value of Adjuvant Chemotherapy in Colon Cancer With Solitary Tumor Deposit

Purpose

The aim of this study is to investigate the survival benefit of adjuvant chemotherapy in patients with colon cancer with the solitary tumor deposit (TD).

Methods

The primary study outcomes used in this study were colon cancer–specific survival (CSS) and overall survival (OS). The differences of the distribution of categorical variables in patients with colon cancer with the solitary TD according to adjuvant chemotherapy administration were tested using the Pearson’s chi-square test. The Kaplan–Meier method was utilized to evaluate CSS and OS. Hazard ratio (HR) and 95% confidence interval (CI) were calculated on the basis of Cox regression models to assess the prognostic value of different demographic and clinicopathological characteristics.

Results

A total of 877 patients with TanyN1cM0 colon cancer with solitary TD were identified in our analysis. It was found that OS (75.4% vs. 42.8% for 5-year OS rate, p < 0.001) and CSS (82.9% vs. 69.3% for 5-year CSS rate, p < 0.001) of patients with colon cancer with adjuvant chemotherapy administration were significantly better than those without adjuvant chemotherapy administration. Multivariate Cox survival analyses revealed that the overall and colon cancer–specific mortality risks of patients with adjuvant chemotherapy administration were decreased by 64.4% (HR = 0.356, 95% CI = 0.265–0.479, p < 0.001) and 57.4% (HR = 0.426, 95% CI = 0.286–0.634, p < 0.001) compared with those without adjuvant chemotherapy administration, respectively.

Conclusions

Adjuvant chemotherapy administration could significantly improve OS and CSS in patients with colon cancer with the solitary TD. This is the first study to investigate and demonstrate the survival benefit of adjuvant chemotherapy in patients with colon cancer with the solitary TD.