Prognostic Nomogram for Rectal Cancer Patients With Tumor Deposits

Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer

AIM

Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer.

MATERIALS AND METHODS

A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n ​= ​159) and a validation set (n ​= ​40). The total incidence of TDs was 37.2 ​% (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI ​+ ​ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores.

RESULTS

The Rad-score (T2WI ​+ ​ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI ​+ ​ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 ​% in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 ​%, 92.0 ​%, 83.3 ​%, and 82.1 ​%, respectively.

CONCLUSION

The radiomics nomogram based on Rad-score (T2WI ​+ ​ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.

Effect of Tumor Regression Grade on Survival and Disease-Free Interval in Patients Operated on for Locally Advanced Rectal Cancer

INTRODUCTION

Colorectal cancer is the fourth leading cause of cancer-related death in both men and women in our population. In this regard, rectal cancer accounts for more than half of colorectal cancer deaths, and its incidence is expected to increase in the coming years. There have been significant changes in neoadjuvant therapy regimens, with promising results, as demonstrated by the recent RAPIDO and PRODIGE23 studies. Around 40% of patients diagnosed with locally advanced rectal cancer show some degree of response to neoadjuvant treatment, with complete tumor regression observed in up to one in five patients.

MATERIALS AND METHODS

Retrospective observational study. A total of 181 patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy followed by surgery were analyzed. Clinical and pathological data were collected from the patients, including assessment of tumor regression through histopathological studies after surgery. The Mandard tumor regression grading system was used to categorize tumor response into different grades.

RESULTS

The results showed a significant association between the degree of tumor regression and several important clinical outcomes. Specifically, patients with higher tumor regression had significantly better disease-free survival than those with less regression (p = 0.004). In addition, tumor regression was also correlated with the incidence of local recurrence (p = 0.018) and distant metastasis (p = 0.032). These associations suggest that tumor responsiveness to neoadjuvant therapy may influence the long-term progression of the disease. Regarding tumor deposits and the presence of lymphadenopathy, these factors were also found to be significantly associated with clinical outcomes. Patients with tumor deposits had a higher incidence of local recurrence (p = 0.025) and distant metastases (p = 0.041), while the presence of lymphadenopathy increased the risk of local recurrence (p = 0.013). These findings highlight the importance of evaluating not only tumor regression but also other pathological markers to predict prognosis and guide clinical management.

CONCLUSIONS

The degree of tumor regression was not an independent predictor of survival compared to other variables such as nodal stage and presence of tumor deposits. This indicates that while tumor regression is an important factor, other elements also play a crucial role in determining the prognosis of patients with locally advanced rectal cancer. This study provides additional evidence for the importance of tumor regression, tumor deposits, and lymphadenopathy as predictors of clinical outcomes in patients with rectal cancer treated with neoadjuvant chemoradiotherapy.

Development and validation of comprehensive nomograms from the SEER database for predicting early mortality in metastatic rectal cancer patients

BACKGROUND

Metastatic rectal cancer is an incurable malignancy, which is prone to early mortality. We aimed to establish nomograms for predicting the risk of early mortality in patients with metastatic rectal cancer.

METHODS

In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database.We utilized X-tile software to determine the optimal cut-off points of age and tumor size in diagnosis. Significant independent risk factors for all-cause and cancer-specific early mortality were determined by the univariate and multivariate logistic regression analyses, then we construct two practical nomograms. In order to assess the predictive performance of nomograms, we performed calibration plots, time-dependent receiver-operating characteristic curve (ROC), decision curve analysis (DCA) and clinical impact curve (CIC).

RESULTS

A total of 2570 metastatic rectal cancer patients were included in the study. Multivariate logistic regression analyses revealed that age at diagnosis, CEA level, tumor size, surgical intervention, chemotherapy, radiotherapy, and metastases to bone, brain, liver, and lung were independently associated with early mortality of metastatic rectal cancer patients in the training cohort. The area under the curve (AUC) values of nomograms for all-cause and cancer-specific early mortality were all higher than 0.700. Calibration curves indicated that the nomograms accurately predicted early mortality and exhibited excellent discrimination. DCA and CIC showed moderately positive net benefits.

CONCLUSIONS

This study successfully generated applicable nomograms that predicted the high-risk early mortality of metastatic rectal cancer patients, which can assist clinicians in tailoring more effective treatment regimens.

A narrative review of prognostic indices in the evaluation of gastrointestinal cancers

Background and Objective

Accurate cancer prognostication allows for conscious decision-making. There is a need for precise indices, along with predictive biomarkers, which aid cancer prognostication. We sought to conduct an overview of the current state of prognostic indices and biomarkers in the evaluation of gastrointestinal (GI) cancers, specifically esophageal, colon and rectal.

Methods

We conducted a comprehensive review of articles in the PubMed database between September 2001 and February 2022. Only articles written in English were included. We reviewed retrospective analyses and prospective observational studies.

Key Content and Findings

Nomograms are well-described tools that provide estimates of specific cancer-related events, such as overall survival (OS). They are also useful in unroofing specific patient-related variables, which may be associated with cancer survival. Certain prognostic indices have been tested against each other with the goal of discerning superiority. Finally, specific biomarkers have emerged as promising prognostic indicators.

Conclusions

Nomograms play a significant role in the prognostication of GI cancer. The identification of specific biomarkers in cancer prognostication is evolving. As we embark on the era of precision medicine, further investigation of reliable prognostic indices and biomarkers is needed.

Establishment of a nomogram model for prediction of postoperative heterochronous liver metastasis in young and middle-aged patients with rectal cancer

BACKGROUND

The incidence of rectal cancer is increasing year by year. Radical surgery is often used for the treatment of rectal cancer in clinical practice, but postoperative liver metastasis has become an important reason for the increase in mortality. Therefore, establishing a model to predict the trend of metachronous liver metastasis has become a research focus. Nomogram model has been widely used in the medical field, but there has been no widely accepted nomogram model available for prediction of metachronous liver metastasis after rectal cancer surgery.

AIM

To constuct a nomogram model based on the risk factors for postoperative metachronous liver metastasis in young and middle-aged patients with rectal cancer, and to evaluate the performance of the model for predicting the risk of postoperative metachronous liver metastasis, so as to provide some guidance for clinical prevention and treatment.

METHODS

A total of 120 young and middle-aged patients with rectal cancer admitted to our hospital from March 2019 to February 2022 were selected as research subjects to observe the incidence of postoperative heterochronous liver metastasis. Univariate and multivariate Logistic regression analyses were performed to identify the risk factors for postoperative heterochronous liver metastasis and to construct a nomogram model. ROC curve, decision curve, and correction curve analyses were used to verify the value of nomogram model for the prediction of postoperative heterochronous liver metastasis.

RESULTS

The incidence of anomalous liver metastasis 1 year after surgery was 23.33% in 120 young and middle-aged patients with rectal cancer. Low differentiation, lymph node metastasis, depth of invasion (T3/T4), margin width of primary cancer < 2 cm, high expression of peripheral blood telomerase reverse transcriptase (hTERT), and elevated serum levels of carcinoembryonic antigen (CEA), vascular endothelial growth factor (VEGF), lemur tyrosine kinase-3 (LMTK3), squamous cell carcinoma-associated antigen (SCC-Ag), and axon-guided factor-1 (Netrin-1) were identified to be risk factor for postoperative hetero-chronic liver metastasis (P < 0.05). The C-index and area under the curve of the nomogram model were 0.860 and 0.957, respectively, and the net benefit value was high (P < 0.05).

CONCLUSION

Low differentiation, lymph node metastasis, depth of invasion (T3/T4), margin width of primary cancer < 2 cm, high expression of hTERT in peripheral blood, and elevated levels of serum CEA, VEGF, LMTK3, SC-AG and Netrin-1 are risk factors for postoperative xenotemporal liver metastasis in young and middle-aged patients with rectal cancer. Based on the above risk factors, a nomogram model has been established to predict postoperative heterochronous liver metastasis in such patients.

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.