Prognostic value of tumor deposits in locally advanced rectal cancer: a retrospective study with propensity score matching

Prediction of pathological complete response and prognosis in locally advanced rectal cancer

BACKGROUND

Colorectal cancer is currently the third most common malignant tumor and the second leading cause of cancer-related death worldwide. Neoadjuvant chemoradiotherapy (nCRT) is standard for locally advanced rectal cancer (LARC). Except for pathological examination after resection, it is not known exactly whether LARC patients have achieved pathological complete response (pCR) before surgery. To date, there are no clear clinical indicators that can predict the efficacy of nCRT and patient outcomes.

AIM

To investigate the indicators that can predict pCR and long-term outcomes following nCRT in patients with LARC.

METHODS

Clinical data of 128 LARC patients admitted to our hospital between September 2013 and November 2022 were retrospectively analyzed. Patients were categorized into pCR and non-pCR groups. Univariate analysis (using the χ 2 test or Fisher's exact test) and logistic multivariate regression analysis were used to study clinical predictors affecting pCR. The 5-year disease-free survival (DFS) and overall survival (OS) rates were calculated using Kaplan-Meier analysis, and differences in survival curves were assessed with the log-rank test.

RESULTS

Univariate analysis showed that pretreatment carcinoembryonic antigen (CEA) level, lymphocyte-monocyte ratio (LMR), time interval between neoadjuvant therapy completion and total mesorectal excision, and tumor size were correlated with pCR. Multivariate results showed that CEA ≤ 5 ng/mL (P = 0.039), LMR > 2.73 (P = 0.023), and time interval > 10 wk (P = 0.039) were independent predictors for pCR. Survival analysis demonstrated that patients in the pCR group had significantly higher 5-year DFS rates (94.7% vs 59.7%, P = 0.002) and 5-year OS rates (95.8% vs 80.1%, P = 0.019) compared to the non-pCR group. Tumor deposits (TDs) were significantly correlated with shorter DFS (P = 0.002) and OS (P < 0.001).

CONCLUSION

Pretreatment CEA, LMR, and time interval contribute to predicting nCRT efficacy in LARC patients. Achieving pCR demonstrates longer DFS and OS. TDs correlate with poor prognosis.

Prediction of pathological complete response and prognosis in locally advanced rectal cancer

BACKGROUND

Colorectal cancer is currently the third most common malignant tumor and the second leading cause of cancer-related death worldwide. Neoadjuvant chemoradiotherapy (nCRT) is standard for locally advanced rectal cancer (LARC). Except for pathological examination after resection, it is not known exactly whether LARC patients have achieved pathological complete response (pCR) before surgery. To date, there are no clear clinical indicators that can predict the efficacy of nCRT and patient outcomes.

AIM

To investigate the indicators that can predict pCR and long-term outcomes following nCRT in patients with LARC.

METHODS

Clinical data of 128 LARC patients admitted to our hospital between September 2013 and November 2022 were retrospectively analyzed. Patients were categorized into pCR and non-pCR groups. Univariate analysis (using the χ² test or Fisher’s exact test) and logistic multivariate regression analysis were used to study clinical predictors affecting pCR. The 5-year disease-free survival (DFS) and overall survival (OS) rates were calculated using Kaplan-Meier analysis, and differences in survival curves were assessed with the log-rank test.

RESULTS

Univariate analysis showed that pretreatment carcinoembryonic antigen (CEA) level, lymphocyte-monocyte ratio (LMR), time interval between neoadjuvant therapy completion and total mesorectal excision, and tumor size were correlated with pCR. Multivariate results showed that CEA ≤ 5 ng/mL (P = 0.039), LMR > 2.73 (P = 0.023), and time interval > 10 wk (P = 0.039) were independent predictors for pCR. Survival analysis demonstrated that patients in the pCR group had significantly higher 5-year DFS rates (94.7% vs 59.7%, P = 0.002) and 5-year OS rates (95.8% vs 80.1%, P = 0.019) compared to the non-pCR group. Tumor deposits (TDs) were significantly correlated with shorter DFS (P = 0.002) and OS (P < 0.001).

CONCLUSION

Pretreatment CEA, LMR, and time interval contribute to predicting nCRT efficacy in LARC patients. Achieving pCR demonstrates longer DFS and OS. TDs correlate with poor prognosis.

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.

A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study

The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.

Development and validation of a competitive risk model in patients with rectal cancer: based on SEER database

BACKGROUND

Rectal cancer is one of the most common malignancies. To predict the specific mortality risk of rectal cancer patients, we constructed a predictive nomogram based on a competing risk model.

METHODS

The information on rectal cancer patients was extracted from the SEER database. Traditional survival analysis and specific death analysis were performed separately on the data.

RESULTS

The present study included 23,680 patients, with 16,580 in the training set and 7100 in the validation set. The specific mortality rate calculated by the competing risk model was lower than that of the traditional survival analysis. Age, Marriage, Race, Sex, ICD-O-3Hist/Behav, Grade, AJCC stage, T stage, N stage, Surgery, Examined LN, RX SUMM-SURG OTH, Chemotherapy, CEA, Deposits, Regional nodes positive, Brain, Bone, Liver, Lung, Tumor size, and Malignant were independent influencing factors of specific death. The overall C statistic of the model in the training set was 0.821 (Se = 0.001), and the areas under the ROC curve for cancer-specific survival (CSS) at 1, 3, and 5 years were 0.842, 0.830, and 0.812, respectively. The overall C statistic of the model in the validation set was 0.829 (Se = 0.002), and the areas under the ROC curve for CSS at 1, 3, and 5 years were 0.851, 0.836, and 0.813, respectively.

CONCLUSIONS

The predictive nomogram based on a competing risk model for time-specific mortality in patients with rectal cancer has very desirable accuracy. Thus, the application of the predictive nomogram in clinical practice can help physicians make clinical decisions and follow-up strategies.

Distinguishing mesorectal tumor deposits from metastatic lymph nodes by using diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in rectal cancer

OBJECTIVES

This study aimed to identify whether apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are helpful in distinguishing mesorectal tumor deposits (TD) from metastatic lymph nodes (MLN) in rectal cancer (RC).

METHODS

Thirty patients (59 lesions, including 30 TD and 29 MLN) with RC who underwent pretreatment-MRI between February 2016 and August 2018 were enrolled. The morphological features, ADC values, and semi-quantitative parameters of DCE-MRI, including relative enhancement (RE), maximum enhancement (ME), maximum relative enhancement (MRE), time to peak (TTP), wash-in rates (WIR), wash-out rates (WOR), brevity of enhancement (BRE), and area under the curve (AUC) were measured on lesions (TD or MLN) and RC. The parameters were compared between TD and MLN, tumor with and without TD group by using Fisher's exact test, independent-samples t-test, and Mann-Whitney U test. The ratio (lesion-to-tumor) of the parameters was compared between TD and MLN. Receiver operating characteristic curve analysis and binary logistic regression analysis were used to assess the diagnostic ability of single and combined metrics for distinguishing TD from MLN.

RESULTS

The morphological features, including size, shape, and border, were significantly different between TD and MLN. TD exhibited significantly lower RE, MRE, RE-ratio, MRE-ratio, ADC_min-ratio, and ADC_mean-ratio than MLN. RE-ratio showed the highest AUC (0.749) and accuracy (77.97%) among single parameters. The combination of DCE-MRI and DWI parameters together showed higher diagnostic efficiency (AUC = 0.825).

CONCLUSIONS

Morphological features, ADC values, and DCE-MRI parameters can preoperatively help distinguish TD from MLN in RC.

KEY POINTS

• DWI and DCE-MRI can facilitate early detection and distinguishing mesorectal TD (tumor deposits) from MLN (metastatic lymph nodes) in rectal cancer preoperatively. • TD has some specific morphological features, including relatively larger size, lower short- to long-axis ratio, irregular shape, and ill-defined border on T2-weighted MR images in rectal cancer. • The combination of ADC values and semi-quantitative parameters of DCE-MRI (RE, MRE) can help to improve the diagnostic efficiency of TD in rectal cancer.

Prognostic Nomogram for Rectal Cancer Patients With Tumor Deposits

Aim

Tumor deposits (TDs) are an aggressive hallmark of rectal cancer, but their prognostic value has not been addressed in current staging systems. This study aimed to construct and validate a prognostic nomogram for rectal cancer patients with TDs.

Methods

A total of 1,388 stage III–IV rectal cancer patients who underwent radical surgical resection from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed to identify the clinical value of TDs. TD-positive rectal cancer patients in the SEER database were used as the training set to construct a prognostic model, which was validated by Fujian Cancer Hospital. Three models were constructed to predict the prognosis of rectal cancer patients with TDs, including the least absolute shrinkage and selection operator regression (LASSO, model 1), backward stepwise regression (BSR, model 2), and LASSO followed by BSR (model 3). A nomogram was established among the three models.

Results

In the entire cohort, TD was also identified as an independent risk factor for overall survival (OS), even after adjusting for baseline factors, stage, other risk factors, treatments, and all the included variables in this study (all P < 0.05). Among patients with TDs, model 3 exhibited a higher C-index and area under the curves (AUCs) at 3, 4, and 5 years compared with the American Joint Committee on Cancer staging system both in the training and validation sets (all P < 0.05). The nomogram obtained from model 3 showed good consistency based on the calibration curves and excellent clinical applicability by the decision curve analysis curves. In addition, patients were divided into two subgroups with apparently different OS according to the current nomogram (both P < 0.05), and only patients in the high-risk subgroup were found to benefit from postoperative radiotherapy (P < 0.05).

Conclusion

We identified a novel nomogram that could not only predict the prognosis of rectal cancer patients with TDs but also provide reliable evidence for clinical decision-making.