Development and validation of a novel prognostic nomogram including tumor deposits could better predict survival for colorectal cancer: a population-based study

Prognostic scoring system based on indicators reflecting the tumor glandular differentiation and microenvironment for patients with colorectal cancer

Prognostic stratification is an urgent concern for patients with colorectal cancer (CRC). The desmoplastic reaction (DR) is speculated to mirror the tumor microenvironment. DR types are considered independent prognostic indicators in CRC, but have not been incorporated in previous prognostic nomograms. We aimed to assess the prognostic significance of a novel approach incorporating histopathological indicators reflecting tumor glandular differentiation and microenvironment. We evaluated 329 consecutive patients with CRC who underwent surgical resection at Kansai Medical University. Histological glandular differentiation was scored as 2 (0 point), 3 (1 point), or 4 (2 points). Tumor buddings (TBs) were classified as TB1 (0 point), TB2 (1 point), or TB3 (2 points). pT1 or 2 was considered as 0 point, pT3 or 4 + DR non-immature type as 1 point, and pT3 or 4 + DR immature type as 2 points. Lymph node metastasis was classified as pN0 (0 point), pN1 (1 point), or pN2 (2 points). The preoperative carcinoembryonic antigen levels were categorized as < 5.0 ng/mL (0 point) and ≧5.0 (1 point). Considering these factors, the following D&M (tumor differentiation and microenvironment) scoring system was applied: I (0-2 points), II (3-4 points), III (5-6 points), and IV (7-9 points). Kaplan-Meier curves showed significant differences in disease-specific survival and recurrence-free survival among the assigned scores, highlighting their enhanced utility compared with the American Joint Committee on Cancer 8th edition staging system. The D&M scoring system was valuable as the initial prognostic nomogram, including DR.

Clinical and Pathologic Predictors of Tumor Deposits in Colorectal Cancer

BACKGROUND

Tumor deposits (TDs) are a special metastatic pattern of colorectal cancer (CRC). This study aims to explore the pathological characteristics of TD and find out the risk factors of TD in CRC.

METHODS

TDs cases of CRC were selected and validated by HE staining. The correlation between TDs and T stages, N stages, and microsatellite instability was calculated by the chi-squared (χ2) test.

RESULTS

A total of 2553 patients with colorectal cancer undergoing intestinal resection were included in this study. Two hundred fifty-nine cases of TDs patients were included. The positive rate of TDs was 1.9% (2/105) in T1, 3.8% (10/266) in T2, 11% (231/2305) in T3, and 22.8% (16/77) in T4. T3 and T4 were more prone to TDs than T1 and T2, but there was no difference between T3 and T4. The positive rate of TDs was 7.2% (107/1491) in N0, 14.3% (152/1062) in N + , and N + was more prone to TDs than N0. The positive rate of TDs was 10.5% (256/2432) in MSS, 2.5% (3/121) in MSI, and MSS was more prone to TDs than MSI. Multivariate analysis showed lymph node invasion, T stage, and MSS were independent risk factors for TDs.

CONCLUSION

Lymph node invasion, T stage, and MSS were independent risk factors for TDs.

Readjustment of nodal staging by integrating tumor deposits and positive lymph nodes in patients with stage III colon cancer: a population-based analysis

Whether tumor deposits (TDs) should be classified as lymph node metastasis or distant metastasis remains controversial. To address this predicament, we conducted this study to identify the predictive value of TDs on the survival of patients diagnosed with stage III colon cancer (CC). 12,904 eligible patients diagnosed with stage III CC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The best cutoff point of TD quantity was determined based on the difference in survival. Cox proportional hazards model was employed to perform univariate and multivariate analyses. The Kaplan-Meier method and log-rank test were performed to calculate the differences between overall survival (OS). Our results showed that the number of TDs was a significant prognostic factor in patients with stage III CC (P < 0.0001). We added the number of TDs to the pN stage and devised a new pN stage, there were no significant differences in the survival of npN, except npN2a (P > 0.05). Upon re-staging to the same npN stage, the difference in survival between TDs+ and TDs- disappeared (P > 0.05). The median survival times for N2aTDs > 4 and N2bTDs > 4 were 33 and 37 months, respectively, which were significantly shorter than that of N2TDs- (65 months) and represented the worst survival rates among all groups. In conclusion, the number of TDs indicated a poor prognosis for patients with stage III CC. Incorporating TDs into the pN is feasible to predict prognosis.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models

Background

This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC).

Methods

In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation.

Results

A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04).

Conclusions

A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

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.

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

Background

More and more evidence indicated that tumor deposit (TD) was significantly associated with local recurrence, distant metastasis (DM), and poor prognosis for patients with colorectal cancer (CRC). This study aims to explore the main clinical risk factors for the presence of TD in CRC patients with no DM (CRC-NDM) and the prognostic factors for TD-positive patients after surgery.

Methods

The data of patients with CRC-NDM between 2010 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A logistic regression model was used to identify risk factors for TD presence. Fine and Gray’s competing-risk model was performed to analyze prognostic factors for TD-positive CRC-NDM patients. A predictive nomogram was constructed using the multivariate logistic regression model. The concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration were used to evaluate the predictive nomogram. Also, a prognostic nomogram was built based on multivariate competing-risk regression. C-index, the calibration, and decision-curve analysis (DCA) were performed to validate the prognostic model.

Results

The predictive nomogram to predict the presence of TD had a C-index of 0.785 and AUC of 0.787 and 0.782 in the training and validation sets, respectively. From the competing-risk analysis, chemotherapy (subdistribution hazard ratio (SHR) = 0.542, p < 0.001) can significantly reduce CRC-specific death (CCSD). The prognostic nomogram for the outcome prediction in postoperative CRC-NDM patients with TD had a C-index of 0.727. The 5-year survival of CCSD was 17.16%, 36.20%, and 63.19% in low-, medium-, and high-risk subgroups, respectively (Gray’s test, p < 0.001).

Conclusions

We constructed an easily predictive nomogram in identifying the high-risk TD-positive CRC-NDM patients. Besides, a prognostic nomogram was built to help clinicians identify poor-outcome individuals in postoperative CRC-NDM patients with TD. For the high-risk or medium-risk subgroup, additional chemotherapy may be more advantageous for the TD-positive patients rather than radiotherapy.

Significance of tumor deposits combined with lymph node metastasis in stage III colorectal cancer patients: a retrospective multi-center cohort study from China

PURPOSE

The study aimed to explore the value of tumor deposits in stage III colorectal cancer (CRC) and verify whether patients with more tumor deposit numbers have higher risk of recurrence.

METHODS

The retrospective cohort analysis was performed at two cancer centers of China. Stage III CRC patients who underwent radical resection at the center between April 2008 and February 2019 were identified. The Univariate/Multivariate Cox regression, Kaplan-Meier analysis, and PSM were recurrence-free survival (RFS) used.

RESULTS

Total 1080 stage III CRC patients (634 [58.7%] men; median [IQR] age, 60 [50-68] years) who underwent radical surgical resection were identified for inclusion in this study. Patients with tumor deposits had a 12.8% lower 3-year RFS (n = 236 [69.9%]) than the patients without tumor deposits (n = 844 [82.7%]) (P ≤ 0.0001). The 3-year RFS of patients with stage N2 (n = 335 [61.2%]) was 18.6% lower (P ≤ 0.0001) than the original cohort of patients with stage N1 (n = 745 [79.8%]), but it was similar to the RFS of patients with 4 or more tumor deposits plus lymph node metastases (n = 58 [61.4%]) (P = 0.91). The RFS for patients with 4 or more tumor deposits plus number of lymph node metastases (n = 58 [61.4%]) was 15.8% lower than the cohort of patients with 1-3 tumor deposits + number of lymph node metastases (n = 687 [77.2%]) (P = 0.001). Multivariate analysis confirmed that patients with 4 or more tumor deposits + the number of lymph node metastases (hazard ratio [HR], 1.88; 95% CI, 1.24-2.87) were independently associated with a shorter RFS.

CONCLUSION

The number of tumor deposits is an indicator of poor postoperative prognosis. It is necessary to incorporate the number of tumor deposits combined with the number of lymph node metastases to stratify postoperative stratification of stage III CRC, which may provide a new theoretical basis for adjuvant therapy for patients with N1 stage CRC after surgery.

Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer

Background

The overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively.

Methods

Records from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis.

Results

The LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827-0.846 and 0.780, 95% CI 0.773-0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort.

Conclusions

An original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.