The new 'coN' staging system combining lymph node metastasis and tumour deposit provides a more accurate prognosis for TNM stage III colon cancer

Validated Integration of Tumor Deposits in N Staging for Prognostication in Colon Cancer

Importance

Tumor deposits have prognostic value in colon cancer, but the current American Joint Committee on Cancer (AJCC) staging only considers them if there are no concurrent positive lymph nodes.

Objective

To devise a staging system for colon cancer by integrating counts of tumor deposits with positive lymph nodes while retaining the current AJCC staging framework.

Design, Setting, and Participants

This retrospective cohort study examines data from a large-volume, tertiary care center database (January 2010 through March 2023 with follow-up until December 2023) and the population-based National Cancer Database (January 2010 through December 2020 with follow-up until December 2021). Participants were adults (age 18-75 years) with stage III colon adenocarcinoma who underwent chemotherapy, and had a specified positive lymph node count and tumor deposit count were selected.

Exposure

A real positive lymph nodes count was developed and used to derive Sassun-Mayo N/tumor, lymph node, and metastasis (TNM) stages that were compared with the AJCC N/TNM stages.

Main Outcomes and Measures

Receiver operating characteristic (ROC) curves and Kaplan-Meier analyses for 3-year overall survival were performed to assess the efficiency of the 2 staging systems. The concordance index was used for validation using the National Cancer Database.

Results

From a total patient number of 11 162 (institutional) and 848 704 (national), the final patient numbers were 788 and 77 790, respectively. The institutional database patients had a mean (SD) age of 58.5 (11.5) years; there were 433 male patients (54.9%) and 355 female (45.1%). The national database patients had a mean (SD) age of 59.3 (10.6) years; there were 40 315 male patients (51.8%) and 37 475 female (48.2%). ROC curve areas were improved using the Sassun-Mayo stages (3-year death for AJCC TMN, 0.63 [95% CI, 0.57-0.69] vs 0.66 [95% CI, for 0.60-0.72] for Sassun-Mayo TNM). Kaplan-Meier curves revealed visible overlaps among AJCC N stages, which were absent in the Sassun-Mayo N stages. The concordance index in the Sassun-Mayo N/TNM stages was 0.611 and 0.616, respectively, while in the AJCC N/TNM stages, it was 0.598 and 0.606, respectively. Patients upstaged from N1 to N2 (n = 10 307; 13.2%) had a 3-year overall survival rate nearly identical to that of AJCC N2a patients. Additionally, 3001 patients (3.9%) were upstaged from N2a to N2b, indicating that 13 308 patients (17.1%) with stage III colon cancer across cohorts were understaged.

Conclusions and Relevance

This study found that Sassun-Mayo N/TNM staging provided superior overall survival stratification compared with the current AJCC staging, suggesting that their implementation could improve prognostication in colon cancer.

Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI

OBJECTIVE

To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.

MATERIALS AND METHODS

A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (Ktrans, kep, and ve) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups. Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.

RESULTS

All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, ve had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, ve, and ADCmean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).

CONCLUSION

The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome

RATIONALE AND OBJECTIVES

To investigate the predictive value of a deep learning model based on multiparametric MRI (mpMRI) for tumor deposit (TD) in rectal cancer (RC) patients and to analyze their prognosis.

MATERIALS AND METHODS

Data from 529 RC patients who underwent radical surgery at two centers were retrospectively collected. 379 patients from center one were randomly divided into a training set (n = 265) and an internal validation (invad) set (n = 114) in a 7:3 ratio. 150 patients from center two were included in the external validation (exvad) set. Univariate and multivariate analyses were performed to identify independent clinical predictors and to construct a clinical model. Preoperative mpMRI images were utilized to extract deep features through the ResNet-101 model. Following feature selection, a deep learning model was developed. A nomogram was created by combining the clinical model with the deep learning model. The clinical applicability of each model was assessed using ROC curves, decision curve analysis (DCA), clinical impact curves (CIC), and deLong test. Kaplan-Meier survival analysis was conducted to evaluate prognostic outcome among patients.

RESULTS

Among the 529 patients, 142 (26.8%) were TD positive. In the training set, clinical model was constructed based on clinical independent predictors (cT and cN). 30 deep features were selected to calculate the deep learning radscore (DLRS) and develop the deep learning (DL) model. The AUC values for the clinical model were 0.724, 0.836, and 0.763 in the training set, invad set, and exvad set, respectively. The AUC values for the DL model were 0.903, 0.853, and 0.874, respectively. The nomogram achieved higher AUC values of 0.925, 0.919, and 0.9, respectively. The DeLong test indicated that the predictive performance of the nomogram was superior to both the DL model and the clinical model in training and invad sets. Kaplan-Meier survival analysis showed that both the DL model and the nomogram effectively stratified patients into high-risk and low-risk groups for 3-year DFS (p < 0.05).

CONCLUSION

The nomogram, which integrates mpMRI-based deep radiomic features and clinical characteristics, effectively predicts preoperative TD status in RC. Both the DL model and the nomogram can effectively stratify patients' 3-year DFS risk.

Adjuvant Chemotherapy Duration and Disease-Free Survival in Low-Risk Stage III Colon Cancer with N1a-b and N1c Disease: Insights from a Single-Center Retrospective Analysis

BACKGROUND

Tumor deposits (TDs) are known to have a poor prognosis independent of lymph node (LN) involvement and are considered equivalent to LN metastases in the latest staging system. In stage III colon cancer (CC), high-risk patients (pT4 or pN2) receive 6 months of adjuvant chemotherapy, while low-risk patients (pT1-3 and N1) are recommended either 3 or 6 months of CAPOX or 6 months of FOLFOX therapy. However, the optimal chemotherapy duration for low-risk patients classified as pN1c remains unknown. The aim of this study is to investigate the impact of adjuvant chemotherapy duration (3 months vs. 6 months) on survival in patients with low-risk stage III CC either in pN1a-b and pN1c patient groups.

METHODS

We retrospectively analyzed patients with stage III CC who underwent surgery at a tertiary center between January 2014 and May 2024. Demographic and pathological data of patients were retrospectively collected. The primary outcome was disease-free survival (DFS).

RESULTS

A total of 142 patients were included. Among the patients, 116 were pT1-3N1a-b and 26 were pT1-3N1c. Local (23.1% vs. 1.7%, P < 0.001) and overall (38.5% vs 14.6%, P = 0.011) recurrences were significantly higher in the pN1c group. Univariate and multivariate analyses revealed no significant impact of adjuvant chemotherapy duration on DFS in the pN1a-b group (P = 0.359), whereas in the pN1c group, 3-month chemotherapy resulted in significantly shorter DFS (P = 0.044) in univariate analysis.

CONCLUSION

Our study indicates that shorter duration of adjuvant chemotherapy is associated with worse survival and 6-month chemotherapy is recommended for patients with pT1-3 and N1c disease.

Negative prognostic impact of tumor deposits in stage III colorectal cancer patients

BACKGROUND

The prognostic value of tumor deposits (TDs) in stage III colorectal cancer (CRC) patients is poorly described based on the current tumor node metastasis (TNM) stage system.

MATERIALS AND METHODS

Based on the data from the Surveillance, Epidemiology, and End Result (SEER) database between 2010 to 2020 and local hospital between 2006 to 2022, the clinicopathological features of stage III CRC patients with TDs were screened by Chi-square test. Kaplan-Meier curves were performed to describe the significant difference in overall survival (OS) among the different groups, and log-rank tests were used to compare the cumulative survival distributions.

RESULT

Patients with TDs exhibited more aggressive tumors, characterized by advanced T staging (T3&T4), N staging (N2), perineural invasion, and more advanced TNM stage. The presence of TDs was identified as a negative prognostic factor in stage III CRC patients, with the co-existence of TDs and lymph node metastasis associated the poorest prognosis. A pairwise comparison revealed no statistically significant difference between TD+N1a/b and N1c groups, while the OS of TD-LN+ (TD- N1a/b) patients was the most favorable within the N1 stage. Notably, patients with a single lymph node positive had a significantly better OS than those with a single TD positive.

CONCLUSION

The presence of tumor deposits was a negative prognostic factor in stage III colorectal cancer patients, and the significance of tumor deposits was underestimated in the current TNM staging system.

qRT-PCR analysis of CEACAM5, KLK6, SLC35D3, MUC2 and POSTN in colon cancer lymph nodes-An improved method for assessment of tumor stage and prognosis

One fourth of colorectal cancer patients having curative surgery will relapse of which the majority will die. Lymph node (LN) metastasis is the single most important prognostic factor and a key factor when deciding on postoperative treatment. Presently, LN metastases are identified by histopathological examination, a subjective method analyzing only a small LN volume and giving no information on tumor aggressiveness. To better identify patients at risk of relapse we constructed a qRT-PCR test, ColoNode, that determines levels of CEACAM5, KLK6, SLC35D3, MUC2 and POSTN mRNAs. Combined these biomarkers estimate the tumor cell load and aggressiveness allocating patients to risk categories with low (0, -1), medium (1), high (2) and very high (3) risk of recurrence. Here we present result of a prospective, national multicenter study including 196 colon cancer patients from 8 hospitals. On average, 21 LNs/patient, totally 4698 LNs, were examined by both histopathology and ColoNode. At 3-year follow-up, 36 patients had died from colon cancer or lived with recurrence. ColoNode identified all patients that were identified by histopathology and in addition 9 patients who were undetected by histopathology. Thus, 25% of the patients who recurred were identified by ColoNode only. Multivariate Cox regression analysis proved ColoNode (1, 2, 3 vs 0, -1) as a highly significant risk factor with HR 4.24 [95% confidence interval, 1.42-12.69, P = .01], while pTN-stage (III vs I/II) lost its univariate significance. In conclusion, ColoNode surpassed histopathology by identifying a significantly larger number of patients with future relapse and will be a valuable tool for decisions on postoperative treatment.

A new lymph node ratio-based staging system for rectosigmoid cancer: a retrospective study with external validation

BACKGROUND

This study evaluated the clinical value of a new American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging prediction model based on lymph node ratio (LNR) in rectosigmoid cancer (RSC).

METHODS

The analysis included 1444 patients with nonmetastatic RSC diagnosed pathologically between 2010 and 2016 who were collected from the National Cancer Institute Surveillance, Epidemiology, and Results database. The AJCC N-stage was redefined according to the LNR cutoff point, and the ability of the new staging system to predict prognosis was compared with that of the AJCC TNM staging system. Data from 739 patients from our hospital were used for external validation.

RESULTS

According to the number of examined lymph nodes and LNR, the N stage was divided into five groups (LNR0-5). The 5-year OS of patients divided according to the new T lymph node ratio M (TLNRM) staging into stage I (T1LNR1, T1LNR2), IIA (T1LNR3, T2LNR1, T2LNR2, T2LNR3, T1LNR4, T3LNR1), IIB (T2LNR4), IIC (T3LNR2, T4a LNR1, T1LNR5), IIIA (T3LNR3, T2LNR5, T4b LNR1, T4a LNR2, T3LNR4), IIIB (T3LNR5, T4a LNR3, T4a LNR4, T4b LNR2), and IIIC (T4b LNR3, T4a LNR5, T4b LNR4, T4b LNR5) was significantly different ( P <0.05). Decision curve analysis showed that the net income of the new TLNRM staging system for different decision thresholds was higher than the prediction line of the traditional eighth TNM staging system. The smaller Akaike information criterion and Bayesian information suggested that the new staging system had a higher sensitivity for predicting prognosis than the traditional staging system. TLNRM II and III patients benefited from adjuvant chemotherapy, while adjuvant chemotherapy did not improve the prognosis of TNM II patients. These findings were confirmed by the external validation data.

CONCLUSION

The new TLNRM staging system was superior to the eighth edition AJCC staging system for staging and predicting the prognosis of patients with RSC and may become an effective tool in clinical practice.