Morphological predictors for lymph node metastases on computed tomography in colon cancer

A scoring system for stratifying the risk of postoperative bone metastases in colorectal cancer

BACKGROUND

Surveillance recommendations for postoperative high-risk colorectal bone metastases patients remain in a gray area of guidelines. We aimed to develop a risk stratification system to select ideal candidates for follow-up of colorectal bone metastases status.

METHODS

Postoperative colorectal cancer patients were included to develop a risk-scoring system to predict bone metastases. Risk scores were calculated based on the predictive factors for bone metastases, which were identified using the Cox proportional hazard regression model. Kaplan-Meier curves visualize the differences between risk groups.

RESULTS

Eight risk factors (age, lymph node metastasis, pathologic tumor deposit, KRAS mutation status, suspicious retroperitoneal lymph node metastasis, lung metastasis status, largest thickness of colorectal cancer lesion, largest short diameter of lymph node) were predictors of colorectal bone metastases and incorporated into the risk scoring system, and the patients were categorized into 2 risk groups. In the low-risk group, the 1, 3, and 5-year colorectal bone metastases rates were 2.4%, 4.6%, and 3.7%, respectively, whereas in the high-risk group, the 1, 3, and 5-year colorectal bone metastases rates were 15.6%, 29.9%, and 44.4%, respectively. The risk scoring system exhibited a C-index of 0.706, 0.795, and 0.841 in 1, 3, and 5 years, respectively. The Kaplan-Meier curve demonstrates that the incidence of colorectal bone metastases was higher in the high-risk group than in the low-risk group (50.5% vs 11.4%, P < .001).

CONCLUSION

This risk-scoring system may be valuable in predicting colorectal bone metastases in colorectal cancer patients, and we suggest that colorectal bone metastases status surveillance be added in the high-risk group.

Comparative evaluation of CT and MRI in the preoperative staging of colon cancer

The aim of this study is to compare the diagnostic performance of magnetic resonance imaging (MRI) against computed tomography (CT) in various aspects of local staging in colon cancer patients. This study was a prospective single arm diagnostic accuracy study. All consecutive adult patients with confirmed colon cancer that met the current criteria for surgical resection were considered as eligible. Diagnostic performance assessment included T (T1/T2 vs T3/T4 and < T3ab vs > T3cd) and N (N positive) staging, serosa and retroperitoneal surgical margin (RSM) involvement and extramural vascular invasion (EMVI). Imaging was based on a 3 Tesla MRI system and the evaluation of all sequences (T1, T2 and diffusion-weighted imaging-DWI series) by two independent readers. CT scan was performed in a 128 row multidetector (MD) CT scanner (slice thickness: 1 mm) with intravenous contrast. Pathology report was considered as the gold standard for local staging. Sensitivity (SE), specificity (SP), and area under the curve (AUC) were calculated for both observers. MRI displayed a higher diagnostic performance over CT in terms of T1/T2 vs T3/T4 (SE: 100% vs 83.9%, SP: 96.6% vs 81%, AUC: 0.825 vs 0.983, p < 0.001), N positive (p < 0.001) and EMVI (p = 0.023) assessment. An excellent performance of MRI was noted in the T3ab vs T3cd (CT AUCReader1: 0.636, AUCReader2: 0.55 vs MRI AUCReader1: 0.829 AUCReader2 0.846, p = 0.01) and RSM invasion diagnosis. In contrast to these, MRI did not perform well in the identification of serosa invasion. MRI had a higher diagnostic yield than CT in several local staging parameters.

Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT

BACKGROUND

To compare the diagnostic performance of the Node-RADS scoring system and lymph node (LN) size in preoperative LN assessment for rectal cancer (RC), and to investigate whether the selection of size as the primary criterion whereas morphology as the secondary criterion for LNs can be considered the preferred method for clinical assessment.

METHODS

Preoperative CT data of 146 RC patients treated with radical resection surgery were retrospectively analyzed. The Node-RADS score and short-axis diameter of size-prioritized LNs and the morphology-prioritized LNs were obtained. The correlations of Node-RADS score to the pN stage, LNM number and lymph node ratio (LNR) were investigated. The performances on assessing pathological lymph node metastasis were compared between Node-RADS score and short-axis diameter. A nomogram combined the Node-RADS score and clinical features was also evaluated.

RESULTS

Node-RADS score showed significant correlation with pN stage, LNM number and LNR (Node-RADS of size-prioritized LN: r = 0.600, 0.592, and 0.606; Node-RADS of morphology-prioritized LN: r = 0.547, 0.538, and 0.527; Node-RADSmax: r = 0.612, 0.604, and 0.610; all p < 0.001). For size-prioritized LN, Node-RADS achieved an AUC of 0.826, significantly superior to short-axis diameter (0.826 vs. 0.743, p = 0.009). For morphology-prioritized LN, Node-RADS exhibited an AUC of 0.758, slightly better than short-axis diameter (0.758 vs. 0.718, p = 0.098). The Node-RADS score of size-prioritized LN was significantly better than that of morphology-prioritized LN (0.826 vs. 0.758, p = 0.038). The nomogram achieved the best diagnostic performance (AUC = 0.861) than all the other assessment methods (p < 0.05).

CONCLUSIONS

The Node-RADS scoring system outperforms the short-axis diameter in predicting lymph node metastasis in RC. Size-prioritized LN demonstrates superior predictive efficacy compared to morphology-prioritized LN. The nomogram combined the Node-RADS score of size-prioritized LN with clinical features exhibits the best diagnostic performance. Moreover, a clear relationship was demonstrated between the Node-RADS score and the quantity-dependent pathological characteristics of LNM.

Evaluation of colon cancer prognostic factors by CT and MRI: an up-to-date review

Colorectal cancer (CRC) is a significant global health concern. Prognostication of CRC traditionally relies on the Union for International Cancer Control (UICC) and American Joint Committee on Cancer (AJCC) TNM staging classifications, yet clinical outcomes often vary independently of stage. Despite similarities, rectal and colon cancers are distinct in their diagnostic methodologies and treatments, with MRI and CT scans primarily used for staging rectal and colon cancers, respectively. This paper examines the challenges in accurately assessing prognostic factors of colon cancer such as primary tumor extramural extension, retroperitoneal surgical margin (RSM) involvement, extramural vessel invasion (EMVI), and lymph node metastases through preoperative CT and MRI. It highlights the importance of these factors in risk stratification, treatment decisions, and surgical planning for colon cancer patients. Advancements in imaging techniques are crucial for improving clinical management and optimizing patient outcomes, underscoring the necessity for ongoing research to refine diagnostic methods and incorporate novel findings into practice.

Performance of CT in the locoregional staging of colon cancer: detailed radiology-pathology correlation with special emphasis on tumor deposits, extramural venous invasion and T staging

PURPOSE

To investigate the performance of computed tomography (CT) in the local staging of colon cancer in different segments, with emphasis on parameters that have been found to be significant for rectal cancer, namely, extramural venous invasion (EMVI) and tumor deposits (TDs).

METHODS

CT and pathology data from 137 patients were independently reviewed by radiology and pathology teams. The performance of CT in categorizing a given patient into good, versus poor prognostic groups was assessed for each segment, as well as the presence of lymph nodes (LNs), TDs and EMVIs. Discordant cases were re-evaluated to determine potential sources of error. Elastic stain was applied for EMVI discordance.

RESULTS

The T staging accuracy was 80.2%. For T stage stratification, CT performed slightly better in the left colon, and the lowest accuracy was in the transverse colon. Under-staging was more common (in 12.4%), and most of the mis-staged cases were in sigmoid colon. According to the first comprehensive correlative analysis, the sensitivity, specificity, and accuracy of CT for detecting TDs were found to be 57.9%, 92.4%, 87.6%, respectively. These figures were 44.7%, 72.7%, and 63.5% for LN, and 58.5%, 82.1% and 73% for EMVI. The detection rate was better for multifocal EMVI. The detection rate was also comparable (although substantially underestimated) for LNs, with the half of the LNs missed by CT being < 5 mm. Four patients that were classified as TD by CT, disclosed to be LNs by pathology. Correlative analysis led to refinement of the pathology criteria, with subsequent modifications of the initial reports in 13 (9.5%) patients.

CONCLUSION

Overall, CT performed well in the evaluation of colon cancer, as did TD and EMVI. It is advisable to include these parameters in CT-based staging. Radiologists should be aware of the pitfalls that occur more commonly in different segments.

Are Metastatic Central Lymph Nodes (D3 volume) in right-sided Colon Cancer a Sign of Systemic Disease? A sub-group Analysis of an Ongoing Multicenter Trial

OBJECTIVE

Assess outcomes of patients with right-sided colon cancer with metastases in the D3 volume after personalized surgery.

BACKGROUND

Patients with central lymph node metastasis (D3-PNG) are considered to have a systemic disease with a poor prognosis. A 3-dimensional definition of the dissection volume allows the removal of all central nodes.

MATERIALS AND METHODS

D3-PNG includes consecutive patients from an ongoing clinical trial. Patients were stratified into residual disease negative (D3-RDN) and residual disease positive (D3-RDP) groups. D3-RDN was further stratified into 4 periods to identify a learning curve. A personalized D3 volume (defined through arterial origins and venous confluences) was removed " en bloc" through medial-to-lateral dissection, and the D3 volume of the specimen was analyzed separately.

RESULTS

D3-PNG contained 42 (26 females, 63.1 SD 9.9 y) patients, D3-RDN:29 (17 females, 63.4 SD 10.1 y), and D3-RDP:13 (9 females, 62.2 SD 9.7 y). The mean overall survival (OS) days were D3-PNG:1230, D3-RDN:1610, and D3-RDP:460. The mean disease-free survival (DFS) was D3-PNG:1023, D3-RDN:1461, and D3-RDP:74 days. The probability of OS/DFS were D3-PNG:52.1%/50.2%, D3-RDN:72.9%/73.1%, D3-RDP: 7.7%/0%. There is a significant change in OS/DFS in the D3-RDN from 2011-2013 to 2020-2022 (both P =0.046) and from 2014-2016 to 2020-2022 ( P =0.028 and P =0.005, respectively).

CONCLUSION

Our results indicate that surgery can achieve survival in most patients with central lymph node metastases by removing a personalized and anatomically defined D3 volume. The extent of mesenterectomy and the quality of surgery are paramount since a learning curve has demonstrated significantly improved survival over time despite the low number of patients. These results imply a place for the centralization of this patient group where feasible.

Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer

The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0-2 points) and high likelihood (HLM: 3-7 points) of malignancy. The T-test and the Mann-Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN's status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69-0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52-0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer

INTRODUCTION

Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT.

METHODS

In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC.

RESULTS

A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively.

CONCLUSION

A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

Small arteriole sign: an imaging feature for staging T4a colon cancer

OBJECTIVES

By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance.

METHODS

Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test.

RESULTS

In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001).

CONCLUSION

Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer.

CLINICAL RELEVANCE STATEMENT

Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer.

KEY POINTS

• The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Applications of new radiological scores: the Node-rads in colon cancer staging

PURPOSE

The study focuses on the evaluation of the new Node Reporting and Data System 1.0 (Node-rads) scoring accuracy in the assessment of metastatic lymph nodes (LN) in patients with colon carcinoma.

MATERIAL AND METHODS

From April 2021 to May 2022, retrospective chart reviews were performed on 67 preoperative CT (Computed Tomography) of patients undergoing excisional surgery for colon cancer at the Polyclinic of Bari, Italy. Primary endpoints were to assess lymph node size and configuration to express the likelihood of a metastatic site adopting the Node-rads score system, whose categories of risk are defined from 1 (very low) to 5 (very high). The nodal postsurgical histological evaluation was the gold standard. The relationship between Node-rads score, LN size, configuration criteria (texture, border and shape) and the presence of histological metastases was statistically evaluated.

RESULTS

All surgical specimens examined had correlation with Node-rads score. They were significantly more likely to present nodes micrometastasis those patients with (a) spherical LN shape (82.8%), (b) with lymph node necrosis (100%), (c) irregular borders (87%) and (d) the LN short axis more than 10 mm (61.9%).

CONCLUSIONS

Our experience highlights how the Node-rads system proposes an intuitive and effective definition of criteria to standardize the lymph node radiological reports in colon cancer disease. Further studies are needed to streamline the classification of the nodal and peripheral LN in all the oncological imaging.

Diagnostic Performance of Rectal CT for Staging Rectal Cancer: Comparison with Rectal MRI and Histopathology

Purpose

To compare the diagnostic performance of rectal CT with that of high-resolution rectal MRI and histopathology in assessing rectal cancer.

Materials and Methods

Sixty-seven patients with rectal cancer who underwent rectal CT with rectal distension using sonographic gel and high-resolution MRI were enrolled in this study. The distance from the anal verge/anorectal junction, distance to the mesorectal fascia (MRF), extramural depth (EMD), extramesorectal lymph node (LN) involvement, extramural venous invasion (EMVI), and T/N stages in rectal CT/MRI were analyzed by two gastrointestinal radiologists. The CT findings of 20 patients who underwent radical surgery without concurrent chemoradiotherapy were compared using histopathology. Interclass correlations and kappa statistics were used.

Results

The distance from the anal verge/anorectal junction showed an excellent intraclass correlation between CT and MRI for both reviewers. For EMD, the distance to the MRF, presence of LNs, extramesorectal LN metastasis, EMVI, T stage, and intermodality kappa or weighted kappa values between CT and MRI showed excellent agreement. Among the 20 patients who underwent radical surgery, T staging, circumferential resection margin involvement, EMVI, and LN metastasis on rectal CT showed acceptable concordance rates with histopathology.

Conclusion

Dedicated rectal CT may be on par with rectal MRI in providing critical information to patients with rectal cancer.

Effect of visceral fat area on the accuracy of preoperative CT-N staging of colorectal cancer

OBJECTIVE

To investigate the effect of visceral fat area (VFA) on the accuracy of preoperative CT-N staging of colorectal cancer.

METHODS

We retrospectively reviewed the clinical and imaging data of 385 CRC patients who underwent surgical resection for colorectal cancer between January 2018 and July 2021. Preoperative CT-N staging and imaging features were determined independently by two radiologists. Using postoperative pathology as the gold standard, patients were divided into accurately and incorrectly staged groups, and clinical and imaging characteristics were compared between the two groups. VFA and subcutaneous fat area (SFA) at the L3 vertebral level, sex, age, BMI, tumor location, size, and tumor circumference ratio (TCR) were included. Logistic regression analysis was used to evaluate the independent factors influencing the accuracy of preoperative N staging of colorectal cancer.

RESULTS

Of the 385 patients enrolled, 259 (67.27%) were in the preoperative N-stage accurate staging group, and 126 (32.73%) were in the incorrectly staged group. Univariate analysis showed that there were significant differences in BMI, tumor location, VFA, SFA, size and TCR between the two groups (P<0.05). Logistic regression analysis showed that VFA (95% CI: 1.277, 3.813; P=0.005) and TCR (95% CI: 1.649, 17.545; P=0.005) were independent factors affecting the accuracy of N staging. The optimal cutoff points for VFA and TCR in predicting incorrect staging were 110 cm2 and 0.675, respectively.

CONCLUSIONS

Colorectal cancer patients with lower VFA and higher TCR and preoperative CT-N staging had an increased risk for diagnostic errors.

CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer

OBJECTIVES

The aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC).

METHODS

A total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index.

RESULTS

LNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted ORright-sided colon = 0.05 [0.01-0.60], p = 0.018; adjusted ORrectum = 0.22 [0.06-0.83], p = 0.026). In the internal validation cohort, the model showed good calibration and good discrimination with a C-index of 0.89.

CONCLUSIONS

There are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection.

KEY POINTS

• LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.

MRI Evaluation of Rectal Cancer Lymph Node Staging Using Apparent Diffusion Coefficient

Introduction Colorectal cancer is the third most diagnosed cancer globally. Lymph node metastases significantly affect prognosis, emphasizing the importance of early detection and management. Despite significant advances in conventional MRI's role in staging, improvements in advanced functional imaging such as diffusion-weighted imaging (DWI) in identifying lymph node metastases persist. Objectives The aim is to evaluate the effectiveness of apparent diffusion coefficient (ADC) MRI in evaluating lymph node staging in rectal cancer. Patients and methods In a prospective study, 89 patients with stage II-III rectal cancer were grouped into two treatments: pre-operative FOLFOX4 chemotherapy and standard pre-operative chemoradiotherapy. All underwent 1.5T MRI, with T2-weighted and DWI sequences. A radiologist defined regions of interest on the tumor, lymph nodes, and intact rectal wall to calculate ADC values. Results Rectal cancer ADC's receiver operating characteristic curve had an area under the curve (AUC) of 0.688 (P < 0.001), with optimal ADC cutoff at 0.99 x 10-3 mm2/s (sensitivity: 75%, specificity: 83%). For lymph nodes, AUC was 0.508 (P < 0.001), with a cutoff of 0.9 x 10-3 mm2/s (sensitivity: 78%, specificity: 67%). No correlation between tumor and lymph node ADC values was observed. In chemotherapy patients, "healthy" inguinal lymph nodes had higher ADC values than affected ones pre-treatment (P = 0.001), a disparity fading post-treatment (P = 0.313). For chemoradiotherapy patients, the ADC difference persisted pre and post-treatment (P = 0.001). Conclusion The study of ADC-MRI showed different ADC values between tumors and lymph nodes and highlighted ADC differences between treatment groups. Notably, no correlation was observed between tumor and lymph node ADC values. However, differences were apparent when comparing "healthy" inguinal nodes with lymph nodes affected by cancer.

Imaging of colon and rectal cancer

Colon and rectal cancer imaging has traditionally been performed to assess for distant disease (typically lung and liver metastases) and to assess the resectability of the primary tumor. With technological and scientific advances in imaging and the evolution of treatment options, the role of imaging has expanded. Radiologists are now expected to provide a precise description of primary tumor invasion extent, including adjacent organ invasion, involvement of the surgical resection plane, extramural vascular invasion, lymphadenopathy, and response to neoadjuvant treatment, and to monitor for recurrence after clinical complete response.

Size and number of lymph nodes were risk factors of recurrence in stage II colorectal cancer

BACKGROUND

Size and number of lymph nodes (LNs) were reported to be associated with the prognosis of stage II colorectal cancer (CRC). The purpose of this study was to determine the prognostic role of the size of LNs (SLNs) measured by computer tomography (CT) and the number of retrieved LNs (NLNs) in the relapse-free survival (RFS) and overall survival (OS) among stage II CRC patients.

METHODS

Consecutive patients diagnosed with stage II CRC at Fudan University Shanghai Cancer Center (FUSCC) from January 2011 to December 2015 were reviewed, and 351 patients were randomly divided into two cohorts for cross-validation. The optimal cut-off values were obtained using X-tile program. Kaplan-Meier curves and Cox regression analyses were conducted for the two cohorts.

RESULTS

Data from 351 stage II CRC patients were analyzed. The cut-off values for SLNs and NLNs were 5.8 mm and 22, respectively, determined by the X-tile in the training cohort. In the validation cohort, Kaplan-Meier curves demonstrated SLNs (P = 0.0034) and NLNs (P = 0.0451) were positively correlated with RFS but not with OS. The median follow-up time in the training cohort and the validation cohort were 60.8 months and 61.0 months respectively. Univariate and multivariate analysis revealed that both SLNs (training cohort: Hazard Ratio (HR) = 2.361, 95% Confidence interval (CI): 1.044-5.338, P = 0.039; validation cohort: HR = 2.979, 95%CI: 1.435-5.184, P = 0.003) and NLNs (training cohort: HR = 0.335, 95%CI: 0.113-0.994, P = 0.049; validation cohort: HR = 0.375, 95%CI: 0.156-0.900, P = 0.021) were independent prognostic factors for RFS whereas not for OS.

CONCLUSION

SLNs and NLNs are independent prognostic factors for patients with stage II CRC. Patients with SLNs > 5.8 mm and NLNs ≤ 22 are apt to have higher risk of recurrence.

Preoperative computed tomography assessment of circumferential resection margin in retroperitonealized colon cancer predicts disease-free survival

OBJECTIVES

The aim of this retrospective study was to predict circumferential resection margin (CRM) involvement on preoperative CT, and prognostic impact of CRM assessment by CT (ctCRM) in patients with retroperitonealized colon cancer.

METHODS

This study included patients who underwent resection for ascending or descending colon cancer between July 2010 and February 2013. Positive ctCRM was defined as tumor distance to the retromesenteric plane of ≤ 1 mm. The origin of positive CRM was divided into primary tumor or other tumor components including lymph nodes, tumor deposits, or extramural venous invasions. Logistic regression analysis was performed to identify preoperative factors to predict pathologic CRM (pCRM). A Cox proportional hazards model was used in multivariable analysis to determine the preoperative factors affecting disease-free survival (DFS).

RESULTS

A total of 274 patients (mean age, 64.0 years ± 11.0 [standard deviation]; 157 men) with retroperitonealized colon cancer were evaluated. Of 274 patients, 67 patients (24.5%) had positive CRM on surgical pathology. The accuracy of preoperative CT in predicting pCRM was 79.6% (218/274). Among preoperative factors, only CRM assessment on CT was independently associated with pCRM (p < 0.001). Positive ctCRM by primary tumor was an independent factor for DFS (HR, 3.362 [1.714-6.593]) and systemic recurrence (HR, 3.715 [1.787-7.724], but not for local recurrence on multivariable analyses.

CONCLUSIONS

Preoperative CT can accurately predict pCRM, and positive ctCRM by primary tumor is an independent risk factor for DFS and systemic recurrence, but not for local recurrence in retroperitonealized colon cancer.

KEY POINTS

• Preoperative CT can predict pathologic circumferential resection margin (CRM) with approximately 80% of accuracy in patients with retroperitonealized colon cancer. • Positive CRM by a primary tumor on preoperative CT is a poor prognostic factor for disease-free survival and systemic recurrence in patients with retroperitonealized colon cancer. • CRM involvement on CT was not associated with local recurrence in patients with retroperitonealized colon cancer.

Colon cancer CT staging according to mismatch repair status: Comparison and suggestion of imaging features for high-risk colon cancer

BACKGROUND

Neoadjuvant treatment with either chemotherapy or immunotherapy is gaining momentum in colon cancers (CC). To reduce over-treatment, increasing staging accuracy using computed tomography (CT) is of high importance.

PURPOSE

To assess and compare CT imaging features of CC between mismatch repair-proficient (pMMR) and MMR-deficient (dMMR) tumours and identify CT features that can distinguish high-risk (pT3-4, N+) CC according to MMR status.

METHODS

Primary staging CTs of 266 patients who underwent primary surgical resection of a colon tumour were retrospectively and independently evaluated by two radiologists. Logistic regression analysis was performed to identify significant associations between imaging features and positive lymph node status. Receiver operating characteristic (ROC) curves of significantly associated features were assessed and validated in an external cohort of 104 patients.

RESULTS

Among pT3 tumours only, dMMR CC were significantly larger than pMMR CC in both length and thickness (length 59.39 ± 26.28 mm versus 48.70 ± 23.72, respectively, p = 0.031; thickness 20.54 mm ± 11.17 versus 16.34 ± 8.73, respectively, p = 0.027). For pMMR tumours, nodal internal heterogeneity on CT was significantly associated with a positive lymph node status (odds ratio (OR) = 2.66, p = 0.027), while for dMMR tumours, the largest short diameter of the nodes was associated with lymph node status (OR = 2.01, p = 0.049). The best cut-off value of the largest short diameter of involved nodes was 10.4 mm for dMMR and 7.95 mm for pMMR. In the external validation cohort, AUCs for predicting involved nodes based on the largest short diameter was 0.764 for dMMR tumours using 10 mm size cut-off and 0.624 for pMMR tumours using 7 mm cut-off.

CONCLUSION

These data show that CT imaging features of primary CC differ between dMMR and pMMR tumours, suggesting that the assessment of CT-based CC staging should take MMR status into consideration, especially for lymph node status, and thus may help in selecting patients for neoadjuvant treatment.

Radiomic Cancer Hallmarks to Identify High-Risk Patients in Non-Metastatic Colon Cancer

The study was aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann−Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC <  0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk disease.

Value of Clinical Information on Radiology Reports in Oncological Imaging

Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists’ experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% (p = 0.13) and 42% to 56% (p = 0.13), for detection of hematogenous metastases from 85% to 86% (p = 0.61) and 46% to 60% (p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible.

Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography

Approximately 25% of the patients with muscle-invasive bladder cancer (MIBC) who are clinically node negative have occult lymph node metastases at radical cystectomy (RC) and pelvic lymph node dissection. The aim of this study was to evaluate preoperative CT-based radiomics to differentiate between pN+ and pN0 disease in patients with clinical stage cT2-T4aN0-N1M0 MIBC. Patients with cT2-T4aN0-N1M0 MIBC, of whom preoperative CT scans and pathology reports were available, were included from the prospective, multicenter CirGuidance trial. After manual segmentation of the lymph nodes, 564 radiomics features were extracted. A combination of different machine-learning methods was used to develop various decision models to differentiate between patients with pN+ and pN0 disease. A total of 209 patients (159 pN0; 50 pN+) were included, with a total of 3153 segmented lymph nodes. None of the individual radiomics features showed significant differences between pN+ and pN0 disease, and none of the radiomics models performed substantially better than random guessing. Hence, CT-based radiomics does not contribute to differentiation between pN+ and pN0 disease in patients with cT2-T4aN0-N1M0 MIBC.

Correlation and prognostic value of CT-detected extramural venous invasion and pathological lymph-vascular invasion in colon cancer

PURPOSE

To explore the association between CT-detected extramural vascular invasion (ctEMVI) and lymph-vascular invasion (LVI) in colon cancer, and analyze the prognostic value of ctEMVI in different conditions of LVI.

METHODS

This single-center, retrospective study included 448 colon cancer patients from January 2015 to December 2017. Preoperative CT features and clinical and pathological data were collected. Associations between ctEMVI and LVI were tested. Univariate and multivariate logistic regression was performed. Multivariate Cox regression was performed adjusted with propensity score(PS). Kaplan-Meier method was used to compare survival differences between the ctEMVI and LVI groups. A 1:1 patient pairing was conducted using PS matching to assess the prognostic effect of ctEMVI in LVI subgroups.

RESULTS

Among the 448 patients, there were 261 men and 187 women, with an average age of 63 ± 12 years. The coincidence rate of ctEMVI and LVI was 73.9%. The k coefficient for identifying ctEMVI was 0.84. ctEMVI and LVI were both independent risk factors for overall survival (ctEMVI: HR 2.8, 95% CI 1.5-5.5; LVI: HR 2.2, 95% CI 1.2-4.1) and metastasis-free survival (ctEMVI: HR 3.3, 95% CI 1.7-6.4; LVI: HR 2.4, 95% CI 1.3-4.5) adjusted with PS. In the LVI(+) subgroup, the prognosis of ctEMVI(+) was significantly worse than that of ctEMVI(-); in the LVI(-) subgroup, the prognosis of different ctEMVI states was similar.

CONCLUSION

ctEMVI is an independent prognostic risk factor and has different prognostic value in different LVI states. It is recommended to perform the evaluation in routine work, especially for patients with positive LVI.

Imaging findings of gastrointestinal tract tumors in children and adolescents

Gastrointestinal (GI) tract tumors are rarely seen in children and adolescents, and can easily be misdiagnosed. Lymphoma is the most frequent GI tract tumor, and the common locations are ileum and ileocecal area. GI tract tumors may present as large heterogeneous mass lesions. For gastric and colonic tumors, increased wall thickening usually prompts the diagnosis of GI tract tumors. Computed tomography and magnetic resonance imaging might be used in clinically suspected cases for correct/appropriate diagnosis and management. Awareness as regards the most common tumors and their locations is paramount for radiologists. Likewise, the aim of this article was to define the imaging findings of primary benign and malignant GI tract tumors in children and adolescents.

Transabdominal Ultrasonography for Preoperative Diagnosis of Lymph Node Metastasis in Colon Cancer: A Retrospective Cohort Study

Background/Aim

Although computed tomography (CT) is the standard modality for diagnosing lymph node metastasis (LNM), transabdominal ultrasonography (US) can be useful due to its high spatial resolution and use of Doppler signals to precisely analyse lymph nodes. This study aimed to evaluate the accuracy of US for lymph node assessment, establish US-based diagnostic criteria for LNM, and compare the capability of US with that of CT for the diagnosis of LNM.

Patients and Methods

This retrospective, single-institution, cohort study included patients who underwent radical surgery for clinical stage 0-III colon cancer, between March 2012 and February 2019.

Results

Overall, 34.9% (66/189) of patients had pathological LNM. The optimal US diagnostic criteria were 1) short axis ≥7 mm and short/long ratio ≥0.75 and 2) at least two of the following: the absence of hilar echoes, expansive appearance, or peripheral/mixed vascularity by the colour Doppler and/or contrast-enhanced method. Compared to CT, US showed a higher diagnostic sensitivity (54.5% vs. 43.9%; p=0.296), higher concordance with the number of pathological LNM (correlation coefficient: US, 0.42; CT, 0.27) and pathological N diagnosis (weighted ĸ: US, 0.35; CT, 0.18), and higher sensitivity for advanced LNM, including multiple LNMs (47.4% vs. 18.4%; p=0.014) and N2 stage (27.8% vs. 5.6%; p=0.177).

Conclusion

US has higher sensitivity than CT for diagnosing LNM in colon cancer, along with a more accurate preoperative diagnosis of the N stage. Additionally, US may be more helpful than CT alone for preoperatively deciding the appropriateness of neoadjuvant treatment in colon cancer with advanced LNM.

CT for lymph node staging of Colon cancer: not only size but also location and number of lymph node count

PURPOSE

To evaluate the diagnostic accuracy of imaging features to predict lymph node status of colon cancer using CT.

METHODS

This was a retrospective study from 2 tertiary hospitals in South Korea and Netherlands. 317 Colon cancer patients who underwent primary surgical treatment were included. Number of lymph nodes according to the anatomical location, size, cluster, degree of attenuation, shape, presence of internal heterogeneity and ill-defined margin of the lymph node were assessed and compared according to histological lymph node status.

RESULTS

The largest short diameter of lymph node and presence of internal heterogeneity of lymph node showed significant association with malignant lymph node status (P < 0.001 and P = 0.041, respectively). The ROC curve analysis revealed AUC of 0.703 for the largest short diameter of lymph node (P < 0.001), and AUC of the presence of internal heterogeneity was 0.630 (P < 0.001). In addition, our study showed that a total number of lymph nodes, regardless of size, (P = 0.022) and number of lymph nodes in peritumoral area (P < 0.001) and along the mesenteric vessels (P < 0.001) on CT demonstrated significant association with malignant status of lymph nodes in colon cancer.

CONCLUSIONS

There were significant associations between lymph node status and imaging features of lymph nodes on CT in colon cancer patients. The largest short diameter of lymph node and presence of internal heterogeneity can be used to predict the malignant status of lymph node in colon cancer patients. Also, the number of lymph nodes near the colonic tumor should be considered in assessment of colon cancer lymph node involvement on CT.

Tumor size measured by multidetector CT in resectable colon cancer: correlation with regional lymph node metastasis and N stage

Background

Lymph node metastasis (LNM) is a risk factor for poor long-term outcomes and a prognostic factor for disease-free survival in colon cancer. Preoperative lymph node status evaluation remains a challenge. The purpose of this study is to determine whether tumor size measured by multidetector computed tomography (MDCT) could be used to predict LNM and N stage in colon cancer.

Material and methods

One hundred six patients with colon cancer who underwent radical surgery within 1 week of MDCT scan were enrolled. Tumor size including tumor length (Tlen), tumor maximum diameter (Tdia), tumor maximum cross-sectional area (Tare), and tumor volume (Tvol) were measured to be correlated with pathologic LNM and N stage using univariate logistic regression analysis, multivariate logistic analysis, and receiver operating characteristic (ROC) curve analysis.

Results

The inter- and intraobserver reproducibility of Tlen (intraclass correlation coefficient [ICC] = 0.94, 0.95, respectively), Tdia (ICC = 0.81, 0.93, respectively), Tare (ICC = 0.97, 0.91, respectively), and Tvol (ICC = 0.99, 0.99, respectively) parameters measurement are excellent. Univariate logistic regression analysis showed that there were significant differences in Tlen, Tdia, Tare, and Tvol between positive and negative LNM (p < 0.001, 0.001, < 0.001, < 0.001, respectively). Multivariate logistic regression analysis revealed that Tvol was independent risk factor for predicting LNM (odds ratio, 1.082; 95% confidence interval for odds ratio, 1.039, 1.127, p<0.001). Tlen, Tdia, Tare, and Tvol could distinguish N0 from N1 stage (p < 0.001, 0.041, < 0.001, < 0.001, respectively), N0 from N2 (all p < 0.001), N0 from N1-2 (p < 0.001, 0.001, < 0.001, < 0.001, respectively), and N0-1 from N2 (p < 0.001, 0.001, < 0.001, < 0.001, respectively). The area under the ROC curve (AUC) was higher for Tvol than that of Tlen, Tdia, and Tare in identifying LNM (AUC = 0.83, 0.82, 0.69, 0.79), and distinguishing N0 from N1 stage (AUC = 0.79, 0.78, 0.63, 0.74), N0 from N2 stage (AUC = 0.92, 0.89, 0.80, 0.89, respectively), and N0-1 from N2 stage (AUC = 0.84, 0.79, 0.76, 0.83, respectively).

Conclusion

Tumor size was correlated with regional LNM in resectable colon cancer. In particularly, Tvol showed the most potential for noninvasive preoperative prediction of regional LNM and N stage.

Accuracy of various criteria for lymph node staging in ductal adenocarcinoma of the pancreatic head by computed tomography and magnetic resonance imaging

BACKGROUND

Lymph node staging of ductal adenocarcinoma of the pancreatic head (PDAC) by cross-sectional imaging is limited. The aim of this study was to determine the diagnostic accuracy of expanded criteria in nodal staging in PDAC patients.

METHODS

Sixty-six patients with histologically confirmed PDAC that underwent primary surgery were included in this retrospective IRB-approved study. Cross-sectional imaging studies (CT and/or MRI) were evaluated by a radiologist blinded to histopathology. Number and size of lymph nodes were measured (short-axis diameter) and characterized in terms of expanded morphological criteria of border contour (spiculated, lobulated, and indistinct) and texture (homogeneous or inhomogeneous). Sensitivities and specificities were calculated with histopathology as a reference standard.

RESULTS

Forty-eight of 66 patients (80%) had histologically confirmed lymph node metastases (pN+). Sensitivity, specificity, and Youden's Index for the criterion "size" were 44.2%, 82.4%, and 0.27; for "inhomogeneous signal intensity" 25.6%, 94.1%, and 0.20; and for "border contour" 62.7%, 52.9%, and 0.16, respectively. There was a significant association between the number of visible lymph nodes on preoperative CT and lymph node involvement (pN+, p = 0.031).

CONCLUSION

Lymph node staging in PDAC is mainly limited due to low sensitivity for detection of metastatic disease. Using expanded morphological criteria instead of size did not improve regional nodal staging due to sensitivity remaining low. Combining specific criteria yields improved sensitivity with specificity and PPV remaining high.

Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study

BACKGROUND

Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models.

METHODS

A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC).

RESULTS

The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method.

CONCLUSIONS

The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

Reporting colon cancer staging using a template

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Template reporting increases key colon tumour descriptors.

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Template reporting was used for less than a third of the patients.

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High risk of underreporting.

Purpose

The purpose of this study was to evaluate the effect of completeness of the radiological reports in primary local staging colon cancer when using a template.

Methods

The study used primary staging reports retrieved from the departments RIS/PACS. Five key tumour descriptors were evaluated within each report: tumour morphology (polypoid or annular), information on tumour breach of the colon wall (≥ T3), tumour out-growth in mm, nodal status and TNM in conclusion. The failure to provide a description of the presence or absence of a feature in a report counted as ‘not reported’. To allow comparisons between reporting styles, the template or free-text style of reporting was also recorded.

Results

During a two year period, a total of 666 patients CT reports were evaluated at the colorectal center multidisciplinary team (MDT) conference. In 200 of these reports a template was used. Information on tumour morphology (polypoid or annular) was present in 81% of the template reports vs 9% in free-text style. The figures in percentage for information on tumour breach of the colon wall (≥ T3) were 93% vs 48 %, tumour out-growth in mm: 51% vs 17%, nodal status: 99% vs 86% and TNM in conclusion: 98% vs 51%. P < 0.0001.

Conclusion

The present study provides additional support for the routine use of template reports to improve imaging reporting standards in colonic cancer.

Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography

IMPORTANCE

Preoperative TNM stratification of colon cancer on computed tomography (CT) does not identify patients who are at high risk of recurrence that could be selected for preoperative treatment.

OBJECTIVE

To evaluate the utility of CT findings for prognosis of sigmoid colon cancer.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study used retrospective data from patients who underwent bowel resection for sigmoid colon cancer between January 1, 2006, and January 1, 2015, at a tertiary care center receiving international and national referrals for colorectal cancer. Statistical analysis was performed in April 2019.

MAIN OUTCOMES AND MEASURES

Cox proportional hazards regression analysis was performed to investigate CT findings associated with disease recurrence. Kaplan-Meier survival plots were calculated for disease-free survival using CT staging systems.

RESULTS

Of the 414 patients who had sigmoid colon cancer (248 [60.0%] men; mean [SD] age, 66.1 [12.7] years), with median follow-up of 61 months (interquartile range, 40-87 months), 122 patients (29.5%) developed disease recurrence. On multivariate analysis, nodal disease was not associated with disease recurrence; only tumor deposits (hazard ratio [HR], 1.90; 95% CI, 1.21-2.98; P = .006) and extramural venous invasion (HR, 1.97; 95% CI, 1.26-3.06; P = .003) on CT were associated with disease recurrence. Significant differences in disease-free survival were found using CT-T3 substage classification (HR, 1.88; 95% CI, 1.32-2.68) but not CT-TNM (HR, 1.55; 95% CI, 0.94-2.55). The presence of tumor deposits or extramural venous invasion on CT (HR, 2.45; 95% CI, 1.68-3.56) had the strongest association with poor outcome.

CONCLUSIONS AND RELEVANCE

In this study, T3 substaging and detection of tumor deposits or extramural venous invasion on preoperative CT scans of sigmoid colon cancer were prognostic factors for disease-free survival, whereas TNM and nodal staging on CT had no prognostic value. T3 substaging and detection of tumor deposits or extramural venous invasion of sigmoid colon cancer was superior to TNM on CT and could be used to preoperatively identify patients at high risk of recurrence.