CT and 3 Tesla MRI in the TN Staging of Colon Cancer: A Prospective, Blind Study

Imaging in the era of risk-adapted treatment in colon cancer

The treatment landscape for patients with colon cancer is continuously evolving. Risk-adapted treatment strategies, including neoadjuvant chemotherapy and immunotherapy, are slowly finding their way into clinical practice and guidelines. Radiologists are pivotal in guiding clinicians toward the most optimal treatment for each colon cancer patient. This review provides an overview of recent and upcoming advances in the diagnostic management of colon cancer and the radiologist's role in the multidisciplinary approach to treating colon cancer.

Predictors of nodal positivity in clinically under-staged patients with colon cancer: A National Cancer Database study and proposal of a predictive scoring system

BACKGROUND

Colon cancer pathological and clinical staging may be disoncordant. This study assessed patients with colon cancer in whom the nodal status was clinically understaged.

METHODS

Patients with stage I-III clinical node-negative colon cancer from the National Cancer Database were included. Regression analyses were conducted to elucidate risk factors for clinical nodal understaging and a scoring system was developed to identify high-risk patients.

RESULTS

The study included 94,945 patients with 78.4 ​% of patients correctly staged and 21.6 ​% clinically understaged. The predictors of nodal positivity in clinically understaged patients were age <65 (OR 1.43), left-sided tumors (OR 1.41), elevated CEA (OR 2.03), moderately (OR 1.81) or poorly/undifferentiated tumors (OR 3.76), T1 tumors (OR 1.29), signet-ring cell histology (OR 2.26), and microsatellite-stable tumors (OR 1.4).

CONCLUSION

Patients with colon cancer and the above factors are more likely to have their nodal status clinically understaged. A scoring system has been developed to identify high-risk patients.

Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer

PURPOSE

To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms.

MATERIALS AND METHODS

This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated.

RESULTS

There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage.

CONCLUSION

The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.

The diagnostic accuracy of local staging in colon cancer based on computed tomography (CT): evaluating the role of extramural venous invasion and tumour deposits

PURPOSE

The shift from adjuvant to neoadjuvant treatment in colon cancer demands the radiological selection of patients for systemic therapy. The aim of this study was to evaluate the accuracy of the CT-based TNM stage and high-risk features, including extramural venous invasion (EMVI) and tumour deposits, in the identification of patients with histopathological advanced disease, currently considered for neoadjuvant treatment (T3-4 disease).

METHODS

All consecutive patients surgically treated for non-metastatic colon cancer between January 2018 and January 2020 in a referral centre for colorectal cancer were identified retrospectively. All tumours were staged on CT according to the TNM classification system. Additionally, the presence of EMVI and tumour deposits on CT was evaluated. The histopathological TNM classification was used as reference standard.

RESULTS

A total of 176 patients were included. Histopathological T3-4 colon cancer was present in 85.0% of the patients with CT-detected T3-4 disease. Histopathological T3-4 colon cancer was present in 96.4% of the patients with CT-detected T3-4 colon cancer in the presence of both CT-detected EMVI and CT-detected tumour deposits. Histopathological T0-2 colon cancer was present in 50.8% of the patients with CT-detected T0-2 disease, and in 32.4% of the patients without CT-detected EMVI and tumour deposits.

CONCLUSION

The diagnostic accuracy of CT-based staging was comparable with previous studies. The presence of high-risk features on CT increased the probability of histopathological T3-4 colon cancer. However, a substantial part of the patients without CT-detected EMVI and tumour deposits was diagnosed with histopathological T3-4 disease. Hence, more accurate selection criteria are required to correctly identify patients with locally advanced disease.

Diffusion-Weighted MRI as a Quantitative Imaging Biomarker in Colon Tumors

PURPOSE

To assess the use of quantitative diffusion-weighted MRI (DW-MRI) as a diagnostic imaging biomarker in differentiating between benign colon adenoma, early, and advanced cancer of the colon, as well as predicting lymph node involvement, and finally comparing mucinous-producing colon cancer with adenomas and non-mucinous colon cancer.

METHOD

Patients with a confirmed tumor on colonoscopy were eligible for inclusion in this study. Using a 3.0 Tesla MRI machine, the main tumor mean apparent diffusion coefficient (mADC) was obtained. Surgically resected tumor specimens served as an endpoint, except in mucinous colon cancers, which were classified based on T2 images.

RESULTS

A total of 152 patients were included in the study population. The mean age was 71 years. A statistically significant mADC mean difference of -282 × 10-6 mm2/s [-419--144 95% CI, p < 0.001] was found between colon adenomas and early colon cancer, with an AUC of 0.80 [0.68-0.93 95% CI] and an optimal cut off value of 1018 × 10-6 mm2/s. Only a small statistically significant difference (p = 0.039) in mADC was found between benign tumors and mucinous colon cancer. We found no statistical difference in mADC mean values between early and advanced colon cancer, and between colon cancer with and without lymph node involvement.

CONCLUSION

Quantitative DW-MRI is potentially useful for determining whether a colonic tumor is benign or malignant. Mucinous colon cancer shows less diffusion restriction when compared to non-mucinous colon cancer, a potential pitfall.

Diagnostic Accuracy of Abdominal CT for Locally Advanced Colon Tumors: Can We Really Entrust Certain Decisions to the Reliability of CT?

Many different options of neoadjuvant treatments for advanced colon cancer are emerging. An accurate preoperative staging is crucial to select the most appropriate treatment option. A retrospective study was carried out on a national series of operated patients with T4 tumors. Considering the anatomo-pathological analysis of the surgical specimen as the gold standard, a diagnostic accuracy study was carried out on the variables T and N staging and the presence of peritoneal metastases (M1c). The parameters calculated were sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios, as well as the overall accuracy. A total of 50 centers participated in the study in which 1950 patients were analyzed. The sensitivity of CT for correct staging of T4 colon tumors was 57%. Regarding N staging, the overall accuracy was 63%, with a sensitivity of 64% and a specificity of 62%; however, the positive and negative likelihood ratios were 1.7 and 0.58, respectively. For the diagnosis of peritoneal metastases, the accuracy was 94.8%, with a sensitivity of 40% and specificity of 98%; in the case of peritoneal metastases, the positive and negative likelihood ratios were 24.4 and 0.61, respectively. The diagnostic accuracy of CT in the setting of advanced colon cancer still has some shortcomings for accurate diagnosis of stage T4, correct classification of lymph nodes, and preoperative detection of peritoneal metastases.

Nationwide practice in CT-based preoperative staging of colon cancer and concordance with definitive pathology

INTRODUCTION

In an era of exploring patient-tailored treatment options for colon cancer, preoperative staging is increasingly important. This study aimed to evaluate completeness and reliability of CT-based preoperative locoregional colon cancer staging in Dutch hospitals.

MATERIALS AND METHODS

Patients who underwent elective oncological resection of colon cancer without neoadjuvant treatment in 77 Dutch hospitals were evaluated between 2011 and 2021. Completeness of T-stage was calculated for individual hospitals and stratified based on a 60% cut-off. Concordance between routine CT-based preoperative locoregional staging (cTN) and definitive pathological staging (pTN) was examined.

RESULTS

A total of 59,558 patients were included with an average completeness of 43.4% and 53.4% for T and N-stage, respectively. Completeness of T-stage improved from 4.9% in 2011-2014 to 74.4% in 2019-2021. Median completeness for individual hospitals was 53.9% (IQR 27.3-80.5%) and were not significantly different between low and high-volume hospitals. Sensitivity and specificity for T3-4 tumours were relatively low: 75.1% and 76.0%, respectively. cT1-2 tumours were frequently understaged based on a low negative predictive value of 56.8%. Distinction of cT4 and cN2 disease had a high specificity (>95%), but a very low sensitivity (<50%). Positive predictive values of <60% indicated that cT4 and cN1-2 were often overstaged. Completeness and time period did not influence reliability of staging.

CONCLUSION

Completeness of locoregional staging of colon cancer improved during recent years and varied between hospitals independently from case volume. Discriminating cT1-2 from cT3-4 tumours resulted in substantial understaging and overstaging, additionally cT4 and cN1-2 were overstaged in >40% of cases.

Radiologic T staging of colon cancer: renewed interest for clinical practice

Radiologic imaging, especially MRI, has long been the mainstay for rectal cancer staging and patient selection for neoadjuvant therapy prior to surgical resection. In contrast, colonoscopy and CT have been the standard for colon cancer diagnosis and metastasis staging with T and N staging often performed at the time of surgical resection. With recent clinical trials exploring the expansion of the use of neoadjuvant therapy beyond the anorectum to the remainder of the colon, the current and future state of colon cancer treatment is evolving with a renewed interest in evaluating the role radiology may play in the primary T staging of colon cancer. The performance of CT, CT colonography, MRI, and FDG PET-CT for colon cancer staging will be reviewed. N staging will also be briefly discussed. It is expected that accurate radiologic T staging will significantly impact future clinical decisions regarding the neoadjuvant versus surgical management of colon cancer.

Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives

This review focuses on the role of magnetic resonance imaging (MRI) in the evaluation of the gastrointestinal tract (GI MRI), analyzing the major technical advances achieved in this field, such as diffusion-weighted imaging, molecular imaging, motility studies, and artificial intelligence. Today, MRI performed with the more advanced imaging techniques allows accurate assessment of many bowel diseases, particularly inflammatory bowel disease and rectal cancer; in most of these diseases, MRI is invaluable for diagnosis, staging, and disease monitoring under treatment. Several MRI parameters are currently considered activity biomarkers for inflammation and neoplastic disease. Furthermore, in younger patients with acute or chronic GI disease, MRI can be safely used for short-term follow-up studies in many critical clinical situations because it is radiation-free. MRI assessment of functional gastro-esophageal and small bowel disorders is still in its infancy but very promising, while it is well established and widely used for dynamic assessment of anorectal and pelvic floor dysfunction; MRI motility biomarkers have also been described. There are still some limitations to GI MRI related to high cost and limited accessibility. However, technical advances are expected, such as faster sequences, more specific intestinal contrast agents, AI analysis of MRI data, and possibly increased accessibility to GI MRI studies. Clinical interest in the evaluation of bowel disease using MRI is already very high, but is expected to increase significantly in the coming years.

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.

Current Controversies in the Management of Locally Advanced Colon Cancer

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Estimation of colon cancer grade and metastatic lymph node involvement using DWI/ADC sequences

BACKGROUND

The potential benefit of neoadjuvant chemotherapy (NAC) in colon cancer is under evaluation. There is a need to improve preoperative non-invasive diagnostics using techniques that provide more accurate staging information in assessing patient eligibility for NAC.

PURPOSE

To investigate the link between the tumor grade (pathohistological confirmed) and the N status (corresponding to lymph node involvement) with apparent diffusion coefficient (ADC) values.

MATERIAL AND METHODS

A total of 17 patients planned for surgical resection had a biopsy confirming colon carcinoma and participated in the study. Abdominal magnetic resonance imaging with diffusion-weighted imaging/ADC sequence was recorded before surgery. The tumor and all visible lymph nodes were manually delineated directly on a grayscale ADC map for every single slice and detected to access the total tumor and summarized lymph node volume. The mean ADC value was further calculated for the mean tumor and mean lymph node values.

RESULTS

Low-grade tumors had a mean ADC equivalent to 1225 ± 170×10^-6 mm²/s, and the coefficient of high-grade tumors was 1444 ± 69×10^-6 mm²/s. The group of patients with positive lymph nodes in operative tissue samples (N+) exhibited lower mean ADC values (1023 ± 142×10^-6 mm²/s) as opposed to the group without metastatic lymph nodes (N-) with ADC values of 1260 ± 231×10^-6 mm²/s.

CONCLUSION

The mean whole-tumor ADC is associated with the histological tumor grade, and the mean ADC value of whole-volume abdominal lymph nodes could assume real nodal infiltration.

Diagnostic Performance of Diffusion-Weighted Imaging for Colorectal Cancer Detection: An Updated Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging (MRI), which uses strong magnetic fields and radio waves (radiofrequency energy) to make images, is one of the best imaging methods for soft tissues and can clearly display unique anatomical structures. Diffusion-weighted imaging (DWI) has been developed for identifying various malignant tumors.

Aim

To investigate the diagnostic value of DWI-MRI quantitative analysis in colorectal cancer detection.

Methods

The PubMed, Cochrane Library, and Embase databases were searched from inception to May 29, 2020. Studies published in English that used DWI-MRI for diagnosing colorectal cancer were included. Case reports, letters, reviews, and studies conducted in non-humans or in-vitro experiments were excluded. The pooled diagnostic odds ratio (DOR) and hierarchical summary receiver operating characteristic (HSROC) curves were computed for DWI, and the area under the curve (AUC) and associated standard error (SE) and 95% confidence intervals (CIs) were also used.

Results

In total, 15 studies with 1,655 participants were finally included in this meta-analysis. There were four prospective studies and 11 retrospective studies. Eight studies focused on rectal cancer, six on colorectal cancer, and one on colonic cancer. The performance of DWI-MRI for diagnosing colorectal cancer was accurate, with pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 0.88 (95% CI = 0.85–0.91), 0.92 (95% CI = 0.91–0.94), 30.36 (95% CI = 11.05–83.43), and 0.44 (95% CI = 0.30–0.64), respectively. The DOR and HSROC curves were 121 (95% CI = 56–261) and 0.92 (λ: 4.79), respectively.

Conclusion

DWI showed high diagnostic accuracy for colorectal cancer detection. Further studies with large sample sizes and prospective design are needed to confirm these results.