Differentiation of poorly differentiated colorectal adenocarcinomas from well- or moderately differentiated colorectal adenocarcinomas at contrast-enhanced multidetector CT

KRAS mutation status between left- and right-sided colorectal cancer: are there any differences in computed tomography?

PURPOSE

To investigate the differences in clinicopathological and imaging features according to KRAS mutation status in left- and right-sided colorectal cancer.

METHOD

A total of 157 patients with pathologically proven colorectal cancer and preoperative contrast-enhanced multidetector CT examinations were enrolled. According to the tumor location and KRAS status, they were divided into two groups: the left-sided colorectal cancer (LCC) group (wild type, mutant type) and the right-sided colorectal cancer (RCC) group (wild type, mutant type). Clinicopathological and imaging features were recorded in each group. The imaging observation indicators included short axis diameter (SAD), longitudinal tumor length (LTL), tumor shape, pericolic fat stranding, bowel stenosis, intratumoral low-density range, enhancement pattern, and bowel obstruction. Univariate and multivariate logistic regression analyses were performed to compare the difference in KRAS mutation status between groups.

RESULTS

In the LCC group, SAD, tumor shape, degree of pericolic fat stranding, and bowel obstruction were significant indicators for predicting KRAS status (P < 0.05). In the RCC group, CA19-9, SAD, and intratumoral low-density range were significant indicators for predicting KRAS status (P < 0.05.). The area under the curve (AUC) of the combination image indicators in the LCC group was 0.802 [cutoff point 0.372, 95% confidence interval (CI) 0.718-0.888, sensitivity 85.4%, specificity 72.0%]. The AUC in the RCC group was 0.828 (cutoff point 0.647, 95% CI 0.726-0.931, sensitivity 79.5%, specificity 75.0%).

CONCLUSION

The CT imaging features associated with KRAS mutation status in the LCC and RCC groups were different. The combination of tumor location and imaging features can help to further improve the predictive value of KRAS status.

MDCT diagnosis of synchronous primary gastrointestinal tract carcinoma and other solid malignancies: case series study

Background

The presentation of synchronous multiple primary tumors is rare. The aim of this report was to report an uncommon series of cases diagnosed with synchronous gastrointestinal tract carcinoma and other solid malignancies by multidetector computed tomography.

Case presentation

Our report included 34 patients with synchronous gastrointestinal tract carcinoma and other solid malignancies from November 2009 to September 2019. They were 14 men and 20 women (mean age, 65.5 year; range, 52–82 years). The highest number of GIT cases were colonic carcinomas detected in 70% (24/34) of the patients. The most frequent extra-gastrointestinal primary malignancy sites were renal cell and breast carcinomas, 17.6% (6/34) of each.

Conclusions

Careful preoperative evaluation is recommended to detect this pattern of synchronous extra-gastrointestinal tumors. More reports of such cases should help to clarify the pathogenesis of this phenomenon and may lead to a new treatment strategy for synchronous gastrointestinal malignancy and other solid malignancies.

T staging with functional and radiomics parameters of computed tomography in colorectal cancer patients

Preoperative T staging is closely related to operation planning and prognosis of colorectal cancer (CRC). This study aimed to re-investigate the value of computed tomography (CT) in T stage evaluation of CRC patients with both functional and radiomics parameters.The functional and radiomics parameters of CT images and the clinical information were collected from 32 CRC patients. The radiomics parameters were measured based on manually labelled 5-mm circles using software Syngo. The radiomics parameters were computed based on labelled tumor regions using Python software package.A total of 125 parameters were collected and analyzed by using decision tree analysis. The decision tree analysis identified 6 rules. Based on the rules, the shape elongation, flow extraction of nodule and blood volume of tumor region were found to be of significance and could define a high-risk group and a low-risk group.This study shows the combination of functional parameters and radiomics parameters of CT is helpful for the diagnosis and T staging of CRC.

Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations.

Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging.

Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4–90.6, p &lt; 0.001; validation cohort: HR: 2.04, 95% CI: 1.44–2.89, p &lt; 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8–69.3, p &lt; 0.001; validation cohort: HR: 1.83, 95% CI: 1.19–2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways.

Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer

Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC).

Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns.

Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26–14.13, P < 0.001; validation cohort: HR: 8.49, 95% CI: 2.05–35.17, P < 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14–13.39, P < 0.001; validation cohort: HR: 5.19, 95% CI: 1.22–22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69–0.80) in the primary cohort and 0.82 (0.77–0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways.

Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.

Characterizing MRI features of rectal cancers with different KRAS status

BACKGROUND

To investigate whether MRI findings, including texture analysis, can differentiate KRAS mutation status in rectal cancer.

METHODS

Totally, 158 patients with pathologically proved rectal cancers and preoperative pelvic MRI examinations were enrolled. Patients were stratified into two groups: KRAS wild-type group (KRAS^wt group) and KRAS mutation group (KRAS^mt group) according to genomic DNA extraction analysis. MRI findings of rectal cancers (including texture features) and relevant clinical characteristics were statistically evaluated to identify the differences between the two groups. The independent samples t test or Mann-Whitney U test were used for continuous variables. The differences of the remaining categorical polytomous variables were analyzed using the Chi-square test or Fisher exact test. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory power of MRI features. The area under the ROC curve (AUC) and the optimal cut-off values were calculated using histopathology diagnosis as a reference; meanwhile, sensitivity and specificity were determined.

RESULTS

Mean values of six texture parameters (Mean, Variance, Skewness, Entropy, gray-level nonuniformity, run-length nonuniformity) were significantly higher in KRAS^mt group compared to KRAS^wt group (p < 0.0001, respectively). The AUC values of texture features ranged from 0.703~0.813. In addition, higher T stage and lower ADC values were observed in the KRAS^mt group compared to KRAS^wt group (t = 7.086, p = 0.029; t = - 2.708, p = 0.008).

CONCLUSION

The MRI findings of rectal cancer, especially texture features, showed an encouraging value for identifying KRAS status.

CT-based Radiomics Signature to Discriminate High-grade From Low-grade Colorectal Adenocarcinoma

RATIONALE AND OBJECTIVES

To develop and validate a computed tomography-based radiomics signature for preoperatively discriminating high-grade from low-grade colorectal adenocarcinoma (CRAC).

MATERIALS AND METHODS

This retrospective study was approved by our institutional review board, and the informed consent requirement was waived. This study enrolled 366 patients with CRAC (training dataset: n = 222, validation dataset: n = 144) from January 2008 to August 2015. A radiomics signature was developed with the least absolute shrinkage and selection operator method in training dataset. Mann-Whitney U test was applied to explore the correlation between radiomics signature and histologic grade. The discriminative power of radiomics signature was investigated with the receiver operating characteristics curve. An independent validation dataset was used to confirm the predictive performance. We further performed a stratified analysis to validate the predictive performance of radiomics signature in colon adenocarcinoma and rectal adenocarcinoma.

RESULTS

The radiomics signature demonstrated discriminative performance for high-grade and low-grade CRAC, with an area under the curve of 0.812 (95% confidence interval [CI]: 0.749-0.874) in training dataset and 0.735 (95%CI: 0.644-0.826) in validation dataset. Stratified analysis demonstrated that radiomics signature also showed distinguishing ability for histologic grade in both colon adenocarcinoma and rectal adenocarcinoma, with area under the curve of 0.725 (95%CI: 0.653-0.797) and 0.895 (95%CI: 0.838-0.952), respectively.

CONCLUSIONS

We developed and validated a radiomics signature as a complementary tool to differentiate high-grade from low-grade CRAC preoperatively, which may make contribution to personalized treatment.

Intravoxel Incoherent Motion MRI of Rectal Cancer: Correlation of Diffusion and Perfusion Characteristics With Prognostic Tumor Markers

OBJECTIVE

The objective of our study was to evaluate the intravoxel incoherent motion (IVIM)-DWI derived parameters and their relationships with tumor prognostic markers using 3-T MRI in patients with rectal cancer.

SUBJECTS AND METHODS

Fifty-two patients with histopathologically proven rectal cancer who underwent preoperative pelvic MRI were prospectively enrolled in this study. Diffusion and perfusion parameters including the apparent diffusion coefficient (ADC), pure diffusion coefficient, perfusion fraction, and pseudodiffusion coefficient derived from IVIMDWI were independently measured by two radiologists. Comparisons of IVIM-DWI-derived parameters in patients with different tumor prognostic markers were made using the independent-samples t test, ANOVA, and Mann-Whitney U test. The correlations between IVIM-DWI-derived parameters and tumor grade and tumor stage were further evaluated using Spearman correlation analysis. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC).

RESULTS

Excellent interobserver reproducibility was obtained for the IVIM-DWI-derived parameters (range of ICCs with 95% limits of agreement = 0.9309-0.9948, which is narrow). ADC, pseudodiffusion coefficient, and perfusion fraction tended to rise with greater tumor differentiation (r = 0.520, p < 0.001; r = 0.447, p = 0.001; r = 0.354, p = 0.010, respectively). The pure diffusion coefficient and pseudodiffusion coefficient showed a trend of decreasing with increasing tumor stages (r = 0.479, p < 0.001; r = 0.517, p < 0.001). The group of patients with extramural vascular invasion (EMVI) showed lower pseudodiffusion coefficient values than the group of patients with no EMVI (p < 0.05).

CONCLUSION

IVIM-DWI-derived parameters in patients with rectal cancer, especially the pseudodiffusion coefficient, are associated with tumor grade and tumor stage and show statistically significant differences between subjects with EMVI and those without EMVI. IVIM-DWI-derived parameters would be helpful in predicting tumor aggressiveness and prognosis.

Correlations of ICAM-1 gene polymorphisms with susceptibility and multidrug resistance in colorectal cancer in a Chinese population

BACKGROUND

Colorectal cancer (CRC) is a malignant gastrointestinal tumor with a high mortality rate, including both colon and rectal cancer. In order to provide clinical guidance for the treatment of CRC, this study is conducted to investigate the correlations of intercellular adhesion molecule 1 (ICAM-1) gene polymorphisms with susceptibility and multidrug resistance (MDR) of colorectal cancer (CRC).

METHODS

A total of 195 patients with CRC were selected as the observation group and 188 healthy people enrolled as the control group. Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) was used to test ICAM-1 A13848G and K469E polymorphisms. The expressions of MDR-associated protein topoisomerase II (Topo II) and P-glycoprotein (P-gp) in CRC tissues were detected by immunohistochemistry. The analysis on association of clinical indexes of CRC patients with ICAM-1 gene polymorphisms was performed.

RESULTS

The frequencies of KK genotype and K allele of K469E in the observation group were significantly higher than that in the control group. KE + EE genotype and E allele might be protective factors for CRC. The distribution of genotypes, K469E KK and KE+EE, was highly correlated with histologic grade of tumor differentiation. Compared with adjacent normal tissues, positive rates of Topo II and P-gp expression were significantly increased in CRC tissues. Topo II expression in CRC patients was positively associated with lymph node metastasis and depth of tumor invasion, whereas P-gp expression was only associated with depth of tumor invasion. Higher positive rates of Topo II and P-gp expression were observed in ICAM-1 K469E KK genotype carriers, indicating that ICAM-1 K469E KK genotype might be related to MDR in CRC.

CONCLUSION

These findings in the current study suggested that ICAM-1 K469E polymorphism is highly correlated with susceptibility and MDR in CRC.

Pre-operative to post-operative serum carcinoembryonic antigen ratio is a prognostic indicator in colorectal cancer

We explored the prognostic significance of the pre-operative-to-post-operative serum carcinoembryonic antigen (pre-post-CEA) ratio in colorectal cancer (CRC). We detected pre- and post-operative CEA levels in 2035 CRC patients surgically treated at First Affiliated Hospital of Zhengzhou University between June 2001 and June 2011. Univariate analysis revealed the pre-post-CEA ratio is associated with distant metastasis and degree of tumor differentiation (both P < 0.05). Multivariate analysis showed that the pre-post-CEA ratio is associated with lymphatic and distant metastasis, tumor-node-metastasis (TNM) stage and degree of tumor differentiation (all P < 0.01). The pre-CEA levels, pre-post-CEA ratios, distant metastasis, TNM stage and degree of tumor differentiation were all associated with 5-yr overall survival (all P < 0.05) based on multivariate analysis. Consequently, pre-CEA levels, pre-post-CEA ratios, distant metastasis and TNM stage are independent risk factors for CRC. We have thus demonstrated that the pre-post-CEA ratio is a prognostic indicator for CRC patients.

The diagnostic accuracy of pericolonic fat extension and attenuation for colorectal tumors

OBJECTIVE

To evaluate the utility of quantitative analysis of the extension and attenuation of pericolonic fat in the local staging of colorectal cancer (CRC) using multi detector computed tomography (MDCT).

MATERIALS AND METHODS

This was a retrospective study of 110 patients who were operated due to pathologically proven CRC from January 2007 to January 2010, and who underwent preoperative MDCT of the abdomen and pelvis with administration of intravenous contrast material and image acquisition during the portal venous phase. The mean age was 69 years (range of 38-90 years). Pathological reports were reviewed for TNM staging. All MDCT studies were reviewed by two certified radiologists for maximal and minimal tumor diameter, extent of the infiltrated pericolonic fat (measured in mm), attenuation of the infiltrated pericolonic fat (measured in Hounsfield units), and attenuation of normally appearing fat next to the tumor. The sensitivity and specificity of these parameters in detecting patients with ≥ T3 CRC were calculated.

RESULTS

The overall sensitivity, specificity, and accuracy of pericolonic fat infiltration in detecting patients with ≥T3 stage were 95% (95% CI, 89.0-98.7%), 20% (5.8-43.7%), and 81.9% (74.7-89%) respectively. The mean extent and attenuation of the infiltrated pericolonic fat, in addition to the maximal tumor diameter, were higher in the ≥T3 group (p<0.05). By defining threshold values to these parameters, the positive predictive value for detecting ≥T3 stage tumors approaches 100%.

CONCLUSION

Quantitative analysis of pericolonic fat increased the accuracy of MDCT in the detection of local tumor spread in CRC.