Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

Dual-energy CT combined with histogram parameters in the assessment of perineural invasion in colorectal cancer

PURPOSE

The purpose is to evaluate the predictive value of dual-energy CT (DECT) combined with histogram parameters and a clinical prediction model for perineural invasion (PNI) in colorectal cancer (CRC).

METHODS

We retrospectively analyzed clinical and imaging data from 173 CRC patients who underwent preoperative DECT-enhanced scanning at two centers. Data from Qinghai University Affiliated Hospital (n = 120) were randomly divided into training and validation sets, while data from Lanzhou University Second Hospital (n = 53) served as the external validation set. Regions of interest (ROIs) were delineated to extract spectral and histogram parameters, and multivariate logistic regression identified optimal predictors. Six machine learning models-support vector machine (SVM), decision tree (DT), random forest (RF), logistic regression (LR), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost)-were constructed. Model performance and clinical utility were assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

Four independent predictive factors were identified through multivariate analysis: entropy, CT40KeV, CEA, and skewness. Among the six classifier models, RF model demonstrated the best performance in the training set (AUC = 0.918, 95% CI: 0.862-0.969). In the validation set, RF outperformed other models (AUC = 0.885, 95% CI: 0.772-0.972). Notably, in the external validation set, the XGBoost model achieved the highest performance (AUC = 0.823, 95% CI: 0.672-0.945).

CONCLUSION

Dual-energy CT-based combined with histogram parameters and clinical prediction modeling can be effectively used for preoperative noninvasive assessment of perineural invasion in colorectal cancer.

The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography

OBJECTIVES

To investigate the value of dual-layer spectral computed tomography (DLCT) parameters derived from primary tumors in predicting lymph node metastasis (LNM) of resectable pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

In this retrospective study, patients with resectable PDAC who underwent DLCT within 2-week intervals before surgery were enrolled and randomly divided into training and validation sets at a 7:3 ratio. The patients' clinical data, CT morphological features, and DLCT parameters were analyzed. Univariate and multivariate logistic analyses were used to identify the predictors and construct a predictive model, and receiver operator characteristic (ROC) curves were programmed to evaluate the predictive efficacy.

RESULTS

We enrolled 107 patients (44 patients with LNM and 63 patients without LNM). Among all variables, iodine concentration in the venous phase, extracellular volume, and tumor size were identified as independent predictors of LNM. The nomogram model, incorporating the two DLCT parameters and the morphological feature, achieved an area under the curve (AUC) of 0.877 (95% confidence interval [CI]: 0.803-0.952) and 0.842 (95% CI: 0.707-0.977) for predicting LNM in the training and validation sets, respectively. Furthermore, the AUC of the nomogram model was greater than that of morphological features of lymph nodes in the training (AUC = 0.877 vs. 0.570) and validation (AUC = 0.842 vs. 0.583) sets.

CONCLUSIONS

DLCT has the potential to predict LNM in patients with resectable PDAC and show a better predictive value than morphological features of lymph nodes.

KEY POINTS

Question Morphological features of lymph nodes are of limited value in detecting metastatic lymph nodes in pancreatic ductal adenocarcinoma (PDAC). Findings Dual-layer spectral computed tomography (DLCT) parameters and morphological features derived from PDAC lesions show good preoperatively predictive efficacy for lymph node metastasis. Clinical relevance The proposed DLCT-based nomogram model may serve as an effective and convenient tool for preoperatively predicting lymph node metastasis of resectable PDAC.

Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer

BackgroundLymph node metastasis (LNM) is a poor prognostic predictor and is highly correlated with local recurrence in rectal cancer patients.ObjectiveTo investigate the value of radiomics from dual-energy CT-derived iodine maps for the preoperative prediction of LNM in rectal cancer patients.MethodsA total of 176 patients were enrolled in this study (training group, n = 123; validation group, n = 53). A radiomic signature was constructed via support vector machine (SVM) modeling. Seven models, including a clinical feature model (Model 1), an arterial model (Model 2), a venous model (Model 3), an arterial-venous model (Model 4), an arterial-clinical model (Model 5), a venous-clinical model (Model 6) and an arterial-venous-clinical model (Model 7), were established via logistic regression modeling. Diagnostic performance was assessed via receiver operating characteristic (ROC) curves.ResultsTumor location and carcinoembryonic antigen levels were used to construct Model 1 (training group, AUC [area under the ROC curve] = 0.721, 95% CI [confidence intervals], 0.630-0.813; validation group, AUC = 0.729, 95% CI, 0.593-0.865). Model 6 and Model 7 further improved the discriminatory performance in the training (AUC = 0.850 and 0.869, 95% CI, 0.782-0.919 and 0.807-0.932, respectively; p = 0.250) and validation groups (AUC = 0.780 and 0.716, 95% CI, 0.653-0.906 and 0.576-0.856, respectively; p = 0.115). Moreover, decision curve analysis revealed a greater net benefit with Model 6.ConclusionsThe combination of radiomic features based on dual-energy CT-derived iodine maps and clinical features provides better diagnostic performance for predicting LNM in rectal cancer patients.

Spectral computed tomography parameters of primary tumors and lymph nodes for predicting tumor deposits in colorectal cancer

BACKGROUND

Tumor deposits (TDs) are an independent predictor of poor prognosis in colorectal cancer (CRC) patients. Enhanced follow-up and treatment monitoring for TD+ patients may improve survival rates and quality of life. However, the detection of TDs relies primarily on postoperative pathological examination, which may have a low detection rate due to sampling limitations.

AIM

To evaluate the spectral computed tomography (CT) parameters of primary tumors and the largest regional lymph nodes (LNs), to determine their value in predicting TDs in CRC.

METHODS

A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available. Patients were divided into the TDs+ group and the TDs- group on the basis of their pathological results. Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured, including the normalized iodine concentration (NIC) in both the arterial and venous phases, and the LN-to-primary tumor ratio was calculated. Statistical methods were used to evaluate the diagnostic efficacy of each spectral parameter.

RESULTS

Among the 121 CRC patients, 33 (27.2%) were confirmed to be TDs+. The risk of TDs positivity was greater in patients with positive LN metastasis, higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels. The NIC (LNs in both the arterial and venous phases), NIC (primary tumors in the venous phase), and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs (P < 0.05). In multivariate logistic regression analysis, the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs, demonstrating the highest diagnostic performance (area under the curve: 0.812, sensitivity: 0.879, specificity: 0.648, cutoff value: 1.145).

CONCLUSION

The spectral CT parameters of the primary colorectal tumor and the largest regional LNs, especially the LN-to-primary tumor ratio, have significant clinical value in predicting TDs in CRC.

Construction of a postoperative liver metastasis prediction model for colorectal cancer based on spectral CT imaging, CEA, and CA19-9

Background

Most existing models for predicting liver metastasis primarily rely on single clinical indicators or traditional imaging features, which, while useful, offer limited accuracy and reliability. In recent years, spectral computed tomography (CT) has emerged as a dual-energy imaging technology that provides detailed quantitative analyses of the blood supply characteristics and metabolic activity of tumors. The integration of serum biomarkers such as carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA19-9) with spectral CT features holds great potential for significantly enhancing the accuracy of liver metastasis predictions. This study aims to develops a novel nomogram prediction model based on spectral CT, CEA, and CA19-9 to predict the risk of liver metastasis after colorectal cancer (CRC) surgery.

Methods

This study recruited 100 patients diagnosed with CRC and receiving initial treatment at Jiangsu Cancer Hospital between June 2020 and June 2022. All patients underwent preoperative spectral CT examination. Patients were categorized into two groups based on the occurrence of liver metastasis within two years post-surgery: the liver metastasis group (n=60) and the non-metastasis group (n=40). A comparison was made between the two groups regarding the clinical and pathological characteristics and the changes in spectral CT parameters of the primary lesion before surgery. The predictive efficacy of preoperative spectral CT parameters of the primary lesion for liver metastasis was assessed. The risk factors for liver metastasis following CRC surgery were determined using multivariable logistic regression analysis, and a nomogram prediction model was established. A 7:3 ratio was used to randomly divide the dataset into a training set (n=70) and a validation set (n=30). The model's performance was evaluated using the receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA).

Results

Following CRC surgery, liver metastasis was found to be independently associated with CEA, cancer antigen 19-9 (CA19-9), and preoperative spectral CT characteristics of the original lesion during the venous phase, including lesion iodine concentration (IClesion), spectral slope in Hounsfield units (λHU), and the CT values of the tumor lesions on the 40-keV (CT40 keV). The nomogram developed from these predictors demonstrated high discriminative ability, with area under the curve (AUC) of 0.9078 [95% confidence interval (CI): 0.8419-0.9738] in the training cohort and 0.9502 (95% CI: 0.8792-1.0000) in the internal validation cohort at the optimal cutoff of 0.6460. The calibration curve showed that the observed and expected values agreed well. According to DCA, the nomogram model had good clinical value.

Conclusions

The nomogram model constructed based on spectral CT parameters, CEA, and CA19-9 demonstrates potential in predicting postoperative liver metastasis in CRC, providing a reference for preoperative personalized treatment. However, its generalizability needs to be further confirmed through multi-center external validation.

Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer

OBJECTIVES

To develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer.

METHODS

A total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models.

RESULTS

The combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison).

CONCLUSION

A Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.

Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer

OBJECTIVE

To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).

MATERIALS AND METHODS

A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy.

RESULTS

The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).

CONCLUSION

Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.

CLINICAL RELEVANCE STATEMENT

The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.

KEY POINTS

Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.

Preoperative dual-energy computed tomography and positron-emission tomography evaluation of lymph node metastasis in esophageal squamous cell carcinoma

PURPOSE

To investigate the detectability of lymph node metastasis in patients with esophageal squamous cell carcinoma using a combination of dual-energy computed tomography (CT) and positron-emission tomography (PET) parameters.

METHODS

We analyzed dual-energy CT and PET preoperative data in 27 consecutive patients with esophageal squamous cell carcinoma (23 men, 4 women; mean age, 73.7 years). We selected lymph nodes with a short-axis diameter of ≥5 mm and measured CT values, iodine concentrations, fat fractions, long- and short-axis diameters, and ratio of long- and short-axis diameters. We performed visual assessment of lymph node characteristics based on dual-energy CT and determined the maximum standardized uptake value via PET. The measured values were postoperatively compared between pathologically confirmed metastatic and nonmetastatic lymph nodes. Stepwise logistic regression analysis was performed to determine factors associated with lymph node metastasis. Diagnostic accuracy was assessed via receiver operating characteristic curve analysis.

RESULTS

Overall, 18 metastatic and 37 nonmetastatic lymph nodes were detected. CT values, iodine concentrations, fat fractions, and the maximum standardized uptake values differed significantly between metastatic and nonmetastatic lymph nodes (p < 0.05). Stepwise logistic regression showed that iodine concentration and the maximum standardized uptake value were significant predictors of metastatic lymph nodes. The areas under the curve of iodine concentrations and maximum standardized uptake values were 0.809 and 0.833, respectively. The area under the curve of the combined parameters was 0.884, with 83.3% sensitivity and 86.5% specificity.

CONCLUSION

Combined dual-energy CT and PET parameters improved the diagnosis of lymph node metastasis in patients with esophageal cancer.

Prediction of lymph node metastasis in T1 colorectal cancer based on combination of body composition and vascular invasion

OBJECTIVES

Lymph node metastasis (LNM) in colorectal cancer (CRC) patients is not only associated with the tumor's local pathological characteristics but also with systemic factors. This study aims to assess the feasibility of using body composition and pathological features to predict LNM in early stage colorectal cancer (eCRC) patients.

METHODS

A total of 192 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in the study. The cross-sectional areas of skeletal muscle, subcutaneous fat, and visceral fat at the L3 vertebral body level in CT scans were measured using Image J software. Logistic regression analysis were conducted to identify the risk factors for LNM. The predictive accuracy and discriminative ability of the indicators were evaluated using receiver operating characteristic (ROC) curves. Delong test was applied to compare area under different ROC curves.

RESULTS

LNM was observed in 32 out of 192 (16.7%) patients with eCRC. Multivariate analysis revealed that the ratio of skeletal muscle area to visceral fat area (SMA/VFA) (OR = 0.021, p = 0.007) and pathological indicators of vascular invasion (OR = 4.074, p = 0.020) were independent risk factors for LNM in eCRC patients. The AUROC for SMA/VFA was determined to be 0.740 (p < 0.001), while for vascular invasion, it was 0.641 (p = 0.012). Integrating both factors into a proposed predictive model resulted in an AUROC of 0.789 (p < 0.001), indicating a substantial improvement in predictive performance compared to relying on a single pathological indicator.

CONCLUSION

The combination of the SMA/VFA ratio and vascular invasion provides better prediction of LNM in eCRC.

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data.

METHODS

Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted.

RESULTS

Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001).

CONCLUSION

Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

Dual-energy computed tomography in a multiparametric regression model for diagnosing lymph node metastases in pancreatic ductal adenocarcinoma

OBJECTIVE

To investigate the diagnostic value of dual-energy computed tomography (DECT) quantitative parameters in the identification of regional lymph node metastasis in pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective diagnostic study assessed 145 patients with pathologically confirmed pancreatic ductal adenocarcinoma from August 2016-October 2020. Quantitative parameters for targeted lymph nodes were measured using DECT, and all parameters were compared between benign and metastatic lymph nodes to determine their diagnostic value. A logistic regression model was constructed; the receiver operator characteristics curve was plotted; the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each energy DECT parameter; and the DeLong test was used to compare AUC differences. Model evaluation was used for correlation analysis of each DECT parameter.

RESULTS

Statistical differences in benign and metastatic lymph nodes were found for several parameters. Venous phase iodine density had the highest diagnostic efficacy as a single parameter, with AUC 0.949 [95% confidence interval (CI):0.915-0.972, threshold: 3.95], sensitivity 79.80%, specificity 96.00%, and accuracy 87.44%. Regression models with multiple parameters had the highest diagnostic efficacy, with AUC 0.992 (95% CI: 0.967-0.999), sensitivity 95.96%, specificity 96%, and accuracy 94.97%, which was higher than that for a single DECT parameter, and the difference was statistically significant.

CONCLUSION

Among all DECT parameters for regional lymph node metastasis in PDAC, venous phase iodine density has the highest diagnostic efficacy as a single parameter, which is convenient for use in clinical settings, whereas a multiparametric regression model has higher diagnostic value compared with the single-parameter model.

Overexpression of circulating CD38+ NK cells in colorectal cancer was associated with lymph node metastasis and poor prognosis

Introduction

Lymph node metastasis (LNM) is a critical prognostic factor for colorectal cancer (CRC). Due to the potential influence of immune system on CRC progression, investigation into lymphocyte subsets as clinical markers has gained attention. The objective of this study was to assess the capability of lymphocyte subsets in evaluating the lymph node status and prognosis of CRC.

Methods

Lymphocyte subsets, including T cells (CD3+), natural killer cells (NK, CD3- CD56+), natural killer-like T cells (NK-like T, CD3+ CD56+), CD38+ NK cells (CD3- CD56+ CD38+) and CD38+ NK-like T cells (CD3+ CD56+ CD38+), were detected by flow cytometry. Univariate and multivariate analyses were used to assess the risk factors of LNM. The prognostic role of parameters was evaluated by survival analysis.

Results

The proportion of CD38+ NK cells within the NK cell population was significantly higher in LNM-positive patients (p <0.0001). However, no significant differences were observed in the proportions of other lymphocyte subsets. Poorer histologic grade (odds ratio [OR] =4.76, p =0.03), lymphovascular invasion (LVI) (OR =22.38, p <0.01), and CD38+ NK cells (high) (OR =4.54, p <0.01) were identified as independent risk factors for LNM. Furthermore, high proportion of CD38+ NK cells was associated with poor prognosis of CRC patients (HR=2.37, p =0.03).

Conclusions

It was demonstrated that the proportion of CD38+ NK cells was a marker overexpressed in LNM-positive patients compared with LNM-negative patients. Moreover, an elevated proportion of CD38+ NK cells is a risk factor for LNM and poor prognosis in CRC.

Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

Predictive modeling based on tumor spectral CT parameters and clinical features for postoperative complications in patients undergoing colon resection for cancer

BACKGROUND

Colon cancer is a particularly prevalent malignancy that produces postoperative complications (POCs). However, limited imaging modality exists on the accurate diagnosis of POCs. The purpose of this study was therefore to construct a model combining tumor spectral CT parameters and clinical features to predict POCs before surgery in colon cancer.

METHODS

This retrospective study included 85 patients who had preoperative abdominal spectral CT scans and underwent radical colon cancer resection at our institution. The patients were divided into two groups based on the absence (no complication/grade I) or presence (grades II-V) of POCs according to the Clavien-Dindo grading system. The visceral fat areas (VFA) of patients were semi-automatically outlined and calculated on L3-level CT images using ImageJ software. Clinical features and tumor spectral CT parameters were statistically compared between the two groups. A combined model of spectral CT parameters and clinical features was established by stepwise regression to predict POCs in colon cancer. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve, including area under the curve (AUC), sensitivity, and specificity.

RESULTS

Twenty-seven patients with POCs and 58 patients without POCs were included in this study. MonoE40keV-VP and VFA were independent predictors of POCs. The combined model based on predictors yielded an AUC of 0.84 (95% CI: 0.74-0.91), with a sensitivity of 77.8% and specificity of 87.9%.

CONCLUSIONS

The model combining MonoE40keV-VP and VFA can predict POCs before surgery in colon cancer and provide a basis for individualized management plans.

CRITICAL RELEVANCE STATEMENT

The model combining MonoE40keV-VP and visceral fat area can predict postoperative complications before surgery in colon cancer and provide a basis for individualized management plans.

KEY POINTS

• Visceral fat area and MonoE40keV-VP were independent predictors of postoperative complications in colon cancer. • The combined model yielded a high AUC, sensitivity, and specificity in predicting postoperative complications. • The combined model was superior to the single visceral fat area or MonoE40keV-VP in predicting postoperative complications.

Preoperative prediction of the lymphovascular tumor thrombus of colorectal cancer with the iodine concentrations from dual-energy spectral CT

BACKGROUND

The aim of this study was to explore application value of iodine concentration from dual-energy spectral computed tomography (DESCT) in preoperative prediction of lymphovascular tumor thrombus in patients with colorectal cancer (CRC).

METHODS

We finally retrospectively analyzed 50 patients with CRC who underwent abdominal DESCT before receiving any preoperative treatment and underwent surgery to obtain pathological specimens which were stained with hematoxylin-eosin (HE) staining. According to the presence of cancer cell nests in blood vessels and lymphatic vessels, the subjects were divided into the positive group and negative group of lymphovascular tumor thrombus. Two radiologists independently measured the normalized iodine concentration (NIC) values, effective atomic number (Zeff) and CT values of virtual monochromatic images (VMIs) at 40-90 keV of the primary tumors in the arterial phase (AP) and venous phase (VP). Used SPSS 17.0 to calculate the receiver operating characteristic (ROC) curve to evaluate diagnostic value.

RESULTS

The patients were divided into lymphovascular tumor thrombus positive group(n = 16) and negative group(n = 34). The values of NIC-AP and NIC-VP in the positive group were 0.17 ± 0.09, 0.51 ± 0.13, respectively. And those in the negative group were 0.15 ± 0.06, 0.43 ± 0.12, respectively. There was significant difference in NIC-VP value between the two groups (p = 0.039), but there was no significant difference in NIC-AP value (p = 0.423). The optimal threshold value of NIC-VP value for diagnosis of lymphovascular tumor thrombus was 0.364. The sensitivity was 68.8% and the specificity was 67.6%.

CONCLUSIONS

The NIC-VP value of DESCT can be used to predict the presence or absence of the lymphovascular tumor thrombus in CRC patients before operation, which is helpful to select the best treatment scheme and evaluate its prognosis.