Prognostic value of CT radiomics in evaluating lymphovascular invasion in rectal cancer: Diagnostic performance based on different volumes of interest

Lymphovascular invasion in colorectal cancers: can we predict it preoperatively?

PURPOSE

This study aimed to investigate preoperative predictors of lymphovascular invasion (LVI), which is a poor prognostic factor usually detected postoperatively in patients with colorectal cancer.

METHODS

Results for all patients operated on for colorectal cancer between January 1, 2006, and December 31, 2021, were retrospectively analyzed. Potential preoperative factors and postoperative pathology results were recorded. The patients were categorized as those with LVI and those without LVI. Potential factors that may be associated with LVI were compared between the 2 groups.

RESULTS

The study included 335 patients. The incidence of LVI was 3.11 times higher in patients with ascending colon tumors (odds ratio [OR], 3.11; 95% confidence interval [CI], 1.34-7.23; P=0.008) and 4.28 times higher in those with metastatic tumors (OR, 4.28; 95% CI, 2.18-8.39; P<0.001). Diabetes mellitus was inversely related to LVI in colorectal cancer patients; specifically, LVI was 56% less common in colorectal cancer patients with diabetes mellitus, irrespective of its duration (OR, 0.44; 95% CI, 0.25-0.76; P<0.001).

CONCOUSION

The presence of preoperative LVI in colorectal cancer patients is difficult to predict. In particular, the effect of the effect of factors such as chronic disease accompanied by microvascular pathologies on LVI is still unclear. Advances in the neoadjuvant treatment of colorectal cancer patients, who are becoming more widespread every day, will encourage the investigation of different methods of preoperatively predicting LVI as a poor prognostic factor in these patients.

Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors

Purpose

The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC).

Methods

A total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves.

Results

Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05).

Conclusion

The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study

OBJECTIVE

To develop and externally validate a CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion (LVI) in patients with colorectal cancer (CRC).

METHODS

357 patients derived from 2 centers with pathologically confirmed CRC were included in this retrospective study. Two-dimensional (2D) and three-dimensional (3D) radiomics features were extracted from portal venous phase CT images. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. The radiomics nomogram was developed by integrating the radiomics score (rad-score) and the clinical risk factor.

RESULTS

The rad-score was significantly higher in the LVI+ group than in the LVI- group (p < 0.05). The area under the curve (AUC), accuracy, sensitivity and specificity of the 3D radiomics model were higher than those of the 2D radiomics model. The AUCs of 3D and 2D radiomics models in the training set were 0.82 (95% CI: 0.75-0.89) and 0.74 (95% CI: 0.66-0.82); in the internal validation set were 0.75 (95% CI: 0.65-0.85) and 0.67 (95% CI: 0.56-0.78); in the external validation set were 0.75 (95% CI: 0.64-0.86) and 0.57 (95% CI: 0.45-0.69); respectively. The AUCs of the nomogram integrating the optimal 3D rad-score and clinical risk factors (CT-reported T stage, CT-reported lymph node status) in the internal set and external validation set were 0.82 (95% CI: 0.73-0.91) and 0.80 (95% CI: 0.68-0.91), respectively.

CONCLUSION

Both 2D and 3D radiomics models can predict LVI status of CRC. The nomogram combining the optimal 3D rad-score and clinical risk factors further improved predictive performance.

ADVANCES IN KNOWLEDGE

This is the first study to compare the difference in performance of CT-based 2D and 3D radiomics models for the pre-operative prediction of LVI in CRC. The prediction of the nomogram could be improved by combining the 3D radiomics model with the imaging model, suggesting its potential for clinical application.

An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer

Objective

To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery.

Methods

A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model.

Results

Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits.

Conclusion

The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00813-6.