Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer?

The Value of Transanal Normal Saline Infusion-Assisted Multipath Ultrasonography in the Diagnosis of T1/T2 Rectal Cancer

ABSTRACT

This study aims to assess the application value of transanal normal saline infusion-assisted multipath ultrasonography (TNSI-MU) in the diagnosis of T1/T2 rectal cancer (RC). All patients first received single-path 360-degree transrectal ultrasonography and then received 360-degree transrectal ultrasonography, transabdominal ultrasonography, or transvaginal ultrasonography after TNSI to determine the T stage. With surgical pathology as the criterion standard, the detection rates of T1/T2 RC lesions and the T-staging results of single-path 360-degree transrectal ultrasonography, TNSI-MU, and contrast-enhanced magnetic resonance imaging (MRI) were compared and analyzed. T1/T2 RC was surgically and pathologically confirmed in 52 patients. Single-path 360-degree transrectal ultrasonography had a lesion detection rate of 57.69% (30/52) and a T-staging accuracy of 80.0% (24/30), the sensitivity was 57.69%, and the specificity was 88.46%. Transanal normal saline infusion-assisted multipath ultrasonography had a lesion detection rate of 100%, and its T-staging accuracy was 84.62% (44/52), the sensitivity was 100%, and the specificity was 88.61%. Transanal normal saline infusion-assisted multipath ultrasonography had a significantly higher detection rate of T1/T2 RC lesions than single-path 360-degree transrectal ultrasonography ( P < 0.001), but the 2 methods had similar T-staging accuracy for T1/T2 RC (χ 2 = 0.286, P = 0.593). Contrast-enhanced MRI had a lesion detection rate of 100% and a T-staging accuracy of 40.38% (21/52), the sensitivity was 98.07%, and the specificity was 61.54%. Transanal normal saline infusion-assisted multipath ultrasonography had significantly higher diagnostic accuracy than contrast-enhanced MRI for T staging of T1/T2 RC ( P < 0.001), and the diagnostic results of the 2 methods were not consistent (κ = 0.151). Transanal normal saline infusion-assisted multipath ultrasonography outperformed single-path 360-degree transrectal ultrasonography in the detection rate of T1/T2 RC lesions and contrast-enhanced MRI in the staging accuracy for T1/T2 RC.

The Role of Radiomics in Rectal Cancer

PURPOSE

Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT.

METHODS

We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically.

RESULTS

The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice.

CONCLUSION

Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.

Deep learning models for preoperative T-stage assessment in rectal cancer using MRI: exploring the impact of rectal filling

Background

The objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models.

Methods

A retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans. The datasets were randomly divided into a training set (n = 265) and a test set (n = 52). Initially, an automatic segmentation model based on T2-weighted imaging (T2WI) was constructed using nn-UNet. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, three types of DL-models were constructed: Model 1 trained on the total training dataset, Model 2 trained on the rectal-filling dataset, and Model 3 trained on the non-filling dataset. The diagnostic values were evaluated and compared using receiver operating characteristic (ROC) curve analysis, confusion matrix, net reclassification index (NRI), and decision curve analysis (DCA).

Results

The automatic segmentation showed excellent performance. The rectal-filling dataset exhibited superior results in terms of DSC and ASD (p = 0.006 and 0.017). The DL-models demonstrated significantly superior classification performance to the subjective evaluation in predicting T-stages for all test datasets (all p < 0.05). Among the models, Model 1 showcased the highest overall performance, with an area under the curve (AUC) of 0.958 and an accuracy of 0.962 in the filling test dataset.

Conclusion

This study highlighted the utility of DL-based automatic segmentation and classification models for preoperative T-stage assessment of RC on T2WI, particularly in the rectal-filling dataset. Compared with subjective evaluation, the models exhibited superior performance, suggesting their noticeable potential for enhancing clinical diagnosis and treatment practices.

MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study

OBJECTIVE

To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer.

MATERIALS AND METHODS

A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis.

RESULTS

For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)-based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively.

CONCLUSION

Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk.

CLINICAL RELEVANCE STATEMENT

Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery.

KEY POINTS

• Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD. • The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.