Apparent diffusion coefficient cannot discriminate metastatic and non-metastatic lymph nodes in rectal cancer: a meta-analysis

A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer

OBJECTIVE

The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients.

METHODS

This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets.

RESULTS

The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0.

CONCLUSION

Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.

Differentiating nasopharyngeal carcinoma from lymphoma in the head and neck region using the apparent diffusion coefficient (ADC) value: a systematic review and meta-analysis

Purpose

This study aimed to assess the applicability of the apparent diffusion coefficient (ADC) for differentiating nasopharyngeal carcinoma (NPC) from lymphomas in the head and neck region.

Material and methods

Four databases, including PubMed, the Cochrane Library, EMBASE, and Web of Science, were searched systematically to find relevant literature. The search date was updated to 8 September 2022, with no starting time restriction. The methodological quality of the studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Firstly, a random-effects model was used in a meta-analysis of continuous variables with low heterogeneity to determine the overall effect size, which was reported as the standard mean difference (SMD). Then, bivariate random effects modelling was used to calculate the combined sensitivity and specificity. The area under the curve (AUC) for each diffusion parameter was calculated after constructing summary receiver operating characteristic curves. The presence of heterogeneity was evaluated using subgroup and meta-regression analysis.

Results

Twelve studies involving 181 lymphoma and 449 NPC lesions (N = 630) in the head and neck region were included, of which 5 studies provided sufficient data for pooling diagnostic test accuracy. A meta-analysis of the 12 studies using a random-effects model yielded an SMD of 1.03 (CI = 0.76-1.30; p = 0.00001), implying that NPC lesions had a significantly higher ADC value than lymphoma lesions. By pooling 5 standard DWI studies, the pooled sensitivity and specificity of ADC were 0.90 (95% CI: 0.82-0.95) and 0.63 (95% CI: 0.52-0.72), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.74 (95% CI: 0.70-0.78).

Conclusions

According to this systematic review and meta-analysis, nasopharyngeal carcinoma has a significantly higher ADC value than lymphomas. Furthermore, while ADC has excellent sensitivity for distinguishing these 2 types of tumours, its specificity is relatively low, yielding a moderate diagnostic performance. Further investigations with larger sample sizes are required.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning

OBJECTIVES

This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.

METHODS

In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.

RESULTS

In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).

CONCLUSION

The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

KEY POINTS

• Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma

OBJECTIVES

Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively.

METHODS

A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis.

RESULTS

The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively.

CONCLUSION

The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.

Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective

The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.