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Published on: November 20, 2015
A radiomics nomogram based on multiparametric MRI for diagnosing focal cortical dysplasia and initially identifying
Shi-Qi Chen1, Liang Wei2, Keng He1
1Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China.
Background:
Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.
Methods:
Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.
Results:
The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.
Conclusion:
The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.
Insights
This study developed a radiomics nomogram using multiparametric MRI to diagnose focal cortical dysplasia (FCD), a common cause of epilepsy. The AI-powered tool demonstrated superior diagnostic performance compared to radiologists, aiding in early FCD detection and treatment.
Area of Science:
- Medical Imaging
- Radiology
- Neurology
Background:
- Focal cortical dysplasia (FCD) is the most prevalent epileptogenic malformation.
- Diagnosing FCD presents significant challenges.
- Early and accurate diagnosis is crucial for effective patient management.
Purpose of the Study:
- To develop a radiomics nomogram for diagnosing FCD using multiparametric MRI.
- To assess the nomogram's ability to identify FCD laterality.
- To improve diagnostic accuracy and clinical utility in FCD cases.
Main Methods:
- Retrospective analysis of 43 FCD patients and 43 controls.
- Inclusion of T1w, T2w, FLAIR, and combined MRI sequences.
- Feature selection using LASSO and model development with logistic regression.
- Performance evaluation using AUC, NRI, IDI, calibration, and clinical utility analysis.
Main Results:
- The combined MRI sequence radiomics nomogram achieved high diagnostic performance (AUCs ranging from 0.828 to 0.857).
- The nomogram significantly outperformed inexperienced radiologists in all tested sets.
- Radiomics features showed significant correlation with FCD duration, indicating clinical relevance.
Conclusions:
- The developed radiomics nomogram represents a significant advancement in FCD diagnosis.
- Multiparametric MRI-based radiomics offers a promising tool for FCD detection and characterization.
- This approach can aid clinicians in making timely and accurate FCD diagnoses and treatment decisions.
