Related Experiment Video
Updated: Jun 16, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in
Sofia Netti1, Oriana D'Ecclesiis2, Federica Corso3,4
1Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy. sofia.netti@ieo.it.
Aim:
To investigate whether methodological aspects may influence the performance of MRI-radiomic models to predict response to neoadjuvant treatment (NAT) in breast cancer (BC) patients.
Materials And Methods:
We conducted a systematic review until March 2023. A random-effects meta-analysis was performed to combine the area under the receiver operating characteristic curve (AUC) values. Publication bias was assessed using Egger's test and heterogeneity was estimated by I2. A meta-regression was conducted to investigate the impact of various factors, including scanner, features' number/transformation/type, pixel/voxel scaling, etc. RESULTS: Forty-two studies were included. The summary AUC was 0.77 (95% CI: 0.74-0.81). Substantial heterogeneity was observed (I2 = 81%) with no publication bias (p = 0.35). Radiomic model accuracy was influenced by the scanner vendor, with lower AUCs in studies using mixed scanner vendors (AUC; 95% CI: 0.70; 0.61-0.78) compared to studies including images obtained from the same scanner (AUC (95% CI): 0.83 (0.77-0.88), 0.74 (0.67-0.82), 0.83 (0.78-0.89) for three different vendors; vendors 1, 2, and 3, respectively; p-value = 0.03 for comparison with vendor 1). Feature type also seemed to have an impact on the AUC, with higher prediction accuracy observed for studies using 3D than 2D/2.5D images (AUC; 95% CI: 0.81; 0.78-0.85 and 0.73; 0.65-0.81, respectively, p-value = 0.03). Non-significant between-study heterogeneity was observed in the studies including 3D images (I2 = 33%) and Vendor 1 scanners (I2 = 40%).
Conclusion:
MRI-radiomics has emerged as a potential method for predicting the response to NAT in BC patients, showing promising outcomes. Nevertheless, it is important to acknowledge the diversity among the methodological choices applied. Further investigations should prioritize achieving standardized protocols, and enhancing methodological rigor in MRI-radiomics.
Key Points:
Question Do methodological aspects influence the performance of MRI-radiomic models in predicting response to NAT in BC patients? Findings Radiomic model accuracy was influenced by the scanner vendor and feature type. Clinical relevance Methodological discrepancies affect the performance of MRI-radiomic models. Developing standardized protocols and enhancing methodological rigor in these studies should be prioritized.
