What question did this study set out to answer?

This study aims to correlate preoperative MRI radiomics features with patient-reported outcomes before and after surgery for supraspinatus lesions.

June 1, 2026BMC Musculoskeletal Disorders0 citationsOpen Access

Discriminative and predictive power of radiomics features from preoperative MRI studies in patients with supraspinatus lesions

ULUmile Giuseppe Longo PGPaolo Giaccone LBLuca Bacco

Key Points

This study aims to correlate preoperative MRI radiomics features with patient-reported outcomes before and after surgery for supraspinatus lesions.
44 patients diagnosed with supraspinatus lesions underwent preoperative MRI scans.
Radiomics features were extracted, and relationships with Patient Reported Outcome Measures were analyzed using linear and non-linear methods.
Feature importance was ranked based on association strength with clinical outcomes.
The strongest associations with PROM scores were found for features like Maximum2DDiameterColumn and properties from the Gray Level Co-occurrences Matrix (GLCM).
Linear and non-linear analyses provided complementary insights into the radiomics-PROM relationship.
Top-ranked features were identified as pivotal for assessing patient health status pre- and post-surgery.

Abstract

Relying solely on MRI scans may prove insufficient for obtaining a complete view of a patient's supraspinatus health status comprising both objective and subjective disability level. On the other hand, while Patient Reported Outcome Measures (PROM) capture patients' personal experiences, they may not provide objective data.This study assessed the correlation level between preoperative MRI radiomics features and pre-and post-operative PROM scores in patients diagnosed with supraspinatus lesions. 44 patients who underwent arthroscopic repair for full-thickness Rotator Cuff Tears (RCT) of any grade between January 2019 and January 2022 in a single surgical centre were enrolled in the study. All coronal T2-weighted MRI scans were evaluated independently by two orthopedic surgeons blinded to the patients' clinical information, and the radiomics features were extracted. The strength of linear and non-linear relationships were assessed respectively through Fisher's statistical test applied to linear regression models, namely F-statistics, and through a model-free AI-based Mutual Information estimator. Together, these methods provided a comprehensive feature selection framework, with overlapping results reinforcing feature relevance and differences revealing complementary patterns in the radiomics-PROM relationship. Besides, they allowed us to construct an integrated ranking of radiomic features based on their strength of association with clinical outcomes, from which we selected those ranking in the top 10 most important of at least three PROMs.The radiomics features most frequently related to the patient's health condition were Maxi- mum2DDiameterColumn, Flatness, LeastAxisLength, and features gathered from the Gray Level Co-occurrences Matrix (GLCM), like Correlation, Maximum Correlation Coefficient (MCC), and Informational Measure of Correlation 2 (Imc2).Identifying crucial visual features pertaining to the patient's health status highlighted the potential for radiomics-guided personalized medicine in rotator cuff surgery. Incorporating both linear and non-linear analyses adds depth to the findings, establishing a strong groundwork for future advancements in the field.

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