Validation of a new topographic classification of bone marrow lesions in the knee: the six-letter system

Purpose The aim of this study was to propose and validate a new six-item topographical classification of knee bone marrow lesions in coronal MRI images, to provide an easy-to-use aid to describe their location in a more reproducible and accurate way. Methods This study was conducted in four phases. The first was to do a literature search for methods of describing bone marrow lesions in MRI of the knee. The second was creation of a six-area topographic classification of bone marrow lesions in coronal MRI of the knee. The third phase was selection of cases with bone marrow lesions on knee MRI performed in a single hospital between January of 2017 and December of 2018. The fourth phase was categorization of the bone marrow lesions' location according to the new proposed classification by three independent examiners, two orthopedic surgeons and one radiologist. Patient's demographic data and associated lesions were collected. The inter-observer and intra-observer reliability of the proposed classification was then calculated. Results MRI examination of 4000 patients were studied and in 520 patients a total of 666 bone marrow lesions were identified and their location classified using the new system. The inter-observer and intra-observer reliability analysis found a Fleiss' Kappa value of 0.96 (0.95-0.97) and 0.97 (0.96-0.97), respectively, confirming the high reproducibility of the proposed classification. Conclusions The proposed six-location classification of bone marrow lesions is highly reproducible and can help researchers develop studies and share information in a more accurate and reliable way. The correct classification of bone marrow lesions can lead to a more accurate description of this pathology and help clinicians to propose appropriate therapies for this group of patients.

Automated summary

The study proposed and validated a highly reproducible six-location classification of knee bone marrow lesions in MRI images, which can assist researchers in conducting studies and sharing information in a more accurate and reliable manner.