MRI of the hip: current concepts on bone marrow oedema

Bone marrow edema of the hip: a narrative review

Bone marrow edema (BME) of the hip is a radiological-clinical condition with symptoms ranging from asymptomatic to severe, and it is characterized by increased interstitial fluid within the bone marrow, usually at the femur. Depending on the etiology it can be classified as primary or secondary. The primary cause of BME is unknown, while the secondary forms include traumatic, degenerative, inflammatory, vascular, infectious, metabolic, iatrogenic, and neoplastic etiologies. BME could be classified as reversible or progressive. Reversible forms include transient BME syndrome and regional migratory BME syndrome. Progressive forms include avascular necrosis of the femoral head (AVNH), subchondral insufficiency fracture, and hip degenerative arthritis. The diagnosis can be difficult, because at the beginning, the outbreak of hip pain, typically acute and disabling without any prior trauma or exceptional physical activity, is poorly supported by radiographic findings. MRI is the gold standard, and it shows an area of intermediate signal on T1-weighted MRI scans and a high signal on T2-weighted scans, usually lacking sharps margins. In the reversible form, BME is typically self-limiting, and it can be managed conservatively by means of pharmacological and physical therapy. Surgery is generally required for progressive forms in patients who failed non-operative treatment, and it ranges from femoral head and neck core decompression to total hip arthroplasty.

Bone marrow lesions of the femoral head: can radiomics distinguish whether it is reversible?

Purpose

Contrary to the self-limiting nature of reversible bone marrow lesions, irreversible bone marrow lesions require early surgical intervention to prevent further morbidity. Thus, early discrimination of irreversible pathology is necessitated. The purpose of this study is to evaluate the efficacy of radiomics and machine learning regarding this topic.

Material and methods

A database was scanned for patients who had undergone MRI of the hip for differential diagnosis of bone marrow lesions and had had follow-up images acquired within 8 weeks after the first imaging. Images that showed resolution of oedema were included in the reversible group. The remainders that showed progression into characteristic signs of osteonecrosis were included in the irreversible group. Radiomics was performed on the first MR images, calculating first- and second-order parameters. Support vector machine and random forest classifiers were performed using these parameters.

Results

Thirty-seven patients (seventeen osteonecrosis) were included. A total of 185 ROIs were segmented. Fortyseven parameters were accepted as classifiers with an area under the curve value ranging from 0.586 to 0.718. Support vector machine yielded a sensitivity of 91.3% and a specificity of 85.1%. Random forest classifier yielded a sensitivity of 84.8% and a specificity of 76.7%. Area under curves were 0.921 for support vector machine and 0.892 for random forest classifier.

Conclusions

Radiomics analysis could prove useful for discrimination of reversible and irreversible bone marrow lesions before the irreversible changes occur, which could prevent morbidities of osteonecrosis by guiding the decisionmaking process for management.

Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip

Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep learning methodology with the use of transfer learning and a convolutional neural network (CNN) ensemble, for the accurate differentiation between the two diseases. An augmented dataset of 210 hips with TOH and 210 hips with AVN was used to finetune three ImageNet-trained CNNs (VGG-16, InceptionResNetV2, and InceptionV3). An ensemble decision was reached in a hard-voting manner by selecting the outcome voted by at least two of the CNNs. Inception-ResNet-V2 achieved the highest AUC (97.62%) similar to the model ensemble, followed by InceptionV3 (AUC of 96.82%) and VGG-16 (AUC 96.03%). Precision for the diagnosis of AVN and recall for the detection of TOH were higher in the model ensemble compared to Inception-ResNet-V2. Ensemble performance was significantly higher than that of an MSK radiologist and a fellow (P < 0.001). Deep learning was highly successful in distinguishing TOH from AVN, with a potential to aid treatment decisions and lead to the avoidance of unnecessary surgery.

Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks

PURPOSE

Subchondral insufficiency fractures (SIF) and advanced osteoarthritis (OA) of the knee are usually seen in conjunction with bone marrow lesions (BMLs) and their differentiation may pose a significant diagnostic challenge. We aimed to develop a convolutional neural network (CNN) ensemble which could successfully differentiate between these two entities.

MATERIALS AND METHODS

A total of 212 knees with SIF and 102 knees with advanced OA with BMLs were retrospectively included. Coronal fat suppressed PD-w images were augmented, resized and normalized, reaching a total of 1174 images. Data was used to fine-tune three ImageNet-pretrained CNNs (VGG-16, InceptionV3 and Inception-ResNet-V2). Agreement of at least two networks was recorded as the decision of the network ensemble. Ensemble performance was compared to that of two MSK radiologists on the validation set. Receiver operating characteristics (ROC) curves and the respective areas under the curve (AUC) were used to evaluate human and machine performance.

RESULTS

InceptionV3 achieved the highest AUC (93.68%) and VGG-16 the lowest AUC (82.18%) among individual CNNs. CNN ensemble achieved the highest overall performance with an AUC of 95.97%. The first of the two MSK radiologists achieved a performance similar to the ensemble, reaching an AUC of 91.95%. The second radiologist achieved lower AUC of 82.76% which was lower than both the other specialist and the ensemble (P < 0.001).

CONCLUSION

A CNN ensemble was highly accurate in differentiating between SIF and OA, achieving a higher or equal performance to MSK radiologists.

Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip

Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose of this study was to utilize MRI-based radiomics and machine learning (ML) for accurate differentiation between the two entities. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included. Femoral heads and necks with segmented radiomics features were extracted. Three ML classifiers (XGboost, CatBoost and SVM) using 38 relevant radiomics features were trained on 70% and validated on 30% of the dataset. ML performance was compared to two musculoskeletal radiologists, a general radiologist and two radiology residents. XGboost achieved the best performance with an area under the curve (AUC) of 93.7% (95% CI from 87.7 to 99.8%) among ML models. MSK radiologists achieved an AUC of 90.6% (95% CI from 86.7% to 94.5%) and 88.3% (95% CI from 84% to 92.7%), respectively, similar to residents. The general radiologist achieved an AUC of 84.5% (95% CI from 80% to 89%), significantly lower than of XGboost (p = 0.017). In conclusion, radiomics-based ML achieved a performance similar to MSK radiologists and significantly higher compared to general radiologists in differentiating between TOH and AVN.

Absence of Posttraumatic Bone Marrow Edema in the Setting of Preeclampsia

Magnetic resonance imaging is a powerful tool in the diagnosis of missed or occult fractures on radiographic and computed tomographic (CT) imaging, through the detection of bone marrow edema. Although radiologists often rely on bone marrow edema as a guide for diagnosing subtle underlying fractures, it is important to recognize its limitations as a diagnostic metric. We present a rare case demonstrating the absence of bone marrow edema after acute trauma and confirmed Lisfranc fracture in a patient with preeclampsia and propose an interesting physiologic mechanism to explain this manifestation.

Transient osteoporosis of the hip and subclinical hypothyroidism: an unusual dangerous duet? Case report and pathogenetic hypothesis

BACKGROUND

Transient osteoporosis of the hip (TOH) is a rare and temporary clinical condition characterised by bone marrow edema (BME), severe pain, and functional limitation. It commonly occurs in middle-aged men or in women in the last trimester of pregnancy. TOH usually resolves with conservative therapy but may predispose to hip fracture or progression to avascular necrosis (AVN). Etiology is still unclear, although several pathophysiological mechanisms underpinning this condition has been proposed. We describe the management of an unusual case of TOH occurred in a patient with subclinical hypothyroidism.

CASE PRESENTATION

A clinical case of a 46-year-old man with severe pain in the left anterior thigh is presented. After a comprehensive clinical and radiological approach, a TOH was diagnosed. Moreover, biochemical assessment suggested the presence of subclinical hypothyroidism. After 3 months of treatment with clodronate, physical therapy and hormone replacement therapy (HRT) a significant improvement of clinical and radiological outcomes was observed.

CONCLUSION

Several pathological conditions have been related to development of TOH. In our case, we suggested for the first time a role of subclinical hypothyroidism as novel contributory factor for the onset of this condition, providing pathophysiological mechanisms and a scientific rationale for pharmacological treatment.

[Differential diagnoses of osteonecrosis of large joints]

Subchondral insufficiency fractures and idiopathic bone marrow edema syndrome are the most important differential diagnoses to be distinguished from osteonecrosis because they have a deviating and as a rule more favorable prognosis and also different therapeutic strategies. Osteochondritis dissecans represents a different entity in many respects, which should not be confused or unified with osteonecrosis. Based on recent knowledge intractable subchondral insufficiency fractures may be the underlying cause of rapidly destructive osteoarthritis at least in some cases. Septic involvement of bone and joints can eo ipso cause severe articular damage due to direct destruction but also secondary to septic vascular occlusion resulting in septic osteonecrosis. Whereas bone marrow infiltrating systemic diseases and the therapeutic regimens can lead to osteonecrosis, bone tumors or tumor-like diseases, rarely pose a differential diagnostic problem with respect to the differentiation from osteonecrosis.