Diagnostic value of necrotic lesion boundary in bone collapse of femoral head osteonecrosis

A delphi-based model for prognosis of femoral head collapse in osteonecrosis: a multi-factorial approach

BACKGROUND

Osteonecrosis of the femoral head (ONFH) is a progressive and debilitating condition characterized by the death of bone tissue due to inadequate blood supply. Despite advances in diagnostic imaging and treatment strategies, predicting the risk of femoral head collapse remains a significant clinical challenge. This study seeks to address this gap by developing a robust prognostic model that integrates clinical, imaging, and laboratory data to improve early diagnosis and guide therapeutic decision-making.

METHODS

We conducted a qualitative systematic review and employed the Delphi method to select key prognostic factors from clinical data, imaging findings, and laboratory indicators. The study included ONFH patients treated from January 2014 to December 2021. We used univariate and multivariate Cox regression analyses to develop a nomogram for predicting the risk of femoral head collapse. The model's performance was evaluated using the concordance index (C-index), calibration plots, and decision curve analysis (DCA).

RESULTS

The study included 297 patients (454 hips) with ONFH. Key prognostic factors identified included pain presence (p < 0.001, RR = 0.185, 95% CI: 0.11-0.31), JIC classification (C1: p < 0.001, RR = 0.096, 95% CI: 0.054-0.171; C2: p < 0.001, RR = 0.323, 95% CI: 0.215-0.487), necrotic area (3 < MNAI < 6: p < 0.001, RR = 0.107, 95% CI: 0.061-0.190; MNAI ≥ 6: p < 0.001, RR = 0.466, 95% CI: 0.314-0.692), weight-bearing reduction (p < 0.001, RR = 0.466, 95% CI: 0.323-0.672), preservation of the anterolateral pillar (p < 0.001, RR = 0.223, 95% CI: 0.223-0.473), and subchondral bone fracture on CT (p < 0.001, RR = 0.32, 95% CI: 0.217-0.472). The nomogram demonstrated a high C-index of 0.88, indicating excellent predictive accuracy. Calibration plots showed good agreement between predicted and observed outcomes, and DCA confirmed the model's clinical utility.

CONCLUSIONS

The prognostic model developed in this study provides a reliable tool for predicting femoral head collapse in ONFH patients. It allows for early identification of high-risk patients, guiding personalized treatment strategies to improve patient outcomes and reduce the need for invasive surgical procedures.

Risk assessment for femoral head collapse in osteonecrosis utilizing MRI-derived large lesion ratio: a retrospective cohort study

PURPOSE

This study aimed to developed a novel and practical method to quantify the involvement of lesion in osteonecrosis of the femoral head (ONFH). We hypothesized that the new metric large lesion ratio (LLR) had promising prognostic value.

METHODS

A total of 131 hips with non-traumatic ONFH were included in this retrospective study. Patient aged 18-60 with MRI-confirmed diagnosis, and a minimum of 2-year follow-up or radiographic collapse progression during follow-up were included. Patients with prior hip surgery, incomplete data or advanced ONFH at baseline (femoral head collapse > 2 mm or osteoarthritis) were excluded. Involvement of necrotic lesion was evaluated by calculating LLR. The differences of LLR between collapse progression and non-progression groups were investigated, and the differences among different scanning parameters groups were also examined. Prognostic value of LLR was examined by multivariate regression analysis. Receiver operating characteristic curves (ROC) were constructed and areas under the curve (AUC) were compared.

RESULTS

The median of LLR was 66.67% in the collapse progression group, which was significantly higher compared with 25.00% in the non-progression group (P < 0.001). Subgroups analysis showed that LLR were significantly higher in the collapse progression group of Japanese Investigation Committee type C1 (P < 0.001)and C2 (P = 0.002). Multivariate regression showed that LLR were independently correlated with collapse progression (OR, 1.46 [95% CI, 1.24-1.78]; P < 0.001). ROC analysis showed that the AUC for LLR was 0.84, outperforming the 0.74 AUC OF the JIC classification.

CONCLUSION

LLR could served as a efficient tool to assess the risk of collapse progression and guide the selection of treatment strategy.

Factors related to collapse progression in Japanese Investigation Committee classification type B osteonecrosis of the femoral head

PURPOSE

This study aimed to identify factors related to collapse progression in Japanese Investigation Committee classification type B osteonecrosis of the femoral head (ONFH) and to identify patients who would benefit from surgical treatment.

METHODS

This study included 41 patients (56 hips) with type B ONFH with a minimum follow-up of three years. Based on a ≥ 3 mm collapse progression in ONFH, we categorised patients into two groups: collapse progression and no collapse progression. Sagittal and coronal computed tomography images were used to measure the necrotic region relative to the intact femoral head diameter. The ratios of the necrotic regions of transverse and vertical diameter in coronal and sagittal images are defined as mediolateral transverse and mediolateral vertical, anteroposterior transverse and anteroposterior vertical, respectively. Demographic data and these imaging findings were compared between the two groups. We established a cut-off value for predicting collapse progression through receiver operating characteristic analysis and determined survival rates.

RESULTS

Type B ONFH had a 17.8% collapse progression rate. The mediolateral transverse, mediolateral vertical, anteroposterior transverse, and anteroposterior vertical were significantly higher in the collapse progression group (P < 0.01). Mediolateral transverse was an independent risk factor of collapse progression (hazard ratio, 1.27; 95% confidence interval, 1.03-1.57; P = 0.03), with an optimal cut-off of 45.6%. The 5-year survival rates with collapse progression as the endpoints were 57.0 and 94.9% in the mediolateral transverse of ≥ 45.6 and < 45.6%, respectively.

CONCLUSION

A mediolateral transverse of ≥ 45.6% predicts collapse progression in patients with type B ONFH.

Panoramic heat map for spatial distribution of necrotic lesions

Aims

In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method.

Methods

We retrospectively collected CT imaging data of 108 hips with non-traumatic osteonecrosis of the femoral head from 76 consecutive patients (mean age 34.3 years (SD 8.1), 56.58% male (n = 43)) in two clinical centres. The femoral head was divided into 288 standard units (based on the orientation of units within the femoral head, designated as N[Superior], S[Inferior], E[Anterior], and W[Posterior]) using a new measurement system called the longitude and latitude division system (LLDS). A computer-aided design (CAD) measurement tool was also developed to visualize the measurement of the spatial location of necrotic lesions in CT images. Two orthopaedic surgeons independently performed measurements, and the results were used to draw 2D and 3D heat maps of spatial distribution of necrotic lesions in the femoral head, and for statistical analysis.

Results

The results showed that the LLDS has high inter-rater reliability. As illustrated by the heat map, the distribution of Japanese Investigation Committee (JIC) classification type C necrotic lesions exhibited clustering characteristics, with the lesions being concentrated in the northern and eastern regions, forming a hot zone (90% probability) centred on the N4-N6E2, N3-N6E units of outer ring blocks. Statistical results showed that the distribution difference between type C2 and type C1 was most significant in the E1 and E2 units and, combined with the heat map, indicated that the spatial distribution differences at N3-N6E1 and N1-N3E2 units are crucial in understanding type C1 and C2 necrotic lesions.

Conclusion

The LLDS can be used to accurately measure the spatial location of necrotic lesions and display their distribution characteristics.

Effects of the location of both anterior and lateral boundaries of the necrotic lesion on collapse progression in osteonecrosis of the femoral head

BACKGROUND

The location of the lateral boundary of the necrotic lesion to the weight-bearing portion of the acetabulum (Type classification) is an important factor for collapse in osteonecrosis of the femoral head (ONFH). Recent studies also reported the significance of the location of the anterior boundary of the necrotic lesion on the occurrence of collapse. We aimed to assess the effects of the location of both anterior and lateral boundaries of the necrotic lesion on collapse progression in ONFH.

METHODS

We recruited 55 hips with post-collapse ONFH from 48 consecutive patients, who were conservatively followed for more than one year. Using a plain lateral radiograph (Sugioka's lateral view), the location of the anterior boundary of the necrotic lesion to the weight-bearing portion of the acetabulum was classified as follows: Anterior-area I (two hips) occupying the medial one-third or less; Anterior-area II (17 hips) occupying the medial two-thirds or less; and Anterior-area III (36 hips) occupying greater than the medial two-thirds. The amount of femoral head collapse was measured by biplane radiographs at the onset of hip pain and each follow-up period, and Kaplan-Meier survival curves with collapse progression (≥1 mm) as the endpoint were produced. The probability of collapse progression was also assessed by the combination of Anterior-area and Type classifications.

RESULTS

Collapse progression was observed in 38 of the 55 hips (69.0%). The survival rate of hips with Anterior-area III/Type C2 was significantly lower. Among hips with Type B/C1, collapse progression occurred more frequently in hips with Anterior-area III (21 of 24 hips) than in hips with Anterior-area I/II (3 of 17 hips, P < 0.0001).

CONCLUSIONS

Adding the location of the anterior boundary of the necrotic lesion to Type classification was useful to predict collapse progression especially in hips with Type B/C1.

The Preserved Thickness Ratio of the Femoral Head Contributes to the Collapse Predictor of Osteonecrosis

OBJECTIVES

The collapse of femoral head is a serious symptom of osteonecrosis of the femoral head (ONFH), resulting in hip pain and deformity. However, it is hardly possible to reestablish the femoral head nonoperatively once the collapse happens. Predicting femoral head collapse is of great value for the prognosis of ONFH. This study aimed to develop a new method to quantify the preserved thickness of femoral head and to assess its diagnostic contribution in predicting femoral head collapse on plain radiographs.

METHODS

This was a single-center retrospective study. A total of 101 hips (85 patients) with ARCO stage II from January 2008 to December 2016 were included in this study. The preserved thickness was measured on standard anteroposterior (AP) and frog-leg (FL) radiographs. The anteroposterior view's preserved thickness ratio (APTR) and the frog-leg view's preserved thickness ratio (FPTR) were calculated to show the preserved thickness ratio of the femoral head anteriorly and laterally. Univariate and multivariate logistic regression was performed to determine the risk factors for collapse. Sensitivity, specificity, and cut-off values for APTR and FPTR were determined by the receiver operating characteristic (ROC) curve analysis. Kaplan-Meier (K-M) analysis was applied to determine femoral head survival in ONFH patients.

RESULTS

The mean age of the 27 females and 58 males was 38.93 years old. The mean follow-up time was 74.62 (36-124) months in the non-collapse group and 18.66 (3-82) months in the collapse group. Femoral head collapse was observed in 62 hips during the follow-up period. Logistic regression analysis and ROC results showed that APTR <24.79% and FPTR <10.62% were significantly correlated with femoral head collapse. The Kaplan-Meier survival curve suggested that the overall survival rate of APTR ≥24.79% was 68.2% at 5 and 10 years and FPTR ≥10.62% was 71.63% at 5 and 10 years. At the last follow-up, 26 hips had collapse on the anterior side of the femoral head, 12 hips occurred on the lateral side, and 24 hips happened to collapse on both anterior and lateral sides.

CONCLUSION

Femoral head collapse predominantly occurred anteriorly rather than laterally in ONFH patients. The measurements of APTR and FPTR have noticeable implications for the prediction of femoral head collapse, and contribute to the selection of treatment options for ONFH patients with types B and C1 according to the JIC classification.

Development and Validation of an Automated Classification System for Osteonecrosis of the Femoral Head Using Deep Learning Approach: A Multicenter Study

BACKGROUND

Accurate classification can facilitate the selection of appropriate interventions to delay the progression of osteonecrosis of the femoral head (ONFH). This study aimed to perform the classification of ONFH through a deep learning approach.

METHODS

We retrospectively sampled 1,806 midcoronal magnetic resonance images (MRIs) of 1,337 hips from 4 institutions. Of these, 1,472 midcoronal MRIs of 1,155 hips were divided into training, validation, and test datasets with a ratio of 7:1:2 to develop a convolutional neural network model (CNN). An additional 334 midcoronal MRIs of 182 hips were used to perform external validation. The predictive performance of the CNN and the review panel was also compared.

RESULTS

A multiclass CNN model was successfully developed. In internal validation, the overall accuracy of the CNN for predicting the severity of ONFH based on the Japanese Investigation Committee classification was 87.8%. The macroaverage values of area under the curve (AUC), precision, recall, and F-value were 0.90, 84.8, 84.8, and 84.6%, respectively. In external validation, the overall accuracy of the CNN was 83.8%. The macroaverage values of area under the curve, precision, recall, and F-value were 0.87, 79.5, 80.5, and 79.9%, respectively. In a human-machine comparison study, the CNN outperformed or was comparable to that of the deputy chief orthopaedic surgeons.

CONCLUSION

The CNN is feasible and robust for classifying ONFH and correctly locating the necrotic area. These findings suggest that classifying ONFH using deep learning with high accuracy and generalizability may aid in predicting femoral head collapse and clinical decision-making.

Analysis of the natural collapse course of non-traumatic osteonecrosis of the femoral head based on the matrix model

BACKGROUND

There are many predictions about the progression of natural collapse course of osteonecrosis of the femoral head. Here, we aimed to combine the three classical prediction methods to explore the progression of the natural collapse course.

METHODS

This retrospective study included 127 patients admitted to our hospital from October 2016 to October 2017, in whom the femoral head had not collapsed. Logistic regression analysis was performed to determine the collapse risk factors, and Kaplan-Meier survival curves were used for femoral head survival analysis. The collapse rate of the femoral head was recorded within 5 years based on the matrix model. The specificity of the matrix model was analyzed using the receiver operating characteristic curve.

RESULTS

A total of 127 patients with a total of 202 hips were included in this study, and 98 hips collapsed during the follow-up period. Multivariate logistics regression analysis showed that the predictive ability of the matrix model was stronger than Association Research Circulation Osseous staging, Japanese Investigation Committee classification, and area (P < 0.05). Kaplan-Meier survival curve showed that the median survival time of femoral head in patients was 3 years. The result of the receiver operating characteristic curve analysis showed that the area under the curve (AUC) of the matrix model had better predictive value (AUC = 0.771, log-rank test: P < 0.001).

CONCLUSION

We creatively combined the three classical prediction methods for evaluating the progression of the natural collapse course based on the matrix model and found that the higher the score of the matrix model, the higher the femoral head collapse rate. Specifically, the matrix model has a potential value in predicting femoral head collapse and guiding treatment selection.

Predictive value of clinical features and CT radiomics in the efficacy of hip preservation surgery with fibula allograft

BACKGROUND

Despite being an effective treatment for osteonecrosis of the femoral head (ONFH), hip preservation surgery with fibula allograft (HPS&FA) still experiences numerous failures. Developing a prediction model based on clinical and radiomics predictors holds promise for addressing this issue.

METHODS

This study included 112 ONFH patients who underwent HPS&FA and were randomly divided into training and validation cohorts. Clinical data were collected, and clinically significant predictors were identified using univariate and multivariate analyses to develop a clinical prediction model (CPM). Simultaneously, the least absolute shrinkage and selection operator method was employed to select optimal radiomics features from preoperative hip computed tomography images, forming a radiomics prediction model (RPM). Furthermore, to enhance prediction accuracy, a clinical-radiomics prediction model (CRPM) was constructed by integrating all predictors. The predictive performance of the models was evaluated using receiver operating characteristic curve (ROC), area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis.

RESULTS

Age, Japanese Investigation Committee classification, postoperative use of glucocorticoids or alcohol, and non-weightbearing time were identified as clinical predictors. The AUC of the ROC curve for the CPM was 0.847 in the training cohort and 0.762 in the validation cohort. After incorporating radiomics features, the CRPM showed improved AUC values of 0.875 in the training cohort and 0.918 in the validation cohort. Decision curves demonstrated that the CRPM yielded greater medical benefit across most risk thresholds.

CONCLUSION

The CRPM serves as an efficient prediction model for assessing HPS&FA efficacy and holds potential as a personalized perioperative intervention tool to enhance HPS&FA success rates.

Revisiting prediction of collapse in hip osteonecrosis with artificial intelligence and machine learning: a new approach for quantifying and ranking the contribution and association of factors for collapse

PURPOSE

This study proposes machine learning to analyze the risk factors of the collapse in patients with non-traumatic hip osteonecrosis of the femoral head.

METHODS

We collected data of 900 consecutive patients (634 males) with bilateral (428) or unilateral non-traumatic osteonecrosis diagnosed before collapse (at stage I or stage II). The follow-up was average five years (3 to 8 years). A total of 50 variables related to the osteonecrosis were included in the study. The osteonecroses were randomly divided into a training set (80%) and a validation set (20%) with a similar percentage of hips with collapse in the two groups. Machine learning (ML) algorithms were trained with the selected variables. Performance was evaluated and the different factors (variables) for collapse were ranked with Shapley values. The primary outcome was prediction of occurrence of collapse from automated inventory systems.

RESULTS

In this series of patients, the accuracy with machine learning for predicting collapse within three years follow-up was 81.2%. Accuracies for predicting collapse within six to 12-24 months were 54.2%, 67.3%, and 71.2%, respectively, demonstrating that the accuracy is lower for a prevision in the short term than for the mid-term. Despite none of the risk-factors alone achieving statistical significance for prediction, the system allowed ranking the different variables for risk of collapse. The highest risk factors for collapse were sickle cell disease, liver, and cardiac transplantation treated with corticosteroids, osteonecrosis volume > 50% of the femoral head. Cancer (such as leukemia), alcohol abuse, lupus erythematosus, Crohn's disease, pemphigus vulgaris treated with corticosteroids, and osteonecrosis volume between 40 and 50% were medium risk factors for collapse. Familial cluster of collapse, HIV infection, chronic renal failure, nephrotic syndrome, and renal transplantation, when treated with corticosteroids, stage II, osteonecrosis volume between 30 and 40%, chemotherapy, hip pain with VAS > 6, and collapse progression on the contralateral side, were also significant but lowest risk factors. A heat map is proposed to illustrate the ranking of the combinations of the different variables. The highest risk of collapse is obtained with association of various risks factors.

CONCLUSION

This study, for the first time, demonstrated prediction of collapse and ranking of factors for collapse with a machine learning system. This study also shows that collapse is due to a multifactorial risk factors.

Evaluation of the predictive values of collapse and necrotic lesion boundary for osteonecrosis of the femoral head prognosis

OBJECTIVE

Osteonecrosis of the femoral head (ONFH) is a disabling and intractable orthopedic disease largely affecting young and middle-aged groups. Current standard of treatment relies on the collapse of femoral head as a predictor for prognosis. However, a wide range of variability in repair potentials is observed in patients with femoral head collapse. Therefore, the present study aimed to evaluate the accuracy of femoral head collapse as a predictor and to propose the necrotic lesion boundary as a novel yet reliable measure for ONFH prognosis.

METHODS

A retrospective cross-sectional study was conducted at the First Affiliated Hospital of Guangzhou University of Chinese Medicine, 203 hips with ONFH from 134 patients were included. The occurrences and progression of femoral head collapse were recorded. Necrosis lesion boundary was quantified and classified for each case based on anteroposterior view intact ratio (APIR) and the frog-leg view intact ratio (FLIR) as independent variables. Dependent variables were defined as progressive collapse or terminal collapse for Association Research Circulation Osseous (ARCO) stage II and III respectively. Logistic regression analysis, Receiver Operating Characteristic (ROC) curve and Kaplan-Meier (K-M) survival analysis was performed and results were interpreted.

RESULTS

Out of the 106 hips in ARCO stage II, 31 hips collapsed with further progression, while 75 hips had no collapse or collapse with repair of the necrotic areas. Out of the 97 hips in ARCO stage IIIA, the collapse continued to progress in 58 hips while the necrotic areas were repaired in 39 hips. Logistic regression analysis demonstrated that both APIR and FLIR, were independent risk factors. Further ROC curve analysis indicated that the cutoff values of APIR and FLIR could be considered as indications for evaluating the prognosis of ONFH. Contrary to the traditional view of poor prognosis after femoral head collapse, K-M survival analysis demonstrated a high value of APIR and FLIR for ONFH prognosis.

CONCLUSION

The present study found that the occurrence of collapse is an oversimplified predictor for ONFH prognosis. The collapse of the femoral head in ONFH does not predict a poor prognosis. The necrosis lesion boundary has a high value in predicting ONFH prognosis and informing clinical treatment strategies.

Clinical Outcome of Surgical Hip Dislocation Combined with Impacting Bone Grafts and Implanting Iliac Bone Flaps in the Treatment of Osteonecrosis of the Femoral Head: A Mid-term Retrospective Study

Objective

To report the medium‐term outcomes of surgical hip dislocation (SHD) combined with impacting bone grafts and implanting iliac bone flaps in the treatment of osteonecrosis of the femoral head (ONFH) and to define the indications for this treatment.

Methods

This was a single‐center retrospective study. In total, 64 patients (70 hips) with ONFH treated from January 2014 to December 2017 were included in this study. There were 51 males and 13 females aged 18–55 years with an average age of 32 years. All patients underwent surgery for SHD combined with impacting bone grafts and implanting iliac bone flaps. Preoperative and postoperative clinical outcomes were assessed. The clinical outcome was assessed using the Harris hip score (HHS) and the conversion rate of total hip arthroplasty (THA). Univariate and multivariate logistic regression analyses were performed to identify risk factors affecting the clinical outcome. Kaplan–Meier (K‐M) analysis was applied to calculate the survival rate of the femoral head.

Results

At the last follow‐up (60 ± 15.08 months), the HHS was excellent for 41 hips, good for 17 hips, fair for three hips, and poor for nine hips. All nine hips with poor HHS underwent THA, including five in the first 2 years following the index surgery and four between three and 5 years. The conversion rate of total hip arthroplasty was 12.86%. Univariate and multivariate logistic regression analyses showed that the duration of hip pain and JIC classification type were significantly associated with clinical outcomes. Elderly age and advanced ONFH stage tended to lead to worse surgical outcomes. The overall survival rate of JIC classification type C1 and duration of pain ≤6 months was 98.1% and 97.8% at 72 months, respectively, as estimated by the Kaplan–Meier method.

Conclusion

Surgical hip dislocation combined with impacting bone grafts and implanting iliac bone flaps in the treatment of ONFH had a good mid‐term clinical outcome, especially for patients with retention of the lateral column of the femoral head and hip pain less than 1 year.