Classification of endplate lesions in the lumbar spine and association with risk factors, biochemistry, and genetics.

PURPOSE: To detect the associations between the degree of the endplate (EP) lesions with the presence of risk factors, biochemical and genetic markers previously observed in low back pain (LBP) patients with EP defects in comparison with hernia/discopathy patients and healthy controls. METHODS: In this observational retrospective study, T2-weighted sagittal MRI images (n = 223 LBP patients) were scored for EP lesions by two independent observers. Total MRI score and number of affected levels (L1/L2-L5/S1) have been considered for the correlation with demographic, behavioral, clinical, biochemical (25(OH)D, CTx-I and CTx-II levels, n = 69 males) and VDR variables. RESULTS: Males showed higher BMI and total MRI score than females. Patients bearing TT compared to tt VDR genotypes showed significant higher total MRI scores. Among males (n = 125), TT, bb and aa genotypes showed increased total MRI scores. Higher total MRI score directly correlates with higher levels of CTx-I and CTx-II (n = 69 males). CONCLUSIONS: The markers previously identified as associated with the presence of EP lesions have been confirmed as related to their severity and could be used to follow the pathology progression.

Precision of Bone Mineral Density Measurements Around Total Ankle Replacement Using Dual Energy X-ray Absorptiometry.

INTRODUCTION: Joint prosthesis survival is associated with the quality of surrounding bone. Dual-energy X-ray absorptiometry (DXA) is capable to evaluate areal bone mineral density (BMD) around different prosthetic implants, but no studies evaluated periprosthetic bone around total ankle replacement (TAR). Our aim is to determine the precision of the DXA periprosthetic BMD around TAR. METHODOLOGY: Short-term precision was evaluated on 15 consecutive patients. Each ankle was scanned 3 times both in the posteroanterior (PA) and lateral views with a dedicated patient positioning protocol. Up to four squared regions of interest (ROIs) were placed in the periprosthetic bone around tibial and talar implants, with an additional ROI to include the calcaneal body in the lateral scan. Coefficient of variation (CV%) and least significant change were calculated according to the International Society for Clinical Densitometry. RESULTS: The lateral projection showed lower mean CV values compared to the PA projection, with an average precision error of 2.21% (lateral scan) compared to 3.34% (PA scans). Overall, the lowest precision error was found at both "global" ROIs (CV = 1.25% on PA and CV = 1.3% on lateral). The highest CV value on PA was found at the medial aspect of talar side (ROI 3; CV = 4.89%), while on the lateral scan the highest CV value was found on the posterior aspect of talar side (ROI 2; CV = 2.99%). CONCLUSIONS: We found very good reproducibility BMD values of periprosthetic bone around TAR, that were comparable or even better compared to other studies that evaluated periprosthetic BMD around different prosthetic implants. DXA can be used to precisely monitor bone density around ankle prostheses, despite further long-term longitudinal studies are required to assess the clinical utility of such measurements.

Predictive role of ankle MRI for tendon graft choice and surgical reconstruction.

PURPOSE: Tendon transfers have become a common surgical procedure around the ankle. In this study, we sought to evaluate the existence of a correlation between specific anthropometric parameters and the size of some ankle tendons measured on MRI, in particular those mostly used as graft in ankle surgery. METHODS: We recorded gender, height, weight, and body mass index (BMI) of 113 patients (57 females; mean age: 42 ± 18) who underwent ankle MRI. MRI measurements performed by a radiologist were: axial shortest diameter of Achilles (AT), posterior tibialis (PTT), flexor digitorum longus (FDLT), flexor hallucis longus (FHLT), peroneus longus (PLT), and anterior tibialis (ATT) tendons, intermalleolar distance (ID) and talus width (TW). Mann-Whitney U test and Pearson's correlation coefficient were used. After applying the Bonferroni correction for multiple comparisons, statistical significance was set at p < 0.002. RESULTS: The mean patient height, weight and BMI were 169 ± 9.8 cm (range: 140-193), 72.4 ± 16.4 kg (range: 44-142), and 25 ± 5.7 (range: 16-50), respectively. The mean ankle measurements were: AT = 5.3 ± 1.4 mm, PTT = 3.3 ± 0.6 mm, FDLT = 2.6 ± 0.4 mm, FHLT = 2.7 ± 0.4 mm, PLT = 2.9 ± 0.5 mm, ATT = 3±0.6 mm, ID = 62.9 ± 4.5 mm, and TW = 28.8 ± 2.5 mm. A statistical difference between male and female patients was observed regarding ID (z = -6.955, p < .001), TW (z = -6.692, p < .001), AT (z = -3.587, p < .001), PTT (z = -3.783, p < .001), and FDLT (z = -3.744, p < .001). Both PTT and FDLT showed a significant correlation with ID (p < .001) and TW (p < .001). ATT size was significantly correlated with weight, ID and TW (all with p < 0.001). PLT and AT showed a significant correlation only with ID and weight (p ≤ .001), respectively. CONCLUSION: Our data might help orthopaedists in preoperative planning to identify the best graft for ankle surgical procedures including tendon transfers.

Multicentric, multifocal, and recurrent osteoid osteoma of the hip: first case report.

BACKGROUND: Osteoid osteoma is a benign bone-forming tumour, which very unfrequently has multifocal or multicentric presentation. We report the first known case of a multicentric, multifocal and recurrent osteoid osteoma treated using radiofrequency ablation. CASE PRESENTATION: A 39-year-old man with two-year history of left hip pain was admitted at our Institution. The pain was more intense during the night and partially relieved by salicylates. Pelvis CT demonstrated two lytic lesions (8 and 7 mm, respectively) with surrounding sclerotic reactive bone, both with a central focal area of high attenuation, located in the femoral neck and along the anterior portion of the acetabulum, respectively. Both lesions had clinical and imaging findings consistent with multicentric osteoid osteoma. Thus, the two lesions were biopsied - with pathologic confirmation of osteoid osteoma - and treated using radiofrequency ablation. Hip pain decreased but did not disappear, actually increasing a few months after treatment. CT and MRI were performed showing a smaller lesion (5 mm) with the same imaging features, surrounded by marrow oedema, along the posterior column of the acetabulum. The lesion was considered suspicious for osteoid osteoma, overlooked on previous examinations. Therefore, a diagnosis of multicentric and multifocal osteoid osteoma was established. The new lesion was again treated with radiofrequency ablation with symptom disappearance. However, hip pain relapsed after 18 months, and CT and MRI showed an osteoid osteoma recurrence on the posterior column of the acetabulum, which was biopsied and successfully treated using radiofrequency ablation. CONCLUSIONS: To our knowledge, this is the first reported case of multicentric, multifocal, recurrent osteoid osteoma. Our case report highlights the importance of considering a diagnosis of multifocal osteoid osteoma when dealing with multifocal lytic lesions of the bone and with pain persistence after treatment. It also emphasises the combined role of CT and MRI in this setting.

Instability and impending instability in patients with vertebral metastatic disease.

Metastatic disease commonly involves the spine with an increasing incidence due to a worldwide rise of cancer incidence and a longer survival of patients with osseous metastases. Metastases compromise the mechanical integrity of the vertebra and make it susceptible to fracture. Patients with pathological vertebral fracture often become symptomatic, with mechanical pain generally due to intervertebral instability, and may develop spinal cord compression and neurological deficits. Advances in imaging, radiotherapy, as well as in spinal surgery techniques, have allowed the evolution from conventional palliative external beam radiotherapy to modern stereotactic radiosurgery and from traditional open surgery to less-invasive, and sometimes prophylactic stabilization surgical treatments. It is therefore clear that fracture risk prediction, and maintenance or restoration of intervertebral stability, are important objectives in the management of these patients. Correlation between imaging findings and clinical manifestations is crucial, and a common knowledge base for treatment team members rather than a compartmentalized view is very important. This article reviews the literature on the imaging and clinical diagnosis of intervertebral instability and impending instability in the setting of spine metastatic disease, including the spinal instability neoplastic score, which is a reliable tool for diagnosing unstable or potentially unstable metastatic spinal lesions, and on the different elements considered for treatment.

Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study.

PURPOSE: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesion differential diagnosis, employing radiomic data extracted by different software. METHODS: Patients undergoing MRI for a vertebral lesion were retrospectively analyzed (n = 146, 67 males, 79 females; mean age 63 ±â€¯16 years, range 8-89 years) and constituted the train (n = 100) and internal test cohorts (n = 46). Part of the latter had additional prior exams which constituted a multi-scanner, external test cohort (n = 35). Lesions were labeled as benign or malignant (2-label classification), and benign, primary malignant or metastases (3-label classification) for classification analyses. Features extracted via 3D Slicer heterogeneityCAD module (hCAD) and PyRadiomics were independently used to compare different combinations of feature selection methods and ML classifiers (n = 19). RESULTS: In total, 90 and 1548 features were extracted by hCAD and PyRadiomics, respectively. The best feature selection method-ML algorithm combination was selected by 10 iterations of 10-fold cross-validation in the training data. For the 2-label classification ML obtained 94% accuracy in the internal test cohort, using hCAD data, and 86% in the external one. For the 3-label classification, PyRadiomics data allowed for 80% and 69% accuracy in the internal and external test sets, respectively. CONCLUSIONS: MRI radiomics combined with ML may be useful in spinal lesion assessment. More robust pre-processing led to better consistency despite scanner and protocol heterogeneity.

MRI radiomics-based machine-learning classification of bone chondrosarcoma.

PURPOSE: To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI). METHODS: We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensional regions of interest, which were used for first order and texture feature extraction. A Random Forest wrapper was employed for feature selection. The resulting dataset was used to train a locally weighted ensemble classifier (AdaboostM1). Its performance was assessed via 10-fold cross-validation on the training data and then on the previously unseen test set. Thereafter, an experienced musculoskeletal radiologist blinded to histological and radiomic data qualitatively evaluated the cartilaginous tumors in the test group. RESULTS: After feature selection, the dataset was reduced to 4 features extracted from T1-weighted images. AdaboostM1 correctly classified 85.7 % and 75 % of the lesions in the training and test groups, respectively. The corresponding areas under the receiver operating characteristic curve were 0.85 and 0.78. The radiologist correctly graded 81.3 % of the lesions. There was no significant difference in performance between the radiologist and machine learning classifier (P = 0.453). CONCLUSIONS: Our machine learning approach showed good diagnostic performance for classification of low-to-high grade cartilaginous bone tumors and could prove a valuable aid in preoperative tumor characterization.