Optimized CT Attenuation and SUV Prediction Thresholds for Differentiating Enostoses From Untreated and Treated Metastases on Attenuation-Corrected 18F-FDG PET/CT

Utility of absolute apparent diffusion coefficient and chemical-shift imaging versus CT attenuation for predicting malignancy from percutaneous bone biopsies

INTRODUCTION

Bone lesions are sometimes detected on computed tomography studies, and biopsies are performed to evaluate whether these are malignant. The aim of the study is to evaluate whether chemical-shift imaging (CSI) and diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) are more informative than the CT attenuation for predicting malignancy.

METHODS

Retrospective analysis of 86 patients who underwent both diagnostic CT, CSI MRI and DWI MRI within 6 weeks prior to bone biopsy at a tertiary care academic institution between 01/01/2010 and 03/01/2020. The CT attenuation, signal intensity on in-phase sequences (SIIP), signal intensity on out-of-phase sequences (SIOP), signal intensity ratio (SIR = SIOP/SIIP) and the apparent diffusion coefficient (ADC) of the lesions over the region of the biopsy tract were measured.

RESULTS

A threshold CT attenuation of 157 Hounsfield Units (HU) had a sensitivity of 47.7%, specificity of 83.3% and area under the curve (AUC) of 0.59. A threshold ADC of 793 × 10-6 mm2 /s had a sensitivity of 75.8%, specificity of 85.7% and AUC of 0.83 to predict whether a bone biopsy would detect malignancy. A threshold SIR of 0.949 had a sensitivity of 77.8%, specificity of 77.8% and AUC of 0.81 to predict whether a bone biopsy would detect malignancy. ADC (P = 0.029) and SIR (P = 0.009) were significantly better than CT attenuation. There was no predictive difference between SIR and ADC (P = 0.742).

CONCLUSIONS

The CT attenuation of a lesion is a poor predictor of malignancy in bone lesions. CSI and DWI are significantly better for predicting malignancy.

Osteoid Tumors of Bone

We present a review of several bone (osteoid)-forming tumors including enostosis, osteoid osteoma, osteoblastoma, and osteosarcoma. These entities were chosen because they are reasonably common-neither seen every day nor rare. When applicable, recent information about the lesions is included.

CT Attenuation Values Do Not Reliably Distinguish Benign Sclerotic Lesions From Osteoblastic Metastases in Patients Undergoing Bone Biopsy

BACKGROUND. CT attenuation thresholds that accurately distinguish enostoses from untreated osteoblastic metastases have been published. In the Mayo Clinic practices, these thresholds have been applied more broadly to distinguish benign sclerotic bone lesions other than enostoses from osteoblastic metastases. OBJECTIVE. The purpose of this article is to determine if CT attenuation thresholds allow the distinguishing of benign sclerotic bone lesions from osteoblastic metastases in patients undergoing bone biopsy. METHODS. A retrospective search was conducted to identify sclerotic lesions described on CT between October 7, 1998, and July 15, 2018, that underwent subsequent biopsy. Two musculoskeletal radiologists recorded lesions' maximum and mean attenuation. Using previously published attenuation thresholds, sensitivity and specificity for differentiating benign sclerotic lesions from osteoblastic metastases were calculated. ROC curve analysis was performed to determine if more appropriate attenuation thresholds exist. Intraclass correlation coefficients (ICCs) were computed. RESULTS. A total of 280 patients met inclusion criteria. Of those, 162 had malignant biopsy results and 118 had benign biopsy results. Of the 162 malignant lesions, 81 had received prior treatment. Maximum and mean attenuation were not significantly different between benign and malignant lesions for either reader (all p > .05). For reader 1, to distinguish benign from malignant lesions, a maximum attenuation threshold of more than 1060 HU resulted in sensitivity of 23.7%, specificity of 87.0%, and accuracy of 60.6%. A mean attenuation threshold of greater than 885 HU resulted in sensitivity of 19.5%, specificity of 90.7%, and accuracy 60.7%. ROC curve analysis showed AUCs for mean and maximum attenuation thresholds of 51.8% and 54.6%, respectively. Subgroup analyses of benign versus malignant and treated versus untreated lesions had similar results. Similar findings were obtained for reader 2. The two readers' ICC was 0.946 for maximum attenuation and 0.918 for mean attenuation. CONCLUSION. Published attenuation thresholds for distinguishing enostoses from osteoblastic metastases had slightly decreased specificity and markedly decreased sensitivity when applied to the differentiation of benign sclerotic lesions from osteoblastic metastases in our sample of biopsy-proven lesions. ROC analysis showed no high-performing attenuation threshold alternative. CLINICAL IMPACT. Published CT attenuation thresholds intended for distinguishing enostoses from osteoblastic metastases should not be used more broadly. More accurate alternative thresholds could not be derived.

Attenuation correction for human PET/MRI studies

Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.

Predictors of positive bone cultures from CT-guided bone biopsies performed for suspected osteomyelitis

INTRODUCTION

Bone biopsies are often used to direct antibiotic choice in patients with suspected osteomyelitis. The aim of this study was to identify the best predictors of positive bone biopsy cultures.

METHODS

A retrospective review of 845 patients who underwent computed tomography (CT)-guided non-spine bone biopsies at a tertiary academic healthcare institution. Thirty-seven patients (4.4%) had biopsies performed for suspected osteomyelitis. Laboratory markers, as well as imaging features, were measured. t-Tests and Fisher's exact tests were used to compare clinical and demographic variables between patients with positive bone cultures and patients with negative bone cultures. Multivariable logistic regression was used to identify the best predictors of bone culture positivity.

RESULTS

All patients had negative blood cultures; however, only eight patients (21.6%) had positive bone cultures, with Staphyloccocus the most common organism. Multivariable logistic regression analysis showed that an open wound (OR = 14.00, 95% CI (1.74, 112.4), p = 0.013) and any fluid aspirated at the time of biopsy (OR = 10.50, 95% CI (1.21, 91.01), p = 0.033) were the best predictors of bone culture positivity. The area under the curve (AUC) for this multivariable model was 0.784 with sensitivity and specificity of 0.778 and 0.778, respectively. Interestingly, and contrary to popular belief, open wounds with exposed bone did not always yield positive bone cultures, and when cultures were positive, were not polymicrobial.

CONCLUSIONS

Aspiration of fluid at the time of biopsy and the presence of an open wound are the best predictors of positive bone cultures.