Percutaneous CT-Guided Biopsy of the Craniovertebral Junction: Safety, Diagnostic Yield, and Technical Notes

Transpedicular Contrast-enhanced CT-guided biopsy of the body and dens of the axis avoiding the trans-oral approach: Technical report and literature review

This technical report illustrates the technique to perform computed tomography (CT)-guided bone biopsies in the body and dens of the axis (C2 vertebra) through a posterior transpedicular approach with the use of preoperative contrast-enhanced scans to highlight the course of the vertebral artery. The technique is presented through two exemplification cases: a pediatric patient with osteoblastoma and secondary aneurysmal bone cyst and one adult patient with melanoma metastasis. This case highlights the potential of the CT-guided posterolateral/transpedicular approach for performing safe and effective biopsies in the body and dens of C2, even in pediatric patients.

Percutaneous CT-Guided Bone Biopsies: Indications, Feasibility and Diagnostic Yield in the Different Skeletal Sites-From the Skull to the Toe

CT-guided bone biopsies are currently the diagnostic tool of choice for histopathological (and microbiological) diagnoses of skeletal lesions. Several research works have well-demonstrated their safety and feasibility in almost all skeletal regions. This comprehensive review article aims at summarizing the general concepts in regard to bone biopsy procedures, current clinical indications, the feasibility and the diagnostic yield in different skeletal sites, particularly in the most delicate and difficult-to-reach ones. The choice of the correct imaging guidance and factors affecting the diagnostic rate, as well as possible complications, will also be discussed. Since the diagnostic yield, technical difficulties, and complications risk of a CT-guided bone biopsy significantly vary depending on the different skeletal sites, subdivided analyses of different anatomical sites are provided. The information included in the current review article may be useful for clinicians assisting patients with possible bone neoplasms, as well as radiologists involved in the imaging diagnoses of skeletal lesions and/or in performing bone biopsies.

Ultrasound-Guided Percutaneous Bone Biopsy: Feasibility, Diagnostic Yield and Technical Notes

While nowadays, CT-guided bone biopsy represents the gold standard tool for histopathological and microbiological diagnosis of skeletal lesions, the role of US-guided bone biopsy has not yet been fully explored. US-guided biopsy offers several advantages, such as the absence of ionizing radiation, fast acquisition time, as well as good intra-lesional echo, and structural and vascular characterization. Despite that, a consensus in regard to its applications in bone neoplasms has not been established. Indeed CT-guided technique (or fluoroscopic ones) still represents the standard choice in clinical practice. This review article aims to review the literature data about US-guided bone biopsy, underlying clinical-radiological indications, advantages of the procedure and future perspectives. Bone lesions taking the best advantages of the US-guided biopsy are osteolytic, determining the erosion of the overlying bone cortex and/or with an extraosseous soft-tissue component. Indeed, osteolytic lesions with extra-skeletal soft-tissue involvement represent a clear indication for US-guided biopsy. Moreover, even lytic bone lesions with cortical thinning and/or cortical disruption, especially located in the extremities or pelvis, can be safely sampled with US guidance with very good diagnostic yield. US-guided bone biopsy is proven to be fast, effective and safe. Additionally, it offers real-time needle evaluation, an advantage when compared to CT-guided bone biopsy. In the current clinical settings, it seems relevant to select the exact eligibility criteria for this imaging guidance since the effectiveness can vary depending on the type of lesion and body site involved.

CT-Guided Radiofrequency Thermal Ablation for the Treatment of Atypical, Early-Onset Osteoid Osteoma in Children Younger than 4 Years Old: Single-Institution Experience and Literature Review

The aim of our study is to report our experience on CT-guided radiofrequency ablation (RFA) for osteoid osteoma (OO) in children under 4 years of age and to review the literature regarding this atypical, early onset of the disease. We retrospectively reviewed the clinical and radiological records of the patients treated with CT-guided RFA for OO at our institution (2006−2021), including those under 4 years of age. Data regarding technical success, clinical success, and biopsy diagnostic yield were collected. Moreover, we performed a literature review including previous articles on early-onset OO. We found only 12 patients that were under 4 years of age (12/842−1.4%) at the time of RFA treatment: 4 F and 8 M, mean age at the time of the treatment 35.3 months (range 22−46 months). The mean follow-up was 22.8 months (range 6−96 months). Technical success was achieved in all cases (12/12). In all patients (12/12), a complete remission of the pain symptoms was achieved at clinical follow-up controls. No recurrence of pain or complications were documented. The histopathological diagnosis was confirmed in 4 patients (4/12−33.3%). Moreover, we found another 9 articles in the literature with a main focus on early-onset OO (<4 years old), with a total of 12 patients included; 6 of those patients (6/12−50%) were treated with CT-guided RFA, with success reported 5 cases (5/6−83.3%). Our series of cases treated at a single institution, together with the existing data from the literature, confirms that CT-guided RFA is effective and safe for the treatment of osteoid osteoma, even in atypical, early onset in children under 4 years of age.

MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study

Simple Summary

In this study, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma with multiparametric signatures. While these tumors share common radiographic characteristics, clinical behavior is distinct. Therefore, distinguishing these tumors before initial surgical intervention would be useful, potentially impacting the surgical strategy. Although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation, our machine learning model distinguishing chordoma from chondrosarcoma yielded superior diagnostic accuracy to that achieved by 20 board-certified neurosurgeons.

Abstract

Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures.