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Sasha C. Burn, Olaf Ansorge, Reinhard Zeller and James M. Drake

Object

Osteoid osteomas and osteoblastoma of the spine are rare lesions in childhood, and management strategies have changed. The authors reviewed their recent experience with these 2 types of lesions to elucidate current treatment modalities and outcomes.

Methods

Case records and radiographic images from all cases of osteoid osteoma and osteoblastoma diagnosed between 1993 and 2008 were retrospectively reviewed, including those managed nonsurgically.

Results

Thirty cases were identified; 22 were treated surgically and 8 were managed nonsurgically. The patients' mean age at presentation was 13 years (range 3–17 years). Of 30 patients, 29 (97%) presented with pain; 7 (23%) had scoliosis at presentation; 12 (40%) experienced relief with nonsteroidal antiinflammatory medication. Osteoid osteoma was diagnosed in 7 (32%) of the 22 patients who underwent surgery, and osteoblastoma in 15 (68%). Nine (41%) of the 22 surgically treated patients underwent fusion procedures (bone onlay or instrumentation) at the time of surgery. Pain freedom without medication had been achieved in 16 (73%) of the 22 surgically treated patients at a mean follow-up of 28 months (range 2–75 months) and was confirmed in 3 (38%) of the 8 nonsurgically treated patients at a mean follow-up of 33 months (range 24–51 months).

Conclusions

Osteoid osteomas and osteoblastomas can present challenging management problems in pediatric patients. In the majority of cases in which conservative therapy fails or pathological diagnosis is required, surgery using modern intraoperative imaging and spinal instrumentation can provide symptom relief and tumor control.

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Laurent J. Livermore, Martin Isabelle, Ian M. Bell, Oliver Edgar, Natalie L. Voets, Richard Stacey, Olaf Ansorge, Claire Vallance and Puneet Plaha

OBJECTIVE

Raman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)–induced fluorescence.

METHODS

A principal component analysis (PCA)–fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA–induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor.

RESULTS

The PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA–induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA–induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009).

CONCLUSIONS

Raman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA–induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA–induced fluorescence to guide extent of resection in glioma surgery.