A comprehensive epidemiological review of spinal astrocytomas in the United States

Surgical approaches to intramedullary spinal cord astrocytomas in the age of genomics

Intramedullary astrocytomas represent approximately 30%–40% of all intramedullary tumors and are the most common intramedullary tumor in children. Surgical resection is considered the mainstay of treatment in symptomatic patients with neurological deficits. Gross total resection (GTR) can be difficult to achieve as astrocytomas frequently present as diffuse lesions that infiltrate the cord. Therefore, GTR carries a substantial risk of new post-operative deficits. Consequently, subtotal resection and biopsy are often the only surgical options attempted. A midline or paramedian sulcal myelotomy is frequently used for surgical resection, although a dorsal root entry zone myelotomy can be used for lateral tumors. Intra-operative neuromonitoring using D-wave integrity, somatosensory, and motor evoked potentials is critical to facilitating a safe resection. Adjuvant radiation and chemotherapy, such as temozolomide, are often administered for high-grade recurrent or progressive lesions; however, consensus is lacking on their efficacy. Biopsied tumors can be analyzed for molecular markers that inform clinicians about the tumor’s prognosis and response to conventional as well as targeted therapeutic treatments. Stratification of intramedullary tumors is increasingly based on molecular features and mutational status. The landscape of genetic and epigenetic mutations in intramedullary astrocytomas is not equivalent to their intracranial counterparts, with important difference in frequency and type of mutations. Therefore, dedicated attention is needed to cohorts of patients with intramedullary tumors. Targeted therapeutic agents can be designed and administered to patients based on their mutational status, which may be used in coordination with traditional surgical resection to improve overall survival and functional status.

Epidemiology of primary malignant non-osseous spinal tumors in the United States

BACKGROUND CONTEXT

Primary malignant non-osseous spinal tumors are relatively rare and this has led to a paucity of studies specifically examining the epidemiology of malignant spinal tumors.

PURPOSE

To provide an updated and more comprehensive study examining the epidemiology and relative survival of these rare tumors.

STUDY DESIGN/SETTING

Data was retrospectively acquired from the Central Brain Tumor Registry of the United States (CBTRUS).

PATIENT SAMPLE

Primary malignant non-osseous spinal tumor cases diagnosed between 2000 and 2017 in the United States.

OUTCOME MEASURES

Incidence rates (IRs), relative survival rates, and hazard ratios (HR) were measured.

METHODS

IRs were calculated only for histologically-confirmed cases between 2000 and 2017. Relative survival estimates were calculated from survival information on malignant spinal tumors between 2001 and 2016 for death from any cause. Multivariable Cox proportional hazards regression models were constructed to control for age, sex, race, and ethnicity.

RESULTS

From 2000 to 2017, approximately 587 new cases of malignant non-osseous spinal tumors were diagnosed every year in the United States. The overall IR was 0.178 per 100,000 persons. Ependymomas were the most commonly diagnosed tumor in all age groups. The 10-year relative survival rates were 94.1%, 62.1%, 62.0%, and 13.3% for ependymomas, lymphomas, diffuse astrocytomas, and high-grade astrocytomas, respectively. Females have a significantly lower risk of death as compared with males for ependymomas (HR: 0.74, p<.001) and diffuse astrocytomas (HR: 0.70, p=.005). African-Americans have a significantly higher risk of death compared with Caucasians when diagnosed with ependymomas (HR: 1.52, p=.009) or lymphomas (HR: 1.55, p=.009).

CONCLUSION

Primary malignant non-osseous spinal tumors are primarily diagnosed in adulthood or late adulthood. Ependymal tumors are the most commonly diagnosed primary malignant non-osseous spinal tumors and have the highest 10-year relative survival rates. High-grade astrocytomas are rare and portend the worst prognosis.

Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma

Background

The study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA.

Methods

Patients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of patients with SCA in the training dataset and feature importance was obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability.

Results

A total of 818 patients with SCA were included in this study, with an average age of 30.84 ± 21.97 years and an average follow-up time of 117.57 ± 113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy, and post-operation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade, and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in the training and testing dataset illustrated good calibration, with C-indexes of 0.783 and 0.769. The area under the curves (AUCs) of 5-year survival prediction were 0.82 and 0.843, while 10-year survival predictions were 0.849 and 0.881, for training and testing datasets, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performances of nomograms were verified to be superior to that of single indicators, and the prediction performance of nomograms for CSS is also excellent.

Conclusions

Nomograms for patients with SCA prognosis prediction demonstrated good calibration, discrimination, and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation is required for the established web-based online calculators.

Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery

Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision. Integration of various sources of data and application of advanced analytical approaches could improve risk assessment for intramedullary tumors.