Smoking, obesity and the risk of pituitary adenoma: a large prospective cohort study (The HUNT Study)

A Comprehensive Analysis of Tobacco Smoking History as a Risk for Outcomes after Endoscopic Transsphenoidal Resection of Pituitary Adenoma

Objectives  This study seeks to comprehensively analyze the impact of smoking history on outcomes after endoscopic transsphenoidal hypophysectomy (TSH) for pituitary adenoma. Design  This was a retrospective study. Setting  This study was done at the tertiary care center. Participants  Three hundred and ninety-eight adult patients undergoing TSH for a pituitary adenoma. Main Outcome Measures  Clinical and tumor characteristics and operative factors were collected. Patients were categorized as never, former, or active smokers, and the pack-years of smoking history was collected. Years since cessation of smoking was obtained for former smokers. Specific outcomes included postoperative cerebrospinal fluid (CSF) leak, length of hospitalization, 30-day return to the operating room, and 30-day readmission. Smoking history details were comprehensively analyzed for association with outcomes. Results  Any history of smoking tobacco was associated with return to the operating room (odds ratio [OR] = 2.67, 95% confidence interval [CI]: 1.05-6.76, p  = 0.039), which was for persistent CSF leak in 58.3%. Among patients with postoperative CSF leak, any history of smoking was associated with need for return to the operating room to repair the CSF leak (OR = 5.25, 95% CI: 1.07-25.79, p  = 0.041). Pack-years of smoking was positively associated with a return to the operating room (OR = 1.03, 95% CI: 1.01-1.06, p  = 0.048). In all multivariable models, all negative outcomes were significantly associated with the covariate: occurrence of intraoperative CSF leak. Conclusion  This is the first study to show smoking may have a negative impact on healing of CSF leak repairs after TSH, requiring a return to the operating room. This effect appears to be dose dependent on the smoking history. Secondarily, intraoperative CSF leak as covariate in multivariable models was significantly associated with all negative outcomes.

The impact of body mass index and height on risk for primary tumours of the spinal cord, spinal meninges, spinal and peripheral nerves in 1.7 million norwegian women and men: a prospective cohort study

BACKGROUND

Primary tumours of the spinal cord, spinal meninges, spinal and peripheral nerves comprise a heterogenous group of pathology, dominantly represented by meningioma, nerve sheath tumours (NST) and glioma. Body height and body mass index (BMI) are risk factors for certain brain tumour subgroups, but no other study has specifically assessed height and BMI in relation to primary tumours of the spine and peripheral nerves in women and men.

METHODS

In this prospective population-based cohort study height and weight were measured in 1.7 million adult Norwegian women and men at baseline. Incident cases of primary tumours arising from the spinal cord, spinal meninges, spinal and peripheral nerves during follow-up were identified by linkage to the National Cancer Registry. Tumour risk was assessed by Cox regression analyses in relation to height and BMI.

RESULTS

During 49 million person-years of follow-up, 857 primary tumours of the spinal cord, spinal meninges, spinal and peripheral nerves were identified. Overweight and obesity were not associated with risk for all tumours or any tumour subgroup. Height was positively associated with risk for all tumours (HR per 10 cm increase: 1.30, 95% CI 1.16-1.46). The association between height and tumour risk varied between tumour subgroups: while height was not significantly associated with NST, height increased the risk for meningioma (HR 1.42, 95% CI 1.13-1.78) and glioma (HR 1.56, 95% CI 1.06-2.28). The strongest association between height and tumour risk was found for the glioma subgroup of ependymoma in women (HR 3.38, 95% CI 1.64-6.94).

CONCLUSION

This study could not identify overweight and obesity as risk factors for primary tumours of the spinal cord, spinal meninges, spinal and peripheral nerves in women or men. Increasing body height was associated with increased tumour risk overall, but not universal for all tumour subgroups.Importance of the studyPrimary tumours of the spinal cord, spinal meninges, spinal and peripheral nerves have received little focus in epidemiologic studies, although the incidence and histo-pathological tumour subgroups differ significantly from primary brain tumours. Risk factors for these tumours have hardly been assessed in previous studies. Height, overweight and obesity are known risk factors for several cancers, including certain brain tumour subgroups, such as meningioma.This is the first study to report the association between height, overweight and obesity and primary tumours of the spinal cord, spinal meninges, spinal and peripheral nerves. This includes tumour subgroups of meningioma, nerve sheath tumour, glioma and the most common spinal glioma subgroup of ependymoma. While overweight and obesity were not associated with either of the tumour subgroups, an association between increasing body height and risk for spinal meningioma and glioma, including ependymoma, was found. Nerve sheath tumour risk was not associated with increasing body height.

Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task

CONTEXT

The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability.

OBJECTIVE

This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency.

METHODS

Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency.

RESULTS

A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively.

CONCLUSION

The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods' clinical usefulness for supporting neurosurgeons in judging patients' conditions, predicting prognosis, and other downstream tasks during the preoperative period.

Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation

Background: Prediction of tumor consistency before surgery is of vital importance to determine individualized therapeutic schemes for patients with acromegaly. The present study was performed to noninvasively predict tumor consistency based on magnetic resonance imaging and radiomics analysis. Methods: In total, 158 patients with acromegaly were randomized into the primary cohort (n = 100) and validation cohort (n = 58). The consistency of the tumor was classified as soft or firm according to the neurosurgeon's evaluation. The critical radiomics features were determined using the elastic net feature selection algorithm, and the radiomics signature was constructed. The most valuable clinical characteristics were then selected based on the multivariable logistic regression analysis. Next, a radiomics model was developed using the radiomics signature and clinical characteristics, and 30 patients with acromegaly were recruited for multicenter validation of the radiomics model. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, and other associated classification measures. Its calibration, discriminating capacity, and clinical usefulness were also evaluated. Results: The radiomics signature established according to four radiomics features screened in the primary cohort exhibited excellent discriminatory capacity in the validation cohort. The radiomics model, which incorporated both the radiomics signature and Knosp grade, displayed favorable discriminatory capacity and calibration, and the AUC was 0.83 (95% confidence interval, 0.81-0.85) and 0.81 (95% confidence interval, 0.78-0.83) in the primary and validation cohorts, respectively. Furthermore, compared with the clinical characteristics, the as-constructed radiomics model is more effective in prediction of the tumor consistency in patients with acromegaly. Moreover, the multicenter validation and decision curve analysis suggested that the radiomics model was clinically useful. Conclusions: This radiomics model can assist neurosurgeons in predicting tumor consistency in patients with acromegaly before surgery and facilitates the determination of individualized therapeutic schemes.

The impact of body mass index and height on the risk for glioblastoma and other glioma subgroups: a large prospective cohort study

Background

Glioma comprises a heterogeneous group of mostly malignant brain tumors, whereof glioblastoma (GBM) represents the largest and most lethal subgroup. Body height and body mass index (BMI) are risk factors for other cancers, but no previous study has examined anthropometric data in relation to different glioma subgroups.

Methods

This prospective cohort study includes 1.8 million Norwegian women and men between ages 14 and 80 years at baseline. Body weight and height were measured, and incident cases of glioma were identified by linkage to the National Cancer Registry. Cox regression analyses were performed to evaluate risk for different glioma subgroups in relation to anthropometric measures.

Results

During 54 million person-years of follow-up, 4,382 gliomas were identified. Overweight and obesity were not associated with risk for any glioma subgroup. Height was positively associated with risk for GBM and all other gliomas (hazard ratio [HR] per 10 cm increase: 1.24; 95% confidence interval [CI], 1.17-1.31 and 1.18; 95% CI, 1.09-1.29) but not with the proxy for isocitrate dehydrogenase (IDH)-mutant glioma (HR, 1.09; 95% CI, 0.98-1.21). In further subgroup analyses, the effect of height on glioma risk varied significantly with positive associations for oligoastrocytoma (HR, 1.74; 95% CI, 1.20-2.53) and malignant glioma not otherwise specified (NOS) (HR, 1.42; 95% CI, 1.16-1.76, but not with diffuse astrocytoma (WHO grades II and III) or oligodendroglioma.

Conclusion

This epidemiologic study consolidates height as a risk factor for GBM and other gliomas. It further indicates that this association is not universal for gliomas but may differ between different glioma subgroups.