Citations

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft tissue sarcomas. Their resection may lead to serious morbidity. Incidence of postoperative motor and sensory deficits is unknown, and reconstruction aimed at restoring such deficits is infrequently carried out. OBJECTIVE: To identify the incidence and risk factors of postoperative morbidity in MPNST as well as the use and outcomes of functional reconstructions in these patients. METHODS: Postoperative function and treatment of MPNSTs diagnosed from 1988 to 2019 in 10 cancer centers was obtained. Two models were constructed evaluating factors independently associated with postoperative motor (<M3) or critical sensory loss. Critical sensation was defined as partial or complete loss of hand, foot, or buttocks sensation. RESULTS: A total of 756 patients (33.4% neurofibromatosis type 1, NF1) were included. MPNSTs originated in 34.4% from a major nerve. Of 658 surgically treated patients, 27.2% had <M3 muscle power and 24.3% critical sensory loss. Amputations were carried out in 61 patients. Independent risk factors for motor and sensory loss included patients with NF1, symptomatic, deep-seated, extremity, or plexus tumors originating from major nerves (all P < .05). A total of 26 patients underwent functional reconstructions. The majority (64%) of these patients regained at least M3 muscle power and 33% M4 despite 86% receiving multimodal therapy. CONCLUSION: Resection of MPNSTs commonly results in motor and sensory deficits. Patients with NF1, symptomatic, deep-seated tumors, arising from major nerves were associated with a higher risk for developing postoperative morbidity. Functional reconstructions are infrequently performed but can improve functional outcomes.

Abstract Background Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. Methods We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. Results One hundred and seven schwannomas and 59 neurofibromas were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUCs for the Logistic Regression (AUC = 0.923) and K Nearest Neighbor (AUC = 0.923) classifiers were significantly greater than the human evaluators (AUC = 0.766; p = 0.041). Conclusions The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.

Abstract Many neuro-oncology studies commonly assess the association between a prognostic factor (predictor) and disease or outcome, such as the association between age and glioma. Predictors can be continuous (eg, age) or categorical (eg, race/ethnicity). Effects of categorical predictors are frequently easier to visualize and interpret than effects of continuous variables. This makes it an attractive, and seemingly justifiable, option to subdivide the continuous predictors into categories (eg, age &lt;50 years vs age ≥50 years). However, this approach results in loss of information (and power) compared to the continuous version. This review outlines the use cases for continuous and categorized predictors and provides tips and pitfalls for interpretation of these approaches.

Objectives This study evaluated the efficacy of a multidomain brain health intervention on health behavior change and sought to understand whether health literacy or brain health knowledge predicted engagement with the intervention. Methods One-hundred thirty midlife and older adults were assigned to one of three intervention conditions: brain fitness (B-Fit) utilizing education and goal setting, education-only, or waitlist. Questionnaires were completed at baseline and post-intervention. Results Both B-Fit and education-only conditions reported improvements in health behaviors over time. Although effect size for the education-only condition was moderate, only the B-Fit condition differed significantly in health behaviors from the waitlist post-intervention. Lower baseline brain health knowledge predicted improvements in health behaviors for education-only condition. Discussion The multidomain brain health intervention was successful in helping participants change their behaviors, but it was not more effective than the education-only condition. For those with lower brain health knowledge, an education-only intervention may be sufficient to encourage behavior change.