Zhang, Michael

Abstract Purpose To report patient outcomes and local tumor control rates in a cohort of patients with biopsy-proven HER-2 positive breast cancer treated with stereotactic radiosurgery (SRS) for brain metastases (BM). Methods This international, retrospective, multicenter study, included 195 female patients with 1706 SRS-treated BM. Radiologic and clinical outcomes after SRS were determined and prognostic factors identified. Results At SRS, median patient age was 55 years [interquartile range (IQR) 47.6–62.0], and 156 (80%) patients had KPS ≥ 80. The median tumor volume was 0.1 cm3 (IQR 0.1–0.5) and the median prescription dose was 16 Gy (IQR 16–18). Local tumor control (LTC) rate was 98%, 94%, 93%, 90%, and 88% at six-, 12-, 24-, 36- and 60-months post-SRS, respectively. On multivariate analysis, tumor volume (p = < 0.001) and concurrent pertuzumab (p = 0.02) improved LTC. Overall survival (OS) rates at six-, 12-, 24-, 36-, 48-, and 60-months were 90%, 69%, 46%, 27%, 22%, and 18%, respectively. Concurrent pertuzumab improved OS (p = 0.032). In this patient subgroup, GPA scores ≥ 2.5 (p = 0.038 and p = 0.003) and rare primary tumor histologies (p = 0.01) were associated with increased and decreased OS respectively. Asymptomatic adverse radiation events (ARE) occurred in 27 (14.0%) and symptomatic ARE in five (2.6%) patients. Invasive lobular carcinoma primary (p = 0.042) and concurrent pertuzumab (p < 0.001) conferred an increased risk for overall but not for symptomatic ARE. Conclusion SRS affords effective LTC for selected patients with BM from HER-2 positive breast cancer. Concurrent pertuzumab improved LTC and OS with however an increased risk for overall but not symptomatic ARE.

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 BACKGROUND Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans ( P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was ( P = .001). CONCLUSION Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.