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Purpose: Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases. Materials/Methods: Twenty‐eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB‐approved study. 179 irradiated tumors included 42 that locally failed within one‐year follow‐up. Using SRS tumor volumes, radiomic features were calculated on T1‐weighted contrast‐enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis. Results: The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5‐fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p < 0.05). The model was validated on independent holdout data with an AUC of 0.78. Conclusions: Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the leading cause of cancer death in most countries. Although early diagnosis and treatment critically influence prognosis, lung cancers are generally only discovered in the late stages of the disease., Objective: Widely-used screening and diagnostic methods are not suitable for preventive screening, and high-throughput technologies based on serum biomarkers are needed., Methods: We screened 501 serum samples, including 224 lung cancer (LC), 126 disease control (DC), and 151 healthy donor (HC) samples for new serum autoantibodies as biomarkers in the early diagnosis of lung cancer. In phase I, we used HuProt TM microarrays to perform preliminary serum antibody screening on 24 LC and 24 HC samples. In phase II, we screened 60 LC, 60 DC, and 60 HC serum samples using focused arrays constructed with 22 of the candidate autoantibody biomarkers screened out in phase I., Results: After data modeling and validation, we selected four potential early LC protein biomarker candidates, IL2RB, CENPB, TP53, and XAGE1A, with individual specificities >90% and sensitivities ranging from 21.2% to 32.2%. These four biomarkers had a specificity of >90% and a sensitivity of >65.5% for early LC when they combined in a panel. Further evaluation of these four biomarker candidates using ELISA assays and 273 serum samples (140 LC, 66 DC, and 67 HC) gave similar results (specificity of >91.7%, sensitivity >61.43%)., Conclusion: IL2RB, CENPB, TP53, and XAGE1A combined biomarker panel holds potential for rapid screening and improving the diagnosis of early-stage LC, thus potentially also improving its prognosis., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)