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Item A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information(Springer Nature, 2024-02-29) Ramakrishnan, Divya; Jekel, Leon; Chadha, Saahil; Janas, Anastasia; Moy, Harrison; Maleki, Nazanin; Sala, Matthew; Kaur, Manpreet; Cassinelli Petersen, Gabriel; Merkaj, Sara; von Reppert, Marc; Baid, Ujjwal; Bakas, Spyridon; Kirsch, Claudia; Davis, Melissa; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Aneja, Sanjay; Memon, Fatima; Aboian, Mariam S.; Pathology and Laboratory Medicine, School of MedicineResection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.Item A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information(Springer Nature, 2024-02-29) Ramakrishnan, Divya; Jekel, Leon; Chadha, Saahil; Janas, Anastasia; Moy, Harrison; Maleki, Nazanin; Sala, Matthew; Kaur, Manpreet; Cassinelli Petersen, Gabriel; Merkaj, Sara; von Reppert, Marc; Baid, Ujjwal; Bakas, Spyridon; Kirsch, Claudia; Davis, Melissa; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Aneja, Sanjay; Memon, Fatima; Aboian, Mariam S.; Pathology and Laboratory Medicine, School of MedicineResection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.Item Statistical multiscale mapping of IDH1, MGMT, and microvascular proliferation in human brain tumors from multiparametric MR and spatially-registered core biopsy(Nature Research, 2019-11-19) Parker, Jason G.; Diller, Emily E.; Cao, Sha; Nelson, Jeremy T.; Yeom, Kristen; Ho, Chang; Lober, Robert; Radiology and Imaging Sciences, School of MedicineWe propose a statistical multiscale mapping approach to identify microscopic and molecular heterogeneity across a tumor microenvironment using multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR followed by MR-guided stereotactic core biopsy. The locations of the biopsy cores were identified in the pre-surgical images using stereotactic bitmaps acquired during surgery. Feature matrices mapped the multiparametric voxel values in the vicinity of the biopsy cores to the pathologic outcome variables for each patient and logistic regression tested the individual and collective predictive power of the MR contrasts. A non-parametric weighted k-nearest neighbor classifier evaluated the feature matrices in a leave-one-out cross validation design across patients. Resulting class membership probabilities were converted to chi-square statistics to develop full-brain parametric maps, implementing Gaussian random field theory to estimate inter-voxel dependencies. Corrections for family-wise error rates were performed using Benjamini-Hochberg and random field theory, and the resulting accuracies were compared. The combination of all five image contrasts correlated with outcome (P < 10−4) for all four microscopic variables. The probabilistic mapping method using Benjamini-Hochberg generated statistically significant results (α ≤ 0.05) for three of the four dependent variables: (1) IDH1, (2) MGMT, and (3) microvascular proliferation, with an average classification accuracy of 0.984 ± 0.02 and an average classification sensitivity of 1.567% ± 0.967. The images corrected by random field theory demonstrated improved classification accuracy (0.989 ± 0.008) and classification sensitivity (5.967% ± 2.857) compared with Benjamini-Hochberg. Microscopic and molecular tumor properties can be assessed with statistical confidence across the brain from minimally-invasive, mp-MR.