Citations 2
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
LaBella, Dominic , Baid, Ujjwal , Khanna, Omaditya , McBurney-Lin, Shan , McLean, Ryan, Rashid, Arif , Altes, Talissa A. , Bhalerao, Radhika , Dhemesh, Yaseen , Hilal, Fathi , Janas, Anastasia , Kazerooni, Anahita Fathi , Kent, Collin , Leu, Kevin , Maleki, Nazanin , Pajot, Maxence , Saluja, Rachit , Wang, Chunhao , Warman, Pranav , Adewole, Maruf , Albrecht, Jake, Anwar, Syed Muhammad , Bergquist, Timothy , Chen, Sully Francis, Conte, Gian-Marco, Eddy, J. Mark , Ezhov, Ivan , Jiang, Zhifan , Johanson, Elaine, Linguraru, Marius George , Liu, Xinyang , Meier, Zeke, Shinohara, Russell Takeshi, Abayazeed, Aly, Akinola, Rachel , Bilello, Michel , Verdier, Maria Correia de, Moassefi, Mana, Pease, Matthew W., Vollmuth, Phillipp, Diffley, David, Hussain, Ali , Fehringer, Pascal, Bronstein, Yulia, Deptula, Lisa, Stein, Evan G. , Taherzadeh, Mahsa , Oliveira, Eduardo Portela de, Haughey, Aoife , Kontzialis, Marinos , Saba, Luca , Turner, Benjamin , Brüßeler, Melanie, Gkampenis, Athanasios , Weiss, David Maximilian, Mansour, Aya, Shawali, Islam H., Yordanov, Nikolay , Hourani, Roula , Moshebah, Mohammed Yahya, Abouelatta, Ahmed Magdy, Willms, Klara, Martin, Dann C., Okar, Abdullah , Kite, Dominic , Haider, Muhammad Ammar , Aristizábal, Alejandro , Karargyris, Alexandros , Kassem, Hasan , Sheller, Micah , Alonso‐Basanta, Michelle , McLean, Ryan, Nedelec, Pierre , Rashid, Arif, Tahon, Nourel Hoda , Godfrey, Devon , Floyd, Scott , Kirkpatrick, John P. , Kofler, Florian , Menze, Bjoern H. , Reitman, Zachary J. , Rudie, Jeffrey D. , Velichko, Yury , Anazodo, Udunna , Chung, Verena , Chai, Rong, Dako, Farouk , Eddy, James, Khalili, Nastaran , Iglesias, Juan Eugenio , Leemput, Koen Van , Li, Hongwei , Mahtabfar, Aria , Moawad, Ahmed W. , Mongan, John , Piraud, Marie , Shinohara, Russell Taki , Wiggins, Walter F. , Jakab, András Áron , Verdier, Maria Correia de , Crivellaro, Priscila Sacilotto , Davatzikos, Christos , Farahani, Keyvan , Freymann, John , Hess, Christopher Paul , Huang, Raymond Y. , Lohmann, Philipp , Moassefi, Mana , Sollmann, Nico , Nandolia, Khanak , Warren, Daniel I, Saba, Luca , Ansari, Shehbaz , Stein, Joel M. , Rizvi, Tanvir , D’Anna, Gennaro , Taha, Ahmed , Sharifi, Yasaman , Faghani, Shahriar , Pinho, Marco C. , Pati, Sarthak , Villanueva-Meyer, Javier E. , Rauschecker, Andreas M. , Nada, Ayman , Aboian, Mariam , Flanders, Adam E , Wiestler, Benedikt , Bakas, Spyridon , Calabrese, Evan
Machine Learning for Biomedical Imaging | March 7, 2025 | Volume: 3 | Issue: March 2025 | Pages: 38-58
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
Drukker, Karen , Li, Hui , Bonaccio, Ermelinda, Zuley, Margarita L. , Ganott, Marie A., Net, Jose , Sutton, Elizabeth J. , Brandt, Kathleen R. , Conzen, Suzanne D. , Li, Lan , Zhu, Yitan , Jaffe, C. Carl, Huang, Erich P. , Freymann, John , Kirby, Justin , Morris, Elizabeth A. , Burnside, Elizabeth S. , Whitman, Gary J. , Ji, Yuan , Giger, Maryellen , Drukker, Karen
Cancer | November 30, 2015 | Volume: 122 | Issue: 5 | Pages: 748-757
BACKGROUNDThe objective of this study was to demonstrate that computer‐extracted image phenotypes (CEIPs) of biopsy‐proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage.METHODSThe authors used a data set of deidentified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. In total, 91 biopsy‐proven breast cancers were analyzed from patients who had information available on pathologic stage (stage I, n = 22; stage II, n = 58; stage III, n = 11) and surgically verified lymph node status (negative lymph nodes, n = 46; ≥ 1 positive lymph node, n = 44; no lymph nodes examined, n = 1). Tumors were characterized according to 1) radiologist‐measured size and 2) CEIP. Then, models were built that combined 2 CEIPs to predict tumor pathologic stage and lymph node involvement, and the models were evaluated in a leave‐1‐out, cross‐validation analysis with the area under the receiver operating characteristic curve (AUC) as the value of interest.RESULTSTumor size was the most powerful predictor of pathologic stage, but CEIPs that captured biologic behavior also emerged as predictive (eg, stage I and II vs stage III demonstrated an AUC of 0.83). No size measure was successful in the prediction of positive lymph nodes, but adding a CEIP that described tumor “homogeneity” significantly improved discrimination (AUC = 0.62; P = .003) compared with chance.CONCLUSIONSThe current results indicate that MRI phenotypes have promise for predicting breast cancer pathologic stage and lymph node status. Cancer 2016;122:748–757. © 2015 American Cancer Society.