Browsing by Author "Baid, Ujjwal"

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    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 Medicine
    Resection 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.
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    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 Medicine
    Resection 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.
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    Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice
    (Elsevier, 2024) Bakas, Spyridon; Vollmuth, Philipp; Galldiks, Norbert; Booth, Thomas C.; Aerts, Hugo J. W. L.; Bi, Wenya Linda; Wiestler, Benedikt; Tiwari, Pallavi; Pati, Sarthak; Baid, Ujjwal; Calabrese, Evan; Lohmann, Philipp; Nowosielski, Martha; Jain, Rajan; Colen, Rivka; Ismail, Marwa; Rasool, Ghulam; Lupo, Janine M.; Akbari, Hamed; Tonn, Joerg C.; Macdonald, David; Vogelbaum, Michael; Chang, Susan M.; Davatzikos, Christos; Villanueva-Meyer, Javier E.; Huang, Raymond Y.; Response Assessment in Neuro Oncology (RANO) group; Pathology and Laboratory Medicine, School of Medicine
    Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.
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    The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
    (ArXiv, 2023-06-01) Moawad, Ahmed W.; Janas, Anastasia; Baid, Ujjwal; Ramakrishnan, Divya; Jekel, Leon; Krantchev, Kiril; Moy, Harrison; Saluja, Rachit; Osenberg, Klara; Wilms, Klara; Kaur, Manpreet; Avesta, Arman; Cassinelli Pedersen, Gabriel; Maleki, Nazanin; Salimi, Mahdi; Merkaj, Sarah; von Reppert, Marc; Tillmans, Niklas; Lost, Jan; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Maresca, Ryan; Link, Katherine E.; Tahon, Nourel Hoda; Marcus, Daniel; Sotiras, Aristeidis; LaMontagne, Pamela; Chakrabarty, Strajit; Teytelboym, Oleg; Youssef, Ayda; Nada, Ayaman; Velichko, Yuri S.; Gennaro, Nicolo; Connectome Students; Group of Annotators; Cramer, Justin; Johnson, Derek R.; Kwan, Benjamin Y. M.; Petrovic, Boyan; Patro, Satya N.; Wu, Lei; So, Tiffany; Thompson, Gerry; Kam, Anthony; Guzman Perez-Carrillo, Gloria; Lall, Neil; Group of Approvers; Albrecht, Jake; Anazodo, Udunna; Lingaru, Marius George; Menze, Bjoern H.; Wiestler, Benedikt; Adewole, Maruf; Anwar, Syed Muhammad; Labella, Dominic; Li, Hongwei Bran; Iglesias, Juan Eugenio; Farahani, Keyvan; Eddy, James; Bergquist, Timothy; Chung, Verena; Shinohara, Russel Takeshi; Dako, Farouk; Wiggins, Walter; Reitman, Zachary; Wang, Chunhao; Liu, Xinyang; Jiang, Zhifan; Van Leemput, Koen; Piraud, Marie; Ezhov, Ivan; Johanson, Elaine; Meier, Zeke; Familiar, Ariana; Kazerooni, Anahita Fathi; Kofler, Florian; Calabrese, Evan; Aneja, Sanjay; Chiang, Veronica; Ikuta, Ichiro; Shafique, Umber; Memon, Fatima; Conte, Gian Marco; Bakas, Spyridon; Rudie, Jeffrey; Aboian, Mariam; Radiology and Imaging Sciences, School of Medicine
    Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.