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1. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Author

Moawad, Ahmed W., Janas, Anastasia, Baid, Ujjwal, Ramakrishnan, Divya, Saluja, Rachit, Ashraf, Nader, Jekel, Leon, Amiruddin, Raisa, Adewole, Maruf, Albrecht, Jake, Anazodo, Udunna, Aneja, Sanjay, Anwar, Syed Muhammad, Bergquist, Timothy, Calabrese, Evan, Chiang, Veronica, Chung, Verena, Conte, Gian Marco Marco, Dako, Farouk, Eddy, James, Ezhov, Ivan, Familiar, Ariana, Farahani, Keyvan, Iglesias, Juan Eugenio, Jiang, Zhifan, Johanson, Elaine, Kazerooni, Anahita Fathi, Kofler, Florian, Krantchev, Kiril, LaBella, Dominic, Van Leemput, Koen, Li, Hongwei Bran, Linguraru, Marius George, Link, Katherine E., Liu, Xinyang, Maleki, Nazanin, Meier, Zeke, Menze, Bjoern H, Moy, Harrison, Osenberg, Klara, Piraud, Marie, Reitman, Zachary, Shinohara, Russel Takeshi, Tahon, Nourel hoda, Nada, Ayman, Velichko, Yuri S., Wang, Chunhao, Wiestler, Benedikt, Wiggins, Walter, Shafique, Umber, Willms, Klara, Avesta, Arman, Bousabarah, Khaled, Chakrabarty, Satrajit, Gennaro, Nicolo, Holler, Wolfgang, Kaur, Manpreet, LaMontagne, Pamela, Lin, MingDe, Lost, Jan, Marcus, Daniel S., Maresca, Ryan, Merkaj, Sarah, Nada, Ayaman, Pedersen, Gabriel Cassinelli, von Reppert, Marc, Sotiras, Aristeidis, Teytelboym, Oleg, Tillmans, Niklas, Westerhoff, Malte, Youssef, Ayda, Godfrey, Devon, Floyd, Scott, Rauschecker, Andreas, Villanueva-Meyer, Javier, Pflüger, Irada, Cho, Jaeyoung, Bendszus, Martin, Brugnara, Gianluca, Cramer, Justin, Perez-Carillo, Gloria J. Guzman, Johnson, Derek R., Kam, Anthony, Kwan, Benjamin Yin Ming, Lai, Lillian, Lall, Neil U., Memon, Fatima, Patro, Satya Narayana, Petrovic, Bojan, So, Tiffany Y., Thompson, Gerard, Wu, Lei, Schrickel, E. Brooke, Bansal, Anu, Barkhof, Frederik, Besada, Cristina, Chu, Sammy, Druzgal, Jason, Dusoi, Alexandru, Farage, Luciano, Feltrin, Fabricio, Fong, Amy, Fung, Steve H., Gray, R. Ian, Ikuta, Ichiro, Iv, Michael, Postma, Alida A., Mahajan, Amit, Joyner, David, Krumpelman, Chase, Letourneau-Guillon, Laurent, Lincoln, Christie M., Maros, Mate E., Miller, Elka, Morón, Fanny, Nimchinsky, Esther A., Ozsarlak, Ozkan, Patel, Uresh, Rohatgi, Saurabh, Saha, Atin, Sayah, Anousheh, Schwartz, Eric D., Shih, Robert, Shiroishi, Mark S., Small, Juan E., Tanwar, Manoj, Valerie, Jewels, Weinberg, Brent D., White, Matthew L., Young, Robert, Zohrabian, Vahe M., Azizova, Aynur, Brüßeler, Melanie Maria Theresa, Fehringer, Pascal, Ghonim, Mohanad, Ghonim, Mohamed, Gkampenis, Athanasios, Okar, Abdullah, Pasquini, Luca, Sharifi, Yasaman, Singh, Gagandeep, Sollmann, Nico, Soumala, Theodora, Taherzadeh, Mahsa, Yordanov, Nikolay, Vollmuth, Philipp, Foltyn-Dumitru, Martha, Malhotra, Ajay, Abayazeed, Aly H., Dellepiane, Francesco, Lohmann, Philipp, Pérez-García, Víctor M., Elhalawani, Hesham, Al-Rubaiey, Sanaria, Armindo, Rui Duarte, Ashraf, Kholod, Asla, Moamen M., Badawy, Mohamed, Bisschop, Jeroen, Lomer, Nima Broomand, Bukatz, Jan, Chen, Jim, Cimflova, Petra, Corr, Felix, Crawley, Alexis, Deptula, Lisa, Elakhdar, Tasneem, Shawali, Islam H., Faghani, Shahriar, Frick, Alexandra, Gulati, Vaibhav, Haider, Muhammad Ammar, Hierro, Fátima, Dahl, Rasmus Holmboe, Jacobs, Sarah Maria, Hsieh, Kuang-chun Jim, Kandemirli, Sedat G., Kersting, Katharina, Kida, Laura, Kollia, Sofia, Koukoulithras, Ioannis, Li, Xiao, Abouelatta, Ahmed, Mansour, Aya, Maria-Zamfirescu, Ruxandra-Catrinel, Marsiglia, Marcela, Mateo-Camacho, Yohana Sarahi, McArthur, Mark, McDonnell, Olivia, McHugh, Maire, Moassefi, Mana, Morsi, Samah Mostafa, Muntenu, Alexander, Nandolia, Khanak K., Naqvi, Syed Raza, Nikanpour, Yalda, Alnoury, Mostafa, Nouh, Abdullah Mohamed Aly, Pappafava, Francesca, Patel, Markand D., Petrucci, Samantha, Rawie, Eric, Raymond, Scott, Roohani, Borna, Sabouhi, Sadeq, Sanchez-Garcia, Laura M., Shaked, Zoe, Suthar, Pokhraj P., Altes, Talissa, Isufi, Edvin, Dhermesh, Yaseen, Gass, Jaime, Thacker, Jonathan, Tarabishy, Abdul Rahman, Turner, Benjamin, Vacca, Sebastiano, Vilanilam, George K., Warren, Daniel, Weiss, David, Worede, Fikadu, Yousry, Sara, Lerebo, Wondwossen, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Bakas, Spyridon, Rudie, Jeffrey D., and Aboian, Mariam

Subjects
Quantitative Biology - Other Quantitative Biology, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Published
2023

2. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Author

Li, Hongwei Bran, Conte, Gian Marco, Anwar, Syed Muhammad, Kofler, Florian, Ezhov, Ivan, van Leemput, Koen, Piraud, Marie, Diaz, Maria, Cole, Byrone, Calabrese, Evan, Rudie, Jeff, Meissen, Felix, Adewole, Maruf, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W., Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M., Wiest, Roland, Kirschke, Jan, Colen, Rivka R., Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva, Javier, Colak, Meyer Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Yu, Thomas, Baid, Ujjwal, Bakas, Spyridon, Linguraru, Marius George, Menze, Bjoern, Iglesias, Juan Eugenio, and Wiestler, Benedikt

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions., Comment: Technical report of BraSyn

Published
2023

3. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting

Author

Kofler, Florian, Meissen, Felix, Steinbauer, Felix, Graf, Robert, Oswald, Eva, de da Rosa, Ezequiel, Li, Hongwei Bran, Baid, Ujjwal, Hoelzl, Florian, Turgut, Oezguen, Horvath, Izabela, Waldmannstetter, Diana, Bukas, Christina, Adewole, Maruf, Anwar, Syed Muhammad, Janas, Anastasia, Kazerooni, Anahita Fathi, LaBella, Dominic, Moawad, Ahmed W, Farahani, Keyvan, Eddy, James, Bergquist, Timothy, Chung, Verena, Shinohara, Russell Takeshi, Dako, Farouk, Wiggins, Walter, Reitman, Zachary, Wang, Chunhao, Liu, Xinyang, Jiang, Zhifan, Familiar, Ariana, Conte, Gian-Marco, Johanson, Elaine, Meier, Zeke, Davatzikos, Christos, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan M, Wiest, Roland, Kirschke, Jan, Colen, Rivka R, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-André, Mahajan, Abhishek, Mohan, Suyash, Mongan, John, Hess, Christopher, Cha, Soonmee, Villanueva-Meyer, Javier, Colak, Errol, Crivellaro, Priscila, Jakab, Andras, Albrecht, Jake, Anazodo, Udunna, Aboian, Mariam, Iglesias, Juan Eugenio, Van Leemput, Koen, Bakas, Spyridon, Rueckert, Daniel, Wiestler, Benedikt, Ezhov, Ivan, Piraud, Marie, and Menze, Bjoern

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada., Comment: 5 pages, 1 figure

Published
2023

4. Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

Author

Lee, Matthew, Patel, Sohil, Mohan, Suyash, Akbari, Hamed, Bakas, Spyridon, Nasrallah, MacLean, Calabrese, Evan, Rudie, Jeffrey, LaMontagne, Pamela, Marcus, Daniel, Colen, Rivka, Balana, Carmen, Choi, Yoon, Badve, Chaitra, Barnholtz-Sloan, Jill, Sloan, Andrew, Booth, Thomas, Palmer, Joshua, Dicker, Adam, Flanders, Adam, Shi, Wenyin, Griffith, Brent, Poisson, Laila, Chakravarti, Arnab, Mahajan, Abhishek, Chang, Susan, Orringer, Daniel, Davatzikos, Christos, Jain, Rajan, and Villanueva-Meyer, Javier

Subjects
Astrocytoma, Glioblastoma, Isocitrate dehydrogenase, Magnetic resonance imaging, T2-FLAIR mismatch, Adult, Humans, Isocitrate Dehydrogenase, Brain Neoplasms, Retrospective Studies, Glioma, Magnetic Resonance Imaging, Mutation, World Health Organization
Abstract

PURPOSE: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p

Published
2023

5. A universal neocortical mask for Centiloid quantification

Author

Bourgeat, Pierrick, Doré, Vincent, Rowe, Christopher C, Benzinger, Tammie, Tosun, Duygu, Goyal, Manu S, LaMontagne, Pamela, Jin, Liang, Weiner, Michael W, Masters, Colin L, Fripp, Jurgen, Villemagne, Victor L, Initiative, for the Alzheimer's Disease Neuroimaging, and OASIS3, and the AIBL research group

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Acquired Cognitive Impairment, Neurosciences, Dementia, Brain Disorders, Alzheimer's Disease, Biomedical Imaging, Neurodegenerative, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, amyloid positron emission tomography, Centiloid, Florbetaben, Florbetapir, Flutemetamol, NAV4694, PiB, Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group, Genetics, Biological psychology
Abstract

IntroductionThe Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aβ) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aβ tracers. We created a universal mask using an independent dataset of five Aβ tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation.MethodsUsing data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F-florbetapir (N = 147 pairs), 18F-florbetaben (N = 22), 18F-flutemetamol (N = 10), 18F-NAV (N = 42), 11C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI).ResultsIn the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline.DiscussionThe universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aβ tracers.HighlightsThis study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.

Published
2023

6. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Published
2024
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7. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja T., Chen, Jiong, Habes, Mohamad, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C., Morris, John C., LaMontagne, Pamela, Marcus, Daniel S., Benzinger, Tammie L. S., Wolk, David A., Shen, Li, Bao, Jingxuan, Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Published
2023

8. MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Author

Chakrabarty, Satrajit, LaMontagne, Pamela, Shimony, Joshua, Marcus, Daniel S., and Sotiras, Aristeidis

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774). A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location. The models were tested on one internal (TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform a 'virtual biopsy' for tailoring treatment planning and overall clinical management of gliomas.

Published
2022
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9. Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)

Author

Chakrabarty, Satrajit, Abidi, Syed Amaan, Mousa, Mina, Mokkarala, Mahati, Hren, Isabelle, Yadav, Divya, Kelsey, Matthew, LaMontagne, Pamela, Wood, John, Adams, Michael, Su, Yuzhuo, Thorpe, Sherry, Chung, Caroline, Sotiras, Aristeidis, and Marcus, Daniel S.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, iii) delineates tumor tissue subtypes using convolutional neural networks, and iv) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists. Following the implementation of the framework in Docker containers, it was applied to two retrospective glioma datasets collected from the Washington University School of Medicine (WUSM; n = 384) and the M.D. Anderson Cancer Center (MDA; n = 30) comprising preoperative MRI scans from patients with pathologically confirmed gliomas. The scan-type classifier yielded an accuracy of over 99%, correctly identifying sequences from 380/384 and 30/30 sessions from the WUSM and MDA datasets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. Mean Dice scores were 0.882 ($\pm$0.244) and 0.977 ($\pm$0.04) for whole tumor segmentation for WUSM and MDA, respectively. This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology datasets and demonstrating a high potential for integration as an assistive tool in clinical practice.

Published
2022
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10. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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11. β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3

Author

Bourgeat, Pierrick, Doré, Vincent, Burnham, Samantha C, Benzinger, Tammie, Tosun, Duygu, Li, Shenpeng, Goyal, Manu, LaMontagne, Pamela, Jin, Liang, Rowe, Christopher C, Weiner, Michael W, Morris, John C, Masters, Colin L, Fripp, Jurgen, Villemagne, Victor L, and Alzheimer's Disease Neuroimaging Initiative, OASIS3

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Neurosciences, Alzheimer Disease, Amyloid beta-Peptides, Aniline Compounds, Cross-Sectional Studies, Humans, Longitudinal Studies, Positron-Emission Tomography, Amyloid PET, Centiloid, Harmonisation, Alzheimer's Disease Neuroimaging Initiative, OASIS3, and the AIBL research group, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

IntroductionThe Centiloid scale was developed to harmonise the quantification of β-amyloid (Aβ) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies.MethodsAll Aβ PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aβ burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated.ResultsThe smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used.ConclusionsFWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

Published
2022

12. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
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14. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Author

Bakas, Spyridon, Reyes, Mauricio, Jakab, Andras, Bauer, Stefan, Rempfler, Markus, Crimi, Alessandro, Shinohara, Russell Takeshi, Berger, Christoph, Ha, Sung Min, Rozycki, Martin, Prastawa, Marcel, Alberts, Esther, Lipkova, Jana, Freymann, John, Kirby, Justin, Bilello, Michel, Fathallah-Shaykh, Hassan, Wiest, Roland, Kirschke, Jan, Wiestler, Benedikt, Colen, Rivka, Kotrotsou, Aikaterini, Lamontagne, Pamela, Marcus, Daniel, Milchenko, Mikhail, Nazeri, Arash, Weber, Marc-Andre, Mahajan, Abhishek, Baid, Ujjwal, Gerstner, Elizabeth, Kwon, Dongjin, Acharya, Gagan, Agarwal, Manu, Alam, Mahbubul, Albiol, Alberto, Albiol, Antonio, Albiol, Francisco J., Alex, Varghese, Allinson, Nigel, Amorim, Pedro H. A., Amrutkar, Abhijit, Anand, Ganesh, Andermatt, Simon, Arbel, Tal, Arbelaez, Pablo, Avery, Aaron, Azmat, Muneeza, B., Pranjal, Bai, W, Banerjee, Subhashis, Barth, Bill, Batchelder, Thomas, Batmanghelich, Kayhan, Battistella, Enzo, Beers, Andrew, Belyaev, Mikhail, Bendszus, Martin, Benson, Eze, Bernal, Jose, Bharath, Halandur Nagaraja, Biros, George, Bisdas, Sotirios, Brown, James, Cabezas, Mariano, Cao, Shilei, Cardoso, Jorge M., Carver, Eric N, Casamitjana, Adrià, Castillo, Laura Silvana, Catà, Marcel, Cattin, Philippe, Cerigues, Albert, Chagas, Vinicius S., Chandra, Siddhartha, Chang, Yi-Ju, Chang, Shiyu, Chang, Ken, Chazalon, Joseph, Chen, Shengcong, Chen, Wei, Chen, Jefferson W, Chen, Zhaolin, Cheng, Kun, Choudhury, Ahana Roy, Chylla, Roger, Clérigues, Albert, Colleman, Steven, Colmeiro, Ramiro German Rodriguez, Combalia, Marc, Costa, Anthony, Cui, Xiaomeng, Dai, Zhenzhen, Dai, Lutao, Daza, Laura Alexandra, Deutsch, Eric, Ding, Changxing, Dong, Chao, Dong, Shidu, Dudzik, Wojciech, Eaton-Rosen, Zach, Egan, Gary, Escudero, Guilherme, Estienne, Théo, Everson, Richard, Fabrizio, Jonathan, Fan, Yong, Fang, Longwei, Feng, Xue, Ferrante, Enzo, Fidon, Lucas, Fischer, Martin, French, Andrew P., Fridman, Naomi, Fu, Huan, Fuentes, David, Gao, Yaozong, Gates, Evan, Gering, David, Gholami, Amir, Gierke, Willi, Glocker, Ben, Gong, Mingming, González-Villá, Sandra, Grosges, T., Guan, Yuanfang, Guo, Sheng, Gupta, Sudeep, Han, Woo-Sup, Han, Il Song, Harmuth, Konstantin, He, Huiguang, Hernández-Sabaté, Aura, Herrmann, Evelyn, Himthani, Naveen, Hsu, Winston, Hsu, Cheyu, Hu, Xiaojun, Hu, Xiaobin, Hu, Yan, Hu, Yifan, Hua, Rui, Huang, Teng-Yi, Huang, Weilin, Van Huffel, Sabine, Huo, Quan, HV, Vivek, Iftekharuddin, Khan M., Isensee, Fabian, Islam, Mobarakol, Jackson, Aaron S., Jambawalikar, Sachin R., Jesson, Andrew, Jian, Weijian, Jin, Peter, Jose, V Jeya Maria, Jungo, Alain, Kainz, B, Kamnitsas, Konstantinos, Kao, Po-Yu, Karnawat, Ayush, Kellermeier, Thomas, Kermi, Adel, Keutzer, Kurt, Khadir, Mohamed Tarek, Khened, Mahendra, Kickingereder, Philipp, Kim, Geena, King, Nik, Knapp, Haley, Knecht, Urspeter, Kohli, Lisa, Kong, Deren, Kong, Xiangmao, Koppers, Simon, Kori, Avinash, Krishnamurthi, Ganapathy, Krivov, Egor, Kumar, Piyush, Kushibar, Kaisar, Lachinov, Dmitrii, Lambrou, Tryphon, Lee, Joon, Lee, Chengen, Lee, Yuehchou, Lee, M, Lefkovits, Szidonia, Lefkovits, Laszlo, Levitt, James, Li, Tengfei, Li, Hongwei, Li, Wenqi, Li, Hongyang, Li, Xiaochuan, Li, Yuexiang, Li, Heng, Li, Zhenye, Li, Xiaoyu, Li, Zeju, Li, XiaoGang, Lin, Zheng-Shen, Lin, Fengming, Lio, Pietro, Liu, Chang, Liu, Boqiang, Liu, Xiang, Liu, Mingyuan, Liu, Ju, Liu, Luyan, Llado, Xavier, Lopez, Marc Moreno, Lorenzo, Pablo Ribalta, Lu, Zhentai, Luo, Lin, Luo, Zhigang, Ma, Jun, Ma, Kai, Mackie, Thomas, Madabushi, Anant, Mahmoudi, Issam, Maier-Hein, Klaus H., Maji, Pradipta, Mammen, CP, Mang, Andreas, Manjunath, B. S., Marcinkiewicz, Michal, McDonagh, S, McKenna, Stephen, McKinley, Richard, Mehl, Miriam, Mehta, Sachin, Mehta, Raghav, Meier, Raphael, Meinel, Christoph, Merhof, Dorit, Meyer, Craig, Miller, Robert, Mitra, Sushmita, Moiyadi, Aliasgar, Molina-Garcia, David, Monteiro, Miguel A. B., Mrukwa, Grzegorz, Myronenko, Andriy, Nalepa, Jakub, Ngo, Thuyen, Nie, Dong, Ning, Holly, Niu, Chen, Nuechterlein, Nicholas K, Oermann, Eric, Oliveira, Arlindo, Oliveira, Diego D. C., Oliver, Arnau, Osman, Alexander F. I., Ou, Yu-Nian, Ourselin, Sebastien, Paragios, Nikos, Park, Moo Sung, Paschke, Brad, Pauloski, J. Gregory, Pawar, Kamlesh, Pawlowski, Nick, Pei, Linmin, Peng, Suting, Pereira, Silvio M., Perez-Beteta, Julian, Perez-Garcia, Victor M., Pezold, Simon, Pham, Bao, Phophalia, Ashish, Piella, Gemma, Pillai, G. N., Piraud, Marie, Pisov, Maxim, Popli, Anmol, Pound, Michael P., Pourreza, Reza, Prasanna, Prateek, Prkovska, Vesna, Pridmore, Tony P., Puch, Santi, Puybareau, Élodie, Qian, Buyue, Qiao, Xu, Rajchl, Martin, Rane, Swapnil, Rebsamen, Michael, Ren, Hongliang, Ren, Xuhua, Revanuru, Karthik, Rezaei, Mina, Rippel, Oliver, Rivera, Luis Carlos, Robert, Charlotte, Rosen, Bruce, Rueckert, Daniel, Safwan, Mohammed, Salem, Mostafa, Salvi, Joaquim, Sanchez, Irina, Sánchez, Irina, Santos, Heitor M., Sartor, Emmett, Schellingerhout, Dawid, Scheufele, Klaudius, Scott, Matthew R., Scussel, Artur A., Sedlar, Sara, Serrano-Rubio, Juan Pablo, Shah, N. Jon, Shah, Nameetha, Shaikh, Mazhar, Shankar, B. Uma, Shboul, Zeina, Shen, Haipeng, Shen, Dinggang, Shen, Linlin, Shen, Haocheng, Shenoy, Varun, Shi, Feng, Shin, Hyung Eun, Shu, Hai, Sima, Diana, Sinclair, M, Smedby, Orjan, Snyder, James M., Soltaninejad, Mohammadreza, Song, Guidong, Soni, Mehul, Stawiaski, Jean, Subramanian, Shashank, Sun, Li, Sun, Roger, Sun, Jiawei, Sun, Kay, Sun, Yu, Sun, Guoxia, Sun, Shuang, Suter, Yannick R, Szilagyi, Laszlo, Talbar, Sanjay, Tao, Dacheng, Teng, Zhongzhao, Thakur, Siddhesh, Thakur, Meenakshi H, Tharakan, Sameer, Tiwari, Pallavi, Tochon, Guillaume, Tran, Tuan, Tsai, Yuhsiang M., Tseng, Kuan-Lun, Tuan, Tran Anh, Turlapov, Vadim, Tustison, Nicholas, Vakalopoulou, Maria, Valverde, Sergi, Vanguri, Rami, Vasiliev, Evgeny, Ventura, Jonathan, Vera, Luis, Vercauteren, Tom, Verrastro, C. A., Vidyaratne, Lasitha, Vilaplana, Veronica, Vivekanandan, Ajeet, Wang, Guotai, Wang, Qian, Wang, Chiatse J., Wang, Weichung, Wang, Duo, Wang, Ruixuan, Wang, Yuanyuan, Wang, Chunliang, Wen, Ning, Wen, Xin, Weninger, Leon, Wick, Wolfgang, Wu, Shaocheng, Wu, Qiang, Wu, Yihong, Xia, Yong, Xu, Yanwu, Xu, Xiaowen, Xu, Peiyuan, Yang, Tsai-Ling, Yang, Xiaoping, Yang, Hao-Yu, Yang, Junlin, Yang, Haojin, Yang, Guang, Yao, Hongdou, Ye, Xujiong, Yin, Changchang, Young-Moxon, Brett, Yu, Jinhua, Yue, Xiangyu, Zhang, Songtao, Zhang, Angela, Zhang, Kun, Zhang, Xuejie, Zhang, Lichi, Zhang, Xiaoyue, Zhang, Yazhuo, Zhang, Lei, Zhang, Jianguo, Zhang, Xiang, Zhang, Tianhao, Zhao, Sicheng, Zhao, Yu, Zhao, Xiaomei, Zhao, Liang, Zheng, Yefeng, Zhong, Liming, Zhou, Chenhong, Zhou, Xiaobing, Zhou, Fan, Zhu, Hongtu, Zhu, Jin, Zhuge, Ying, Zong, Weiwei, Kalpathy-Cramer, Jayashree, Farahani, Keyvan, Davatzikos, Christos, van Leemput, Koen, and Menze, Bjoern

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset., Comment: The International Multimodal Brain Tumor Segmentation (BraTS) Challenge

Published
2018

16. Integration of resting state functional MRI into clinical practice - A large single institution experience.

Author

Leuthardt, Eric C, Guzman, Gloria, Bandt, S Kathleen, Hacker, Carl, Vellimana, Ananth K, Limbrick, David, Milchenko, Mikhail, Lamontagne, Pamela, Speidel, Benjamin, Roland, Jarod, Miller-Thomas, Michelle, Snyder, Abraham Z, Marcus, Daniel, Shimony, Joshua, and Benzinger, Tammie LS

Subjects
Cerebral Cortex, Humans, Brain Neoplasms, Nervous System Diseases, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Algorithms, Rest, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Female, Male, Vascular Malformations, Young Adult, Preoperative Period, Drug Resistant Epilepsy, and over, Preschool, Image Interpretation, Computer-Assisted, General Science & Technology
Abstract

Functional magnetic resonance imaging (fMRI) is an important tool for pre-surgical evaluation of eloquent cortex. Classic task-based paradigms require patient participation and individual imaging sequence acquisitions for each functional domain that is being assessed. Resting state fMRI (rs-fMRI), however, enables functional localization without patient participation and can evaluate numerous functional domains with a single imaging session. To date, post-processing of this resting state data has been resource intensive, which limits its widespread application for routine clinical use. Through a novel automated algorithm and advanced imaging IT structure, we report the clinical application and the large-scale integration of rs-fMRI into routine neurosurgical practice. One hundred and ninety one consecutive patients underwent a 3T rs-fMRI, 83 of whom also underwent both motor and language task-based fMRI. Data were processed using a novel, automated, multi-layer perceptron algorithm and integrated into stereotactic navigation using a streamlined IT imaging pipeline. One hundred eighty-five studies were performed for intracranial neoplasm, 14 for refractory epilepsy and 33 for vascular malformations or other neurological disorders. Failure rate of rs-fMRI of 13% was significantly better than that for task-based fMRI (38.5%,) (p

Published
2018

17. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Author

Skampardoni, Ioanna, primary, Nasrallah, Ilya M., additional, Abdulkadir, Ahmed, additional, Wen, Junhao, additional, Melhem, Randa, additional, Mamourian, Elizabeth, additional, Erus, Guray, additional, Doshi, Jimit, additional, Singh, Ashish, additional, Yang, Zhijian, additional, Cui, Yuhan, additional, Hwang, Gyujoon, additional, Ren, Zheng, additional, Pomponio, Raymond, additional, Srinivasan, Dhivya, additional, Govindarajan, Sindhuja Tirumalai, additional, Parmpi, Paraskevi, additional, Wittfeld, Katharina, additional, Grabe, Hans J., additional, Bülow, Robin, additional, Frenzel, Stefan, additional, Tosun, Duygu, additional, Bilgel, Murat, additional, An, Yang, additional, Marcus, Daniel S., additional, LaMontagne, Pamela, additional, Heckbert, Susan R., additional, Austin, Thomas R., additional, Launer, Lenore J., additional, Sotiras, Aristeidis, additional, Espeland, Mark A., additional, Masters, Colin L., additional, Maruff, Paul, additional, Fripp, Jurgen, additional, Johnson, Sterling C., additional, Morris, John C., additional, Albert, Marilyn S., additional, Bryan, R. Nick, additional, Yaffe, Kristine, additional, Völzke, Henry, additional, Ferrucci, Luigi, additional, Benzinger, Tammie L.S., additional, Ezzati, Ali, additional, Shinohara, Russell T., additional, Fan, Yong, additional, Resnick, Susan M., additional, Habes, Mohamad, additional, Wolk, David, additional, Shou, Haochang, additional, Nikita, Konstantina, additional, and Davatzikos, Christos, additional

Published
2024
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18. Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Author

Thakur, Siddhesh, Doshi, Jimit, Pati, Sarthak, Rathore, Saima, Sako, Chiharu, Bilello, Michel, Ha, Sung Min, Shukla, Gaurav, Flanders, Adam, Kotrotsou, Aikaterini, Milchenko, Mikhail, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., LaMontagne, Pamela, Nazeri, Arash, Talbar, Sanjay, Kulkarni, Uday, Marcus, Daniel, Colen, Rivka, Davatzikos, Christos, Erus, Guray, and Bakas, Spyridon

Published
2020
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19. Genomic loci influence patterns of structural covariance in the human brain

Author

Wen, Junhao, primary, Nasrallah, Ilya M., additional, Abdulkadir, Ahmed, additional, Satterthwaite, Theodore D., additional, Yang, Zhijian, additional, Erus, Guray, additional, Robert-Fitzgerald, Timothy, additional, Singh, Ashish, additional, Sotiras, Aristeidis, additional, Boquet-Pujadas, Aleix, additional, Mamourian, Elizabeth, additional, Doshi, Jimit, additional, Cui, Yuhan, additional, Srinivasan, Dhivya, additional, Skampardoni, Ioanna, additional, Chen, Jiong, additional, Hwang, Gyujoon, additional, Bergman, Mark, additional, Bao, Jingxuan, additional, Veturi, Yogasudha, additional, Zhou, Zhen, additional, Yang, Shu, additional, Dazzan, Paola, additional, Kahn, Rene S., additional, Schnack, Hugo G., additional, Zanetti, Marcus V., additional, Meisenzahl, Eva, additional, Busatto, Geraldo F., additional, Crespo-Facorro, Benedicto, additional, Pantelis, Christos, additional, Wood, Stephen J., additional, Zhuo, Chuanjun, additional, Shinohara, Russell T., additional, Gur, Ruben C., additional, Gur, Raquel E., additional, Koutsouleris, Nikolaos, additional, Wolf, Daniel H., additional, Saykin, Andrew J., additional, Ritchie, Marylyn D., additional, Shen, Li, additional, Thompson, Paul M., additional, Colliot, Olivier, additional, Wittfeld, Katharina, additional, Grabe, Hans J., additional, Tosun, Duygu, additional, Bilgel, Murat, additional, An, Yang, additional, Marcus, Daniel S., additional, LaMontagne, Pamela, additional, Heckbert, Susan R., additional, Austin, Thomas R., additional, Launer, Lenore J., additional, Espeland, Mark, additional, Masters, Colin L., additional, Maruff, Paul, additional, Fripp, Jurgen, additional, Johnson, Sterling C., additional, Morris, John C., additional, Albert, Marilyn S., additional, Bryan, R. Nick, additional, Resnick, Susan M., additional, Fan, Yong, additional, Habes, Mohamad, additional, Wolk, David, additional, Shou, Haochang, additional, and Davatzikos, Christos, additional

Published
2023
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21. Assessing a universal neocortical mask for Centiloid quantification

Author

Bourgeat, Pierrick, primary, Dore, Vincent, additional, Rowe, Christopher C, additional, Benzinger, Tammie L.S., additional, Tosun, Duygu, additional, Goyal, Manu S., additional, LaMontagne, Pamela J., additional, Jin, Liang, additional, Weiner, Michael S. W., additional, Morris, John C, additional, Masters, Colin L, additional, Fripp, Jurgen, additional, and Villemagne, Victor L, additional

Published
2023
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22. NIMG-13. ROBUSTNESS OF PROGNOSTIC STRATIFICATION IN DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: INSIGHTS FROM THE RESPOND CONSORTIUM

Author

Akbari, Hamed, primary, Bakas, Spyridon, additional, Garcia, Jose, additional, Kazerooni, Anahita Fathi, additional, Sako, Chiharu, additional, Villanueva-Meyer, Javier, additional, Baid, Ujjwal, additional, Mamourian, Elizabeth, additional, Brem, Steven, additional, Lustig, Robert A, additional, Nasrallah, MacLean P, additional, O'Rourke, Donald M, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Chang, Susan, additional, Rauschecker, Andreas, additional, LaMontagne, Pamela, additional, Marcus, Daniel S, additional, Balana, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Barnholtz-Sloan, Jill, additional, Badve, Chaitra, additional, Sloan, Andrew, additional, Waite, Kristin, additional, Colen, Rivka, additional, Choi, Yoon Seong, additional, Ahn, Sung Soo, additional, Dicker, Adam P, additional, Flanders, Adam E, additional, Shi, Wenyin, additional, Griffith, Brent, additional, Poisson, Laila M, additional, Rogers, Lisa R, additional, Booth, Thomas C, additional, Jain, Rajan, additional, Chakravarti, Arnab, additional, Palmer, Joshua, additional, Cepeda, Santiago, additional, Wiestler, Benedikt, additional, Stefano, Anna Luisa Di, additional, Alexander, Kimberley, additional, Melhem, Elias R, additional, Woodworth, Graeme F, additional, Kamel, Peter I, additional, Tiwari, Pallavi, additional, Aboian, Mariam, additional, Mohan, Suyash, additional, and Davatzikos, Christos, additional

Published
2023
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23. EPID-15. SEX-SPECIFIC DIFFERENCES IN GLIOBLASTOMA IN THE RESPOND CONSORTIUM

Author

Gongala, Sree, primary, Garcia, Jose, additional, Korakavi, Nisha, additional, Patil, Nirav, additional, Akbari, Hamed, additional, Tippareddy, Charit, additional, Sloan, Andrew, additional, Barnholtz-Sloan, Jill, additional, Bakas, Spyridon, additional, Kazerooni, Anahita Fathi, additional, Sako, Chiharu, additional, Baid, Ujjwal, additional, Brem, Steven, additional, Lustig, Robert A, additional, Capellades, Jaume, additional, Nasrallah, MacLean, additional, O'Rourke, Donald M, additional, LaMontagne, Pamela, additional, Marcus, Daniel S, additional, Balana, Carmen, additional, Puig, Josep, additional, Waite, Kristin, additional, Colen, Rivka, additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Dicker, Adam P, additional, Flanders, Adam E, additional, Shi, Wenyin, additional, Griffith, Brent, additional, Poisson, Laila M, additional, Rogers, Lisa R, additional, Booth, Thomas C, additional, Jain, Rajan, additional, Chakravarti, Arnab, additional, Palmer, Joshua, additional, Mohan, Suyash, additional, Tiwari, Pallavi, additional, Aboian, Mariam, additional, Ahn, Sung Soo, additional, Davatzikos, Christos, additional, and Badve, Chaitra, additional

Published
2023
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25. Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO)

Author

Chakrabarty, Satrajit, primary, Abidi, Syed Amaan, additional, Mousa, Mina, additional, Mokkarala, Mahati, additional, Hren, Isabelle, additional, Yadav, Divya, additional, Kelsey, Matthew, additional, LaMontagne, Pamela, additional, Wood, John, additional, Adams, Michael, additional, Su, Yuzhuo, additional, Thorpe, Sherry, additional, Chung, Caroline, additional, Sotiras, Aristeidis, additional, and Marcus, Daniel S., additional

Published
2023
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28. Brain aging patterns in a large and diverse cohort of 49,482 individuals

Author

Yang, Zhijian, Wen, Junhao, Erus, Guray, Govindarajan, Sindhuja T., Melhem, Randa, Mamourian, Elizabeth, Cui, Yuhan, Srinivasan, Dhivya, Abdulkadir, Ahmed, Parmpi, Paraskevi, Wittfeld, Katharina, Grabe, Hans J., Bülow, Robin, Frenzel, Stefan, Tosun, Duygu, Bilgel, Murat, An, Yang, Yi, Dahyun, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L. S., Heckbert, Susan R., Austin, Thomas R., Waldstein, Shari R., Evans, Michele K., Zonderman, Alan B., Launer, Lenore J., Sotiras, Aristeidis, Espeland, Mark A., Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Toga, Arthur W., O’Bryant, Sid, Chakravarty, Mallar M., Villeneuve, Sylvia, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Yaffe, Kristine, Völzke, Henry, Ferrucci, Luigi, Nick Bryan, R., Shinohara, Russell T., Fan, Yong, Habes, Mohamad, Lalousis, Paris Alexandros, Koutsouleris, Nikolaos, Wolk, David A., Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Abstract

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Published
2024
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29. Discovering Alzheimer's disease subtypes with imaging and genetic signatures via multi‐view weakly‐supervised deep clustering.

Author

Yang, Zhijian, Wen, Junhao, Abdulkadir, Ahmed, Cui, Yuhan, Erus, Guray, Mamourian, Elizabeth, Melhem, Randa, Srinivasan, Dhivya, Govindarajan, Sindhuja Tirumalai, Chen, Jiong, Habes, Mohamad, Masters, Colin L, Maruff, Paul, Fripp, Jurgen, Ferrucci, Luigi, Albert, Marilyn S., Johnson, Sterling C, Morris, John C, LaMontagne, Pamela, and Marcus, Daniel S.

Abstract

Background: The heterogeneity of neurodegenerative diseases, including Alzheimer's disease (AD), has hampered precision diagnosis and treatment. Machine learning methods enable the identification of genetically‐explained disease subtypes with distinct brain phenotypes. Method: Gene‐SGAN is a novel, multi‐view, weakly‐supervised deep clustering method that jointly considers phenotypic and genetic data, thereby deriving subtypes of disease‐related brain changes with genetic associations. We apply the method to derive subtypes related to AD. Analyses used structural MRIs and whole‐genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (1,533 participants, including controls and patients with Mild Cognitive Impairment (MCI) and AD). Result: Gene‐SGAN identified four distinct subtypes within MCI/AD: A1, normal anatomy; A2, focal medial temporal lobe (MTL) atrophy; A3, severe atrophy in neocortex and MTL; A4, severe cortical but relatively modest MTL atrophy (Figure 1). The four subtypes reveal significant differences in seven known AD‐related genetic variants (Figure 2). The identified subtype‐associated SNPs not only resemble previous findings, but also suggest genetic protective effects contributing to the observed resilience (A1), potential inflammatory mechanisms underlying subtypes (A2&A3), and the effect of SNPs on atypical atrophy patterns of AD (A4). Clinically, A1 and A3 participants showed the best and the worst cognitive performances (Figure 3). A3 participants had the most abnormal CSF Aβ (p<0.001 vs. A1&A4 and p = 0.039 vs. A2), and exhibited the highest white matter hyperintensity (WMH) volumes (p<0.001 vs. A1&A4 and p = 0.017 vs. A2). However, A2 participants showed significantly higher CSF phospho‐tau than A3 participants (p = 0.019). A4 participants were the youngest group (p<0.001 vs. all other groups), suggesting the inclusion of early onset AD participants, who have a higher prevalence of hippocampal‐sparing variants such as posterior cortical atrophy. The four genetically‐associated subtypes also possess significant differences in a large set of plasma/CSF biomarkers, which are related to distinct biological mechanisms contributing to the heterogeneity of AD. Conclusion: Deep learning revealed four morphologic/genetic MCI/AD subtypes. The derived subtypes display distinct neuroanatomical patterns and genetic determinants and also differ in many clinically interpretable biomarkers. These subtypes provide potential for drug discovery and repurposing, optimization of clinical trial recruitment, and personalized medicine based on an individual's genetic profile. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Predicting Cognitive Decline: Which is More Useful, Baseline Amyloid Levels or Longitudinal Change?

Author

Chen, Gengsheng, primary, McKay, Nicole S., additional, Gordon, Brian A., additional, Liu, Jingxia, additional, Joseph-Mathurin, Nelly, additional, Schindler, Suzanne, additional, Hassenstab, Jason, additional, Aschenbrenner, Andrew J., additional, Wang, Qing, additional, Schultz, Stephanie A., additional, Su, Yi, additional, LaMontagne, Pamela J., additional, Keefe, Sarah J., additional, Massoumzadeh, Parinaz, additional, Cruchaga, Carlos, additional, Morris, John C., additional, and Benzinger, Tammie, additional

Published
2023
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32. Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, primary, Baid, Ujjwal, additional, Edwards, Brandon, additional, Sheller, Micah, additional, Wang, Shih-Han, additional, Reina, G. Anthony, additional, Foley, Patrick, additional, Gruzdev, Alexey, additional, Karkada, Deepthi, additional, Davatzikos, Christos, additional, Sako, Chiharu, additional, Ghodasara, Satyam, additional, Bilello, Michel, additional, Mohan, Suyash, additional, Vollmuth, Philipp, additional, Brugnara, Gianluca, additional, Preetha, Chandrakanth J., additional, Sahm, Felix, additional, Maier-Hein, Klaus, additional, Zenk, Maximilian, additional, Bendszus, Martin, additional, Wick, Wolfgang, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, Cha, Soonmee, additional, Ingalhalikar, Madhura, additional, Jadhav, Manali, additional, Pandey, Umang, additional, Saini, Jitender, additional, Garrett, John, additional, Larson, Matthew, additional, Jeraj, Robert, additional, Currie, Stuart, additional, Frood, Russell, additional, Fatania, Kavi, additional, Huang, Raymond Y., additional, Chang, Ken, additional, Balaña, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Trenkler, Johannes, additional, Pichler, Josef, additional, Necker, Georg, additional, Haunschmidt, Andreas, additional, Meckel, Stephan, additional, Shukla, Gaurav, additional, Liem, Spencer, additional, Alexander, Gregory S., additional, Lombardo, Joseph, additional, Palmer, Joshua D., additional, Flanders, Adam E., additional, Dicker, Adam P., additional, Sair, Haris I., additional, Jones, Craig K., additional, Venkataraman, Archana, additional, Jiang, Meirui, additional, So, Tiffany Y., additional, Chen, Cheng, additional, Heng, Pheng Ann, additional, Dou, Qi, additional, Kozubek, Michal, additional, Lux, Filip, additional, Michálek, Jan, additional, Matula, Petr, additional, Keřkovský, Miloš, additional, Kopřivová, Tereza, additional, Dostál, Marek, additional, Vybíhal, Václav, additional, Vogelbaum, Michael A., additional, Mitchell, J. Ross, additional, Farinhas, Joaquim, additional, Maldjian, Joseph A., additional, Yogananda, Chandan Ganesh Bangalore, additional, Pinho, Marco C., additional, Reddy, Divya, additional, Holcomb, James, additional, Wagner, Benjamin C., additional, Ellingson, Benjamin M., additional, Cloughesy, Timothy F., additional, Raymond, Catalina, additional, Oughourlian, Talia, additional, Hagiwara, Akifumi, additional, Wang, Chencai, additional, To, Minh-Son, additional, Bhardwaj, Sargam, additional, Chong, Chee, additional, Agzarian, Marc, additional, Falcão, Alexandre Xavier, additional, Martins, Samuel B., additional, Teixeira, Bernardo C. A., additional, Sprenger, Flávia, additional, Menotti, David, additional, Lucio, Diego R., additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Wiestler, Benedikt, additional, Kofler, Florian, additional, Ezhov, Ivan, additional, Metz, Marie, additional, Jain, Rajan, additional, Lee, Matthew, additional, Lui, Yvonne W., additional, McKinley, Richard, additional, Slotboom, Johannes, additional, Radojewski, Piotr, additional, Meier, Raphael, additional, Wiest, Roland, additional, Murcia, Derrick, additional, Fu, Eric, additional, Haas, Rourke, additional, Thompson, John, additional, Ormond, David Ryan, additional, Badve, Chaitra, additional, Sloan, Andrew E., additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Colen, Rivka R., additional, Pei, Linmin, additional, Ak, Murat, additional, Srinivasan, Ashok, additional, Bapuraj, J. Rajiv, additional, Rao, Arvind, additional, Wang, Nicholas, additional, Yoshiaki, Ota, additional, Moritani, Toshio, additional, Turk, Sevcan, additional, Lee, Joonsang, additional, Prabhudesai, Snehal, additional, Morón, Fanny, additional, Mandel, Jacob, additional, Kamnitsas, Konstantinos, additional, Glocker, Ben, additional, Dixon, Luke V. M., additional, Williams, Matthew, additional, Zampakis, Peter, additional, Panagiotopoulos, Vasileios, additional, Tsiganos, Panagiotis, additional, Alexiou, Sotiris, additional, Haliassos, Ilias, additional, Zacharaki, Evangelia I., additional, Moustakas, Konstantinos, additional, Kalogeropoulou, Christina, additional, Kardamakis, Dimitrios M., additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Chang, Jong Hee, additional, Ahn, Sung Soo, additional, Luo, Bing, additional, Poisson, Laila, additional, Wen, Ning, additional, Tiwari, Pallavi, additional, Verma, Ruchika, additional, Bareja, Rohan, additional, Yadav, Ipsa, additional, Chen, Jonathan, additional, Kumar, Neeraj, additional, Smits, Marion, additional, van der Voort, Sebastian R., additional, Alafandi, Ahmed, additional, Incekara, Fatih, additional, Wijnenga, Maarten M. J., additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W., additional, Dubbink, Hendrikus J., additional, Vincent, Arnaud J. P. E., additional, van den Bent, Martin J., additional, French, Pim J., additional, Klein, Stefan, additional, Yuan, Yading, additional, Sharma, Sonam, additional, Tseng, Tzu-Chi, additional, Adabi, Saba, additional, Niclou, Simone P., additional, Keunen, Olivier, additional, Hau, Ann-Christin, additional, Vallières, Martin, additional, Fortin, David, additional, Lepage, Martin, additional, Landman, Bennett, additional, Ramadass, Karthik, additional, Xu, Kaiwen, additional, Chotai, Silky, additional, Chambless, Lola B., additional, Mistry, Akshitkumar, additional, Thompson, Reid C., additional, Gusev, Yuriy, additional, Bhuvaneshwar, Krithika, additional, Sayah, Anousheh, additional, Bencheqroun, Camelia, additional, Belouali, Anas, additional, Madhavan, Subha, additional, Booth, Thomas C., additional, Chelliah, Alysha, additional, Modat, Marc, additional, Shuaib, Haris, additional, Dragos, Carmen, additional, Abayazeed, Aly, additional, Kolodziej, Kenneth, additional, Hill, Michael, additional, Abbassy, Ahmed, additional, Gamal, Shady, additional, Mekhaimar, Mahmoud, additional, Qayati, Mohamed, additional, Reyes, Mauricio, additional, Park, Ji Eun, additional, Yun, Jihye, additional, Kim, Ho Sung, additional, Mahajan, Abhishek, additional, Muzi, Mark, additional, Benson, Sean, additional, Beets-Tan, Regina G. H., additional, Teuwen, Jonas, additional, Herrera-Trujillo, Alejandro, additional, Trujillo, Maria, additional, Escobar, William, additional, Abello, Ana, additional, Bernal, Jose, additional, Gómez, Jhon, additional, Choi, Joseph, additional, Baek, Stephen, additional, Kim, Yusung, additional, Ismael, Heba, additional, Allen, Bryan, additional, Buatti, John M., additional, Kotrotsou, Aikaterini, additional, Li, Hongwei, additional, Weiss, Tobias, additional, Weller, Michael, additional, Bink, Andrea, additional, Pouymayou, Bertrand, additional, Shaykh, Hassan F., additional, Saltz, Joel, additional, Prasanna, Prateek, additional, Shrestha, Sampurna, additional, Mani, Kartik M., additional, Payne, David, additional, Kurc, Tahsin, additional, Pelaez, Enrique, additional, Franco-Maldonado, Heydy, additional, Loayza, Francis, additional, Quevedo, Sebastian, additional, Guevara, Pamela, additional, Torche, Esteban, additional, Mendoza, Cristobal, additional, Vera, Franco, additional, Ríos, Elvis, additional, López, Eduardo, additional, Velastin, Sergio A., additional, Ogbole, Godwin, additional, Soneye, Mayowa, additional, Oyekunle, Dotun, additional, Odafe-Oyibotha, Olubunmi, additional, Osobu, Babatunde, additional, Shu’aibu, Mustapha, additional, Dorcas, Adeleye, additional, Dako, Farouk, additional, Simpson, Amber L., additional, Hamghalam, Mohammad, additional, Peoples, Jacob J., additional, Hu, Ricky, additional, Tran, Anh, additional, Cutler, Danielle, additional, Moraes, Fabio Y., additional, Boss, Michael A., additional, Gimpel, James, additional, Veettil, Deepak Kattil, additional, Schmidt, Kendall, additional, Bialecki, Brian, additional, Marella, Sailaja, additional, Price, Cynthia, additional, Cimino, Lisa, additional, Apgar, Charles, additional, Shah, Prashant, additional, Menze, Bjoern, additional, Barnholtz-Sloan, Jill S., additional, Martin, Jason, additional, and Bakas, Spyridon, additional

Published
2022
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33. Classification of amyloid positivity in PET imaging using end‐to‐end deep learning: a multi‐cohort, multi‐tracer analysis

Author

Fan, Shuyang, primary, Ha, Sung Min, additional, Chakrabarty, Satrajit, additional, Lee, John, additional, Flores, Shaney, additional, LaMontagne, Pamela, additional, Gordon, Brian A., additional, Raji, Cyrus A., additional, Benzinger, Tammie L.S., additional, Morris, John C., additional, Ances, Beau M, additional, Marcus, Daniel S., additional, and Sotiras, Aristeidis, additional

Published
2022
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34. Machine‐learning based MRI neuro‐anatomical signatures associated with cardiovascular and metabolic risk factors

Author

Govindarajan, Sindhuja Tirumalai, primary, Mamourian, Elizabeth, additional, Erus, Guray, additional, Abdulkadir, Ahmed, additional, Melhem, Randa, additional, Doshi, Jimit, additional, Pomponio, Raymond, additional, Tosun, Duygu, additional, Bilgel, Murat, additional, An, Yang, additional, Sotiras, Aristeidis, additional, Marcus, Daniel S., additional, LaMontagne, Pamela J., additional, Espeland, Mark A., additional, Masters, Colin L., additional, Maruff, Paul, additional, Launer, Lenore J., additional, Fripp, Jurgen, additional, Johnson, Sterling C., additional, Morris, John C., additional, Albert, Marilyn S., additional, Bryan, R. Nick, additional, Resnick, Susan M., additional, Habes, Mohamad, additional, Shou, Haochang, additional, Wolk, David A., additional, Nasrallah, Ilya M., additional, and Davatzikos, Christos, additional

Published
2022
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35. Investigating a new neocortical mask for Centiloid quantification

Author

Bourgeat, Pierrick, primary, Dore, Vincent, additional, Benzinger, Tammie L.S., additional, Tosun, Duygu, additional, Li, Shenpeng, additional, Goyal, Manu S., additional, LaMontagne, Pamela, additional, Jin, Liang, additional, Rowe, Christopher, additional, Weiner, Michael W., additional, Morris, John C., additional, Masters, Colin L, additional, Fripp, Jurgen, additional, and Villemagne, Victor L, additional

Published
2022
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36. NIMG-67. MULTI-PARAMETRIC MRI-BASED MACHINE LEARNING ANALYSIS FOR PREDICTION OF NEOPLASTIC INFILTRATION AND RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: UPDATES FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

Author

Akbari, Hamed, primary, Mohan, Suyash, additional, Garcia, Jose, additional, Kazerooni, Anahita Fathi, additional, Sako, Chiharu, additional, Bakas, Spyridon, additional, Bilello, Michel, additional, Bagley, Stephen, additional, Baid, Ujjwal, additional, Brem, Steven, additional, Lustig, Robert, additional, Nasrallah, MacLean, additional, O'Rourke, Donald, additional, Barnholtz-Sloan, Jill, additional, Badve, Chaitra, additional, Sloan, Andrew, additional, Jain, Rajan, additional, Lee, Matthew, additional, Chakravarti, Arnab, additional, Palmer, Joshua, additional, Taylor, William, additional, Cepeda, Santiago, additional, Dicker, Adam, additional, Flanders, Adam, additional, Shi, Wenyin, additional, Shukla, Gaurav, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Balana, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Ak, Murat, additional, Colen, Rivka, additional, Ahn, Sung Soo, additional, Chang, Jong Hee, additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Griffith, Brent, additional, Poisson, Laila, additional, Rogers, Lisa, additional, Booth, Thomas, additional, Mahajan, Abhishek, additional, Wiestler, Benedikt, additional, and Davatzikos, Christos, additional

Published
2022
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37. β-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3

Author

Bourgeat, Pierrick, primary, Doré, Vincent, additional, Burnham, Samantha C., additional, Benzinger, Tammie, additional, Tosun, Duygu, additional, Li, Shenpeng, additional, Goyal, Manu, additional, LaMontagne, Pamela, additional, Jin, Liang, additional, Rowe, Christopher C, additional, Weiner, Michael W., additional, Morris, John C, additional, Masters, Colin L, additional, Fripp, Jurgen, additional, and Villemagne, Victor L, additional

Published
2022
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38. NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM

Author

Akbari, Hamed, primary, Bakas, Spyridon, additional, Sako, Chiharu, additional, Kazerooni, Anahita Fathi, additional, Villanueva-Meyer, Javier, additional, Garcia, Jose, additional, Bagley, Stephen, additional, Baid, Ujjwal, additional, Bilello, Michel, additional, Brem, Steven, additional, Lustig, Robert, additional, Mohan, Suyash, additional, Nasrallah, MacLean, additional, O'Rourke, Donald, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Balana, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Barnholtz-Sloan, Jill, additional, Badve, Chaitra, additional, Sloan, Andrew, additional, Ak, Murat, additional, Colen, Rivka, additional, Ahn, Sung Soo, additional, Chang, Jong Hee, additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Dicker, Adam, additional, Flanders, Adam, additional, Shi, Wenyin, additional, Shukla, Gaurav, additional, Griffith, Brent, additional, Poisson, Laila, additional, Rogers, Lisa, additional, Booth, Thomas, additional, Jain, Rajan, additional, Lee, Matthew, additional, Mahajan, Abhishek, additional, Chakravarti, Arnab, additional, Palmer, Joshua, additional, Taylor, William, additional, Cepeda, Santiago, additional, Wiestler, Benedikt, additional, and Davatzikos, Christos, additional

Published
2022
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41. Mega-analysis of brain structural covariance, genetics, and clinical phenotypes

Author

Wen, Junhao, primary, Nasrallah, Ilya, additional, Abdulkadir, Ahmed, additional, Satterthwaite, Theodore, additional, Erus, Guray, additional, Robert-Fitzgerald, Timothy, additional, Singh, Ashish, additional, Sotiras, Aristeidis, additional, Boquet-Pujadas, Aleix, additional, Yang, Zhijian, additional, Mamourian, Elizabeth, additional, Doshi, Jimit, additional, Cui, Yuhan, additional, Sriniva, Dhivya, additional, Bergman, Mark, additional, Bao, Jingxuan, additional, Veturi, Yogasudha, additional, Zhou, Zhen, additional, Yang, Shu, additional, Dazzan, Paola, additional, Kahn, René, additional, Schnack, Hugo, additional, Zanetti, Marcus, additional, Meisenzahl, Eva, additional, Busatto, Geraldo, additional, Crespo-Facorro, Benedicto, additional, Pantelis, Christos, additional, Wood, Stephen, additional, Zhuo, Chuanjun, additional, Shinohara, Russell, additional, Gur, Ruben, additional, Gur, Raquel, additional, Koutsouleris, Nikolaos, additional, Wolf, Daniel H., additional, Saykin, Andrew J., additional, Ritchie, Marylyn, additional, Shen, Li, additional, Thompson, Paul, additional, Colliot, Olivier, additional, Wittfeld, Katharina, additional, Grabe, Hans, additional, Tosun, Duygu, additional, Bilgel, Murat, additional, An, Yang, additional, Marcus, Daniel, additional, LaMontagne, Pamela, additional, Heckbert, Susan, additional, Austin, Thomas, additional, Launer, Lenore, additional, Espeland, Mark, additional, Masters, Colin, additional, Maruff, Paul, additional, Fripp, Jurgen, additional, Johnson, Sterling, additional, Morris, John, additional, Albert, Marilyn, additional, Bryan, Nick, additional, Resnick, Susan, additional, Fan, Yong, additional, Habes, Mohamad, additional, Wolk, David, additional, Shou, Haochang, additional, and Davatzikos, Christos, additional

Published
2022
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46. NIMG-39. RADIOMIC ANALYSIS FOR NON-INVASIVE IN VIVO PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS: A MULTI-INSTITUTIONAL EVALUATION FOR GENERALIZABILITY IN THE RESPOND CONSORTIUM

Author

Akbari*, Hamed, primary, Bakas*, Spyridon, additional, Sako*, Chiharu, additional, Kazerooni, Anahita Fathi, additional, Garcia, Jose A, additional, Bagley, Stephen J, additional, Mohan, Suyash, additional, Ahn, Sung Soo, additional, Ak, Murat, additional, Alexander, Gregory S, additional, Ali, Ayesha S, additional, Baid, Ujjwal, additional, Bavde, Chaitra, additional, Bilello, Michel, additional, Brem, Steven, additional, Capellades, Jaume, additional, Chang, Jong Hee, additional, Choi, Yoon Seong, additional, Dicker, Adam P, additional, Fathallah-Shaykh, Hassan, additional, Flanders, Adam E, additional, Griffith, Brent D, additional, LaMontagne, Pamela, additional, Lee, Matthew, additional, Lee, Seung-Koo, additional, Liem, Spencer, additional, Lombardo, Joseph, additional, Lustig, Robert A, additional, Mahajan, Abhishek, additional, Milchenko, Mikhail, additional, Nasrallah, MacLean, additional, Nazeri, Arash, additional, Puig, Josep, additional, Shukla, Gaurav, additional, Sloan, Andrew, additional, Taylor, William, additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Balana, Carmen, additional, Booth, Thomas C, additional, Cepeda, Santiago, additional, Poisson, Laila, additional, Colen, Rivka R, additional, Marcus, Daniel S, additional, Palmer, Joshua, additional, Jain, Rajan, additional, Shi, Wenyin, additional, O’Rourke, Donald M, additional, Barnholtz-Sloan, Jill, additional, and Davatzikos, Christos, additional

Published
2021
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47. NIMG-22. PREDICTION OF GLIOBLASTOMA CELLULAR INFILTRATION AND RECURRENCE USING MACHINE LEARNING AND MULTI-PARAMETRIC MRI ANALYSIS: RESULTS FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

Author

Akbari, Hamed, primary, Mohan, Suyash, additional, Garcia, Jose A, additional, Kazerooni, Anahita Fathi, additional, Sako, Chiharu, additional, Bakas, Spyridon, additional, Shukla, Gaurav, additional, Bagley, Stephen J, additional, Ahn, Sung Soo, additional, Ak, Murat, additional, Alexander, Gregory S, additional, Ali, Ayesha S, additional, Baid, Ujjwal, additional, Bavde, Chaitra, additional, Brem, Steven, additional, Capellades, Jaume, additional, Chang, Jong Hee, additional, Choi, Yoon Seong, additional, Dicker, Adam P, additional, Fathallah-Shaykh, Hassan, additional, Flanders, Adam E, additional, Griffith, Brent D, additional, LaMontagne, Pamela, additional, Lee, Matthew, additional, Lee, Seung-Koo, additional, Liem, Spencer, additional, Lombardo, Joseph, additional, Mahajan, Abhishek, additional, Milchenko, Mikhail, additional, Nazeri, Arash, additional, Puig, Josep, additional, Sloan, Andrew, additional, Taylor, William, additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Nasrallah, MacLean, additional, Bilello, Michel, additional, Lustig, Robert A, additional, Balana, Carmen, additional, Booth, Thomas C, additional, Cepeda, Santiago, additional, Poisson, Laila, additional, Colen, Rivka R, additional, Marcus, Daniel S, additional, Palmer, Joshua, additional, Jain, Rajan, additional, Shi, Wenyin, additional, O’Rourke, Donald M, additional, Barnholtz-Sloan, Jill, additional, and Davatzikos, Christos, additional

Published
2021
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