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  1. AU="Peoples, Jacob"
  2. AU="Cohen, Ehud" AU="Cohen, Ehud"
  3. AU="Ciechanover, Isaac"
  4. AU="Li, Shuyun"
  5. AU="de Oliveira E Silva, Ita"
  6. AU="Wang, Zhishan"
  7. AU="Grandel, Markus"
  8. AU="Abu-Asab, Mones"
  9. AU="Sikorska, Ewa"
  10. AU="Dye, S."
  11. AU="Smith, Jacqueline A M"
  12. AU="Fung, Hoki"
  13. AU="Schilizzi, B M"
  14. AU="Zhang, Nan"
  15. AU="Hu, Zhanggui"
  16. AU=Carty E
  17. AU="Ohki, Yutaro"
  18. AU="Nikolic, Luka"
  19. AU="Chan, Ho-Yin Edwin"
  20. AU="William Tam"
  21. AU="Kitandwe, Paul Kato"
  22. AU="Di Berardino, Chiara"
  23. AU="Li, Changlin"
  24. AU="Poku, Ohemaa"
  25. AU="Fallah, Milad"
  26. AU="Singh, Meetali"
  27. AU="Hickerson K. P."
  28. AU="Arvaniti, Christina"
  29. AU="Lu, Hongxia"

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  1. Article ; Online: Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

    Simpson, Amber L / Peoples, Jacob / Creasy, John M / Fichtinger, Gabor / Gangai, Natalie / Keshavamurthy, Krishna N / Lasso, Andras / Shia, Jinru / D'Angelica, Michael I / Do, Richard K G

    Scientific data

    2024  Volume 11, Issue 1, Page(s) 172

    Abstract: The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is ... ...

    Abstract The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.
    MeSH term(s) Humans ; Colorectal Neoplasms/pathology ; Hepatectomy/adverse effects ; Liver Neoplasms/secondary ; Tomography, X-Ray Computed
    Language English
    Publishing date 2024-02-06
    Publishing country England
    Document type Dataset ; Journal Article
    ZDB-ID 2775191-0
    ISSN 2052-4463 ; 2052-4463
    ISSN (online) 2052-4463
    ISSN 2052-4463
    DOI 10.1038/s41597-024-02981-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Deformable multimodal registration for navigation in beating-heart cardiac surgery.

    Peoples, Jacob J / Bisleri, Gianluigi / Ellis, Randy E

    International journal of computer assisted radiology and surgery

    2019  Volume 14, Issue 6, Page(s) 955–966

    Abstract: Purpose: Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation.: Methods: ...

    Abstract Purpose: Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation.
    Methods: The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT.
    Results: Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3 mm. Ultrasound and CT scans were registered to a mean of 3 mm, which is comparable to the error of 2.8 mm expected by merging ultrasound registration with uncertainty of cardiac CT.
    Conclusion: The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.
    MeSH term(s) Cardiac Surgical Procedures/methods ; Echocardiography, Transesophageal/methods ; Humans ; Imaging, Three-Dimensional/methods ; Minimally Invasive Surgical Procedures/methods ; Myocardial Contraction ; Surgery, Computer-Assisted/methods ; Tomography, X-Ray Computed
    Language English
    Publishing date 2019-03-19
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2365628-1
    ISSN 1861-6429 ; 1861-6410
    ISSN (online) 1861-6429
    ISSN 1861-6410
    DOI 10.1007/s11548-019-01932-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6615: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.

    Hu, Ricky / Chen, Ishita / Peoples, Jacob / Salameh, Jean-Paul / Gönen, Mithat / Romesser, Paul B / Simpson, Amber L / Reyngold, Marsha

    Physics and imaging in radiation oncology

    2022  Volume 24, Page(s) 36–42

    Abstract: Background and purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of ... ...

    Abstract Background and purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI).
    Materials and methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances.
    Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62-0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93-2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05-6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56-0.69), suggesting that predictive signals exist in radiomics and clinical data.
    Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.
    Language English
    Publishing date 2022-09-13
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2405-6316
    ISSN (online) 2405-6316
    DOI 10.1016/j.phro.2022.09.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A primer on texture analysis in abdominal radiology.

    Horvat, Natally / Miranda, Joao / El Homsi, Maria / Peoples, Jacob J / Long, Niamh M / Simpson, Amber L / Do, Richard K G

    Abdominal radiology (New York)

    2021  Volume 47, Issue 9, Page(s) 2972–2985

    Abstract: The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the ... ...

    Abstract The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.
    MeSH term(s) Humans ; Medical Oncology ; Precision Medicine ; Radiography ; Radiography, Abdominal ; Radiology
    Language English
    Publishing date 2021-11-26
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural
    ZDB-ID 2839786-1
    ISSN 2366-0058 ; 2366-004X
    ISSN (online) 2366-0058
    ISSN 2366-004X
    DOI 10.1007/s00261-021-03359-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1262: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: Mechanisms of Exercise Intolerance Across the Breast Cancer Continuum: A Pooled Analysis of Individual Patient Data.

    Scott, Jessica M / Lee, Jasme / Michalski, Meghan G / Batch, Karen / Simpson, Amber L / Peoples, Jacob / Lee, Catherine P / Harrison, Jenna N / Yu, Anthony F / Sasso, John P / Dang, Chau / Moskowitz, Chaya S / Jones, Lee W / Eves, Neil D

    Medicine and science in sports and exercise

    2023  Volume 56, Issue 4, Page(s) 590–599

    Abstract: Purpose: The purpose of this study is to evaluate the prevalence of abnormal cardiopulmonary responses to exercise and pathophysiological mechanism(s) underpinning exercise intolerance across the continuum of breast cancer (BC) care from diagnosis to ... ...

    Abstract Purpose: The purpose of this study is to evaluate the prevalence of abnormal cardiopulmonary responses to exercise and pathophysiological mechanism(s) underpinning exercise intolerance across the continuum of breast cancer (BC) care from diagnosis to metastatic disease.
    Methods: Individual participant data from four randomized trials spanning the BC continuum ([1] prechemotherapy [n = 146], [2] immediately postchemotherapy [n = 48], [3] survivorship [n = 138], and [4] metastatic [n = 47]) were pooled and compared with women at high-risk of BC (BC risk; n = 64). Identical treadmill-based peak cardiopulmonary exercise testing protocols evaluated exercise intolerance (peak oxygen consumption; V̇O2peak) and other resting, submaximal, and peak cardiopulmonary responses. The prevalence of 12 abnormal exercise responses was evaluated. Graphical plots of exercise responses were used to identify oxygen delivery and/or uptake mechanisms contributing to exercise intolerance. Unsupervised, hierarchical cluster analysis was conducted to explore exercise response phenogroups.
    Results: Mean V̇O2peak was 2.78 ml O2.kg-1·min-1 (95% confidence interval [CI], -3.94, -1.62 mL O2.kg-1·min-1; P < 0.001) lower in the pooled BC cohort (52 ± 11 yr) than BC risk (55 ± 10 yr). Compared with BC risk, the pooled BC cohort had a 2.5-fold increased risk of any abnormal cardiopulmonary response (odds ratio, 2.5; 95% confidence interval, 1.2, 5.3; P = 0.014). Distinct exercise responses in BC reflected impaired oxygen delivery and uptake relative to control, although considerable inter-individual heterogeneity within cohorts was observed. In unsupervised, hierarchical cluster analysis, six phenogroups were identified with marked differences in cardiopulmonary response patterns and unique clinical characteristics.
    Conclusions: Abnormal cardiopulmonary response to exercise is common in BC and is related to impairments in oxygen delivery and uptake. The identification of exercise response phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions.
    MeSH term(s) Humans ; Female ; Breast Neoplasms ; Oxygen Consumption/physiology ; Heart ; Exercise Test/methods ; Oxygen
    Chemical Substances Oxygen (S88TT14065)
    Language English
    Publishing date 2023-11-27
    Publishing country United States
    Document type Meta-Analysis ; Journal Article
    ZDB-ID 603994-7
    ISSN 1530-0315 ; 0195-9131 ; 0025-7990
    ISSN (online) 1530-0315
    ISSN 0195-9131 ; 0025-7990
    DOI 10.1249/MSS.0000000000003348
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 869: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: The RSNA Cervical Spine Fracture CT Dataset.

    Lin, Hui Ming / Colak, Errol / Richards, Tyler / Kitamura, Felipe C / Prevedello, Luciano M / Talbott, Jason / Ball, Robyn L / Gumeler, Ekim / Yeom, Kristen W / Hamghalam, Mohammad / Simpson, Amber L / Strika, Jasna / Bulja, Deniz / Angkurawaranon, Salita / Pérez-Lara, Almudena / Gómez-Alonso, María Isabel / Ortiz Jiménez, Johanna / Peoples, Jacob J / Law, Meng /
    Dogan, Hakan / Altinmakas, Emre / Youssef, Ayda / Mahfouz, Yasser / Kalpathy-Cramer, Jayashree / Flanders, Adam E

    Radiology. Artificial intelligence

    2023  Volume 5, Issue 5, Page(s) e230034

    Abstract: This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/. ...

    Abstract This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/.
    Language English
    Publishing date 2023-08-30
    Publishing country United States
    Document type Journal Article
    ISSN 2638-6100
    ISSN (online) 2638-6100
    DOI 10.1148/ryai.230034
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Author Correction: Federated learning enables big data for rare cancer boundary detection.

    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 / Bakas, Spyridon

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 436

    Language English
    Publishing date 2023-01-26
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-36188-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Federated learning enables big data for rare cancer boundary detection.

    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 /
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    Nature communications

    2022  Volume 13, Issue 1, Page(s) 7346

    Abstract: Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various ... ...

    Abstract Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
    MeSH term(s) Humans ; Big Data ; Glioblastoma ; Machine Learning ; Rare Diseases ; Information Dissemination
    Language English
    Publishing date 2022-12-05
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-022-33407-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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