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Your search keyword '"Farahani K"' showing total 213 results
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213 results on '"Farahani K"'

1. Operational Ontology for Oncology (O3) – A Professional Society Based, Multi-Stakeholder, Consensus Driven Informatics Standard Supporting Clinical and Research use of “Real -World” Data from Patients Treated for Cancer Operational Ontology for Radiation Oncology

Author

Mayo, CS, Feng, MU, Brock, KK, Kudner, R, Balter, P, Buchsbaum, JC, Caissie, A, Covington, E, Daugherty, EC, Dekker, AL, Fuller, CD, Hallstrom, AL, Hong, DS, Hong, JC, Kamran, SC, Katsoulakis, E, Kildea, J, Krauze, AV, Kruse, JJ, McNutt, T, Mierzwa, M, Moreno, A, Palta, JR, Popple, R, Purdie, TG, Richardson, S, Sharp, GC, Shiraishi, S, Tarbox, L, Venkatesan, AM, Witztum, A, Woods, KE, Yao, J, Farahani, K, Aneja, S, Gabriel, PE, Hadjiiski, L, Ruan, D, Siewerdsen, JH, Bratt, S, Casagni, M, Chen, S, Christodouleas, J, DiDonato, A, Hayman, J, Kapoor, R, Kravitz, S, Sebastian, S, Von Siebenthal, M, and Xiao, Y

Subjects
Medical and Biological Physics, Physical Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Networking and Information Technology R&D (NITRD), Patient Safety, Good Health and Well Being, Other Physical Sciences, Clinical Sciences, Oncology & Carcinogenesis, Oncology and carcinogenesis, Theoretical and computational chemistry, Medical and biological physics
Abstract

PurposeThe ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), Radiation Oncology Information Systems (ROIS), treatment planning systems (TPSs), and other cancer care and outcomes databases. The effort sought to create a standardized ontology for clinical data, social determinants of health (SDOH), and other radiation oncology concepts and interrelationships.Methods and materialsThe American Association of Physicists in Medicine's (AAPM's) Big Data Science Committee (BDSC) was initiated July of 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra- institutional databases from EHRs. The BDSC adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community.ResultsWe developed the Operational Ontology for Oncology (O3) which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, or the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to four constituencies: device manufacturers, centers of clinical care, researchers, and professional societies.ConclusionsO3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used creating large, representative, findable, accessible, interoperable and reusable (FAIR) datasets supporting the scientific objectives of grant programs. The construction of comprehensive "real world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative datasets.

Published
2023

2. Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Author

McNitt-Gray, M, Napel, S, Jaggi, A, Mattonen, SA, Hadjiiski, L, Muzi, M, Goldgof, D, Balagurunathan, Y, Pierce, LA, Kinahan, PE, Jones, EF, Nguyen, A, Virkud, A, Chan, HP, Emaminejad, N, Wahi-Anwar, M, Daly, M, Abdalah, M, Yang, H, Lu, L, Lv, W, Rahmim, A, Gastounioti, A, Pati, S, Bakas, S, Kontos, D, Zhao, B, Kalpathy-Cramer, J, and Farahani, K

Subjects
Information and Computing Sciences, Biomedical and Clinical Sciences, Software Engineering, Biomedical Imaging, Bioengineering, Humans, Image Processing, Computer-Assisted, Neoplasms, Positron Emission Tomography Computed Tomography, Radiometry, Reference Standards, Software, Radiomics, Quantitative Imaging, Standardization, Multi-center, Feature Definitions
Abstract

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography-computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Published
2020

3. Metrics reloaded: recommendations for image analysis validation

Author

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A., Wiesenfarth, M., Kavur, A.E., Sudre, C.H., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Radsch, T., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M.B., Cardoso, M.J., Cheplygina, V., Cimini, B.A., Collins, G.S., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Haase, R., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Karthikesalingam, A., Kofler, F., Kopp-Schneider, A., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Mattson, P., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rajpoot, N., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Smeden, M. van, Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Jager, P.F., Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A., Wiesenfarth, M., Kavur, A.E., Sudre, C.H., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Radsch, T., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M.B., Cardoso, M.J., Cheplygina, V., Cimini, B.A., Collins, G.S., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Haase, R., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Karthikesalingam, A., Kofler, F., Kopp-Schneider, A., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Mattson, P., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rajpoot, N., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Smeden, M. van, Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., and Jager, P.F.

Abstract

Contains fulltext : 305368.pdf (Publisher’s version ) (Closed access), Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Published
2024

4. Understanding metric-related pitfalls in image analysis validation

Author

Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Kavur, A.E., Radsch, T., Sudre, C.H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M.J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B.A., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Glocker, B., Godau, P., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Isensee, F., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S.M., Rajpoot, N., Reyes, M., Riegler, M.A., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Yaniv, Z.R., Jager, P.F., Maier-Hein, L., Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Kavur, A.E., Radsch, T., Sudre, C.H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M.J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B.A., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Glocker, B., Godau, P., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Isensee, F., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S.M., Rajpoot, N., Reyes, M., Riegler, M.A., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Yaniv, Z.R., Jager, P.F., and Maier-Hein, L.

Abstract

Contains fulltext : 305370.pdf (Publisher’s version ) (Closed access), Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Published
2024

5. Magnetic resonance imaging of plantar plate rupture.

Author

Yao, L, Cracchiolo, A, Farahani, K, and Seeger, LL

Subjects
Toes, Ligaments, Articular, Metatarsophalangeal Joint, Humans, Synovitis, Rupture, Spontaneous, Diagnosis, Differential, Magnetic Resonance Imaging, Sensitivity and Specificity, Reference Values, Aged, Middle Aged, Female, Male, Orthopedics, Clinical Sciences, Human Movement and Sports Sciences, Biomedical Engineering
Abstract

Degenerative plantar plate failure is an under-recognized cause of lesser metatarsalgia. We performed magnetic resonance imaging (MRI) with a small receiver coil in 13 patients in whom plantar plate ruptures of the second or third metatarsophalangeal joint were clinically suspected. In eight patients, MRI showed focal hyperintensity in the plantar plate that was interpreted as a rupture of the plate. Ruptures were confirmed in all five patients who underwent an operative procedure to treat the unstable, painful metatarsophalangeal joint. MRI is a noninvasive technique that can visualize plantar plate abnormalities and aid the clinical evaluation of problematic lesser metatarsalgia.

Published
1996

6. NCI Imaging Data Commons

Author

Fedorov, A., primary, Longabaugh, W., additional, Pot, D., additional, Clunie, D., additional, Pieper, S., additional, Lewis, R., additional, Aerts, H., additional, Homeyer, A., additional, Herrmann, M., additional, Wagner, U., additional, Pihl, T., additional, Farahani, K., additional, and Kikinis, R., additional

Published
2021
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7. Common limitations of image processing metrics: a picture story

Author

Reinke, A, Eisenmann, M, Tizabi, MD, Sudre, CH, Rädsch, T, Antonelli, M, Arbel, T, Bakas, S, Cardoso, MJ, Cheplygina, V, Farahani, K, Glocker, B, Heckmann-Nötzel, D, Isensee, F, Jannin, P, Kahn, CE, Kleesiek, J, Kurc, T, Kozubek, M, Landman, BA, Litjens, G, Maier-Hein, K, Menze, B, Müller, H, Petersen, J, Reyes, M, Rieke, N, Stieltjes, B, Summers, RM, Tsaftaris, SA, Ginneken, BV, Kopp-Schneider, A, Jäger, P, and Maier-Hein, L

Subjects
eess.IV, cs.CV
Abstract

While the importance of automatic image analysis is increasing at an enormous pace, recent meta-research revealed major flaws with respect to algorithm validation. Specifically, performance metrics are key for objective, transparent and comparative performance assessment, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. A common mission of several international initiatives is therefore to provide researchers with guidelines and tools to choose the performance metrics in a problem-aware manner. This dynamically updated document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts.

Published
2021

8. Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists

Author

Jacobs, C., Setio, A.A.A., Scholten, E.T., Gerke, P.K., Bhattacharya, H., Hoesein, F.A.M., Brink, M., Ranschaert, E., Jong, Pa. de, Silva, M., Geurts, B.H.J., Chung, K., Schalekamp, S., Meersschaert, J., Devaraj, A., Pinsky, P.F., Lam, S.C., Ginneken, B. van, Farahani, K., Jacobs, C., Setio, A.A.A., Scholten, E.T., Gerke, P.K., Bhattacharya, H., Hoesein, F.A.M., Brink, M., Ranschaert, E., Jong, Pa. de, Silva, M., Geurts, B.H.J., Chung, K., Schalekamp, S., Meersschaert, J., Devaraj, A., Pinsky, P.F., Lam, S.C., Ginneken, B. van, and Farahani, K.

Abstract

Contains fulltext : 245169.pdf (Publisher’s version ) (Closed access), Purpose: To determine whether deep learning algorithms developed in a public competition could identify lung cancer on low-dose CT scans with a performance similar to that of radiologists. Materials and Methods: In this retrospective study, a dataset consisting of 300 patient scans was used for model assessment; 150 patient scans were from the competition set and 150 were from an independent dataset. Both test datasets contained 50 cancer-positive scans and 100 cancer-negative scans. The reference standard was set by histopathologic examination for cancer-positive scans and imaging follow-up for at least 2 years for cancer-negative scans. The test datasets were applied to the three top-performing algorithms from the Kaggle Data Science Bowl 2017 public competition: grt123, Julian de Wit and Daniel Hammack (JWDH), and Aidence. Model outputs were compared with an observer study of 11 radiologists that assessed the same test datasets. Each scan was scored on a continuous scale by both the deep learning algorithms and the radiologists. Performance was measured using multireader, multicase receiver operating characteristic analysis. Results: The area under the receiver operating characteristic curve (AUC) was 0.877 (95% CI: 0.842, 0.910) for grt123, 0.902 (95% CI: 0.871, 0.932) for JWDH, and 0.900 (95% CI: 0.870, 0.928) for Aidence. The average AUC of the radiologists was 0.917 (95% CI: 0.889, 0.945), which was significantly higher than grt123 (P = .02); however, no significant difference was found between the radiologists and JWDH (P = .29) or Aidence (P = .26). Conclusion: Deep learning algorithms developed in a public competition for lung cancer detection in low-dose CT scans reached performance close to that of radiologists.Keywords: Lung, CT, Thorax, Screening, Oncology Supplemental material is available for this article. (c) RSNA, 2021.

Published
2021

10. Development of a PET detector system compatible with MRI/NMR systems

Author

Shao, Y., Cherry, S.R., Farahani, K., Slates, R., Silverman, R.W., Meadors, K., Bowery, A., Siegel, S., Marsden, P.K., and Garlick, P.B.

Subjects
PET imaging -- Equipment and supplies, Business, Electronics, Electronics and electrical industries
Abstract

We report the development of a prototype positron emission tomography (PET) scanner compatible with clinical magnetic resonance imaging (MRI) scanners and nuclear magnetic resonance (NMR) spectrometers. This single slice PET system consists of 72 2x2x5 mm lutetium oxyorthosilicate (LSO) crystals coupled by 2 mm diameter, 4 meter long double clad optical fibers to three multi-channel photomultiplier tubes (MC-PMT's) shielded inside an aluminum closure. The ring diameter is 54 mm and the slice thickness is [approximately]1 mm FWHM. Measurements with a point source demonstrate that this PET system has a reconstructed resolution of 2.1 mm, a coincidence time resolution of 26 ns and a typical energy resolution of 45%. Simultaneously acquired PET and MR phantom images show no significant artifacts or distortions. We also obtained simultaneous NMR spectra and PET images from an isolated, perfused rat heart, demonstrating the power of obtaining temporally correlated PET and NMR information in biological systems. Again, no artifacts in the PET or NMR data were apparent, despite the high field strength of 9.4T. The challenge for the future is to scale up the design to develop a high resolution, high sensitivity device that can be used in simultaneous PET and MR studies of in vivo systems.

Published
1997

15. Contributors

Author

Abi-Dargham, Anissa, primary, Abrunhosa, A.J., additional, Aigner, T.G., additional, Alpert, Nathaniel M., additional, Andermann, Mark, additional, Anderson, J.R., additional, Andersson, Jesper L. R,, additional, Andreason, Paul, additional, Antonini, A., additional, Arai, Hiroyuki, additional, Ardekani, B.A., additional, Ashburner, John, additional, Ashworth, S., additional, Bailey, D.L., additional, Bánáti, Richard B., additional, Baron, J.C., additional, Barrio, Jorge R., additional, Bauer, R., additional, Beattie, Bradley J., additional, Bergmann, R., additional, Berman, Karen Faith, additional, Berzdorf, A., additional, Besret, L., additional, Blasberg, Ronald G., additional, Bloomfìeld, P.M., additional, Bonab, Ali A., additional, Bowery, A., additional, Brady, F., additional, Brooks, David J., additional, Brühlmeier, M., additional, Brust, P., additional, Budinger, T.F., additional, Byrne, Helen, additional, Carson, Richard E., additional, Chan, G.L. Y., additional, Chatziioannou, Arion, additional, Chefer, Svetlana I., additional, Chen, Chin-Tu, additional, Cherry, Simon R., additional, Cheung, K., additional, Chugani, Diane C., additional, Chugani, Harry T., additional, Cooper, Malcolm, additional, Cunningham, Vincent J., additional, Dagher, Alain, additional, Dahlbom, M., additional, Danielsen, E.H., additional, DaSilva, J.N., additional, Davis, James, additional, de Lima, J.J., additional, DeJesus, O.T., additional, Derenzo, S.E., additional, Dhawan, V., additional, Dogan, A.S., additional, Doudet, D.J., additional, Drevets, W., additional, Duncan, John, additional, Eidelberg, D., additional, Ellmore, Timothy M., additional, Endres, Christopher J., additional, English, C., additional, Esposito, Giuseppe, additional, Evans, Alan C., additional, Farahani, K., additional, Feng, Dagan, additional, Ficaro, Edward P., additional, Fischer, N., additional, Fischman, Alan J., additional, Fiset, Pierre, additional, Frey, Kirk A., additional, Friston, K.J., additional, Füchtner, F., additional, Fukushi, K., additional, Gee, A.D., additional, Ghaemi, M., additional, Ghez, C., additional, Ghilardi, M.F., additional, Gillispie, Steven B., additional, Gjedde, Albert, additional, Graf, R., additional, Grafton, Scott T., additional, Graham, Michael M., additional, Grasby, Paul M., additional, Greenwald, E., additional, Gunn, Roger N., additional, Günther, I., additional, Hansen, L.K., additional, Hansen, Søren B., additional, Heiss, W.-D., additional, Herholz, K., additional, Higuchi, Makoto, additional, Hirani, E., additional, Ho, D., additional, Hoffman, John M., additional, Holden, J.E., additional, Holt, Daniel, additional, Holt, John L., additional, Hommer, Daniel W., additional, Horwitz, Barry, additional, Houle, Sylvain, additional, Huang, Sung-Cheng, additional, Huang, Yiyun, additional, Huesman, R.H., additional, Hume, S.P., additional, Hussey, D., additional, Ibazizene, M., additional, Ido, Tatsuo, additional, Ilmberger, J., additional, Inaba, T., additional, Innis, Robert B., additional, Irie, T., additional, Ishii, Kenji, additional, Ito, K., additional, Itoh, Masatoshi, additional, Iyo, M., additional, Jivan, S., additional, Johannsen, B., additional, Johannsen, Peter, additional, Jones, Terry, additional, Kanno, Iwao, additional, Kapur, S., additional, Kawashima, Ryuta, additional, Kazumata, K., additional, Kilbourn, Michael R., additional, Klein, Denise, additional, Klein, G.J., additional, Koepp, Matthias, additional, Koeppe, Robert A., additional, Kuhl, David E., additional, Kumura, E., additional, Künig, G., additional, Labbé, Claire, additional, Lammertsma, Adriaan A., additional, Landeau, B., additional, Lange, N., additional, Larson, Steve M., additional, Laruelle, Marc, additional, Lau, K.K., additional, Law, I., additional, Leenders, K.L., additional, Lin, K.P., additional, Litt, Harold, additional, Livieratos, L., additional, Lockwood, Geoff, additional, London, Edythe D., additional, Lopresti, Brian, additional, Löttgen, J., additional, Luthra, S.K., additional, Ma, Yilong, additional, MacLeod, A.M., additional, Marenco, S., additional, Marrett, S., additional, Mason, N. Scott, additional, Mathis, Chester A., additional, Matthews, Julian C., additional, Mawlawi, Osama R., additional, Meadors, Ken, additional, Meikle, S.R., additional, Meyer, Ernst, additional, Miller, David H., additional, Miller, M.P., additional, Minoshima, Satoshi, additional, Missimer, J., additional, Moeller, J.R., additional, Moore, A.H., additional, Moran, L., additional, Moreno-Cantú, Jorge J., additional, Morris, Evan D., additional, Morris, H., additional, Morrish, P.K., additional, Morrison, K.S., additional, Moses, W.W., additional, Muzi, Mark, additional, Muzik, Otto, additional, Myers, Ralph, additional, Nagatsuka, S., additional, Namba, H., additional, Nguyen, Thinh B., additional, O'Sullivan, Finbarr, additional, Oakes, T.R., additional, Oda, Keiichi, additional, Ohta, K., additional, Okamura, Nobuyuki, additional, Opacka-Juffry, J., additional, Osman, S., additional, Østergaard, Leif, additional, Paulesu, Eraldo, additional, Paulson, O.B., additional, Paus, T., additional, Pawlik, G., additional, Perevuznik, Jennifer, additional, Petit-Taboué, M.C., additional, Phelps, Michael E., additional, Pietrzyk, U., additional, Price, Julie C., additional, Price, Pat M., additional, Psylla, M., additional, Raffel, D.M., additional, Rakshi, J.S., additional, Raleigh, Michael J., additional, Rawlings, Robert R., additional, Rehm, K., additional, Reulen, H. -J., additional, Reutens, David C., additional, Reutter, B.W., additional, Richardson, Mark, additional, Rio, Daniel, additional, Rottenberg, D.A., additional, Rousset, Olivier G., additional, Ruszkiewicz, James, additional, Ruth, T.J., additional, Ruttimann, Urs E., additional, Sadato, Norihiro, additional, Sasaki, Hidetada, additional, Schaper, K.A., additional, Schumann, P., additional, Schuster, A., additional, Senda, Michio, additional, Shao, Yiping, additional, Shen, Chenggang, additional, Shinotoh, H., additional, Silverman, Robert W., additional, Simpson, N.R., additional, Siu, Wan-Chi, additional, Slates, R., additional, Smith, D.F., additional, Smith, Gwenn S., additional, Snyder, Scott E., additional, Sobesky, J., additional, Søiling, Thomas, additional, Sossi, V., additional, Spinks, Terry J., additional, Steinbach, J., additional, Stout, David B., additional, Strother, S.C., additional, Sudo, Y., additional, Sugita, M., additional, Suhara, T., additional, Suzuki, K., additional, Tatsumi, Itaru, additional, Teng, X., additional, Thiel, A., additional, Thompson, Christopher J., additional, Thorpe, John, additional, Toussaint, P.-J., additional, Toyama, Hinako, additional, Uema, T., additional, Vafaee, M.S., additional, Van Horn, John Darrell, additional, Venkatachalam, T.K., additional, Virador, P.R.G., additional, von Stockhausen, H.-M., additional, Vontobel, P., additional, Vorwieger, G., additional, Votaw, John R., additional, Walter, B., additional, Wienhard, K., additional, Wilson, A.A., additional, Wong, Dean F., additional, Wong, Koon-Pong, additional, Wu, Chi-Ming, additional, Wu, L.C., additional, Yamaki, Atsushi, additional, Yanai, Kazuhiko, additional, Yang, J., additional, Yap, Jeffrey T., additional, Yokoi, Fuji, additional, Young, A.R., additional, Yu, C.L., additional, and Zatorre, Robert J., additional

Published
1998
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18. Machine learning applications in head and neck radiation oncology

Author

Elhalawani, H., Lin, T.A., Volpe, S., Mohamed, A.S.R., White, A.L., Zafereo, J., Wong, A.J., Berends, J.E., AboHashem, S., Williams, B., Aymard, J.M., Kanwar, A., Perni, S., Rock, C.D., Cooksey, L., Campbell, S., Yang, P., Nguyen, K., Ger, R.B., Cardenas, C.E., Fave, X.J., Sansone, C., Piantadosi, G., Marrone, S., Liu, R., Huang, C., Yu, K., Li, T., Yu, Y., Zhang, Y., Zhu, H., Morris, J.S., Baladandayuthapani, V., Shumway, J.W., Ghosh, A., Pöhlmann, A., Phoulady, H.A., Goyal, V., Canahuate, G., Marai, G.E., Vock, D., Lai, S.Y., Mackin, D.S., Court, L.E., Freymann, J., Farahani, K., Kaplathy-Cramer, J., Fuller, C.D., and Publica

Abstract

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the ""HPV"" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the ""local recurrence"" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Published
2018

19. PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Author

Armato, S.G., Huisman, H.J., Drukker, K., Hadjiiski, L., Kirby, J.S., Petrick, N., Redmond, G., Giger, M.L., Cha, K., Mamonov, A., Kalpathy-Cramer, J., Farahani, K., Armato, S.G., Huisman, H.J., Drukker, K., Hadjiiski, L., Kirby, J.S., Petrick, N., Redmond, G., Giger, M.L., Cha, K., Mamonov, A., Kalpathy-Cramer, J., and Farahani, K.

Abstract

Item does not contain fulltext, Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from - 0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.

Published
2018

21. Characterization of the diffusion properties of different gadolinium-based MRI contrast agents after ultrasound induced blood–brain barrier permeabilization

Author

Fowlkes, B, Ghanouni, P, Sanghvi, N, Coussios, C, Lyon, Pc, Gray, M, Mannaris, C, Victor, Mds, Stride, E, Cleveland, R, Carlisle, R, Feng, W, Middleton, M, Gleeson, F, Aubry, J, Pauly, Kb, Moonen, C, Vortman, J, Sharabi, S, Daniels, D, Last, D, Guez, D, Levy, Y, Volovick, A, Grinfeld, J, Rachmilevich, I, Amar, T, Zibly, Z, Mardor, Y, Harnof, S, Plaksin, M, Weissler, Y, Shoham, S, Kimmel, E, Naor, O, Farah, N, Paeng, D, Zhiyuan, X, Snell, J, Quigg, Ah, Eames, M, Jin, C, Everstine, Ac, Sheehan, Jp, Lopes, Bs, Kassell, N, Looi, T, Khokhlova, V, Mougenot, C, Hynynen, K, Drake, J, Slayton, M, Amodei, Rc, Compton, K, Mcnelly, A, Latt, D, Kearney, J, Melodelima, D, Dupre, A, Chen, Y, Perol, D, Vincenot, J, Chapelon, J, Rivoire, M, Guo, W, Ren, G, Shen, G, Neidrauer, M, Zubkov, L, Weingarten, Ms, Margolis, Dj, Lewin, Pa, Mcdannold, N, Sutton, J, Vykhodtseva, N, Livingstone, M, Kobus, T, Zhang, Y, Schwartz, M, Huang, Y, Lipsman, N, Jain, J, Chapman, M, Sankar, T, Lozano, A, Yeung, R, Damianou, C, Papadopoulos, N, Brokman, O, Zadicario, E, Brenner, O, Castel, D, Shih-Ying, W, Grondin, J, Zheng, W, Heidmann, M, Karakatsani, Me, Sánchez, Cjs, Ferrera, V, Konofagou, Ee, Yiannakou, M, Cho, H, Lee, H, Han, M, Choi, J, Lee, T, Ahn, S, Chang, Y, Park, J, Ellens, N, Partanen, A, Farahani, K, Airan, R, Carpentier, A, Canney, M, Vignot, A, Lafon, C, Delattre, J, Idbaih, A, Odéen, H, Bolster, B, Jeong, Ek, Parker, Dl, Gaur, P, Feng, X, Fielden, S, Meyer, C, Werner, B, Grissom, W, Marx, M, Weber, H, Taviani, V, Hargreaves, B, Tanaka, J, Kikuchi, K, Ishijima, A, Azuma, T, Minamihata, K, Yamaguchi, S, Nagamune, T, Sakuma, I, Takagi, S, Santin, Md, Marsac, L, Maimbourg, G, Monfort, M, Larrat, B, François, C, Lehéricy, S, Tanter, M, Samiotaki, G, Wang, S, Acosta, C, Feinberg, Er, Kovacs, Zi, Tsang-Wei, T, Papadakis, Gz, Reid, Wc, Hammoud, Da, Frank, Ja, Kim, S, Jikaria, N, Bresler, M, Qureshi, F, Xia, J, Tsui, P, Liu, H, Plata, Jc, Sveinsson, B, Salgaonkar, Va, Adams, M, Diederich, C, Ozhinsky, E, Bucknor, Md, Rieke, V, Mikhail, A, Severance, L, Negussie, Ah, Wood, B, de Greef, M, Schubert, G, Ries, M, Poorman, Me, Dockery, M, Chaplin, V, Dudzinski, So, Spears, R, Caskey, C, Giorgio, T, Costa, Mm, Papaevangelou, E, Shah, A, Rivens, I, Box, C, Bamber, J, ter Haar, G, Burks, Sr, Nagle, M, Nguyen, B, Milo, B, Nhan M., L, Song, S, Zhou, K, Nabi, G, Huang, Z, Ben-Ezra, S, Rosen, S, Mihcin, S, Strehlow, J, Karakitsios, I, Nhan, L, Schwenke, M, Demedts, D, Prentice, P, Haase, S, Preusser, T, Melzer, A, Mestas, J, Chettab, K, Gomez, Gs, Dumontet, C, Werle, B, Marquet, F, Bour, P, Vaillant, F, Amraoui, S, Dubois, R, Ritter, P, Haïssaguerre, M, Hocini, M, Bernus, O, Quesson, B, Livneh, A, Adam, D, Robin, J, Arnal, B, Fink, M, Pernot, M, Khokhlova, Td, Schade, Gr, Wang, Y, Kreider, W, Simon, J, Starr, F, Karzova, M, Maxwell, A, Bailey, Mr, Lundt, Je, Allen, Sp, Sukovich, Jr, Hall, T, Zhen, X, May, P, Lin, Dw, Constans, C, Deffieux, T, Park, E, Ahn, Yd, Kang, Sy, Park, D, Lee, Jy, Vidal-Jove, J, Perich, E, Ruiz, A, Jaen, A, Eres, N, del Castillo, Ma, Myers, R, Kwan, J, Coviello, C, Rowe, C, Crake, C, Finn, S, Jackson, E, Pouliopoulos, A, Caiqin, L, Tinguely, M, Tang, M, Garbin, V, Choi, Jj, Folkes, L, Stratford, M, Nwokeoha, S, Tong, L, Farr, N, D’Andrea, S, Gravelle, K, Chen, H, Lee, D, Hwang, Jh, Tardoski, S, Ngo, J, Gineyts, E, Roux, J, Clézardin, P, Conti, A, Magnin, R, Gerstenmayer, M, Lux, F, Tillement, O, Mériaux, S, Penna, Sd, Romani, Gl, Dumont, E, Sun, T, Power, C, Miller, E, Sapozhnikov, O, Tsysar, S, Yuldashev, Pv, Svet, V, Dongli, L, Pellegrino, A, Petrinic, N, Siviour, C, Jerusalem, A, Cunitz, Bw, Dunmire, B, Inserra, C, Guedra, M, Mauger, C, Gilles, B, Solovchuk, M, Sheu, Twh, Thiriet, M, Zhou, Y, Neufeld, E, Baumgartner, C, Payne, D, Kyriakou, A, Kuster, N, Xiao, X, Mcleod, H, Dillon, C, Payne, A, Khokhova, Va, Sinilshchikov, I, Andriyakhina, Y, Rybyanets, A, Shvetsova, N, Berkovich, A, Shvetsov, I, Shaw, Cj, Civale, J, Giussani, D, Lees, C, Ozenne, V, Toupin, S, Salgaonkar, V, Kaye, E, Monette, S, Maybody, M, Srimathveeravalli, G, Solomon, S, Gulati, A, Bezzi, M, Jenne, Jw, Lango, T, Müller, M, Sat, G, Tanner, C, Zangos, S, Günther, M, Dinh, Ah, Niaf, E, Bratan, F, Guillen, N, Souchon, R, Lartizien, C, Crouzet, S, Rouviere, O, Han, Y, Payen, T, Palermo, C, Sastra, S, Olive, K, van Breugel, Jm, van den Bosch, Ma, Fellah, B, Le Bihan, D, Hernandez-Garcia, L, Cain, Ca, Lyka, E, Elbes, D, Chunhui, L, Tamano, S, Jimbo, H, Yoshizawa, S, Fujiwara, K, Itani, K, Umemura, S, Stoianovici, D, Zaini, Z, Takagi, R, Zong, S, Watkins, R, Pascal-Tenorio, A, Jones, P, Butts-Pauly, K, Bouley, D, Lin, C, Hsieh, H, Wei, K, Garnier, C, Renault, G, Seifabadi, R, Wilson, E, Eranki, A, Kim, P, Lübke, D, Huber, P, Georgii, J, Dresky, Cv, Haller, J, Yarmolenko, P, Sharma, K, Celik, H, Guofeng, L, Qiu, W, Zheng, H, Tsai, M, Chu, P, Webb, T, Vyas, U, Walker, M, Zhong, J, Waspe, Ac, Hodaie, M, Yang, F, Huang, S, Zur, Y, Assif, B, Aurup, C, Kamimura, H, Carneiro, Aa, Rothlübbers, S, Schwaab, J, Houston, G, Azhari, H, Weiss, N, Sosna, J, Goldberg, Sn, Barrere, V, Jang, Kw, Lewis, B, Wang, X, Suomi, V, Edwards, D, Larrabee, Z, Hananel, A, Rafaely, B, Debbiny, Re, Dekel, Cz, Assa, M, Menikou, G, Mouratidis, P, Pineda-Pardo, Ja, de Pedro, Mda, Martinez, R, Hernandez, F, Casas, S, Oliver, C, Pastor, P, Vela, L, Obeso, J, Greillier, P, Zorgani, A, Catheline, S, Solovov, V, Vozdvizhenskiy, Mo, Orlov, Ae, Chueh-Hung, W, Sun, M, Shih, Tt, Chen, W, Prieur, F, Pillon, A, Cartron, V, Cebe, P, Chansard, N, Lafond, M, Seya, Pm, Bera, J, Boissenot, T, Fattal, E, Bordat, A, Chacun, H, Guetin, C, Tsapis, N, Maruyama, K, Unga, J, Suzuki, R, Fant, C, Rogez, B, Afadzi, M, Myhre, Of, Vea, S, Bjørkøy, A, Yemane, Pt, van Wamel, A, Berg, S, Hansen, R, Angelsen, B, and Davies, C

Subjects
Settore FIS/07
Published
2017

28. Effetti del trattamento con sildenafil nei pazienti con ipertensione arteriosa polmonare idiopatica, associata e cuore polmonare cronico tromboembolico

Author

ROMANAZZI, SERENA, MANES, ALESSANDRA, GAMBETTI, SIMONA, LECI, ENRI, NEGRO, LUCA, PALAZZINI, MASSIMILIANO, PRESOTTO, LUISA, GALIE', NAZZARENO, BRANZI, ANGELO, Marinelli A, Vaziri Farahani K, NICOLOSI GL, Romanazzi S, Manes A, Gambetti S, Marinelli A, Leci E, Negro L, Vaziri Farahani K, Palazzini M, Presotto L, Galie N, and Branzi A.

Published
2005

30. Significato prognostico dell'aerea del rigurgito tricuspidalico nei pazienti con ipertensione arteriosa polmonare idiopatica trattati secondo le attuali linee guida

Author

MANES, ALESSANDRA, FERLITO, MARINELLA, ROMANAZZI, SERENA, NEGRO, LUCA, PALAZZINI, MASSIMILIANO, PRESOTTO, LUISA, GAMBETTI, SIMONA, LECI, ENRI, GALIE', NAZZARENO, BRANZI, ANGELO, Rocchi G, Vaziri Farahani K, Marinelli A, NICOLOSI GL, Manes A, Rocchi G, Ferlito M, Vaziri Farahani K, Romanazzi S, Negro L, Palazzini M, Presotto L, Marinelli A, Gambetti S, Leci E, Galie N, and Branzi A

Published
2005

31. Il test acuto di vasoreattività nei pazienti con ipertensione arteriosa polmonare

Author

NEGRO, LUCA, MANES, ALESSANDRA, LECI, ENRI, GAMBETTI, SIMONA, PRESOTTO, LUISA, ROMANAZZI, SERENA, PALAZZINI, MASSIMILIANO, GALIE', NAZZARENO, BRANZI, ANGELO, Marinelli A, Vaziri Farahani K, NICOLOSI GL, Negro L, Manes A, Leci E, Gambetti S, Marinelli A, Presotto L, Romanazzi S, Vaziri Farahani K, Palazzini M, Galie N, and Branzi A.

Published
2005

32. Regressione delle alterazioni elettrocardiografiche indicative di ipertrofia ventricolare destra nei pazienti con ipertensione arteriosa polmonare

Author

Vaziri Farahani K, Marinelli A, ROMANAZZI, SERENA, PALAZZINI, MASSIMILIANO, NEGRO, LUCA, PRESOTTO, LUISA, GAMBETTI, SIMONA, LECI, ENRI, MANES, ALESSANDRA, GALIE', NAZZARENO, BRANZI, ANGELO, NICOLOSI GL, Vaziri Farahani K, Romanazzi S, Palazzini M, Negro L, Presotto L, Marinelli A, Gambetti S, Leci E, Manes A, Galie N, and Branzi A.

Published
2005

45. Therapy Monitoring by MRI

Author

Hamilton, D. R., primary, Anzai, Y., additional, Black, K. L., additional, Farahani, K., additional, and Lufkin, R. B., additional

Published
2007
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