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2. 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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3. Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge.

Author

Schmidt K, Bearce B, Chang K, Coombs L, Farahani K, Elbatel M, Mouheb K, Marti R, Zhang R, Zhang Y, Wang Y, Hu Y, Ying H, Xu Y, Testagrose C, Demirer M, Gupta V, Akünal Ü, Bujotzek M, Maier-Hein KH, Qin Y, Li X, Kalpathy-Cramer J, and Roth HR

Subjects
Humans, Female, Machine Learning, Breast Density, Mammography methods, Breast Neoplasms diagnostic imaging, Algorithms
Abstract

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Published
2024
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4. Sutureless versus transcatheter aortic valve replacement: A multicenter analysis of "real-world" data.

Author

Santarpino G, Lorusso R, Moscarelli M, Mikus E, Wisniewski K, Dell'Aquila AM, Margari V, Carrozzo A, Barbato L, Fiorani V, Lamarra M, Fattouch K, Squeri A, Giannini F, Marchese A, Farahani K, Gregorini R, Comoglio C, Martinelli L, Calvi S, Avolio M, Paparella D, Albertini A, and Speziale G

Subjects
Aortic Valve surgery, Humans, Risk Factors, Treatment Outcome, Aortic Valve Stenosis, Heart Valve Prosthesis Implantation methods, Transcatheter Aortic Valve Replacement adverse effects
Abstract

Background: Recent data suggested that transcatheter aortic valve replacement (TAVR) may be indicated also for low-risk patients. However, robust evidence is still lacking, particularly regarding valve performance at follow-up that confers a limitation to its use in young patients. Moreover, a literature gap exists in terms of 'real-world' data analysis. The aim of this study is to compare the cost-effectiveness of sutureless aortic valve replacement (SuAVR) versus transfemoral TAVR., Methods: Prospectively collected data were retrieved from a centralized database of nine cardiac surgery centers between 2010 and 2018. Follow-up was completed in June 2019. A propensity score matching (PSM) analysis was performed., Results: Patients in the TAVR group (n=1002) were older and with more comorbidities than SuAVR patients (n=443). The PSM analysis generated 172 pairs. No differences were recorded between groups in 30-day mortality [SuAVR vs TAVR: n=7 (4%) vs n=5 (2.9%); p=0.7] and need for pacemaker implant [n=10 (5.8%) vs n=20 (11.6%); p=0.1], but costs were lower in the SuAVR group (20486.6±4188€ vs 24181.5±3632€; p<0.01). Mean follow-up was 1304±660 days. SuAVR patients had a significantly higher probability of survival than TAVR patients (no. of fatal events: 22 vs 74; p<0.014). Median follow-up was 2231 days and 2394 days in the SuAVR and TAVR group, respectively., Conclusion: The treatment of aortic valve stenosis with surgical sutureless or transcatheter prostheses is safe and effective. By comparing the two approaches, patients who can undergo surgery after heart team evaluation show longer lasting results and a more favorable cost ratio., (Copyright © 2021. Published by Elsevier Ltd.)

Published
2022
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5. MRI of thermally denatured blood: methemoglobin formation and relaxation effects.

Author

Farahani K, Saxton RE, Yoon HC, De Salles AA, Black KL, and Lufkin RB

Subjects
Animals, Erythrocytes chemistry, In Vitro Techniques, Male, Plasma chemistry, Protein Denaturation, Spectrophotometry, Swine, Blood metabolism, Hot Temperature, Magnetic Resonance Imaging, Methemoglobin biosynthesis
Abstract

Focal regions of T1-shortening have been observed in magnetic resonance imaging (MRI)-monitored thermal ablations of perfused tissues. The aims of this study were two-fold: to find evidence for heat-induced conversion of hemoglobin (Hb) to methemoglobin (mHb), and to investigate the effects of heat treatment of in-vitro blood components upon their MR relaxation times. Spectrophotometric studies were performed to confirm the heat-induced formation of methemoglobin. Preparations of whole and fractionated blood, previously submitted to elevated temperatures of 40 degrees C to 80 degrees C, were imaged and the relaxation times were calculated. Optical absorption spectra of samples containing free Hb, heated to 60 degrees C, showed increased light absorption at 630 nm, evident of mHb presence. Short T1 values in whole blood (1.13 s) and packed red blood cell (0.65 s) compartments, heated at 60 degrees C, compared to their baseline values (1.62 s and 0.83 s, respectively), were attributed to mHb formation. In relation to MRI-guided thermal interventions, these results suggest a possible explanation for observation of hyperintense regions on T1-weighted images.

Published
1999
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