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1. Incorporation of Simulation in Graduate Medical Education: Historical Perspectives, Current Status, and Future Directions

Author

Premila D. Leiphrakpam, Priscila R. Armijo, and Chandrakanth Are

Subjects
Special aspects of education, LC8-6691, Medicine (General), R5-920
Abstract

Technological advancement and improved training strategies have transformed the healthcare practice environment in the last few decades. Simulation has evolved as one of the leading training models for the next generation of healthcare professionals. Simulation-based training enables healthcare professionals to acquire knowledge and skills in a safe and educationally oriented environment and can be a valuable tool for improving clinical practice and patient outcomes. The field of healthcare simulation has been rapidly growing, and various graduate medical education programs around the world have started incorporating this modality into their curricula. In graduate medical education, simulation-based training helps implement an outcome-based curriculum that tests the trainee's actual skill level as the primary factor for the trainee's competency rather than relying on the current model of a predetermined training period. However, the major challenge revolves around developing an educational curriculum incorporating a simulation-based educational model, understanding the value of this new technology, the overall cost factor, and the lack of adequate infrastructure. Hence, embracing the full potential of simulation technology in graduate medical education curricula requires an innovative approach with participation from institutions and stakeholders.

Published
2024
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2. PI3K/Akt/mTOR Signaling Pathway as a Target for Colorectal Cancer Treatment

Author

Premila D. Leiphrakpam and Chandrakanth Are

Subjects
colorectal cancer, PI3K, Akt, mTOR, cell survival, apoptosis, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

In the last decade, pathway-specific targeted therapy has revolutionized colorectal cancer (CRC) treatment strategies. This type of therapy targets a tumor-vulnerable spot formed primarily due to an alteration in an oncogene and/or a tumor suppressor gene. However, tumor heterogeneity in CRC frequently results in treatment resistance, underscoring the need to understand the molecular mechanisms involved in CRC for the development of novel targeted therapies. The phosphatidylinositol 3-kinase/protein kinase B/mammalian target of the rapamycin (PI3K/Akt/mTOR) signaling pathway axis is a major pathway altered in CRC. The aberrant activation of this pathway is associated with CRC initiation, progression, and metastasis and is critical for the development of drug resistance in CRC. Several drugs target PI3K/Akt/mTOR in clinical trials, alone or in combination, for the treatment of CRC. This review aims to provide an overview of the role of the PI3K/Akt/mTOR signaling pathway axis in driving CRC, existing PI3K/Akt/mTOR-targeted agents against CRC, their limitations, and future trends.

Published
2024
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3. Geographic variation in attitudes regarding management of locally advanced pancreatic cancer

Author

Logan R. McNeil, BA, Alex B. Blair, MD, Robert W. Krell, MD, Chunmeng Zhang, MD, Aslam Ejaz, MD, MPH, Vincent P. Groot, MD, PhD, Georgios Gemenetzis, MD, James C. Padussis, MD, Massimo Falconi, MD, Christopher L. Wolfgang, MD, PhD, Matthew J. Weiss, MD, Chandrakanth Are, MD, MBA, Jin He, MD, PhD, and Bradley N. Reames, MD, MS

Subjects
Surgery, RD1-811
Abstract

Background: Recent literature suggests wide variations exist in the international management of locally advanced pancreatic cancer. This study sought to evaluate how geography contributes to variations in management of locally advanced pancreatic cancer. Methods: An electronic survey investigating preferences for the evaluation and management of locally advanced pancreatic cancer was distributed to an international cohort of pancreatic surgeons. Surgeons were classified according to geographic location of practice, and survey responses were compared across locations. Results: A total of 153 eligible responses were received from 4 continents: North and South America (n = 94, 61.4%), Europe (n = 25, 16.3%), and Asia (n = 34, 22.2%). Preferences for the use and duration of neoadjuvant chemotherapy and radiotherapy varied widely. For example, participants in Asia commonly preferred 2 months of neoadjuvant chemotherapy (61.8%), whereas North and South American participants preferred 4 months (52.1%), and responses in Europe were mixed (P = .006). Participants in Asia were less likely to consider isolated liver or lung metastases contraindications to exploration and consequently had a greater propensity to consider exploration in a vignette of oligometastatic disease (56.7% vs North and South America: 25.6%, Europe: 43.5%; P = .007). Conclusion: In an international survey of pancreatic surgeons, attitudes regarding locally advanced pancreatic cancer and metastatic disease management varied widely across geographic locations. Better evidence is needed to define optimal management of locally advanced pancreatic cancer.

Published
2022
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4. Association of gravity drainage and complications following Whipple: an analysis of the ACS-NSQIP targeted database

Author

Bradley R. Hall, Zachary H. Egr, Robert W. Krell, James C. Padussis, Valerie K. Shostrom, Chandrakanth Are, and Bradley N. Reames

Subjects
Pancreatic ductal adenocarcinoma, Drain, Suction, Gravity, Morbidity, Surgery, RD1-811, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background The optimal type of operative drainage following pancreaticoduodenectomy (PD) remains unclear. Our objective is to investigate risk associated with closed drainage techniques (passive [gravity] vs. suction) after PD. Methods We assessed operative drainage techniques utilized in patients undergoing PD in the ACS-NSQIP pancreas-targeted database from 2016 to 2018. Using multivariable logistic regression to adjust for characteristics of the patient, procedure, and pancreas, we examined the association between use of gravity drainage and postoperative outcomes. Results We identified 9665 patients with drains following PD from 2016 to 2018, of which 12.7% received gravity drainage. 61.0% had a diagnosis of adenocarcinoma or pancreatitis, 26.5% had a duct

Published
2021
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5. ReeSPOT: Reeb Graph Models Semantic Patterns of Normalcy in Human Trajectories

Author

Zhang, Bowen, Shailja, S., Gudavalli, Chandrakanth, Levenson, Connor, Khan, Amil, and Manjunath, B. S.

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Social and Information Networks
Abstract

This paper introduces ReeSPOT, a novel Reeb graph-based method to model patterns of life in human trajectories (akin to a fingerprint). Human behavior typically follows a pattern of normalcy in day-to-day activities. This is marked by recurring activities within specific time periods. In this paper, we model this behavior using Reeb graphs where any deviation from usual day-to-day activities is encoded as nodes in the Reeb graph. The complexity of the proposed algorithm is linear with respect to the number of time points in a given trajectory. We demonstrate the usage of ReeSPOT and how it captures the critically significant spatial and temporal deviations using the nodes of the Reeb graph. Our case study presented in this paper includes realistic human movement scenarios: visiting uncommon locations, taking odd routes at infrequent times, uncommon time visits, and uncommon stay durations. We analyze the Reeb graph to interpret the topological structure of the GPS trajectories. Potential applications of ReeSPOT include urban planning, security surveillance, and behavioral research.

Published
2024

10. CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data

Author

Gudavalli, Chandrakanth, Rosten, Erik, Nataraj, Lakshmanan, Chandrasekaran, Shivkumar, and Manjunath, B. S.

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

Content creation and image editing can benefit from flexible user controls. A common intermediate representation for conditional image generation is a semantic map, that has information of objects present in the image. When compared to raw RGB pixels, the modification of semantic map is much easier. One can take a semantic map and easily modify the map to selectively insert, remove, or replace objects in the map. The method proposed in this paper takes in the modified semantic map and alter the original image in accordance to the modified map. The method leverages traditional pre-trained image-to-image translation GANs, such as CycleGAN or Pix2Pix GAN, that are fine-tuned on a limited dataset of reference images associated with the semantic maps. We discuss the qualitative and quantitative performance of our technique to illustrate its capacity and possible applications in the fields of image forgery and image editing. We also demonstrate the effectiveness of the proposed image forgery technique in thwarting the numerous deep learning-based image forensic techniques, highlighting the urgent need to develop robust and generalizable image forensic tools in the fight against the spread of fake media.

Published
2024

11. Neoadjuvant treatment of pancreatic adenocarcinoma: a systematic review and meta-analysis of 5520 patients

Author

Mashaal Dhir, Gautam K. Malhotra, Davendra P.S. Sohal, Nicholas A. Hein, Lynette M. Smith, Eileen M. O’Reilly, Nathan Bahary, and Chandrakanth Are

Subjects
Pancreatic cancer, Pancreatic adenocarcinoma, Neoadjuvant therapy, Outcomes, Survival, Surgery, RD1-811, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Recent years have seen standardization of the anatomic definitions of pancreatic adenocarcinoma, and increasing utilization of neoadjuvant therapy (NAT). The aim of the current review was to summarize the evidence for NAT in pancreatic adenocarcinoma since 2009, when consensus criteria for resectable (R), borderline resectable (BR), and locally advanced (LA) disease were endorsed. Methods PubMed search was undertaken along with extensive backward search of the references of published articles to identify studies utilizing NAT for pancreatic adenocarcinoma. Abstracts from ASCO-GI 2014 and 2015 were also searched. Results A total of 96 studies including 5520 patients were included in the final quantitative synthesis. Pooled estimates revealed 36% grade ≥ 3 toxicities, 5% biliary complications, 21% hospitalization rate and low mortality (0%, range 0–16%) during NAT. The majority of patients (59%) had stable disease. On an intention-to-treat basis, R0-resection rates varied from 63% among R patients to 23% among LA patients. R0 rates were > 80% among all patients who were resected after NAT. Among R and BR patients who underwent resection after NAT, median OS was 30 and 27.4 months, respectively. Conclusions The current study summarizes the recent literature for NAT in pancreatic adenocarcinoma and demonstrates improving outcomes after NAT compared to those historically associated with a surgery-first approach for pancreatic adenocarcinoma.

Published
2017
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27. Weekly paclitaxel, carboplatin and cetuximab (PCC) combination followed by nivolumab in platinum-sensitive recurrent and /or metastatic squamous cell carcinoma of head and neck: a retrospective analysis from two institutions in India [version 2; peer review: 2 approved with reservations]

Author

M V Chandrakanth, Vivek Agarwala, Pradip Mondal, Raajit Chanana, K M Parthasarathy, Sourav Dutta, Harsh Dhar, Suman Mallick, Sayan Das, Devmalya Banerjee, Md Arif Faizan, Moinak Basu, and Subhabrata Kumar

Subjects
Research Article, Articles, Palliative therapy, Cetuximab, Nivolumab, Immunotherapy, Paclitaxel, Carboplatin, Chemotherapy.
Abstract

Background: First line (1L) TP-Ex-like regimen followed by 2nd-line (2L) immunotherapy represents one of the standards of care in platinum-sensitive recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN). We report our experience from 2 tertiary care institutions of India. Methods: This is a retrospective analysis of consecutive patients of platinum-sensitive R/M SCCHN treated with 1L weekly paclitaxel, carboplatin, and cetuximab (PCC) regimen followed by cetuximab maintenance (if non-progressive) or 2L nivolumab or oral metronomic chemotherapy (OMCT) on progression. Overall response rates (ORR), progression-free survival after 1L and 2L (PFS-1 & PFS-2), overall survival (OS), and safety were evaluated. Results: The study included 54 patients; median age 56.5 years; 89% men; 11% had cardiac dysfunction; 13% had renal dysfunction. After 1L PCC, ORR was 59.3%; median PFS-1 was 7.031 months; 61% had progression; 35% were treated with nivolumab and 18% with OMCT. The ORR was 26.3% (nivolumab) and 10% (OMCT). Median PFS-2 was 6.5 months (nivolumab) and 2 months (OMCT). The median OS was 15.01 months (entire cohort), 20.6 months (nivolumab), and 7 months (OMCT). Grade III/IV adverse events on PCC included neutropenia (31.4%), anaemia (35.1%), thrombocytopenia (7.4%), febrile neutropenia (11.1%), and skin reaction (16.6%); no Grade-III/IV treatment-related toxicities on 2L. Conclusions: 1L weekly PCC is an effective regimen for palliative therapy of platinum- sensitive R/MSCCHN with an acceptable toxicity profile. The addition of 2L nivolumab on progression further improves the outcomes.

Published
2024
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30. 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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33. Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation

Author

Dhulipala, Somayajulu L. N., Shields, Michael D., Chakroborty, Promit, Jiang, Wen, Spencer, Benjamin W., Hales, Jason D., Laboure, Vincent M., Prince, Zachary M., Bolisetti, Chandrakanth, and Che, Yifeng

Subjects
Statistics - Applications, Statistics - Computation, Statistics - Machine Learning
Abstract

Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. We used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models consistently called the HF model least often. Next, for the 2D TRISO model, we considered two multifidelity modeling strategies: DNN LF prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction plus Kriging correction (physics-based). The physics-based strategy, as expected, consistently required the fewest calls to the HF model. However, the data-driven strategy had a lower overall simulation time since the DNN predictions are instantaneous, and the 1D TRISO model requires a non-negligible simulation time.

Published
2022

35. Seam Carving Detection and Localization using Two-Stage Deep Neural Networks

Author

Nataraj, Lakshmanan, Gudavalli, Chandrakanth, Mohammed, Tajuddin Manhar, Chandrasekaran, Shivkumar, and Manjunath, B. S.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Seam carving is a method to resize an image in a content aware fashion. However, this method can also be used to carve out objects from images. In this paper, we propose a two-step method to detect and localize seam carved images. First, we build a detector to detect small patches in an image that has been seam carved. Next, we compute a heatmap on an image based on the patch detector's output. Using these heatmaps, we build another detector to detect if a whole image is seam carved or not. Our experimental results show that our approach is effective in detecting and localizing seam carved images.

Published
2021
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38. SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

Author

Gudavalli, Chandrakanth, Rosten, Erik, Nataraj, Lakshmanan, Chandrasekaran, Shivkumar, and Manjunath, B. S.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or insert/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The datasets curated during this work will be released to the public.

Published
2021

43. 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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44. Molecular Pharming

Author

Kelmani Chandrakanth, R., primary, Gubbewad, Sahanapriya, additional, and Oli, Ajay Kumar, additional

Published
2023
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47. Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Author

Rastogi, Aditya, Brugnara, Gianluca, Foltyn-Dumitru, Martha, Mahmutoglu, Mustafa Ahmed, Preetha, Chandrakanth J, Kobler, Erich, Pflüger, Irada, Schell, Marianne, Deike-Hofmann, Katerina, Kessler, Tobias, van den Bent, Martin J, Idbaih, Ahmed, Platten, Michael, Brandes, Alba A, Nabors, Burt, Stupp, Roger, Bernhardt, Denise, Debus, Jürgen, Abdollahi, Amir, Gorlia, Thierry, Tonn, Jörg-Christian, Weller, Michael, Maier-Hein, Klaus H, Radbruch, Alexander, Wick, Wolfgang, Bendszus, Martin, Meredig, Hagen, Kurz, Felix T, and Vollmuth, Philipp

Published
2024
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