Publications
Opportunities to address gaps in early detection and improve outcomes of liver cancer
McMahon, B,
Cohen, C,
Brown, RS,
El-Serag, H,
Ioannou, GN,
Lok, AS,
Roberts, LR,
Singal, AG,
Block, T,
JNCI Cancer Spectr, 2023 May; DOI:10.1093/jncics/pkad034 Dose prescription and reporting in stereotactic body radiotherapy: A multi-institutional study
Das, IJ,
Yadav, P,
Andersen, AD,
Chen, ZJ,
Huang, L,
Langer, MP,
Lee, C,
Li, L,
Popple, RA,
Rice, RK,
Schiff, PB,
Zhu, TC,
Abazeed, ME,
Radiother Oncol, 2023 May; DOI:10.1016/j.radonc.2023.109571 Federated learning enables big data for rare cancer boundary detection
Pati, S,
Baid, U,
Edwards, B,
Sheller, M,
Wang, S-H,
Reina, GA,
Foley, P,
Gruzdev, A,
Karkada, D,
Davatzikos, C,
Sako, C,
Ghodasara, S,
Bilello, M,
Mohan, S,
Vollmuth, P,
Brugnara, G,
Preetha, CJ,
Sahm, F,
Maier-Hein, K,
Zenk, M,
Nat Commun, 2022 Dec; DOI:10.1038/s41467-022-33407-5 Determinants of telemedicine adoption among financially distressed patients with cancer during the COVID-19 pandemic: insights from a nationwide study.
Hassan, AM,
Chu, CK,
Liu, J,
Angove, R,
Rocque, G,
Gallagher, KD,
Momoh, AO,
Caston, NE,
Williams, CP,
Wheeler, S,
Butler, CE,
Offodile, AC,
Support Care Cancer, 2022 Sep; DOI:10.1007/s00520-022-07204-1 Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer.
Dadhania, V,
Gonzalez, D,
Yousif, M,
Cheng, J,
Morgan, TM,
Spratt, DE,
Reichert, ZR,
Mannan, R,
Wang, X,
Chinnaiyan, A,
Cao, X,
Dhanasekaran, SM,
Chinnaiyan, AM,
Pantanowitz, L,
Mehra, R,
BMC Cancer, 2022 May; DOI:10.1186/s12885-022-09559-4 Computerized decision support for bladder cancer treatment response assessment in CT urography: effect on diagnostic accuracy in multi-institution multi-specialty study
Sun, D,
Hadjiiski, L,
Alva, A,
Zakharia, Y,
Joshi, M,
Chan, H-P,
Garje, R,
Pomerantz, L,
Elhag, D,
Cohan, RH,
Caoili, EM,
Kerr, WT,
Cha, KH,
Kirova-Nedyalkova, G,
Davenport, MS,
Shankar, PR,
Francis, IR,
Shampain, K,
Meyer, N,
Barkmeier, D,
Tomography, 2022 Mar; DOI:10.3390/tomography8020054 LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy with Radiologic-Pathologic Correlation in Patients with SBRT Treated Hepatocellular Carcinoma.
Mendiratta-Lala, M,
Aslam, A,
Maturen, KE,
Westerhoff, M,
Maurino, C,
Parikh, ND,
Sun, Y,
Sonnenday, CJ,
Stein, EB,
Shampain, KL,
Kaza, RK,
Cuneo, K,
Masch, W,
Do, RKG,
Lawrence, TS,
Owen, D,
Int J Radiat Oncol Biol Phys, 2022 Mar; DOI:10.1016/j.ijrobp.2021.10.006 Evaluation of Dose Accuracy in the Near-Surface Region for Whole Breast Irradiation Techniques in a Multi-institutional Consortium.
Moncion, A,
Wilson, M,
Ma, R,
Marsh, R,
Burmeister, J,
Dryden, D,
Lack, D,
Grubb, M,
Mayville, A,
Jursinic, P,
Dess, K,
Kamp, J,
Young, K,
Dilworth, JT,
Kestin, L,
Jagsi, R,
Mietzel, M,
Vicini, F,
Pierce, LJ,
Moran, JM,
Pract Radiat Oncol, 2022 Jan; DOI:10.1016/j.prro.2022.01.013 Feasibility of function-guided lung treatment planning with parametric response mapping
Matrosic, CK,
Owen, DR,
Polan, D,
Sun, Y,
Jolly, S,
Schonewolf, C,
Schipper, M,
Haken, RKT,
Galban, CJ,
Matuszak, M,
J Appl Clin Med Phys, 2021 Nov; DOI:10.1002/acm2.13436 The influence of tobacco load versus smoking status on outcomes following lobectomy for lung cancer in a statewide quality collaborative
Al Natour, RH,
He, C,
Clark, MJ,
Welsh, R,
Chang, AC,
Adams, KN,
J Thorac Cardiovasc Surg, 2021 Nov; DOI:10.1016/j.jtcvs.2020.10.162 HUGO Gene Nomenclature Committee (HGNC) recommendations for the designation of gene fusions
Bruford, EA,
Antonescu, CR,
Carroll, AJ,
Chinnaiyan, A,
Cree, IA,
Cross, NCP,
Dalgleish, R,
Gale, RP,
Harrison, CJ,
Hastings, RJ,
Huret, J-L,
Johansson, B,
Le Beau, M,
Mecucci, C,
Mertens, F,
Verhaak, R,
Mitelman, F,
Leukemia, 2021 Nov; DOI:10.1038/s41375-021-01436-6 Proteogenomic characterization of pancreatic ductal adenocarcinoma
Cao, L,
Huang, C,
Cui Zhou, D,
Hu, Y,
Lih, TM,
Savage, SR,
Krug, K,
Clark, DJ,
Schnaubelt, M,
Chen, L,
da Veiga Leprevost, F,
Eguez, RV,
Yang, W,
Pan, J,
Wen, B,
Dou, Y,
Jiang, W,
Liao, Y,
Shi, Z,
Terekhanova, NV,
Cell, 2021 Sep; DOI:10.1016/j.cell.2021.08.023 Chemotherapy-Induced Peripheral Neuropathy Detection via a Smartphone App: Cross-sectional Pilot Study
Chen, C-S,
Kim, J,
Garg, N,
Guntupalli, H,
Jagsi, R,
Griggs, JJ,
Sabel, M,
Dorsch, MP,
Callaghan, BC,
Hertz, DL,
JMIR Mhealth Uhealth, 2021 Jul; DOI:10.2196/27502 New Nodal Staging for Primary Pancreatic Neuroendocrine Tumors: A Multi-institutional and National Data Analysis
Zhang, X-F,
Xue, F,
Dong, D-H,
Lopez-Aguiar, AG,
Poultsides, G,
Makris, E,
Rocha, F,
Kanji, Z,
Weber, S,
Fisher, A,
Fields, R,
Krasnick, BA,
Idrees, K,
Smith, PM,
Cho, C,
Beems, M,
Lv, Y,
Maithel, SK,
Pawlik, TM,
Ann Surg, 2021 Jul; DOI:10.1097/SLA.0000000000003478 Deep convolutional neural network with adversarial training for denoising digital breast east toosynthesis images.
Gao, M,
Fessler, JA,
Chan, H-P,
IEEE Trans Med Imaging, 2021 Jul; DOI:10.1109/TMI.2021.3066896 Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.
Samala, RK,
Chan, H-P,
Hadjiiski, L,
Helvie, MA,
Med Phys, 2021 Jun; DOI:10.1002/mp.14678 Survival benefit with adjuvant radiotherapy after resection of distal cholangiocarcinoma: A propensity-matched National Cancer Database analysis
Kamarajah, SK,
Bednar, F,
Cho, CS,
Nathan, H,
Cancer, 2021 Apr; DOI:10.1002/cncr.33356 Modeling intra-fractional abdominal configuration changes using breathing motion-corrected radial MRI
Liu, L,
Johansson, A,
Cao, Y,
Kashani, R,
Lawrence, TS,
Balter, JM,
Phys Med Biol, 2021 Apr; DOI:10.1088/1361-6560/abef42 Proteogenomic and metabolomic characterization of human glioblastoma
Wang, L-B,
Karpova, A,
Gritsenko, MA,
Kyle, JE,
Cao, S,
Li, Y,
Rykunov, D,
Colaprico, A,
Rothstein, JH,
Hong, R,
Stathias, V,
Cornwell, M,
Petralia, F,
Wu, Y,
Reva, B,
Krug, K,
Pugliese, P,
Kawaler, E,
Olsen, LK,
Liang, W-W,
Cancer Cell, 2021 Apr; DOI:10.1016/j.ccell.2021.01.006 Institutional-Level Differences in Quality and Outcomes of Lung Cancer Resections in the United States
Osarogiagbon, RU,
Sineshaw, HM,
Lin, CC,
Jemal, A,
Chest, 2021 Apr; DOI:10.1016/j.chest.2020.10.075 Digital breast tomosynthesis slab thickness: Impact on reader performance and interpretation time.
Pujara, AC,
Joe, AI,
Patterson, SK,
Neal, CH,
Noroozian, M,
Ma, T,
Chan, H-P,
Helvie, MA,
Maturen, KE,
Radiology, 2020 Dec; DOI:10.1148/radiol.2020192805 Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies
Li, W,
Denton, BT,
Nieboer, D,
Carroll, PR,
Roobol, MJ,
Morgan, TM,
Movember Foundation’s Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium, Array,
Cancer Med, 2020 Dec; DOI:10.1002/cam4.3549 Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning
Lee, J,
Wang, N,
Turk, S,
Mohammed, S,
Lobo, R,
Kim, J,
Liao, E,
Camelo-Piragua, S,
Kim, M,
Junck, L,
Bapuraj, J,
Srinivasan, A,
Rao, A,
Sci Rep, 2020 Nov; DOI:10.1038/s41598-020-77389-0 An Adaptive Bayesian Design for Personalized Dosing in a Cancer Prevention Trial
Sen, A,
Zhao, L,
Djuric, Z,
Turgeon, DK,
Ruffin, MT,
Smith, WL,
Brenner, DE,
Normolle, DP,
Am J Prev Med, 2020 Oct; DOI:10.1016/j.amepre.2020.04.023 Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
Ioannou, GN,
Tang, W,
Beste, LA,
Tincopa, MA,
Su, GL,
Van, T,
Tapper, EB,
Singal, AG,
Zhu, J,
Waljee, AK,
JAMA Netw Open, 2020 Sep; DOI:10.1001/jamanetworkopen.2020.15626 