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Items: 1 to 50 of 102

1.

Bevacizumab reduces permeability and concurrent temozolomide delivery in a subset of patients with recurrent glioblastoma.

Gerstner E, Emblem KE, Chang K, Vakulenko-Lagun B, Yen YF, Beers AL, Dietrich J, Plotkin SR, Catana C, Hooker JM, Duda DG, Rosen B, Kalpathy-Cramer J, Jain RK, Batchelor T.

Clin Cancer Res. 2019 Sep 26. pii: clincanres.1739.2019. doi: 10.1158/1078-0432.CCR-19-1739. [Epub ahead of print]

PMID:
31558474
2.

Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture.

Silva MA, Patel J, Kavouridis V, Gallerani T, Beers A, Chang K, Hoebel KV, Brown J, See AP, Gormley WB, Aziz-Sultan MA, Kalpathy-Cramer J, Arnaout O, Patel NJ.

World Neurosurg. 2019 Jul 9. pii: S1878-8750(19)31889-3. doi: 10.1016/j.wneu.2019.06.231. [Epub ahead of print]

PMID:
31295616
3.

Democratizing AI.

Allen B, Agarwal S, Kalpathy-Cramer J, Dreyer K.

J Am Coll Radiol. 2019 Jul;16(7):961-963. doi: 10.1016/j.jacr.2019.04.023. Epub 2019 May 16. No abstract available.

PMID:
31272590
4.

Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning.

Taylor S, Brown JM, Gupta K, Campbell JP, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kim SJ, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity Consortium.

JAMA Ophthalmol. 2019 Jul 3. doi: 10.1001/jamaophthalmol.2019.2433. [Epub ahead of print]

PMID:
31268518
5.

A Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning to Monitor Disease Regression After Treatment.

Gupta K, Campbell JP, Taylor S, Brown JM, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Kim SJ, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity Consortium.

JAMA Ophthalmol. 2019 Jul 3. doi: 10.1001/jamaophthalmol.2019.2442. [Epub ahead of print]

PMID:
31268499
6.

Classification and comparison via neural networks.

Yıldız İ, Tian P, Dy J, Erdoğmuş D, Brown J, Kalpathy-Cramer J, Ostmo S, Peter Campbell J, Chiang MF, Ioannidis S.

Neural Netw. 2019 Oct;118:65-80. doi: 10.1016/j.neunet.2019.06.004. Epub 2019 Jun 19.

PMID:
31254769
7.

Publisher Correction: Probing tumor microenvironment in patients with newly diagnosed glioblastoma during chemoradiation and adjuvant temozolomide with functional MRI.

Ly KI, Vakulenko-Lagun B, Emblem KE, Ou Y, Da X, Betensky RA, Kalpathy-Cramer J, Duda DG, Jain RK, Chi AS, Plotkin SR, Batchelor TT, Sorensen G, Rosen BR, Gerstner ER.

Sci Rep. 2019 Jun 14;9(1):8721. doi: 10.1038/s41598-019-44365-2.

8.

Automatic assessment of glioma burden: A deep learning algorithm for fully automated volumetric and bi-dimensional measurement.

Chang K, Beers AL, Bai HX, Brown JM, Ly KI, Li X, Senders JT, Kavouridis VK, Boaro A, Su C, Bi WL, Rapalino O, Liao W, Shen Q, Zhou H, Xiao B, Wang Y, Zhang PJ, Pinho MC, Wen PY, Batchelor TT, Boxerman JL, Arnaout O, Rosen BR, Gerstner ER, Yang L, Huang RY, Kalpathy-Cramer J.

Neuro Oncol. 2019 Jun 13. pii: noz106. doi: 10.1093/neuonc/noz106. [Epub ahead of print]

PMID:
31190077
9.

Kinetic Analysis of Lesions Identified on a Rapid Abridged Multiphase (RAMP) Breast MRI Protocol.

Choudhery S, Chou SS, Chang K, Kalpathy-Cramer J, Lehman CD.

Acad Radiol. 2019 May 27. pii: S1076-6332(19)30222-3. doi: 10.1016/j.acra.2019.05.001. [Epub ahead of print]

PMID:
31147233
10.

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Coyner AS, Swan R, Campbell JP, Ostmo S, Brown JM, Kalpathy-Cramer J, Kim SJ, Jonas KE, Chan RVP, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity Research Consortium.

Ophthalmol Retina. 2019 May;3(5):444-450. doi: 10.1016/j.oret.2019.01.015. Epub 2019 Jan 31.

PMID:
31044738
11.

Methods for Segmentation and Classification of Digital Microscopy Tissue Images.

Vu QD, Graham S, Kurc T, To MNN, Shaban M, Qaiser T, Koohbanani NA, Khurram SA, Kalpathy-Cramer J, Zhao T, Gupta R, Kwak JT, Rajpoot N, Saltz J, Farahani K.

Front Bioeng Biotechnol. 2019 Apr 2;7:53. doi: 10.3389/fbioe.2019.00053. eCollection 2019.

12.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, Flanders AE, Lungren MP, Mendelson DS, Rudie JD, Wang G, Kandarpa K.

Radiology. 2019 Jun;291(3):781-791. doi: 10.1148/radiol.2019190613. Epub 2019 Apr 16.

13.

Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO).

Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, Sorace AG, Yankeelov TE, Rutledge N, Chenevert TL, Malyarenko D, Liu Y, Brenner A, Hu LS, Zhou Y, Boxerman JL, Yen YF, Kalpathy-Cramer J, Beers AL, Muzi M, Madhuranthakam AJ, Pinho M, Johnson B, Quarles CC.

Tomography. 2019 Mar;5(1):110-117. doi: 10.18383/j.tom.2018.00041.

14.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O'Sullivan F, Abramson RG, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Laymon CM, Muzi M, Kinahan PE, Schmainda K, Cao Y, Chenevert TL, Taouli B, Yankeelov TE, Fennessy F, Li X.

Tomography. 2019 Mar;5(1):99-109. doi: 10.18383/j.tom.2018.00027.

15.

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

Armato SG 3rd, Huisman H, Drukker K, Hadjiiski L, Kirby JS, Petrick N, Redmond G, Giger ML, Cha K, Mamonov A, Kalpathy-Cramer J, Farahani K.

J Med Imaging (Bellingham). 2018 Oct;5(4):044501. doi: 10.1117/1.JMI.5.4.044501. Epub 2018 Nov 10.

PMID:
30840739
16.

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

Coyner AS, Swan R, Brown JM, Kalpathy-Cramer J, Kim SJ, Campbell JP, Jonas KE, Ostmo S, Chan RVP, Chiang MF.

AMIA Annu Symp Proc. 2018 Dec 5;2018:1224-1232. eCollection 2018.

17.

An in-silico quality assurance study of contouring target volumes in thoracic tumors within a cooperative group setting.

Elhalawani H, Elgohari B, Lin TA, Mohamed ASR, Fitzgerald TJ, Laurie F, Ulin K, Kalpathy-Cramer J, Guerrero T, Holliday EB, Russo G, Patel A, Jones W, Walker GV, Awan M, Choi M, Dagan R, Mahmoud O, Shapiro A, Kong FS, Gomez D, Zeng J, Decker R, Spoelstra FOB, Gaspar LE, Kachnic LA, Thomas CR Jr, Okunieff P, Fuller CD.

Clin Transl Radiat Oncol. 2019 Jan 6;15:83-92. doi: 10.1016/j.ctro.2019.01.001. eCollection 2019 Feb.

18.

The RSNA Pediatric Bone Age Machine Learning Challenge.

Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, Pan I, Pereira LA, Sousa RT, Abdala N, Kitamura FC, Thodberg HH, Chen L, Shih G, Andriole K, Kohli MD, Erickson BJ, Flanders AE.

Radiology. 2019 Feb;290(2):498-503. doi: 10.1148/radiol.2018180736. Epub 2018 Nov 27.

PMID:
30480490
19.

Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.

Redd TK, Campbell JP, Brown JM, Kim SJ, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium.

Br J Ophthalmol. 2018 Nov 23. pii: bjophthalmol-2018-313156. doi: 10.1136/bjophthalmol-2018-313156. [Epub ahead of print]

PMID:
30470715
20.

Probing tumor microenvironment in patients with newly diagnosed glioblastoma during chemoradiation and adjuvant temozolomide with functional MRI.

Ly KI, Vakulenko-Lagun B, Emblem KE, Ou Y, Da X, Betensky RA, Kalpathy-Cramer J, Duda DG, Jain RK, Chi AS, Plotkin SR, Batchelor TT, Sorensen G, Rosen BR, Gerstner ER.

Sci Rep. 2018 Nov 20;8(1):17062. doi: 10.1038/s41598-018-34820-x. Erratum in: Sci Rep. 2019 Jun 14;9(1):8721.

21.

Reply.

Lee A, Taylor P, Kalpathy-Cramer J, Tufail A.

Ophthalmology. 2018 Dec;125(12):e86. doi: 10.1016/j.ophtha.2018.06.036. Epub 2018 Oct 19. No abstract available.

PMID:
30343936
22.

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

Winzeck S, Hakim A, McKinley R, Pinto JAADSR, Alves V, Silva C, Pisov M, Krivov E, Belyaev M, Monteiro M, Oliveira A, Choi Y, Paik MC, Kwon Y, Lee H, Kim BJ, Won JH, Islam M, Ren H, Robben D, Suetens P, Gong E, Niu Y, Xu J, Pauly JM, Lucas C, Heinrich MP, Rivera LC, Castillo LS, Daza LA, Beers AL, Arbelaezs P, Maier O, Chang K, Brown JM, Kalpathy-Cramer J, Zaharchuk G, Wiest R, Reyes M.

Front Neurol. 2018 Sep 13;9:679. doi: 10.3389/fneur.2018.00679. eCollection 2018.

23.

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

Robins M, Kalpathy-Cramer J, Obuchowski NA, Buckler A, Athelogou M, Jarecha R, Petrick N, Pezeshk A, Sahiner B, Samei E.

Acad Radiol. 2019 Jul;26(7):e161-e173. doi: 10.1016/j.acra.2018.07.022. Epub 2018 Sep 12.

PMID:
30219290
24.

A prospective in silico analysis of interdisciplinary and interobserver spatial variability in post-operative target delineation of high-risk oral cavity cancers: Does physician specialty matter?

Ng SP, Dyer BA, Kalpathy-Cramer J, Mohamed ASR, Awan MJ, Gunn GB, Phan J, Zafereo M, Debnam JM, Lewis CM, Colen RR, Kupferman ME, Guha-Thakurta N, Canahuate G, Marai GE, Vock D, Hamilton B, Holland J, Cardenas CE, Lai S, Rosenthal D, Fuller CD.

Clin Transl Radiat Oncol. 2018 Aug 2;12:40-46. doi: 10.1016/j.ctro.2018.07.006. eCollection 2018 Aug.

25.

Erratum: Semi-automated pulmonary nodule interval segmentation using the NLST data.

Balagurunathan Y, Beers A, Kalpathy-Cramer J, McNitt-Gray M, Hadjiiski L, Zhao B, Zhu J, Yang H, Yip SSF, Aerts HJWL, Napel S, Cherezov D, Cha K, Chan HP, Flores C, Garcia A, Gillies R, Goldgof D.

Med Phys. 2018 Jun;45(6):2689-2690. doi: 10.1002/mp.12905. Epub 2018 Apr 24. No abstract available.

26.

Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium.

JAMA Ophthalmol. 2018 Jul 1;136(7):803-810. doi: 10.1001/jamaophthalmol.2018.1934.

27.

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project.

Schmainda KM, Prah MA, Rand SD, Liu Y, Logan B, Muzi M, Rane SD, Da X, Yen YF, Kalpathy-Cramer J, Chenevert TL, Hoff B, Ross B, Cao Y, Aryal MP, Erickson B, Korfiatis P, Dondlinger T, Bell L, Hu L, Kinahan PE, Quarles CC.

AJNR Am J Neuroradiol. 2018 Jun;39(6):1008-1016. doi: 10.3174/ajnr.A5675. Epub 2018 May 24.

28.

Accuracy and Reliability of Eye-Based vs Quadrant-Based Diagnosis of Plus Disease in Retinopathy of Prematurity.

Kim SJ, Campbell JP, Kalpathy-Cramer J, Ostmo S, Jonas KE, Choi D, Chan RVP, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium.

JAMA Ophthalmol. 2018 Jun 1;136(6):648-655. doi: 10.1001/jamaophthalmol.2018.1195.

29.

Distributed deep learning networks among institutions for medical imaging.

Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A, Rosen B, Rubin DL, Kalpathy-Cramer J.

J Am Med Inform Assoc. 2018 Aug 1;25(8):945-954. doi: 10.1093/jamia/ocy017.

30.

Semi-automated pulmonary nodule interval segmentation using the NLST data.

Balagurunathan Y, Beers A, Kalpathy-Cramer J, McNitt-Gray M, Hadjiiski L, Zhao B, Zhu J, Yang H, Yip SSF, Aerts HJWL, Napel S, Cherezov D, Cha K, Chan HP, Flores C, Garcia A, Gillies R, Goldgof D.

Med Phys. 2018 Mar;45(3):1093-1107. doi: 10.1002/mp.12766. Epub 2018 Feb 19. Erratum in: Med Phys. 2018 Jun;45(6):2689-2690.

31.

Field of View Normalization in Multi-Site Brain MRI.

Ou Y, Zöllei L, Da X, Retzepi K, Murphy SN, Gerstner ER, Rosen BR, Grant PE, Kalpathy-Cramer J, Gollub RL.

Neuroinformatics. 2018 Oct;16(3-4):431-444. doi: 10.1007/s12021-018-9359-z.

PMID:
29353341
32.

Standard chemoradiation in combination with VEGF targeted therapy for glioblastoma results in progressive gray and white matter volume loss.

Prust ML, Jafari-Khouzani K, Kalpathy-Cramer J, Polaskova P, Batchelor TT, Gerstner ER, Dietrich J.

Neuro Oncol. 2018 Jan 22;20(2):289-291. doi: 10.1093/neuonc/nox217. No abstract available.

33.
34.

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging.

Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, Zhang B, Capellini A, Liao W, Shen Q, Li X, Xiao B, Cryan J, Ramkissoon S, Ramkissoon L, Ligon K, Wen PY, Bindra RS, Woo J, Arnaout O, Gerstner ER, Zhang PJ, Rosen BR, Yang L, Huang RY, Kalpathy-Cramer J.

Clin Cancer Res. 2018 Mar 1;24(5):1073-1081. doi: 10.1158/1078-0432.CCR-17-2236. Epub 2017 Nov 22.

35.

Machine Learning Has Arrived!

Lee A, Taylor P, Kalpathy-Cramer J, Tufail A.

Ophthalmology. 2017 Dec;124(12):1726-1728. doi: 10.1016/j.ophtha.2017.08.046. No abstract available.

PMID:
29157423
36.

CM-101: Type I Collagen-targeted MR Imaging Probe for Detection of Liver Fibrosis.

Farrar CT, Gale EM, Kennan R, Ramsay I, Masia R, Arora G, Looby K, Wei L, Kalpathy-Cramer J, Bunzel MM, Zhang C, Zhu Y, Akiyama TE, Klimas M, Pinto S, Diyabalanage H, Tanabe KK, Humblet V, Fuchs BC, Caravan P.

Radiology. 2018 May;287(2):581-589. doi: 10.1148/radiol.2017170595. Epub 2017 Nov 20.

37.

ABCD1 dysfunction alters white matter microvascular perfusion.

Lauer A, Da X, Hansen MB, Boulouis G, Ou Y, Cai X, Liberato Celso Pedrotti A, Kalpathy-Cramer J, Caruso P, Hayden DL, Rost N, Mouridsen K, Eichler FS, Rosen B, Musolino PL.

Brain. 2017 Dec 1;140(12):3139-3152. doi: 10.1093/brain/awx262.

38.

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network.

Newitt DC, Malyarenko D, Chenevert TL, Quarles CC, Bell L, Fedorov A, Fennessy F, Jacobs MA, Solaiyappan M, Hectors S, Taouli B, Muzi M, Kinahan PE, Schmainda KM, Prah MA, Taber EN, Kroenke C, Huang W, Arlinghaus LR, Yankeelov TE, Cao Y, Aryal M, Yen YF, Kalpathy-Cramer J, Shukla-Dave A, Fung M, Liang J, Boss M, Hylton N.

J Med Imaging (Bellingham). 2018 Jan;5(1):011003. doi: 10.1117/1.JMI.5.1.011003. Epub 2017 Oct 10.

39.

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.

Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R.

AJNR Am J Neuroradiol. 2018 Feb;39(2):208-216. doi: 10.3174/ajnr.A5391. Epub 2017 Oct 5. Review.

40.

Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study.

Bane O, Hectors SJ, Wagner M, Arlinghaus LL, Aryal MP, Cao Y, Chenevert TL, Fennessy F, Huang W, Hylton NM, Kalpathy-Cramer J, Keenan KE, Malyarenko DI, Mulkern RV, Newitt DC, Russek SE, Stupic KF, Tudorica A, Wilmes LJ, Yankeelov TE, Yen YF, Boss MA, Taouli B.

Magn Reson Med. 2018 May;79(5):2564-2575. doi: 10.1002/mrm.26903. Epub 2017 Sep 14.

41.

Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research.

Saltz J, Almeida J, Gao Y, Sharma A, Bremer E, DiPrima T, Saltz M, Kalpathy-Cramer J, Kurc T.

AMIA Jt Summits Transl Sci Proc. 2017 Jul 26;2017:85-94. eCollection 2017.

42.

Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network.

Farahani K, Kalpathy-Cramer J, Chenevert TL, Rubin DL, Sunderland JJ, Nordstrom RJ, Buatti J, Hylton N.

Tomography. 2016 Dec;2(4):242-249. doi: 10.18383/j.tom.2016.00265.

43.

Toward a severity index for ROP: An unsupervised approach.

Peng Tian, Ataer-Cansizoglu E, Kalpathy-Cramer J, Ostmo S, Jonas K, Chan RV, Campbell JP, Chiang MF, Erdogmus D.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1312-1315. doi: 10.1109/EMBC.2016.7590948.

44.

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Kalpathy-Cramer J, Mamomov A, Zhao B, Lu L, Cherezov D, Napel S, Echegaray S, Rubin D, McNitt-Gray M, Lo P, Sieren JC, Uthoff J, Dilger SK, Driscoll B, Yeung I, Hadjiiski L, Cha K, Balagurunathan Y, Gillies R, Goldgof D.

Tomography. 2016 Dec;2(4):430-437. doi: 10.18383/j.tom.2016.00235.

45.

Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model.

Le M, Delingette H, Kalpathy-Cramer J, Gerstner ER, Batchelor T, Unkelbach J, Ayache N.

IEEE Trans Med Imaging. 2017 Mar;36(3):815-825. doi: 10.1109/TMI.2016.2626443. Epub 2016 Nov 8.

PMID:
28113925
46.

Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 Versus 2007.

Moleta C, Campbell JP, Kalpathy-Cramer J, Chan RVP, Ostmo S, Jonas K, Chiang MF; Imaging & Informatics in ROP Research Consortium.

Am J Ophthalmol. 2017 Apr;176:70-76. doi: 10.1016/j.ajo.2016.12.025. Epub 2017 Jan 11.

47.

Reliable estimation of microvascular flow patterns in patients with disrupted blood-brain barrier using dynamic susceptibility contrast MRI.

Hansen MB, Tietze A, Kalpathy-Cramer J, Gerstner ER, Batchelor TT, Østergaard L, Mouridsen K.

J Magn Reson Imaging. 2017 Aug;46(2):537-549. doi: 10.1002/jmri.25549. Epub 2016 Nov 30.

PMID:
27902858
48.

Phase II study of tivozanib, an oral VEGFR inhibitor, in patients with recurrent glioblastoma.

Kalpathy-Cramer J, Chandra V, Da X, Ou Y, Emblem KE, Muzikansky A, Cai X, Douw L, Evans JG, Dietrich J, Chi AS, Wen PY, Stufflebeam S, Rosen B, Duda DG, Jain RK, Batchelor TT, Gerstner ER.

J Neurooncol. 2017 Feb;131(3):603-610. doi: 10.1007/s11060-016-2332-5. Epub 2016 Nov 16.

PMID:
27853960
49.

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

Campbell JP, Kalpathy-Cramer J, Erdogmus D, Tian P, Kedarisetti D, Moleta C, Reynolds JD, Hutcheson K, Shapiro MJ, Repka MX, Ferrone P, Drenser K, Horowitz J, Sonmez K, Swan R, Ostmo S, Jonas KE, Chan RV, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity Research Consortium.

Ophthalmology. 2016 Nov;123(11):2338-2344. doi: 10.1016/j.ophtha.2016.07.026. Epub 2016 Aug 31.

50.

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis.

Kalpathy-Cramer J, Campbell JP, Erdogmus D, Tian P, Kedarisetti D, Moleta C, Reynolds JD, Hutcheson K, Shapiro MJ, Repka MX, Ferrone P, Drenser K, Horowitz J, Sonmez K, Swan R, Ostmo S, Jonas KE, Chan RV, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity Research Consortium.

Ophthalmology. 2016 Nov;123(11):2345-2351. doi: 10.1016/j.ophtha.2016.07.020. Epub 2016 Aug 24.

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