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Items: 1 to 50 of 170

1.

MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial.

Saadatmand S, Geuzinge HA, Rutgers EJT, Mann RM, de Roy van Zuidewijn DBW, Zonderland HM, Tollenaar RAEM, Lobbes MBI, Ausems MGEM, van 't Riet M, Hooning MJ, Mares-Engelberts I, Luiten EJT, Heijnsdijk EAM, Verhoef C, Karssemeijer N, Oosterwijk JC, Obdeijn IM, de Koning HJ, Tilanus-Linthorst MMA; FaMRIsc study group.

Lancet Oncol. 2019 Jun 17. pii: S1470-2045(19)30275-X. doi: 10.1016/S1470-2045(19)30275-X. [Epub ahead of print]

PMID:
31221620
2.

A systematic review on the use of the breast lesion excision system in breast disease.

Sanderink WBG, Laarhuis BI, Strobbe LJA, Sechopoulos I, Bult P, Karssemeijer N, Mann RM.

Insights Imaging. 2019 May 2;10(1):49. doi: 10.1186/s13244-019-0737-3. Review.

3.

Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program : A retrospective cohort study.

Vreemann S, Dalmis MU, Bult P, Karssemeijer N, Broeders MJM, Gubern-Mérida A, Mann RM.

Eur Radiol. 2019 Feb 22. doi: 10.1007/s00330-019-06020-2. [Epub ahead of print]

PMID:
30796568
4.

Change in mammographic density across birth cohorts of Dutch breast cancer screening participants.

Napolitano G, Lynge E, Lillholm M, Vejborg I, van Gils CH, Nielsen M, Karssemeijer N.

Int J Cancer. 2019 Feb 14. doi: 10.1002/ijc.32210. [Epub ahead of print]

PMID:
30762225
5.

Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI.

Dalmiş MU, Gubern-Mérida A, Vreemann S, Bult P, Karssemeijer N, Mann R, Teuwen J.

Invest Radiol. 2019 Jun;54(6):325-332. doi: 10.1097/RLI.0000000000000544.

PMID:
30652985
6.

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

Balta C, Bouwman RW, Sechopoulos I, Broeders MJM, Karssemeijer N, van Engen RE, Veldkamp WJH.

Med Phys. 2019 Feb;46(2):714-725. doi: 10.1002/mp.13342. Epub 2019 Jan 4.

PMID:
30561108
7.

The added value of mammography in different age-groups of women with and without BRCA mutation screened with breast MRI.

Vreemann S, van Zelst JCM, Schlooz-Vries M, Bult P, Hoogerbrugge N, Karssemeijer N, Gubern-Mérida A, Mann RM.

Breast Cancer Res. 2018 Aug 3;20(1):84. doi: 10.1186/s13058-018-1019-6.

8.

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks.

Tellez D, Balkenhol M, Otte-Holler I, van de Loo R, Vogels R, Bult P, Wauters C, Vreuls W, Mol S, Karssemeijer N, Litjens G, van der Laak J, Ciompi F.

IEEE Trans Med Imaging. 2018 Mar 28. doi: 10.1109/TMI.2018.2820199. [Epub ahead of print]

PMID:
29994086
9.

Improving the Automated Detection of Calcifications Using Adaptive Variance Stabilization.

Bria A, Marrocco C, Borges LR, Molinara M, Marchesi A, Mordang JJ, Karssemeijer N, Tortorella F.

IEEE Trans Med Imaging. 2018 Aug;37(8):1857-1864. doi: 10.1109/TMI.2018.2814058. Epub 2018 Mar 9.

PMID:
29994062
10.

Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening.

van Zelst JCM, Vreemann S, Witt HJ, Gubern-Merida A, Dorrius MD, Duvivier K, Lardenoije-Broker S, Lobbes MBI, Loo C, Veldhuis W, Veltman J, Drieling D, Karssemeijer N, Mann RM.

Invest Radiol. 2018 Oct;53(10):579-586. doi: 10.1097/RLI.0000000000000494.

PMID:
29944483
11.

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.

Ehteshami Bejnordi B, Mullooly M, Pfeiffer RM, Fan S, Vacek PM, Weaver DL, Herschorn S, Brinton LA, van Ginneken B, Karssemeijer N, Beck AH, Gierach GL, van der Laak JAWM, Sherman ME.

Mod Pathol. 2018 Oct;31(10):1502-1512. doi: 10.1038/s41379-018-0073-z. Epub 2018 Jun 13.

PMID:
29899550
12.

Reasons for (non)participation in supplemental population-based MRI breast screening for women with extremely dense breasts.

de Lange SV, Bakker MF, Monninkhof EM, Peeters PHM, de Koekkoek-Doll PK, Mann RM, Rutten MJCM, Bisschops RHC, Veltman J, Duvivier KM, Lobbes MBI, de Koning HJ, Karssemeijer N, Pijnappel RM, Veldhuis WB, van Gils CH.

Clin Radiol. 2018 Aug;73(8):759.e1-759.e9. doi: 10.1016/j.crad.2018.04.002. Epub 2018 Jun 18.

PMID:
29759590
13.

The combined effect of mammographic texture and density on breast cancer risk: a cohort study.

Wanders JOP, van Gils CH, Karssemeijer N, Holland K, Kallenberg M, Peeters PHM, Nielsen M, Lillholm M.

Breast Cancer Res. 2018 May 2;20(1):36. doi: 10.1186/s13058-018-0961-7.

14.

Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts.

van Zelst JCM, Tan T, Clauser P, Domingo A, Dorrius MD, Drieling D, Golatta M, Gras F, de Jong M, Pijnappel R, Rutten MJCM, Karssemeijer N, Mann RM.

Eur Radiol. 2018 Jul;28(7):2996-3006. doi: 10.1007/s00330-017-5280-3. Epub 2018 Feb 7.

15.

The frequency of missed breast cancers in women participating in a high-risk MRI screening program.

Vreemann S, Gubern-Merida A, Lardenoije S, Bult P, Karssemeijer N, Pinker K, Mann RM.

Breast Cancer Res Treat. 2018 Jun;169(2):323-331. doi: 10.1007/s10549-018-4688-z. Epub 2018 Jan 31.

16.

The correlation of background parenchymal enhancement in the contralateral breast with patient and tumor characteristics of MRI-screen detected breast cancers.

Vreemann S, Gubern-Mérida A, Borelli C, Bult P, Karssemeijer N, Mann RM.

PLoS One. 2018 Jan 19;13(1):e0191399. doi: 10.1371/journal.pone.0191399. eCollection 2018.

17.

Fully automated detection of breast cancer in screening MRI using convolutional neural networks.

Dalmış MU, Vreemann S, Kooi T, Mann RM, Karssemeijer N, Gubern-Mérida A.

J Med Imaging (Bellingham). 2018 Jan;5(1):014502. doi: 10.1117/1.JMI.5.1.014502. Epub 2018 Jan 11.

18.

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images.

Bejnordi BE, Zuidhof G, Balkenhol M, Hermsen M, Bult P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak J.

J Med Imaging (Bellingham). 2017 Oct;4(4):044504. doi: 10.1117/1.JMI.4.4.044504. Epub 2017 Dec 14.

19.

New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers.

Rodriguez-Ruiz A, Teuwen J, Vreemann S, Bouwman RW, van Engen RE, Karssemeijer N, Mann RM, Gubern-Merida A, Sechopoulos I.

Acta Radiol. 2018 Sep;59(9):1051-1059. doi: 10.1177/0284185117748487. Epub 2017 Dec 18.

20.

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium, Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, Bult P, Beca F, Beck AH, Wang D, Khosla A, Gargeya R, Irshad H, Zhong A, Dou Q, Li Q, Chen H, Lin HJ, Heng PA, Haß C, Bruni E, Wong Q, Halici U, Öner MÜ, Cetin-Atalay R, Berseth M, Khvatkov V, Vylegzhanin A, Kraus O, Shaban M, Rajpoot N, Awan R, Sirinukunwattana K, Qaiser T, Tsang YW, Tellez D, Annuscheit J, Hufnagl P, Valkonen M, Kartasalo K, Latonen L, Ruusuvuori P, Liimatainen K, Albarqouni S, Mungal B, George A, Demirci S, Navab N, Watanabe S, Seno S, Takenaka Y, Matsuda H, Ahmady Phoulady H, Kovalev V, Kalinovsky A, Liauchuk V, Bueno G, Fernandez-Carrobles MM, Serrano I, Deniz O, Racoceanu D, Venâncio R.

JAMA. 2017 Dec 12;318(22):2199-2210. doi: 10.1001/jama.2017.14585.

21.

One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection: do we need more?

Rodriguez-Ruiz A, Gubern-Merida A, Imhof-Tas M, Lardenoije S, Wanders AJT, Andersson I, Zackrisson S, Lång K, Dustler M, Karssemeijer N, Mann RM, Sechopoulos I.

Eur Radiol. 2018 May;28(5):1938-1948. doi: 10.1007/s00330-017-5167-3. Epub 2017 Dec 11.

22.

A model observer study using acquired mammographic images of an anthropomorphic breast phantom.

Balta C, Bouwman RW, Sechopoulos I, Broeders MJM, Karssemeijer N, van Engen RE, Veldkamp WJH.

Med Phys. 2018 Feb;45(2):655-665. doi: 10.1002/mp.12703. Epub 2017 Dec 21.

PMID:
29193129
23.

Influence of breast compression pressure on the performance of population-based mammography screening.

Holland K, Sechopoulos I, Mann RM, den Heeten GJ, van Gils CH, Karssemeijer N.

Breast Cancer Res. 2017 Nov 28;19(1):126. doi: 10.1186/s13058-017-0917-3.

24.

The importance of early detection of calcifications associated with breast cancer in screening.

Mordang JJ, Gubern-Mérida A, Bria A, Tortorella F, Mann RM, Broeders MJM, den Heeten GJ, Karssemeijer N.

Breast Cancer Res Treat. 2018 Jan;167(2):451-458. doi: 10.1007/s10549-017-4527-7. Epub 2017 Oct 17.

25.

Influence of Risk Category and Screening Round on the Performance of an MR Imaging and Mammography Screening Program in Carriers of the BRCA Mutation and Other Women at Increased Risk.

Vreemann S, Gubern-Mérida A, Schlooz-Vries MS, Bult P, van Gils CH, Hoogerbrugge N, Karssemeijer N, Mann RM.

Radiology. 2018 Feb;286(2):443-451. doi: 10.1148/radiol.2017170458. Epub 2017 Oct 16.

PMID:
29040037
26.

Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.

Kooi T, Karssemeijer N.

J Med Imaging (Bellingham). 2017 Oct;4(4):044501. doi: 10.1117/1.JMI.4.4.044501. Epub 2017 Oct 10.

27.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis.

Gray E, Donten A, Karssemeijer N, van Gils C, Evans DG, Astley S, Payne K.

Value Health. 2017 Sep;20(8):1100-1109. doi: 10.1016/j.jval.2017.04.012. Epub 2017 Jun 1.

28.

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

Ghafoorian M, Karssemeijer N, Heskes T, van Uden IWM, Sanchez CI, Litjens G, de Leeuw FE, van Ginneken B, Marchiori E, Platel B.

Sci Rep. 2017 Jul 11;7(1):5110. doi: 10.1038/s41598-017-05300-5.

29.

Surveillance of Women with the BRCA1 or BRCA2 Mutation by Using Biannual Automated Breast US, MR Imaging, and Mammography.

van Zelst JCM, Mus RDM, Woldringh G, Rutten MJCM, Bult P, Vreemann S, de Jong M, Karssemeijer N, Hoogerbrugge N, Mann RM.

Radiology. 2017 Nov;285(2):376-388. doi: 10.1148/radiol.2017161218. Epub 2017 Jun 13.

PMID:
28609204
30.

The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study.

Wanders JOP, Holland K, Karssemeijer N, Peeters PHM, Veldhuis WB, Mann RM, van Gils CH.

Breast Cancer Res. 2017 Jun 5;19(1):67. doi: 10.1186/s13058-017-0859-9.

31.

Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound.

van Zelst JCM, Balkenhol M, Tan T, Rutten M, Imhof-Tas M, Bult P, Karssemeijer N, Mann RM.

Ultrasound Med Biol. 2017 Sep;43(9):1820-1828. doi: 10.1016/j.ultrasmedbio.2017.03.019. Epub 2017 May 31.

PMID:
28576620
32.

Compressed Sensing for Breast MRI: Resolving the Trade-Off Between Spatial and Temporal Resolution.

Vreemann S, Rodriguez-Ruiz A, Nickel D, Heacock L, Appelman L, van Zelst J, Karssemeijer N, Weiland E, Maas M, Moy L, Kiefer B, Mann RM.

Invest Radiol. 2017 Oct;52(10):574-582. doi: 10.1097/RLI.0000000000000384.

PMID:
28463932
33.

Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.

Ghafoorian M, Karssemeijer N, Heskes T, Bergkamp M, Wissink J, Obels J, Keizer K, de Leeuw FE, Ginneken BV, Marchiori E, Platel B.

Neuroimage Clin. 2017 Feb 4;14:391-399. doi: 10.1016/j.nicl.2017.01.033. eCollection 2017.

34.

Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions.

Mus RD, Borelli C, Bult P, Weiland E, Karssemeijer N, Barentsz JO, Gubern-Mérida A, Platel B, Mann RM.

Eur J Radiol. 2017 Apr;89:90-96. doi: 10.1016/j.ejrad.2017.01.020. Epub 2017 Jan 20.

PMID:
28267555
35.

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection.

van Zelst JCM, Tan T, Platel B, de Jong M, Steenbakkers A, Mourits M, Grivegnee A, Borelli C, Karssemeijer N, Mann RM.

Eur J Radiol. 2017 Apr;89:54-59. doi: 10.1016/j.ejrad.2017.01.021. Epub 2017 Jan 22.

PMID:
28267549
36.

Optimization of volumetric breast density estimation in digital mammograms.

Holland K, Gubern-Mérida A, Mann RM, Karssemeijer N.

Phys Med Biol. 2017 May 7;62(9):3779-3797. doi: 10.1088/1361-6560/aa628f. Epub 2017 Feb 23.

PMID:
28230532
37.

Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings.

Mordang JJ, Gubern-Mérida A, Bria A, Tortorella F, den Heeten G, Karssemeijer N.

Med Phys. 2017 Apr;44(4):1390-1401. doi: 10.1002/mp.12152. Epub 2017 Mar 22.

PMID:
28182277
38.

Quantification of masking risk in screening mammography with volumetric breast density maps.

Holland K, van Gils CH, Mann RM, Karssemeijer N.

Breast Cancer Res Treat. 2017 Apr;162(3):541-548. doi: 10.1007/s10549-017-4137-4. Epub 2017 Feb 4.

39.

Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.

Kooi T, van Ginneken B, Karssemeijer N, den Heeten A.

Med Phys. 2017 Mar;44(3):1017-1027. doi: 10.1002/mp.12110.

PMID:
28094850
40.

3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging.

Mertzanidou T, Hipwell JH, Reis S, Hawkes DJ, Ehteshami Bejnordi B, Dalmis M, Vreemann S, Platel B, van der Laak J, Karssemeijer N, Hermsen M, Bult P, Mann R.

Med Phys. 2017 Mar;44(3):935-948. doi: 10.1002/mp.12077.

PMID:
28064435
41.

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

Dalmış MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern-Mérida A.

Med Phys. 2017 Feb;44(2):533-546. doi: 10.1002/mp.12079.

PMID:
28035663
42.

Volumetric breast density affects performance of digital screening mammography.

Wanders JO, Holland K, Veldhuis WB, Mann RM, Pijnappel RM, Peeters PH, van Gils CH, Karssemeijer N.

Breast Cancer Res Treat. 2017 Feb;162(1):95-103. doi: 10.1007/s10549-016-4090-7. Epub 2016 Dec 23.

43.

Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease.

Ghafoorian M, Karssemeijer N, van Uden IW, de Leeuw FE, Heskes T, Marchiori E, Platel B.

Med Phys. 2016 Dec;43(12):6246.

PMID:
27908171
44.

Large scale deep learning for computer aided detection of mammographic lesions.

Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, den Heeten A, Karssemeijer N.

Med Image Anal. 2017 Jan;35:303-312. doi: 10.1016/j.media.2016.07.007. Epub 2016 Aug 2.

PMID:
27497072
45.

Consistency of breast density categories in serial screening mammograms: A comparison between automated and human assessment.

Holland K, van Zelst J, den Heeten GJ, Imhof-Tas M, Mann RM, van Gils CH, Karssemeijer N.

Breast. 2016 Oct;29:49-54. doi: 10.1016/j.breast.2016.06.020. Epub 2016 Jul 13.

PMID:
27420382
46.

Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model.

Tan T, Gubern-Mérida A, Borelli C, Manniesing R, van Zelst J, Wang L, Zhang W, Platel B, Mann RM, Karssemeijer N.

Med Phys. 2016 Jul;43(7):4074. doi: 10.1118/1.4953206.

PMID:
27370126
47.

Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images.

Ehteshami Bejnordi B, Balkenhol M, Litjens G, Holland R, Bult P, Karssemeijer N, van der Laak JA.

IEEE Trans Med Imaging. 2016 Sep;35(9):2141-2150. doi: 10.1109/TMI.2016.2550620. Epub 2016 Apr 5.

PMID:
27076354
48.

Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications.

Mordang JJ, Gubern-Mérida A, den Heeten G, Karssemeijer N.

Med Phys. 2016 Apr;43(4):1676. doi: 10.1118/1.4943376.

PMID:
27036566
49.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

Kallenberg M, Petersen K, Nielsen M, Ng AY, Pengfei Diao, Igel C, Vachon CM, Holland K, Winkel RR, Karssemeijer N, Lillholm M.

IEEE Trans Med Imaging. 2016 May;35(5):1322-1331. doi: 10.1109/TMI.2016.2532122. Epub 2016 Feb 18.

PMID:
26915120
50.

The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study.

Schalekamp S, Karssemeijer N, Cats AM, De Hoop B, Geurts BH, Berger-Hartog O, van Ginneken B, Schaefer-Prokop CM.

J Thorac Imaging. 2016 Mar;31(2):119-25. doi: 10.1097/RTI.0000000000000195.

PMID:
26783697

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