000125374 001__ 125374
000125374 005__ 20240228145525.0
000125374 0247_ $$2doi$$a10.1002/jmri.25606
000125374 0247_ $$2pmid$$apmid:28152264
000125374 0247_ $$2ISSN$$a1053-1807
000125374 0247_ $$2ISSN$$a1522-2586
000125374 037__ $$aDKFZ-2017-01507
000125374 041__ $$aeng
000125374 082__ $$a610
000125374 1001_ $$0P:(DE-He78)d2d971750bce6217eb90fff9b01e61f9$$aBickelhaupt, Sebastian$$b0$$eFirst author
000125374 245__ $$aPrediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
000125374 260__ $$aNew York, NY$$bWiley-Liss$$c2017
000125374 3367_ $$2DRIVER$$aarticle
000125374 3367_ $$2DataCite$$aOutput Types/Journal article
000125374 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1509717228_12188
000125374 3367_ $$2BibTeX$$aARTICLE
000125374 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000125374 3367_ $$00$$2EndNote$$aJournal Article
000125374 520__ $$aTo assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences.From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC.The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI.In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique.1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.
000125374 536__ $$0G:(DE-HGF)POF3-315$$a315 - Imaging and radiooncology (POF3-315)$$cPOF3-315$$fPOF III$$x0
000125374 588__ $$aDataset connected to CrossRef, PubMed,
000125374 7001_ $$0P:(DE-He78)c6e31fb8f19e185e254174554a0cccfc$$aPaech, Daniel$$b1
000125374 7001_ $$0P:(DE-He78)3da06896bf2a50a84d40c33c3b7a9b3e$$aKickingereder, Philipp$$b2
000125374 7001_ $$0P:(DE-He78)b03f7e5246e8a54dc8b9664fa11324e2$$aSteudle, Franziska$$b3
000125374 7001_ $$aLederer, Wolfgang$$b4
000125374 7001_ $$aDaniel, Heidi$$b5
000125374 7001_ $$0P:(DE-He78)abd768f879e71d08068d48fabb7e96cf$$aGötz, Michael$$b6
000125374 7001_ $$0P:(DE-He78)51c486692a77d77ee40b3760dd3b390b$$aGählert, Nils$$b7$$udkfz
000125374 7001_ $$0P:(DE-He78)2ef631585610340ff425c9c31fcabd03$$aTichy, Diana$$b8
000125374 7001_ $$0P:(DE-He78)1042737c83ba70ec508bdd99f0096864$$aWiesenfarth, Manuel$$b9
000125374 7001_ $$0P:(DE-He78)b709e6df1ec6b63e5ffad4c8131f6f4d$$aLaun, Frederik$$b10
000125374 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b11
000125374 7001_ $$0P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aSchlemmer, Heinz-Peter$$b12
000125374 7001_ $$0P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93$$aBonekamp, David$$b13$$eLast author
000125374 773__ $$0PERI:(DE-600)1497154-9$$a10.1002/jmri.25606$$gVol. 46, no. 2, p. 604 - 616$$n2$$p604 - 616$$tJournal of magnetic resonance imaging$$v46$$x1053-1807$$y2017
000125374 909CO $$ooai:inrepo02.dkfz.de:125374$$pVDB
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)d2d971750bce6217eb90fff9b01e61f9$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c6e31fb8f19e185e254174554a0cccfc$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)3da06896bf2a50a84d40c33c3b7a9b3e$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b03f7e5246e8a54dc8b9664fa11324e2$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)abd768f879e71d08068d48fabb7e96cf$$aDeutsches Krebsforschungszentrum$$b6$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)51c486692a77d77ee40b3760dd3b390b$$aDeutsches Krebsforschungszentrum$$b7$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)2ef631585610340ff425c9c31fcabd03$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)1042737c83ba70ec508bdd99f0096864$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b709e6df1ec6b63e5ffad4c8131f6f4d$$aDeutsches Krebsforschungszentrum$$b10$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aDeutsches Krebsforschungszentrum$$b11$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aDeutsches Krebsforschungszentrum$$b12$$kDKFZ
000125374 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93$$aDeutsches Krebsforschungszentrum$$b13$$kDKFZ
000125374 9131_ $$0G:(DE-HGF)POF3-315$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vImaging and radiooncology$$x0
000125374 9141_ $$y2017
000125374 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000125374 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ MAGN RESON IMAGING : 2015
000125374 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000125374 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000125374 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000125374 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000125374 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000125374 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000125374 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine
000125374 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000125374 9201_ $$0I:(DE-He78)E010-20160331$$kE010$$lRadiologie$$x0
000125374 9201_ $$0I:(DE-He78)E012-20160331$$kE012$$lNeuroonkologische Bildgebung$$x1
000125374 9201_ $$0I:(DE-He78)E132-20160331$$kE132$$lMedizinische Bildverarbeitung$$x2
000125374 9201_ $$0I:(DE-He78)C060-20160331$$kC060$$lBiostatistik$$x3
000125374 9201_ $$0I:(DE-He78)E020-20160331$$kE020$$lMedizinische Physik in der Radiologie$$x4
000125374 980__ $$ajournal
000125374 980__ $$aVDB
000125374 980__ $$aI:(DE-He78)E010-20160331
000125374 980__ $$aI:(DE-He78)E012-20160331
000125374 980__ $$aI:(DE-He78)E132-20160331
000125374 980__ $$aI:(DE-He78)C060-20160331
000125374 980__ $$aI:(DE-He78)E020-20160331
000125374 980__ $$aUNRESTRICTED