diff --git a/diag.bib b/diag.bib index 8f0a89d..cd8a2e6 100644 --- a/diag.bib +++ b/diag.bib @@ -2950,22 +2950,6 @@ @article{Bel22 pmid = {35046392}, } -@article{Bel22, - author = {de Bel, Thomas and Litjens, Geert and Ogony, Joshua and Stallings-Mann, Melody and Carter, Jodi M. and Hilton, Tracy and Radisky, Derek C. and Vierkant, Robert A. and Broderick, Brendan and Hoskin, Tanya L. and Winham, Stacey J. and Frost, Marlene H. and Visscher, Daniel W. and Allers, Teresa and Degnim, Amy C. and Sherman, Mark E. and van der Laak, Jeroen A. W. M.}, - title = {~~Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning}, - doi = {10.1038/s41523-021-00378-7}, - year = {2022}, - abstract = {AbstractConvolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (+-0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted kappa = 0.747 +- 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution.}, - url = {http://dx.doi.org/10.1038/s41523-021-00378-7}, - file = {Bel22.pdf:pdf\\Bel22.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {npj Breast Cancer}, - automatic = {yes}, - citation-count = {5}, - volume = {8}, - pmid = {35046392}, -} - @article{Bell18, author = {Belli, Davide and Hu, Shi and Sogancioglu, Ecem and van Ginneken, Bram}, title = {~~Context Encoding Chest X-rays}, @@ -6988,46 +6972,6 @@ @article{Eerd21 pmid = {34719725}, } -@article{Eerd21, - author = {van der Eerden, A.W. and van den Heuvel, T.L. and Perlbarg, V. and Vart, P. and Vos, P.E. and Puybasset, L. and Galanaud, D. and Platel, B. and Manniesing, R. and Goraj, B.M.}, - title = {~~Traumatic Cerebral Microbleeds in the Subacute Phase Are Practical and Early Predictors of Abnormality of the Normal-Appearing White Matter in the Chronic Phase}, - doi = {10.3174/ajnr.a7028}, - year = {2021}, - abstract = {BACKGROUND AND PURPOSE: In the chronic phase after traumatic brain injury, DTI findings reflect WM integrity. DTI interpretation in the subacute phase is less straightforward. Microbleed evaluation with SWI is straightforward in both phases. We evaluated whether the microbleed concentration in the subacute phase is associated with the integrity of normal-appearing WM in the chronic phase. MATERIALS AND METHODS: Sixty of 211 consecutive patients 18 years of age or older admitted to our emergency department <=24 hours after moderate to severe traumatic brain injury matched the selection criteria. Standardized 3T SWI, DTI, and T1WI were obtained 3 and 26 weeks after traumatic brain injury in 31 patients and 24 healthy volunteers. At baseline, microbleed concentrations were calculated. At follow-up, mean diffusivity (MD) was calculated in the normal-appearing WM in reference to the healthy volunteers (MDz). Through linear regression, we evaluated the relation between microbleed concentration and MDz in predefined structures. RESULTS: In the cerebral hemispheres, MDz at follow-up was independently associated with the microbleed concentration at baseline (left: B = 38.4 [95% CI 7.5-69.3], P = .017; right: B = 26.3 [95% CI 5.7-47.0], P = .014). No such relation was demonstrated in the central brain. MDz in the corpus callosum was independently associated with the microbleed concentration in the structures connected by WM tracts running through the corpus callosum (B = 20.0 [95% CI 24.8-75.2], P < .000). MDz in the central brain was independently associated with the microbleed concentration in the cerebral hemispheres (B = 25.7 [95% CI 3.9-47.5], P = .023). CONCLUSIONS: SWI-assessed microbleeds in the subacute phase are associated with DTI-based WM integrity in the chronic phase. These associations are found both within regions and between functionally connected regions. B - : linear regression coefficient - Bcmb-conc - : linear regression coefficient with microbleed concentration as independent variable - Bcmb-nr - : linear regression coefficient with microbleed number as independent variable - MD - : mean diffusivity - MDz - : Z -score of mean diffusivity, normalized to the healthy control participants - t1 - : 3 (2-5) weeks after TBI - t2 - : 26 (25-28) weeks after TBI - TAI - : traumatic axonal injury - TBI - : traumatic brain injury - FA - : fractional anisotropy - MARS - : Microbleed Anatomical Rating Scale - GCS - : Glasgow Coma Scale}, - url = {http://dx.doi.org/10.3174/ajnr.A7028}, - file = {Eerd21.pdf:pdf\\Eerd21.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {American Journal of Neuroradiology}, - automatic = {yes}, - citation-count = {3}, - pages = {861-867}, - volume = {42}, - pmid = {34719725}, -} - @article{Ehte16, author = {Bejnordi, Babak Ehteshami and Litjens, Geert and Timofeeva, Nadya and Otte-Holler, Irene and Homeyer, Andre and Karssemeijer, Nico and van der Laak, Jeroen}, title = {Stain specific standardization of whole-slide histopathological images}, @@ -10228,23 +10172,6 @@ @article{Grob19a pmid = {30860897}, } -@article{Grob19a, - author = {Grob, Dagmar and Smit, Ewoud and Prince, Jip and Kist, Jakob and St\"{o}ger, Lauran and Geurts, Bram and Snoeren, Miranda M. and van Dijk, Rogier and Oostveen, Luuk J. and Prokop, Mathias and Schaefer-Prokop, Cornelia M. and Sechopoulos, Ioannis and Brink, Monique}, - title = {~~Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study}, - doi = {10.1148/radiol.2019182666}, - year = {2019}, - abstract = {Abstract unavailable}, - url = {http://dx.doi.org/10.1148/radiol.2019182666}, - file = {Grob19a.pdf:pdf\\Grob19a.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Radiology}, - automatic = {yes}, - citation-count = {31}, - pages = {197-205}, - volume = {292}, - pmid = {30860897}, -} - @conference{Grob19b, author = {Grob, Dagmar and Oostveen, Luuk J. and Jacobs, Colin and Prokop, Mathias and Schaefer-Prokop, Cornelia and Sechopoulos, Ioannis and Brink, Monique}, title = {Intra-patient comparison of pulmonary nodule enhancement in subtraction CT and dual-energy CT}, @@ -10855,38 +10782,6 @@ @article{Hare17 citation-count = {3}, } -@article{Harl21, - author = {Harlianto, Netanja I and Oosterhof, Nadine and Foppen, Wouter and Hol, Marjolein E and Wittenberg, Rianne and van der Veen, Pieternella H and van Ginneken, Bram and Mohamed Hoesein, Firdaus A A and Verlaan, Jorrit-Jan and de Jong, Pim A and Westerink, Jan and van Petersen, R and van Dinther, B and Asselbergs, F W and Nathoe, H M and de Borst, G J and Bots, M L and Geerlings, M I and Emmelot, M H and de Jong, P A and Leiner, T and Lely, A T and van der Kaaij, N P and Kappelle, L J and Ruigrok, Y M and Verhaar, M C and Visseren, F L J and Westerink, J and for the UCC-SMART-Studygroup}, - title = {~~Diffuse idiopathic skeletal hyperostosis is associated with incident stroke in patients with increased cardiovascular risk}, - doi = {10.1093/rheumatology/keab835}, - year = {2021}, - abstract = {Abstract - - Objectives - Earlier retrospective studies have suggested a relation between DISH and cardiovascular disease, including myocardial infarction. The present study assessed the association between DISH and incidence of cardiovascular events and mortality in patients with high cardiovascular risk. - - - Methods - In this prospective cohort study, we included 4624 patients (mean age 58.4 years, 69.6% male) from the Second Manifestations of ARTerial disease cohort. The main end point was major cardiovascular events (MACE: stroke, myocardial infarction and vascular death). Secondary endpoints included all-cause mortality and separate vascular events. Cause-specific proportional hazard models were used to evaluate the risk of DISH on all outcomes, and subdistribution hazard models were used to evaluate the effect of DISH on the cumulative incidence. All models were adjusted for age, sex, body mass index, blood pressure, diabetes, non-HDL cholesterol, packyears, renal function and C-reactive protein. - - - Results - DISH was present in 435 (9.4%) patients. After a median follow-up of 8.7 (IQR 5.0-12.0) years, 864 patients had died and 728 patients developed a MACE event. DISH was associated with an increased cumulative incidence of ischaemic stroke. After adjustment in cause-specific modelling, DISH remained significantly associated with ischaemic stroke (HR 1.55; 95% CI: 1.01, 2.38), but not with MACE (HR 0.99; 95% CI: 0.79, 1.24), myocardial infarction (HR 0.88; 95% CI: 0.59, 1.31), vascular death (HR 0.94; 95% CI: 0.68, 1.27) or all-cause mortality (HR 0.94; 95% CI: 0.77, 1.16). - - - Conclusion - The presence of DISH is independently associated with an increased incidence and risk for ischaemic stroke, but not with MACE, myocardial infarction, vascular death or all-cause mortality. - }, - url = {http://dx.doi.org/10.1093/rheumatology/keab835}, - file = {Harl21.pdf:pdf\\Harl21.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Rheumatology}, - automatic = {yes}, - citation-count = {5}, - pages = {2867-2874}, - volume = {61}, - pmid = {34357130}, -} @article{Harl21, author = {Harlianto, Netanja I and Oosterhof, Nadine and Foppen, Wouter and Hol, Marjolein E and Wittenberg, Rianne and van der Veen, Pieternella H and van Ginneken, Bram and Mohamed Hoesein, Firdaus A A and Verlaan, Jorrit-Jan and de Jong, Pim A and Westerink, Jan and van Petersen, R and van Dinther, B and Asselbergs, F W and Nathoe, H M and de Borst, G J and Bots, M L and Geerlings, M I and Emmelot, M H and de Jong, P A and Leiner, T and Lely, A T and van der Kaaij, N P and Kappelle, L J and Ruigrok, Y M and Verhaar, M C and Visseren, F L J and Westerink, J and for the UCC-SMART-Studygroup}, @@ -13850,23 +13745,6 @@ @article{Johk21 pmid = {33434111}, } -@article{Johk21, - author = {Johkoh, Takeshi and Lee, Kyung Soo and Nishino, Mizuki and Travis, William D. and Ryu, Jay H. and Lee, Ho Yun and Ryerson, Christopher J. and Franquet, Tom\'{a}s and Bankier, Alexander A. and Brown, Kevin K. and Goo, Jin Mo and Kauczor, Hans-Ulrich and Lynch, David A. and Nicholson, Andrew G. and Richeldi, Luca and Schaefer-Prokop, Cornelia M. and Verschakelen, Johny and Raoof, Suhail and Rubin, Geoffrey D. and Powell, Charles and Inoue, Yoshikazu and Hatabu, Hiroto}, - title = {~~Chest CT Diagnosis and Clinical Management of Drug-Related Pneumonitis in Patients Receiving Molecular Targeting Agents and Immune Checkpoint Inhibitors}, - doi = {10.1016/j.chest.2020.11.027}, - year = {2021}, - abstract = {Abstract unavailable}, - url = {http://dx.doi.org/10.1016/j.chest.2020.11.027}, - file = {Johk21.pdf:pdf\\Johk21.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Chest}, - automatic = {yes}, - citation-count = {45}, - pages = {1107-1125}, - volume = {159}, - pmid = {33434111}, -} - @article{Jong10, author = {P. A. de Jong and J. A. Achterberg and O. A. M. Kessels and B. van Ginneken and L. Hogeweg and F. J. Beek and S. W. J. Terheggen-Lagro}, title = {Modified {C}hrispin-{N}orman chest radiography score for cystic fibrosis: observer agreement and correlation with lung function}, @@ -16115,20 +15993,6 @@ @article{Laur22 pmid = {35438561}, } -@article{Laur22, - author = {Lauritzen, Andreas D. and von Euler-Chelpin, My Catarina and Lynge, Elsebeth and Vejborg, Ilse and Nielsen, Mads and Karssemeijer, Nico and Lillholm, Martin}, - title = {~~Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk}, - doi = {10.48550/ARXIV.2212.13439}, - year = {2022}, - abstract = {Purpose: Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk. Approach: The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within two years from screening and long-term cancers (LTC) from two years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk. Results: In Danish women, the texture model achieved an area under the receiver operating characteristic (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs. Conclusions: The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.}, - url = {https://arxiv.org/abs/2212.13439}, - file = {Laur22.pdf:pdf\\Laur22.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {arXiv:2212.13439}, - automatic = {yes}, - pmid = {35438561}, -} - @article{Leac12, author = {Leach, M. O. and Morgan, B. and Tofts, P. S. and Buckley, D. L. and Huang, W. and Horsfield, M. A. and Chenevert, T. L. and Collins, D. J. and Jackson, A. and Lomas, D. and Whitcher, B. and Clarke, L. and Plummer, R. and Judson, I. and Jones, R. and Alonzi, R. and Brunner, T. and Koh, D. M. and Murphy, P. and Waterton, J. C. and Parker, G. and Graves, M. J. and Scheenen, T W J. and Redpath, T. W. and Orton, M. and Karczmar, G. and Huisman, H. and Barentsz, J. and Padhani, A. and , on behalf of the Experimental Cancer Medicine Centres Imaging Network Steering Committee}, title = {Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging}, @@ -16612,23 +16476,6 @@ @article{Leij18 pmid = {29724890}, } -@article{Leij18, - author = {van Leijsen, Esther M. C. and Tay, Jonathan and van Uden, Ingeborg W. M. and Kooijmans, Eline C. M. and Bergkamp, Mayra I. and van der Holst, Helena M. and Ghafoorian, Mohsen and Platel, Bram and Norris, David G. and Kessels, Roy P. C. and Markus, Hugh S. and Tuladhar, Anil M. and de Leeuw, Frank-Erik}, - title = {~~Memory decline in elderly with cerebral small vessel disease explained by temporal interactions between white matter hyperintensities and hippocampal atrophy}, - doi = {10.1002/hipo.23039}, - year = {2018}, - abstract = {AbstractWhite matter hyperintensities (WMH) constitute the visible spectrum of cerebral small vessel disease (SVD) markers and are associated with cognitive decline, although they do not fully account for memory decline observed in individuals with SVD. We hypothesize that WMH might exert their effect on memory decline indirectly by affecting remote brain structures such as the hippocampus. We investigated the temporal interactions between WMH, hippocampal atrophy and memory decline in older adults with SVD. Five hundred and three participants of the RUNDMC study underwent neuroimaging and cognitive assessments up to 3 times over 8.7 years. We assessed WMH volumes semi-automatically and calculated hippocampal volumes (HV) using FreeSurfer. We used linear mixed effects models and causal mediation analyses to assess both interaction and mediation effects of hippocampal atrophy in the associations between WMH and memory decline, separately for working memory (WM) and episodic memory (EM). Linear mixed effect models revealed that the interaction between WMH and hippocampal volumes explained memory decline (WM: b = .067; 95%CI[.024-0.111]; p < .01; EM: b = .061; 95%CI[.025-.098]; p < .01), with better model fit when the WMH*HV interaction term was added to the model, for both WM (likelihood ratio test, kh2[1] = 9.3, p < .01) and for EM (likelihood ratio test, kh2[1] = 10.7, p < .01). Mediation models showed that both baseline WMH volume (b = -.170; p = .001) and hippocampal atrophy (b = 0.126; p = .009) were independently related to EM decline, but the effect of baseline WMH on EM decline was not mediated by hippocampal atrophy (p value indirect effect: 0.572). Memory decline in elderly with SVD was best explained by the interaction of WMH and hippocampal volumes. The relationship between WMH and memory was not causally mediated by hippocampal atrophy, suggesting that memory decline during aging is a heterogeneous condition in which different pathologies contribute to the memory decline observed in elderly with SVD.}, - url = {http://dx.doi.org/10.1002/hipo.23039}, - file = {Leij18.pdf:pdf\\Leij18.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Hippocampus}, - automatic = {yes}, - citation-count = {24}, - pages = {500-510}, - volume = {29}, - pmid = {29724890}, -} - @article{Leij19, author = {van Leijsen, Esther Mc and Bergkamp, Mayra I and van Uden, Ingeborg Wm and Cooijmans, Sjacky and Ghafoorian, Mohsen and van der Holst, Helena M and Norris, David G and Kessels, Roy Pc and Platel, Bram and Tuladhar, Anil M and de Leeuw, Frank-Erik}, title = {Cognitive consequences of regression of cerebral small vessel disease}, @@ -20477,23 +20324,6 @@ @article{Mets18b pmid = {30385691}, } -@article{Mets18b, - author = {Mets, Onno M. and Schaefer-Prokop, Cornelia M. and de Jong, Pim A.}, - title = {~~Cyst-related primary lung malignancies: an important and relatively unknown imaging appearance of (early) lung cancer}, - doi = {10.1183/16000617.0079-2018}, - year = {2018}, - abstract = {It is well known that lung cancer can manifest itself in imaging as solid and subsolid nodules or masses. However, in this era of increased computed tomography use another morphological computed tomography appearance of lung cancer is increasingly being recognised, presenting as a malignancy in relation to cystic airspaces. Despite the fact that it seems to be a relatively common finding in daily practice, literature on this entity is scarce and presumably the overall awareness is limited. This can lead to misinterpretation and delay in diagnosis and, therefore, increased awareness is urgently needed. This review aims to illustrate the imaging appearances of cyst-related primary lung malignancies, demonstrate its mimickers and potential pitfalls, and discuss the clinical implications based on the available literature and our own experience in four different hospitals.}, - url = {http://dx.doi.org/10.1183/16000617.0079-2018}, - file = {Mets18b.pdf:pdf\\Mets18b.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {European Respiratory Review}, - automatic = {yes}, - citation-count = {12}, - pages = {180079}, - volume = {27}, - pmid = {30385691}, -} - @article{Meye20, author = {Anneke Meyer and Grzegorz Chlebus and Marko Rak and Daniel Schindele and Martin Schostak and Bram van Ginneken and Andrea Schenk and Hans Meine and Horst K. Hahn and Andreas Schreiber and Christian Hansen}, title = {Anisotropic {3D} Multi-Stream {CNN} for Accurate Prostate Segmentation from Multi-Planar {MRI}}, @@ -21689,23 +21519,6 @@ @article{Nas22 pmid = {35587346}, } -@article{Nas22, - author = {Nas, J and Thannhauser, J and Vart, P and van Geuns, RJM and Muijsers, HEC and Mol, JHQ and Aarts, GWA and Konijnenberg, LSF and Gommans, DHF and Ahoud-Schoenmakers, SGAM and Vos, JL and van Royen, N and Bonnes, JL and Brouwer, MA}, - title = {~~The impact of alcohol use on the quality of cardiopulmonary resuscitation among festival attendees: A prespecified analysis of a randomised trial}, - doi = {10.1016/j.resuscitation.2022.10.002}, - year = {2022}, - abstract = {Abstract unavailable}, - url = {http://dx.doi.org/10.1016/j.resuscitation.2022.10.002}, - file = {Nas22.pdf:pdf\\Nas22.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Resuscitation}, - automatic = {yes}, - citation-count = {1}, - pages = {12-19}, - volume = {181}, - pmid = {35587346}, -} - @article{Niek09, author = {C. G. van Niekerk and J. A. W. M. van der Laak and M. E. B\"orger and H. Huisman and J. Alfred Witjes and J. O. Barentsz and Hulsbergen-van de Kaa, C. A.}, title = {Computerized whole slide quantification shows increased microvascular density in {pT2} prostate cancer as compared to normal prostate tissue}, @@ -23023,39 +22836,6 @@ @article{Pfob22 pmid = {36283244}, } -@article{Pfob22, - author = {Pfob, Andr\'{e} and Sidey-Gibbons, Chris and Barr, Richard G. and Duda, Volker and Alwafai, Zaher and Balleyguier, Corinne and Clevert, Dirk-Andr\'{e} and Fastner, Sarah and Gomez, Christina and Goncalo, Manuela and Gruber, Ines and Hahn, Markus and Hennigs, Andr\'{e} and Kapetas, Panagiotis and Lu, Sheng-Chieh and Nees, Juliane and Ohlinger, Ralf and Riedel, Fabian and Rutten, Matthieu and Schaefgen, Benedikt and Schuessler, Maximilian and Stieber, Anne and Togawa, Riku and Tozaki, Mitsuhiro and Wojcinski, Sebastian and Xu, Cai and Rauch, Geraldine and Heil, Joerg and Golatta, Michael}, - title = {~~The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis}, - doi = {10.1007/s00330-021-08519-z}, - year = {2022}, - abstract = {Abstract Objectives - AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms. - - Methods - Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC). - - Results - Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons <= 0.05). - - Conclusions - The performance of humans and AI-based algorithms improves with multi-modal information. - - Key Points - * The performance of humans and AI-based algorithms improves with multi-modal information. - * Multimodal AI-based algorithms do not necessarily outperform expert humans. - * Unimodal AI-based algorithms do not represent optimal performance to classify breast masses. - }, - url = {http://dx.doi.org/10.1007/s00330-021-08519-z}, - file = {Pfob22.pdf:pdf\\Pfob22.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {European Radiology}, - automatic = {yes}, - citation-count = {7}, - pages = {4101-4115}, - volume = {32}, - pmid = {36283244}, -} - @inproceedings{Phil13, author = {Philipsen, R.H.H.M. and Maduskar, P. and Hogeweg, L. and van Ginneken, B.}, title = {Normalization of Chest Radiographs}, @@ -24008,21 +23788,6 @@ @inproceedings{Reis15 pmid = {24819231}, } -@inproceedings{Reis15, - author = {Reis, Sara and Eiben, Bjoern and Mertzanidou, Thomy and Hipwell, John and Hermsen, Meyke and van der Laak, Jeroen and Pinder, Sarah and Bult, Peter and Hawkes, David}, - title = {~~Minimum slice spacing required to reconstruct 3D shape for serial sections of breast tissue for comparison with medical imaging}, - doi = {10.1117/12.2081909}, - year = {2015}, - abstract = {There is currently an increasing interest in combining the information obtained from radiology and histology with the intent of gaining a better understanding of how different tumour morphologies can lead to distinctive radiological signs which might predict overall treatment outcome. Relating information at different resolution scales is challenging. Reconstructing 3D volumes from histology images could be the key to interpreting and relating the radiological image signal to tissue microstructure. The goal of this study is to determine the minimum sampling (maximum spacing between histological sections through a fixed surgical specimen) required to create a 3D reconstruction of the specimen to a specific tolerance. We present initial results for one lumpectomy specimen case where 33 consecutive histology slides were acquired.}, - url = {http://dx.doi.org/10.1117/12.2081909}, - file = {Reis15.pdf:pdf\\Reis15.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Medical Imaging 2015: Digital Pathology}, - automatic = {yes}, - citation-count = {2}, - pmid = {24819231}, -} - @article{Rema16, author = {Romain Remark and Taha Merghoub and Niels Grabe and Geert Litjens and Diane Damotte and Jedd D. Wolchok and Miriam Merad and Sacha Gnjatic}, title = {In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide}, @@ -24866,20 +24631,6 @@ @inproceedings{Rodr18c citation-count = {7}, } -@inproceedings{Rodr18c, - author = {Rodriguez-Ruiz, Alejandro and Mordang, Jan-Jurre and Karssemeijer, Nico and Sechopoulos, Ioannis and Mann, Ritse}, - title = {~~Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?}, - doi = {10.1117/12.2317937}, - year = {2018}, - abstract = {For more than a decade, radiologists have used traditional computer aided detection systems to read mammograms, but mainly because of a low computer specificity may not improve their screening performance, according to several studies. The breakthrough in deep learning techniques has boosted the performance of machine learning algorithms, also for breast cancer detection in mammography. The objective of this study was to determine whether radiologists improve their breast cancer detection performance when they concurrently use a deep learningbased computer system for decision support, compared to when they read mammography unaided. A retrospective, fully-crossed, multi-reader multi-case (MRMC) study was designed to compare this. The employed decision support system was Transpara™ (Screenpoint Medical, Nijmegen, the Netherlands). Radiologists interact by clicking an area on the mammogram, for which the computer system displays its cancer likelihood score (1-100). In total, 240 cases (100 cancers, 40 false positive recalls, 100 normals) acquired with two different mammography systems were retrospectively collected. Seven radiologists scored each case once with, and once without the use of decision support, providing a forced BI-RADS® score and a level of suspiciousness (1-100). MRMC analysis of variance of the area under the receiver operating characteristic curves (AUC), and specificity and sensitivity were computed. When using decision support, the AUC increased from 0.87 to 0.89 (P=0.043) and specificity increased from 73% to 78% (P=0.030), while sensitivity did not significantly increment (84% to 87%, P=0.180). In conclusion, radiologists significantly improved their performance when using a deep learningbased computer system as decision support.}, - url = {http://dx.doi.org/10.1117/12.2317937}, - file = {Rodr18c.pdf:pdf\\Rodr18c.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {14th International Workshop on Breast Imaging (IWBI 2018)}, - automatic = {yes}, - citation-count = {7}, -} - @article{Rodr19, author = {Rodriguez-Ruiz, Alejandro and Lang, Kristina and Gubern-Merida, Albert and Teuwen, Jonas and Broeders, Mireille and Gennaro, Gisella and Clauser, Paola and Helbich, Thomas H and Chevalier, Margarita and Mertelmeier, Thomas and Wallis, Matthew G and Andersson, Ingvar and Zackrisson, Sophia and Sechopoulos, Ioannis and Mann, Ritse M}, title = {Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study}, @@ -25121,23 +24872,6 @@ @article{Roth22 pmid = {34100010}, } -@article{Roth22, - author = {Roth, Holger R. and Xu, Ziyue and Tor-D\'{i}ez, Carlos and Sanchez Jacob, Ramon and Zember, Jonathan and Molto, Jose and Li, Wenqi and Xu, Sheng and Turkbey, Baris and Turkbey, Evrim and Yang, Dong and Harouni, Ahmed and Rieke, Nicola and Hu, Shishuai and Isensee, Fabian and Tang, Claire and Yu, Qinji and S\"{o}lter, Jan and Zheng, Tong and Liauchuk, Vitali and Zhou, Ziqi and Moltz, Jan Hendrik and Oliveira, Bruno and Xia, Yong and Maier-Hein, Klaus H. and Li, Qikai and Husch, Andreas and Zhang, Luyang and Kovalev, Vassili and Kang, Li and Hering, Alessa and Vila\c{c}a, Jo\~{a}o L. and Flores, Mona and Xu, Daguang and Wood, Bradford and Linguraru, Marius George}, - title = {~~Rapid artificial intelligence solutions in a pandemic--The COVID-19-20 Lung CT Lesion Segmentation Challenge}, - doi = {10.1016/j.media.2022.102605}, - year = {2022}, - abstract = {Abstract unavailable}, - url = {http://dx.doi.org/10.1016/j.media.2022.102605}, - file = {Roth22.pdf:pdf\\Roth22.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Medical Image Analysis}, - automatic = {yes}, - citation-count = {8}, - pages = {102605}, - volume = {82}, - pmid = {34100010}, -} - @article{Rubi20, author = {Rubin, Geoffrey D. and Ryerson, Christopher J. and Haramati, Linda B. and Sverzellati, Nicola and Kanne, Jeffrey P. and Raoof, Suhail and Schluger, Neil W. and Volpi, Annalisa and Yim, Jae-Joon and Martin, Ian B.K. and Anderson, Deverick J. and Kong, Christina and Altes, Talissa and Bush, Andrew and Desai, Sujal R. and Goldin, Jonathan and Goo, Jin Mo and Humbert, Marc and Inoue, Yoshikazu and Kauczor, Hans-Ulrich and Luo, Fengming and Mazzone, Peter J. and Prokop, Mathias and Remy-Jardin, Martine and Richeldi, Luca and Schaefer-Prokop, Cornelia M. and Tomiyama, Noriyuki and Wells, Athol U. and Leung, Ann N.}, title = {~~The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic}, @@ -25936,23 +25670,6 @@ @article{Sand20a pmid = {32737712}, } -@article{Sand20a, - author = {Sanderink, W.B.G. and Caballo, M. and Strobbe, L.J.A. and Bult, P. and Vreuls, W. and Venderink, D.J. and Sechopoulos, I. and Karssemeijer, N. and Mann, R.M.}, - title = {~~Reliability of MRI tumor size measurements for minimal invasive treatment selection in small breast cancers}, - doi = {10.1016/j.ejso.2020.04.038}, - year = {2020}, - abstract = {Abstract unavailable}, - url = {http://dx.doi.org/10.1016/j.ejso.2020.04.038}, - file = {Sand20a.pdf:pdf\\Sand20a.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {European Journal of Surgical Oncology}, - automatic = {yes}, - citation-count = {3}, - pages = {1463-1470}, - volume = {46}, - pmid = {32737712}, -} - @article{Sand21, author = {Sanderink, Wendelien B.G. and Teuwen, Jonas and Appelman, Linda and Moy, Linda and Heacock, Laura and Weiland, Elisabeth and Karssemeijer, Nico and Baltzer, Pascal A.T. and Sechopoulos, Ioannis and Mann, Ritse M.}, title = {~~Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI}, @@ -26582,23 +26299,6 @@ @article{Scha21 pmid = {33725189}, } -@article{Scha21, - author = {Schaefer-Prokop, Cornelia and Prokop, Mathias}, - title = {~~Chest Radiography in COVID-19: No Role in Asymptomatic and Oligosymptomatic Disease}, - doi = {10.1148/radiol.2020204038}, - year = {2021}, - abstract = {Abstract unavailable}, - url = {http://dx.doi.org/10.1148/radiol.2020204038}, - file = {Scha21.pdf:pdf\\Scha21.pdf:PDF}, - optnote = {DIAG, RADIOLOGY}, - journal = {Radiology}, - automatic = {yes}, - citation-count = {3}, - pages = {E156-E157}, - volume = {298}, - pmid = {33725189}, -} - @article{Scha22, author = {Schaap, M. J. and Cardozo, N. J. and Patel, A. and de Jong, E. M. G. J. and van Ginneken, B. and Seyger, M. M. B.}, title = {Image-based automated Psoriasis Area Severity Index scoring by Convolutional Neural Networks},