Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
8 authors.
Christoph FürböckDepartment of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, 1090 Vienna, Austria.
Ivana JanickovaDepartment of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, 1090 Vienna, Austria.
Georg LangsDepartment of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0002-5536-6873
Thomas H HelbichDepartment of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0003-3169-778X
Paola ClauserDepartment of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, 1090 Vienna, Austria.
Raoul VargaDepartment of Biomedical Imaging and Image-guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0001-9661-1778
Pascal BaltzerDepartment of Biomedical Imaging and Image-guided Therapy, High Field Magnetic Resonance Center (HFMRC), Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0002-3755-5398
Panagiotis KapetasDepartment of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0003-2505-4753
Funding
Austrian Federal Ministry for Digital and Economic Affairs no numberChristian Doppler Research Association CD10277104European Union No.101100633 - EUCAIMNational Foundation for Research, Technology and Development no numberVienna Science and Technology Fund 10.47379/LS20065
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Deep-Learning with Domain-Specific Pretraining for Breast Cancer Neoadjuvant Chemotherapy Response Prediction from Pre-Treatment B-Mode Ultrasound. · full record | OpenQuestion