{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:24:03Z","timestamp":1787171043746,"version":"build-2736575974"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"1185","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"JSPS KAKENHI","award":["24K10803"],"award-info":[{"award-number":["24K10803"]}]},{"name":"JSPS KAKENHI","award":["24K10829"],"award-info":[{"award-number":["24K10829"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To examine the performance of the variable Vision Transformer (vViT) in comparison with that of convolutional neural networks (CNNs) in the task of differentiating clear cell renal cell carcinoma (ccRCC) and non-ccRCC using CT images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The vViT was designed to use patient characteristics, radiomic features, and arterial phase CT images. The training and test datasets were constructed from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) dataset. The training dataset contained 153 patients with 1636 images (818 ccRCC, 818 non-ccRCC) and the test dataset contained 39 patients with 402 images (201 ccRCC, 201 non-ccRCC). After training, metrics including accuracy and the area under the curve of the receiver-operating characteristics (AUC-ROC) were calculated using the test dataset for vViT, Visual Geometry Group (VGG) 16, AlexNet, and GoogleNet in a patient-based approach. The metrics were calculated using CT images containing kidney and tumor. The AUC-ROC of vViT was compared with those of other models using the DeLong test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>vViT, VGG16, AlexNet, and GoogleNet achieved accuracies of 0.82 (95% CI, 0.72-0.86), 0.72 (0.62-0.78), 0.54 (0.45-0.62), and 0.61 (0.52-0.69), respectively. AUC-ROC values for vViT, VGG16, AlexNet, and GoogleNet were 0.91 (0.81-0.99), 0.70 (0.51-0.89), 0.53 (0.34-0.72), and 0.66 (0.46-0.85), respectively. The AUC-ROC of vViT was higher than that of VGG16, AlexNet, and GoogleNet (P\u2009&amp;lt;\u2009.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Variable Vision Transformer achieved comparable performance to CNN using CT images in the task of differentiating ccRCC and non-ccRCC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>We developed a new deep learning model termed vViT that simultaneously analyzes non-image information and medical images. Variable Vision Transformer can evaluate associations between input factors and outcome.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag118","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T03:54:51Z","timestamp":1779335691000},"page":"1788-1794","source":"Crossref","is-referenced-by-count":2,"title":["Performance of a self-attention-based model in the task of differentiating clear cell renal cell carcinoma from other renal tumors: variable Vision Transformer"],"prefix":"10.1093","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3945-3432","authenticated-orcid":false,"given":"Takuma","family":"Usuzaki","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, Tohoku University Hospital , Sendai, Miyagi 980-8574,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kengo","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Department of Clinical Imaging, Graduate School of Medicine, Tohoku University , Sendai, Miyagi, 980-8575,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3071-5803","authenticated-orcid":false,"given":"Hidenobu","family":"Takagi","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tohoku University Hospital , Sendai, Miyagi 980-8574,","place":["Japan"]},{"name":"Department of Advanced MRI Collaborative Research, Graduate School of Medicine, Tohoku University , Sendai, Miyagi, 980-8575,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2884-0023","authenticated-orcid":false,"given":"Mami","family":"Ishikuro","sequence":"additional","affiliation":[{"name":"Division of Molecular Epidemiology, Graduate School of Medicine, Tohoku University , Sendai, Miyagi, 980-8575,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3674-1674","authenticated-orcid":false,"given":"Taku","family":"Obara","sequence":"additional","affiliation":[{"name":"Division of Molecular Epidemiology, Graduate School of Medicine, Tohoku University , Sendai, Miyagi, 980-8575,","place":["Japan"]},{"name":"Division of Molecular Epidemiology, Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University , Sendai, Miyagi, 980-8573,","place":["Japan"]},{"name":"Department of Pharmaceutical Sciences, Tohoku University Hospital , Sendai, Miyagi, 980-8574,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryusei","family":"Inamori","sequence":"additional","affiliation":[{"name":"Department of Clinical Imaging, Graduate School of Medicine, Tohoku University , Sendai, Miyagi, 980-8575,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Kamada","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tohoku University Hospital , Sendai, Miyagi 980-8574,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomomi","family":"Sato","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tohoku University Hospital , Sendai, Miyagi 980-8574,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7101-7382","authenticated-orcid":false,"given":"Sota","family":"Oguro","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tohoku University Hospital , Sendai, Miyagi 980-8574,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kei","family":"Takase","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tohoku University Hospital , Sendai, Miyagi 980-8574,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"2026081915314379200_tqag118-B1","volume-title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","author":"Dosovitskiy","year":"2020"},{"key":"2026081915314379200_tqag118-B2","volume-title":"Attention Is All You Need","author":"Vaswani","year":"2017"},{"key":"2026081915314379200_tqag118-B3","volume-title":"Splitting Expands the Application Range of Vision Transformer\u2014Variable Vision Transformer (vViT)","author":"Usuzaki","year":"2022"},{"key":"2026081915314379200_tqag118-B4","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s00234-024-03329-8","article-title":"Identifying key factors for predicting O6-methylguanine-DNA methyltransferase status in adult patients with diffuse glioma: a multimodal analysis of demographics, radiomics, and MRI by variable Vision Transformer","volume":"66","author":"Usuzaki","year":"2024","journal-title":"Neuroradiology"},{"key":"2026081915314379200_tqag118-B5","doi-asserted-by":"publisher","first-page":"106001","DOI":"10.1016\/j.bspc.2024.106001","article-title":"Grading diffuse glioma based on 2021 WHO grade using self-attention-base deep learning architecture: variable Vision Transformer (vViT)","volume":"91","author":"Usuzaki","year":"2024","journal-title":"Biomed Signal Process Control"},{"key":"2026081915314379200_tqag118-B6","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.mri.2024.05.012","article-title":"Predicting isocitrate dehydrogenase status among adult patients with diffuse glioma using patient characteristics, radiomic features, and magnetic resonance imaging: multi-modal analysis by variable vision transformer","volume":"111","author":"Usuzaki","year":"2024","journal-title":"Magn Reson Imaging"},{"key":"2026081915314379200_tqag118-B7","doi-asserted-by":"publisher","first-page":"3057","DOI":"10.1007\/s10278-024-01180-0","article-title":"Predicting EGFR status after radical nephrectomy or partial nephrectomy for renal cell carcinoma on CT using a self-attention-based model: variable Vision Transformer (vViT)","volume":"37","author":"Usuzaki","year":"2024","journal-title":"J Imaging Inform Med"},{"key":"2026081915314379200_tqag118-B8","doi-asserted-by":"publisher","author":"Usuzaki","DOI":"10.64898\/2026.04.17.26351114"},{"key":"2026081915314379200_tqag118-B9","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1093\/annonc\/mdz056","article-title":"Renal cell carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-updagger","volume":"30","author":"Escudier","year":"2019","journal-title":"Ann Oncol"},{"key":"2026081915314379200_tqag118-B10","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/ng.3146","article-title":"Spectrum of diverse genomic alterations define non-clear cell renal carcinoma subtypes","volume":"47","author":"Durinck","year":"2015","journal-title":"Nat Genet"},{"key":"2026081915314379200_tqag118-B11","doi-asserted-by":"publisher","first-page":"3996","DOI":"10.1007\/s00330-018-5872-6","article-title":"Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective","volume":"29","author":"Li","year":"2019","journal-title":"Eur Radiol"},{"key":"2026081915314379200_tqag118-B12","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/s41698-021-00195-y","article-title":"Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography","volume":"5","author":"Uhm","year":"2021","journal-title":"NPJ Precis Oncol"},{"key":"2026081915314379200_tqag118-B13","doi-asserted-by":"publisher","DOI":"10.7937\/TCIA.2019.IX49E8NX","volume-title":"Data from C4KC-KiTS","author":"Heller","year":"2019"},{"key":"2026081915314379200_tqag118-B14","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The cancer imaging archive (TCIA): maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J Digit Imaging"},{"key":"2026081915314379200_tqag118-B15","doi-asserted-by":"publisher","first-page":"101821","DOI":"10.1016\/j.media.2020.101821","article-title":"The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the KiTS19 challenge","volume":"67","author":"Heller","year":"2021","journal-title":"Med Image Anal"},{"key":"2026081915314379200_tqag118-B16","author":"Heller","year":"2020"},{"key":"2026081915314379200_tqag118-B17","author":"He","year":"2015"},{"key":"2026081915314379200_tqag118-B18","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"2026081915314379200_tqag118-B19","author":"Szegedy","year":"2014"},{"key":"2026081915314379200_tqag118-B20","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1007\/s10278-019-00230-2","article-title":"The classification of renal cancer in 3-phase CT images using a deep learning method","volume":"32","author":"Han","year":"2019","journal-title":"J Digit Imaging"},{"key":"2026081915314379200_tqag118-B21","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.eururo.2011.06.049","article-title":"The epidemiology of renal cell carcinoma","volume":"60","author":"Ljungberg","year":"2011","journal-title":"Eur Urol"},{"key":"2026081915314379200_tqag118-B22","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1038\/bjc.2012.136","article-title":"Alcohol intake and renal cell cancer risk: a meta-analysis","volume":"106","author":"Song","year":"2012","journal-title":"Br J Cancer"},{"key":"2026081915314379200_tqag118-B23","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1148\/radiol.2442060927","article-title":"Solid renal cortical tumors: differentiation with CT","volume":"244","author":"Zhang","year":"2007","journal-title":"Radiology"},{"key":"2026081915314379200_tqag118-B24","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.urolonc.2021.09.004","article-title":"The value of real-time contrast-enhanced ultrasound combined with CT enhancement in the differentiation of subtypes of renal cell carcinoma","volume":"39","author":"Liang","year":"2021","journal-title":"Urol Oncol"}],"container-title":["British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bjr\/advance-article-pdf\/doi\/10.1093\/bjr\/tqag118\/68349713\/tqag118.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/99\/1185\/1788\/68349713\/tqag118.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bjr\/article-pdf\/99\/1185\/1788\/68349713\/tqag118.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:31:50Z","timestamp":1787167910000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/99\/1185\/1788\/8688636"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":24,"journal-issue":{"issue":"1185","published-online":{"date-parts":[[2026,5,20]]},"published-print":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bjr\/tqag118","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"value":"0007-1285","type":"print"},{"value":"1748-880X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,9]]},"published":{"date-parts":[[2026,5,20]]}}}