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).
Objective: To assess the effectiveness of the vViT model for predicting postoperative renal function decline by leveraging clinical data, medical images, and image-derived features; and to identify the most dominant factor influencing this prediction. Materials and Methods: We developed two models,...
| Published in: | Journal of Digital Imaging Vol. 37; no. 6; pp. 3057 - 3070 |
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| Main Authors: | , , , , , , |
| Format: | diagnostic images pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182283988&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182283988 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2024 vid: 37 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182283988 182283988 182283988 10.1007/s10278-024-01180-0 182283988 ppf: 3057 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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). aug: au: Usuzaki, Takuma Inamori, Ryusei Ishikuro, Mami Obara, Taku Takaya, Eichi Homma, Noriyasu Takase, Kei affil: https://ror.org/00kcd6x60 Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan sug: subj: Carcinoma, Renal Cell Surgery Nephrectomy Methods Glomerular Filtration Rate Evaluation Kidney Physiology Kidney Radiography Tomography, X-Ray Computed Methods Treatment Outcomes Deep Learning Methods Deep Learning Evaluation Human Kidney Function Tests Postoperative Period Tomography, X-Ray Computed Equipment and Supplies Cancer Patients Male Female Body Mass Index Comorbidity Validity Machine Learning Neural Networks (Computer) Male Female ab: Objective: To assess the effectiveness of the vViT model for predicting postoperative renal function decline by leveraging clinical data, medical images, and image-derived features; and to identify the most dominant factor influencing this prediction. Materials and Methods: We developed two models, eGFR10 and eGFR20, to identify patients with a postoperative reduction in eGFR of more than 10 and more than 20, respectively, among renal cell carcinoma patients. The eGFR10 model was trained on 75 patients and tested on 27, while the eGFR20 model was trained on 77 patients and tested on 24. The vViT model inputs included class token, patient characteristics (age, sex, BMI), comorbidities (peripheral vascular disease, diabetes, liver disease), habits (smoking, alcohol), surgical details (ischemia time, blood loss, type and procedure of surgery, approach, operative time), radiomics, and tumor and kidney imaging. We used permutation feature importance to evaluate each sector's contribution. The performance of vViT was compared with CNN models, including VGG16, ResNet50, and DenseNet121, using McNemar and DeLong tests. Results: The eGFR10 model achieved an accuracy of 0.741 and an AUC-ROC of 0.692, while the eGFR20 model attained an accuracy of 0.792 and an AUC-ROC of 0.812. The surgical and radiomics sectors were the most influential in both models. The vViT had higher accuracy and AUC-ROC than VGG16 and ResNet50, and higher AUC-ROC than DenseNet121 (p < 0.05). Specifically, the vViT did not have a statistically different AUC-ROC compared to VGG16 (p = 1.0) and ResNet50 (p = 0.7) but had a statistically different AUC-ROC compared to DenseNet121 (p = 0.87) for the eGFR10 model. For the eGFR20 model, the vViT did not have a statistically different AUC-ROC compared to VGG16 (p = 0.72), ResNet50 (p = 0.88), and DenseNet121 (p = 0.64). Conclusion: The vViT model, a transformer-based approach for multimodal data, shows promise for preoperative CT-based prediction of eGFR status in patients with renal cell carcinoma. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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