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Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.
Jpn J Radiol. 2023 Jan;41(1):71-82. doi: 10.1007/s11604-022-01325-7. Epub 2022 Aug 13.
Jpn J Radiol. 2023.
PMID: 35962933
Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment.
Yardimci AH, Sel I, Bektas CT, Yarikkaya E, Dursun N, Bektas H, Afsar CU, Gursu RU, Yardimci VH, Ertas E, Kilickesmez O.
Yardimci AH, et al. Among authors: sel i.
Jpn J Radiol. 2020 Jun;38(6):553-560. doi: 10.1007/s11604-020-00936-2. Epub 2020 Mar 5.
Jpn J Radiol. 2020.
PMID: 32140880
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Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.
Kocak B, Durmaz ES, Ates E, Sel I, Turgut Gunes S, Kaya OK, Zeynalova A, Kilickesmez O.
Kocak B, et al. Among authors: sel i.
Eur Radiol. 2020 Feb;30(2):877-886. doi: 10.1007/s00330-019-06492-2. Epub 2019 Nov 5.
Eur Radiol. 2020.
PMID: 31691122
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Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion.
Yardımcı AH, Koçak B, Turan Bektaş C, Sel İ, Yarıkkaya E, Dursun N, Bektaş H, Usul Afşar Ç, Gürsu RU, Kılıçkesmez Ö.
Yardımcı AH, et al. Among authors: sel i.
Diagn Interv Radiol. 2020 Nov;26(6):515-522. doi: 10.5152/dir.2020.19507.
Diagn Interv Radiol. 2020.
PMID: 32990246
Free PMC article.
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Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.
Sel İ, Çakmakcı M, Özkaya B, Suphi Altan H.
Sel İ, et al.
Waste Manag. 2016 Oct;56:310-7. doi: 10.1016/j.wasman.2016.07.023. Epub 2016 Jul 18.
Waste Manag. 2016.
PMID: 27444845
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