| [1] |
Alvarez CS, Piazuelo MB, Fleitas-Kanonnikoff T, et al. Incidence and survival outcomes of gastrointestinal stromal tumors[J]. JAMA Netw Open, 2024, 7(8): e2428828.
|
| [2] |
Unk M, Jezeršek Novaković B, Novaković S. Molecular mechanisms of gastrointestinal stromal tumors and their impact on systemic therapy decision[J]. Cancers (Basel), 2023, 15(5): 1498.
|
| [3] |
Hong Y, Zhong L, Lv X, et al. Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects[J]. Front Mol Biosci, 2023, 10: 1284549.
|
| [4] |
Sahni VA, Shinagare AB, Silverman SG. Virtual unenhanced CT images acquired from dual-energy CT urography: accuracy of attenuation values and variation with contrast material phase[J]. Clin Radiol, 2013, 68(3): 264–271.
|
| [5] |
Graser A, Johnson TR, Bader M, et al. Dual energy CT characterization of urinary calculi: initial in vitro and clinical experience[J]. Invest Radiol, 2008, 43(2): 112–119.
|
| [6] |
Wolterink JM, Leiner T, de Vos BD, et al. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks[J]. Med Image Anal, 2016, 34: 123–136.
|
| [7] |
Lv P, Zhang Y, Liu J, et al. Material decomposition images generated from spectral CT: detectability of urinary calculi and influencing factors[J]. Acad Radiol, 2014, 21(1): 79–85.
|
| [8] |
Willemink MJ, Persson M, Pourmorteza A, et al. Photon-counting CT: technical principles and clinical prospects[J]. Radiology, 2018, 289(2): 293–312.
|
| [9] |
Sato M, Ichikawa Y, Domae K, et al. Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen[J]. Eur Radiol, 2022, 32(8): 5499–5507.
|
| [10] |
Patino M, Prochowski A, Agrawal MD, et al. Material separation using dual-energy CT: current and emerging applications[J]. Radiographics, 2016, 36(4): 1087–105.
|
| [11] |
Neuhaus V, Große Hokamp N, Abdullayev N, et al. Metal artifact reduction by dual-layer computed tomography using virtual monoenergetic images[J]. Eur J Radiol, 2017, 93: 143–148.
|
| [12] |
Narita K, Nakamura Y, Higaki T, et al. Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma[J]. Sci Rep, 2023, 13(1): 3603.
|
| [13] |
Yunaga H, Ohta Y, Kishimoto J, et al. Effect of energy difference in the evaluation of calcification size and luminal diameter in calcified coronary artery plaque using spectral CT[J]. Jpn J Radiol, 2020, 38(12): 1142–1149.
|
| [14] |
Meisamy S, Hines CD, Hamilton G, et al. Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy[J]. Radiology, 2011, 258(3): 767–775.
|
| [15] |
Wood AM, Shea SM, Medved M, et al. Spectral characterization of tissues in high spectral and spatial resolution MR images: implications for a classification-based synthetic CT algorithm[J]. Med Phys, 2017, 44(5): 1865–1875.
|
| [16] |
Li J, Ye Y, Wang J, et al. Chinese consensus guidelines for diagnosis and management of gastrointestinal stromal tumor[J]. Chin J Cancer Res, 2017, 29(4): 281–293.
|
| [17] |
陈楠, 叶德胜, 邓满红. 能谱CT成像对胃黏膜下微小病变检出率的应用价值[J]. 现代医用影像学, 2024, 33(5): 858–861.
|
| [18] |
张瑞博, 郑艳芬. 能谱CT碘含量与超声内镜比较诊断胃黏膜下肿瘤的临床价值[J]. 中国医疗器械信息, 2023, 29(11): 77–80.
|
| [19] |
Liu J, Chai Y, Zhou J, et al. Spectral computed tomography imaging of gastric schwannoma and gastric stromal tumor[J]. J Comput Assist Tomogr, 2017, 41(3): 417–421.
|
| [20] |
Tsurumaru D, Nishimuta Y, Kai S, et al. Clinical significance of dual-energy dual-layer CT parameters in differentiating small-sized gastrointestinal stromal tumors from leiomyomas[J]. Jpn J Radiol, 2023, 41(12): 1389–1396.
|
| [21] |
吴玉锦. CT能谱成像在胃癌诊断中的初步研究应用[D]. 苏州:苏州大学, 2015.
|
| [22] |
李晶晶, 张梦琪, 刘焱. 能谱CT影像组学及CT征象对GIST危险度分级探讨[J]. 中国CT和MRI杂志, 2024, 22(7): 156–159.
|
| [23] |
于珊珊, 张极峰, 管莹, 等. 能谱CT定量参数与胃肠道间质瘤肿瘤危险度的关系[J]. 现代消化及介入诊疗, 2019, 24(4): 442–444.
|
| [24] |
Zhang X, Bai L, Wang D, et al. Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters[J]. Abdom Radiol (NY), 2019, 44(7): 2329–2336.
|
| [25] |
刘显旺, 谢一婧, 刘宏, 等. 能谱CT碘基图直方图分析预测胃间质瘤Ki-67表达水平的价值[J]. 中国临床医学影像杂志, 2025, 36(4): 260–264, 274.
|
| [26] |
张于凤, 李辉, 李晶晶, 等. 能谱CT多模态参数联合临床参数对胃间质瘤Ki-67的预测价值[J]. 分子影像学杂志, 2022, 45(5): 688–692.
|
| [27] |
Liu X, Han T, Wang Y, et al. Prediction of Ki-67 expression in gastric gastrointestinal stromal tumors using histogram analysis of monochromatic and iodine images derived from spectral CT[J]. Cancer Imaging, 2024, 24(1): 173.
|
| [28] |
Yin YQ, Liu CJ, Zhang B, et al. Association between CT imaging features and KIT mutations in small intestinal gastrointestinal stromal tumors[J]. Sci Rep, 2019, 9(1): 7257.
|
| [29] |
Qi YJ, Su GH, You C, et al. Radiomics in breast cancer: current advances and future directions[J]. Cell Rep Med, 2024, 5(9): 101719.
|
| [30] |
Xu L, Chen Z, Zhu D, Wang Y. The application status of radiomics-based machine learning in intrahepatic cholangiocarcinoma: systematic review and meta-analysis[J]. J Med Internet Res, 2025, 27: e69906.
|
| [31] |
Xu F, Ma X, Wang Y, et al. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation[J]. Eur J Radiol, 2018, 107: 90–97.
|
| [32] |
Liu X, Yin Y, Wang X, et al. Gastrointestinal stromal tumors: associations between contrast-enhanced CT images and KIT exon 11 gene mutation[J]. Ann Transl Med, 2021, 9(19): 1496.
|
| [33] |
Guo C, Zhou H, Chen X, et al. Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors[J]. Heliyon, 2023, 9(10): e20983.
|
| [34] |
Wei Y, Lu Z, Ren Y. Predictive value of a radiomics nomogram model based on contrast-enhanced computed tomography for KIT exon 9 gene mutation in gastrointestinal stromal tumors[J]. Technol Cancer Res Treat, 2023, 22: 15330338231181260.
|
| [35] |
Zhang QW, Zhang RY, Yan ZB, et al. Personalized radiomics signature to screen for KIT-11 mutation genotypes among patients with gastrointestinal stromal tumors: A retrospective multicenter study[J]. J Transl Med, 2023, 21(1): 726.
|
| [36] |
Liu B, Liu H, Zhang L, et al. Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: A two-center study[J]. Diagn Interv Radiol, 2022, 28(1): 29–38.
|
| [37] |
Zhang Y, Yue X, Zhang P, et al. Clinical-radiomics-based treatment decision support for KIT exon 11 deletion in gastrointestinal stromal tumors: A multi-institutional retrospective study[J]. Front Oncol, 2023, 13: 1193010.
|
| [38] |
Yin XN, Wang ZH, Zou L, et al. Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor[J]. World J Gastrointest Oncol, 2024, 16(4): 1296–1308.
|
| [39] |
蒋明巧, 杨彬, 韩福刚, 等. 胃肠道间质瘤的影像组学研究进展[J]. 浙江临床医学, 2023, 25(11): 1735–1738.
|
| [40] |
Cannella R, Tabone E, Porrello G, et al. Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors[J]. Eur Radiol, 2021, 31(11): 8554–8564.
|
| [41] |
孙嘉晨. 能谱CT评估胃肠间质瘤c-KIT外显子11突变状态的研究[D]. 兰州:兰州大学, 2023.
|
| [42] |
Meyer M, Ota H, Messiou C, et al. Prospective evaluation of quantitative response parameter in patients with gastrointestinal stroma tumor undergoing tyrosine kinase inhibitor therapy-impact on clinical outcome[J]. Int J Cancer, 2024, 155(11): 2047–2057.
|
| [43] |
Ekert K, Hinterleitner C, Horger M. Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis[J]. Eur J Radiol, 2019, 116: 98–105.
|
| [44] |
张学凌, 周俊林. 胃肠道间质瘤的影像研究进展[J]. 国际医学放射学杂志, 2017, 40(2): 170–173.
|
| [45] |
Douek PC, Boccalini S, Oei EHG, et al. Clinical applications of photon-counting CT: A review of pioneer studies and a glimpse into the future[J]. Radiology, 2023, 309(1): e222432.
|
| [46] |
Symons R, Reich DS, Bagheri M, et al. Photon-counting computed tomography for vascular imaging of the head and neck: first in vivo human results[J]. Invest Radiol, 2018, 53(3): 135–142.
|