|
[1]
|
Xia, C., Dong, X., Li, H., Cao, M., Sun, D., He, S., et al. (2022) Cancer Statistics in China and United States, 2022: Profiles, Trends, and Determinants. Chinese Medical Journal, 135, 584-590. https://doi.org/10.1097/cm9.0000000000002108
|
|
[2]
|
Cercek, A., Dos Santos Fernandes, G., Roxburgh, C.S., Ganesh, K., Ng, S., Sanchez-Vega, F., et al. (2020) Mismatch Repair-Deficient Rectal Cancer and Resistance to Neoadjuvant Chemotherapy. Clinical Cancer Research, 26, 3271-3279. https://doi.org/10.1158/1078-0432.ccr-19-3728
|
|
[3]
|
De Smedt, L., Lemahieu, J., Palmans, S., Govaere, O., Tousseyn, T., Van Cutsem, E., et al. (2015) Microsatellite Instable vs Stable Colon Carcinomas: Analysis of Tumour Heterogeneity, Inflammation and Angiogenesis. British Journal of Cancer, 113, 500-509. https://doi.org/10.1038/bjc.2015.213
|
|
[4]
|
陈桥梁, 李响, 来瑞鹤, 等. 18F-FDGPET相关代谢参数预测结直肠癌微卫星高度不稳定性及HER2表达[J]. 中华核医学与分子影像杂志, 2025, 45(1): 7-12.
|
|
[5]
|
彭乐平, 张秀玲, 施柳言, 等. CT和MRI及影像组学预测结直肠癌微卫星不稳定状态研究进展[J]. 磁共振成像, 2024, 15(6): 218-223.
|
|
[6]
|
Sinicrope, F.A. and Sargent, D.J. (2012) Molecular Pathways: Microsatellite Instability in Colorectal Cancer: Prognostic, Predictive, and Therapeutic Implications. Clinical Cancer Research, 18, 1506-1512. https://doi.org/10.1158/1078-0432.ccr-11-1469
|
|
[7]
|
梁丽, 李鑫, 农琳, 等. 子宫内膜癌微卫星不稳定性分析: 微小微卫星变换的意义[J]. 北京大学学报(医学版), 2023, 55(2): 254-261.
|
|
[8]
|
王申博, 康飞, 汪静. 多中心/多设备间PET影像组学特征可重复性的研究进展[J]. 中华核医学与分子影像杂志, 2025, 45(4): 246-249.
|
|
[9]
|
张宇, 王城, 常培叶. PET影像组学在结直肠癌诊疗中的应用进展[J]. 国际放射医学核医学杂志, 2025, 49(3): 191-196.
|
|
[10]
|
Mi, M., Weng, S., Xu, Z., Hu, H., Wang, Y. and Yuan, Y. (2023) CSCO Guidelines for Colorectal Cancer Version 2023: Updates and Insights. Chinese Journal of Cancer Research, 35, 233-238. https://doi.org/10.21147/j.issn.1000-9604.2023.03.02
|
|
[11]
|
周志鹏, 赵春雷. PET影像组学临床应用进展[J]. 中国医学影像学杂志, 2023, 31(4): 424-428.
|
|
[12]
|
Cao, Y., Zhang, G., Zhang, J., Yang, Y., Ren, J., Yan, X., et al. (2021) Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study. Frontiers in Oncology, 11, Article 687771. https://doi.org/10.3389/fonc.2021.687771
|
|
[13]
|
Pei, Q., Yi, X., Chen, C., Pang, P., Fu, Y., Lei, G., et al. (2022) Pre-Treatment CT-Based Radiomics Nomogram for Predicting Microsatellite Instability Status in Colorectal Cancer. European Radiology, 32, 714-724. https://doi.org/10.1007/s00330-021-08167-3
|
|
[14]
|
Ma, Y., Lin, C., Liu, S., Wei, Y., Ji, C., Shi, F., et al. (2022) Radiomics Features Based on Internal and Marginal Areas of the Tumor for the Preoperative Prediction of Microsatellite Instability Status in Colorectal Cancer. Frontiers in Oncology, 12, Article 1020349. https://doi.org/10.3389/fonc.2022.1020349
|
|
[15]
|
Hoshino, I., Yokota, H., Iwatate, Y., Mori, Y., Kuwayama, N., Ishige, F., et al. (2025) Prediction of the Differences in Tumor Mutation Burden between Primary and Metastatic Lesions by Radiogenomics. Cancer Science, 113, 229-239. https://doi.org/10.1111/cas.15173
|
|
[16]
|
Song, R., Feng, Q., Pei, E., Li, Z., Yuan, X., Huo, Y., et al. (2026) Subregional Radiomics Analysis on Multiparametric MRI for Evaluating Lymphovascular Invasion and Survival in Gastric Cancer: A Multicenter Study. Journal of Magnetic Resonance Imaging, 63, 1466-1479. https://doi.org/10.1002/jmri.70236
|
|
[17]
|
Chen, T., Yi, R., Liu, Z., Chen, Q., Yuan, W. and Zhou, Q. (2025) Integrative Multi-Region MRI Radiomics and Clinical Nomogram for Preoperative Lymphovascular Invasion Prediction in Rectal Cancer: A Multicenter Validation. BMC Medical Imaging, 26, Article No. 48. https://doi.org/10.1186/s12880-025-02105-1
|
|
[18]
|
Bass, C., Ntelemis, F., Schmidt, J., Wolf, S., Geraldes, A., Mehrotra, D., et al. (2025) H&E-Based MSI/MMR Testing with AI in Colorectal Cancer: A Multi-Centred Blinded Evaluation. npj Digital Medicine, 9, Article No. 44. https://doi.org/10.1038/s41746-025-02218-5
|
|
[19]
|
Li, J., Yang, Z., Xin, B., Hao, Y., Wang, L., Song, S., et al. (2021) Quantitative Prediction of Microsatellite Instability in Colorectal Cancer with Preoperative PET/CT-Based Radiomics. Frontiers in Oncology, 11, Article 702055. https://doi.org/10.3389/fonc.2021.702055
|
|
[20]
|
Liu, H., Ye, Z., Yang, T., Xie, H., Duan, T., Li, M., et al. (2021) Predictive Value of Metabolic Parameters Derived from 18F-FDG PET/CT for Microsatellite Instability in Patients with Colorectal Carcinoma. Frontiers in Immunology, 12, Article 724464. https://doi.org/10.3389/fimmu.2021.724464
|
|
[21]
|
Zhang, J., Zhao, X., Zhao, Y., Zhang, J., Zhang, Z., Wang, J., et al. (2020) Value of Pre-Therapy 18F-FDG PET/CT Radiomics in Predicting EGFR Mutation Status in Patients with Non-Small Cell Lung Cancer. European Journal of Nuclear Medicine and Molecular Imaging, 47, 1137-1146. https://doi.org/10.1007/s00259-019-04592-1
|
|
[22]
|
Giannini, V., Mazzetti, S., Bertotto, I., Chiarenza, C., Cauda, S., Delmastro, E., et al. (2019) Predicting Locally Advanced Rectal Cancer Response to Neoadjuvant Therapy with 18F-FDG PET and MRI Radiomics Features. European Journal of Nuclear Medicine and Molecular Imaging, 46, 878-888. https://doi.org/10.1007/s00259-018-4250-6
|
|
[23]
|
Zhao, H., Su, Y., Wang, Y., Lyu, Z., Xu, P., Gu, W., et al. (2024) Using Tumor Habitat-Derived Radiomic Analysis during Pretreatment 18F-FDG PET for Predicting KRAS/NRAS/BRAF Mutations in Colorectal Cancer. Cancer Imaging, 24, Article No. 26. https://doi.org/10.1186/s40644-024-00670-2
|
|
[24]
|
Chen, S., Chiang, H., Chen, W.T., Hsieh, T., Yen, K., Chiang, S., et al. (2014) Correlation between PET/CT Parameters and KRAS Expression in Colorectal Cancer. Clinical Nuclear Medicine, 39, 685-689. https://doi.org/10.1097/rlu.0000000000000481
|