[1] Vespro V,Bonanno MC,Andrisani MC,et al. CT after lung microwave ablation:normal findings and evolution patterns of treated lesions[J]. Tomography, 2022, 8: 617- 626.
[2] 中华人民共和国国家卫生健康委员会医政医管局. 原发性肝癌诊疗指南(2022年版)[J]. 中国实用外科杂志, 2022, 42:241- 273.
[3] 叶 欣,王 俊,危志刚,等. 热消融治疗肺部亚实性结节专家共识(2021年版)[J]. 中国肺癌杂志, 2021, 24:305- 322.
[4] Zhang G,Yang H,Zhu X,et al. A CT- based radiomics nomogram to predict complete ablation of pulmonary malignancy: a multicenter study[J]. Front Oncol, 2022, 12: 841678.
[5] 邱金霞,尹永波,鲁 北,等. 实时影像融合介入导航系统在小肝癌微波消融中应用评价[J]. 介入放射学杂志, 2023, 32:663- 667.
[6] Rossi G,Petrone MC,Healey AJ,et al. Approaching small neuro-endocrine tumors with radiofrequency ablation[J]. Diagnostics(Basel), 2023, 13: 1561.
[7] Zhou W, Yu M, Mao X, et al. Landscape of the peripheral immune response induced by local microwave ablation in patients with breast cancer[J]. Adv Sci(Weinh), 2022, 9: e2200033.
[8] Faraoni EY, O’Brien BJ, Strickland LN, et al. Radiofrequency ablation remodels the tumor microenvironment and promotes neutrophil- mediated abscopal immunomodulation in pancreatic cancer[J]. Cancer Immunol Res, 2023, 11: 4- 12.
[9] Xiao W,Huang H,Zheng P,et al. The CXCL10/CXCR3 pathway contributes to the synergy of thermal ablation and PD- 1 blockade therapy against tumors[J]. Cancers(Basel), 2023, 15: 1427.
[10] Chen S, Zeng X, Su T, et al. Combinatory local ablation and immunotherapies for hepatocellular carcinoma: rationale,efficacy,and perspective[J]. Front Immunol, 2022,13: 1033000.
[11] Lambin P, Rios- Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48: 441- 446.
[12] Morris LGT, Riaz N, Desrichard A, et al. Pan- cancer analysis of intratumor heterogeneity as a prognostic determinant of survival[J]. Oncotarget, 2016, 7: 10051- 10063.
[13] Liu J, Dang H,Wang XW. The significance of intertumor and intratumor heterogeneity in liver cancer[J]. Exp Mol Med, 2018, 50: e416.
[14] 王 申,李 红,王 曦,等.影像组学在肝细胞癌TACE中的应用进展[J]. 介入放射学杂志, 2022, 31:829- 832.
[15] Gu J,Tong T,Xu D,et al. Deep learning radiomics of ultrasono-graphy for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: a multicenter study[J]. Cancer, 2023, 129: 356- 366.
[16] Ghosn M, Solomon SB. Current management of oligometastatic lung cancer and future perspectives: results of thermal ablation as a local ablative therapy[J]. Cancers(Basel), 2021, 13: 5202.
[17] Kobe A,Kindler Y,Klotz E,et al. Fusion of preinterventional MR imaging with liver perfusion CT after RFA of hepatocellular carcinoma: early quantitative prediction of local recurrence[J]. Invest Radiol, 2021, 56: 188- 196.
[18] Horvat N,Araujo- Filho JAB,Assuncao- Jr AN,et al. Radiomic analysis of mri to predict sustained complete response after radiofrequency ablation in patients with hepatocellular carcinoma:a pilot study[J]. Clinics(Sao Paulo), 2021, 76: e2888.
[19] Gu Y,Huang H,Tong Q,et al. Multi- view radiomics feature fusion reveals distinct immuno- oncological characteristics and clinical prognoses in hepatocellular carcinoma[J]. Cancers(Basel), 2023, 15: 2338.
[20] Guo L, Du S, Gao S, et al. Delta- radiomics based on dynamic contrast- enhanced MRI predicts pathologic complete response in breast cancer patients treated with neoadjuvant chemotherapy[J]. Cancers(Basel), 2022, 14: 3515.
[21] Liu B,Li C,Sun X et al. Assessment and prognostic value of immediate changes in post- ablation intratumor density hetero-geneity of pulmonary tumors via radiomics- based computed tomography features [J]. Front Oncol,2021,11:615174.
[22] Peng Z,Wu X,Li J,et al. The role of neoadjuvant conventional transarterial chemoembolization with radiofrequency ablation in the treatment of recurrent hepatocellular carcinoma after initial hepatectomy with microvascular invasion[J]. Int J Hyperthermia,2022,39:688- 696.
[23] Lv K, Cao X, Du Peng et al. Radiomics for the detection of microvascular invasion in hepatocellular carcinoma[J]. World J Gastroenterol,2022,28:2176- 2183.
[24] Wu J, Ding W, Wang Y, et al. Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation[J]. Int J Hyperthermia, 2022, 39: 595- 604.
[25] Shan QY,Hu HT,Feng ST,et al. CT- based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation[J]. Cancer Imaging, 2019, 19: 11.
[26] Zhang L,Cai P,Hou J,et al. Radiomics model based on gadoxetic acid disodium- enhanced MR imaging to predict hepatocellular carcinoma recurrence after curative ablation[J]. Cancer Manag Res, 2021, 13: 2785- 2796.
[27] Peng W, Jiang X, Zhang W, et al. A radiomics- based model can predict recurrence- free survival of hepatocellular carcinoma after curative ablation[J]. Asian J Surg, 2023, 46: 2689- 2696.
[28] Rizzo S,Botta F,Raimondi S,et al. Radiomics: the facts and the challenges of image analysis[J]. Eur Radiol Exp, 2018, 2: 36.
[29] Liu F, Liu D, Wang K, et al. Deep learning radiomics based on contrast- enhanced ultrasound might optimize curative treatments for very- early or early- stage hepatocellular carcinoma patients[J]. Liver Cancer, 2020, 9: 397- 413.
[30] Taghavi M, Staal F, Gomez Munoz F, et al. CT- based radiomics analysis before thermal ablation to predict local tumor progression for colorectal liver metastases[J]. Cardiovasc Intervent Radiol, 2021, 44: 913- 920.
[31] Fang S,Lai L,Zhu J,et al. A radiomics signature- based nomogram to predict the progression- free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization plus radiofrequency ablation[J]. Front Mol Biosci, 2021, 8: 662366.
[32] Yuan C, Wang Z, Gu D, et al. Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a radiomics nomogram[J]. Cancer Imaging, 2019, 19: 21.
[33] Huang Z, Shu Z, Zhu RH, et al. Deep learning- based radiomics based on contrast- enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma[J]. World J Gastrointest Oncol, 2022, 14: 2380- 2392.
[34] An C, Jiang Y, Huang Z, et al. Assessment of ablative margin after microwave ablation for hepatocellular carcinoma using deep learning- based deformable image registration[J]. Front Oncol, 2020, 10: 573316.