Shang Y, Li G, Zhang B, Wu Y, Chen Y, Li C, et al. Image-guided percutaneous ablation for lung malignancies. Front Oncol. 2022;12:1020296. https://doi.org/10.3389/fonc.2022.1020296.
Article PubMed PubMed Central Google Scholar
Sugarbaker DJ. Lung cancer * 6: the case for limited surgical resection in non-small cell lung cancer. Thorax. 2003;58:639–41.
Article CAS PubMed PubMed Central Google Scholar
Mazzone P. Preoperative evaluation of the lung resection candidate. Cleve Clin J Med. 2012;79:S17-22.
nscl.pdf [Internet]. [cited 2024 Jan 23]. Available from: https://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf
Tselikas L, Garzelli L, Mercier O, Auperin A, Lamrani L, Deschamps F, et al. Radiofrequency ablation versus surgical resection for the treatment of oligometastatic lung disease. Diagn Interv Imaging. 2021;102:19–26.
Article CAS PubMed Google Scholar
Genshaft SJ, Suh RD, Abtin F, Baerlocher MO, Chang AJ, Dariushnia SR, et al. Society of interventional radiology multidisciplinary position statement on percutaneous ablation of non-small cell lung cancer and metastatic disease to the lungs: endorsed by the Canadian association for interventional radiology, the cardiovascular and interventional radiological society of Europe, and the society of interventional oncology. J Vasc Interv Radiol. 2021;32:1241.e1-1241.e12.
Murphy MC, Wrobel MM, Fisher DA, Cahalane AM, Fintelmann FJ. Update on image-guided thermal lung ablation: society guidelines, therapeutic alternatives, and postablation imaging findings. Am J Roentgenol. 2022;219:471–85.
Wu X, Uhlig J, Blasberg JD, Gettinger SN, Suh RD, Solomon SB, et al. Microwave ablation versus stereotactic body radiotherapy for stage i non-small cell lung cancer: a cost-effectiveness analysis. J Vasc Interv Radiol. 2022;33:964-971.e2.
Wang Y, Liu B, Cao P, Wang W, Wang W, Chang H, et al. Comparison between computed tomography-guided percutaneous microwave ablation and thoracoscopic lobectomy for stage I non-small cell lung cancer. Thorac Cancer. 2018;9:1376–82.
Article PubMed PubMed Central Google Scholar
Yao W, Lu M, Fan W, Huang J, Gu Y, Gao F, et al. Comparison between microwave ablation and lobectomy for stage I non-small cell lung cancer: a propensity score analysis. Int J Hyperth. 2018;34:1329–36.
Healey TT, March BT, Baird G, Dupuy DE. Microwave ablation for lung neoplasms: a retrospective analysis of long-term results. J Vasc Interv Radiol JVIR. 2017;28:206–11.
Hasegawa T, Takaki H, Kodama H, Matsuo K, Yamanaka T, Nakatsuka A, et al. Impact of the ablative margin on local tumor progression after radiofrequency ablation for lung metastases from colorectal carcinoma: supplementary analysis of a phase II trial (MLCSG-0802). J Vasc Interv Radiol. 2023;34:31-37.e1.
Ziv E, Erinjeri JP, Yarmohammadi H, Boas FE, Petre EN, Gao S, et al. Lung adenocarcinoma: predictive value of KRAS mutation status in assessing local recurrence in patients undergoing image-guided ablation. Radiology. 2017;282:251–8.
Chen X, Sun S, Bai N, Han K, Liu Q, Yao S, et al. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother Oncol. 2021;160:175–84.
Yadav SS, Jadhav SM. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data. 2019;6:113.
Suzuki K, Abe H, MacMahon H, Doi K. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging. 2006;25:406–16.
Taghavi M, Staal F, Gomez Munoz F, Imani F, Meek DB, Simões R, et al. CT-based radiomics analysis before thermal ablation to predict local tumor progression for colorectal liver metastases. Cardiovasc Intervent Radiol. 2021;44:913–20.
Xv Y, Lv F, Guo H, Zhou X, Tan H, Xiao M, et al. Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study. Insights Imaging. 2021;12:170.
Article PubMed PubMed Central Google Scholar
Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp. 2020;4:50.
Article PubMed PubMed Central Google Scholar
Shariaty F, Hosseinlou S, Rud VY. Automatic lung segmentation method in computed tomography scans. J Phys Conf Ser. 2019;1236:012028.
Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, et al. UNETR: transformers for 3D medical image segmentation [Internet]. arXiv; 2021 [cited 2024 Jan 23]. Available from: http://arxiv.org/abs/2103.10504
Hesamian MH, Jia W, He X, Kennedy P. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging. 2019;32:582–96.
Article PubMed PubMed Central Google Scholar
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–7.
Article PubMed PubMed Central Google Scholar
Pölsterl S. Scikit-survival: a library for time-to-event analysis built on top of scikit-learn. J Mach Learn Res. 2020;21:1–6.
Cifci MA. SegChaNet: a novel model for lung cancer segmentation in CT scans. Appl Bion Biomech. 2022;2022:1139587.
Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH. Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi A, Bakas S, Kuijf H, Menze B, Reyes M, editors. Brainlesion glioma mult scler stroke trauma brain inj. Cham: Springer International Publishing; 2018. p. 287–97.
Tortora M, Gemini L, Scaravilli A, Ugga L, Ponsiglione A, Stanzione A, et al. Radiomics applications in head and neck tumor imaging: a narrative review. Cancers. 2023;15:1174.
Article PubMed PubMed Central Google Scholar
Park S, Sham JG, Kawamoto S, Blair AB, Rozich N, Fouladi DF, et al. CT radiomics-based preoperative survival prediction in patients with pancreatic ductal adenocarcinoma. Am J Roentgenol. 2021;217:1104–12.
Wu Y, Xu L, Yang P, Lin N, Huang X, Pan W, et al. Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine. 2018;34:27–34.
Article PubMed PubMed Central Google Scholar
Hou K-Y, Chen J-R, Wang Y-C, Chiu M-H, Lin S-P, Mo Y-H, et al. Radiomics-based deep learning prediction of overall survival in non-small-cell lung cancer using contrast-enhanced computed tomography. Cancers. 2022;14:3798.
Article PubMed PubMed Central Google Scholar
Zhu F, Yang C, Xia Y, Wang J, Zou J, Zhao L, et al. CT-based radiomics models may predict the early efficacy of microwave ablation in malignant lung tumors. Cancer Imaging. 2023;23:60.
Article PubMed PubMed Central Google Scholar
Huang H, Zheng D, Chen H, Chen C, Wang Y, Xu L, et al. A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases. Front Oncol. 2023;13:1107026. https://doi.org/10.3389/fonc.2023.1107026.
Article PubMed PubMed Central Google Scholar
Zhang G, Yang H, Zhu X, Luo J, Zheng J, Xu Y, et al. A CT-Based radiomics nomogram to predict complete ablation of pulmonary malignancy: a multicenter study. Front Oncol. 2022;12:841678. https://doi.org/10.3389/fonc.2022.841678.
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