Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology
暂无分享,去创建一个
T. Yoshiura | Mitsuho Hirahara | M. Nakajo | M. Jinguji | A. Tani | S. Ito
[1] Zhenxing Jiang,et al. The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma , 2023, EJNMMI Research.
[2] V. Berti,et al. Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques , 2023, Cancers.
[3] Kei Yamada,et al. Four-dimensional quantitative analysis using FDG-PET in clinical oncology , 2023, Japanese Journal of Radiology.
[4] M. Hatt,et al. Multicentric development and evaluation of ^18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer , 2023, European Journal of Nuclear Medicine and Molecular Imaging.
[5] Y. Hu,et al. Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on 18F-FDG PET , 2023, Journal of personalized medicine.
[6] G. Huang,et al. Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study , 2023, Journal of clinical medicine.
[7] Shaoli Song,et al. Preoperative 18F-FDG PET/CT radiomics analysis for predicting HER2 expression and prognosis in gastric cancer , 2023, Quantitative imaging in medicine and surgery.
[8] M. Wang,et al. Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC , 2022, Journal of Cancer Research and Clinical Oncology.
[9] Su Jin Lee,et al. A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer , 2022, AntiCancer Research.
[10] Zhaobang Liu,et al. A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images , 2022, Japanese Journal of Radiology.
[11] Eiryo Kawakami,et al. Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models , 2022, European Journal of Nuclear Medicine and Molecular Imaging.
[12] Hongzan Sun,et al. Development of machine learning models integrating PET/CT radiomic and immunohistochemical pathomic features for treatment strategy choice of cervical cancer with negative pelvic lymph node by mediating COX-2 expression , 2022, Frontiers in Physiology.
[13] Clemens P. Spielvogel,et al. Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer , 2022, European Journal of Nuclear Medicine and Molecular Imaging.
[14] J. A. van der Heide,et al. Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population , 2022, EJNMMI Physics.
[15] Alejandro F Frangi,et al. Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma , 2022, European Radiology.
[16] Xuelei Ma,et al. Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT. , 2022, Medical physics.
[17] Clemens P. Spielvogel,et al. Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma , 2022, European Radiology.
[18] M. Bertolini,et al. Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer , 2022, Current oncology.
[19] T. Yoshiura,et al. The Usefulness of Machine Learning–Based Evaluation of Clinical and Pretreatment [^18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer , 2022, Molecular Imaging and Biology.
[20] Li Yang,et al. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT , 2022, EBioMedicine.
[21] A. Murali,et al. Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases , 2022, Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, India.
[22] L. Papp,et al. Two-Year Event-Free Survival Prediction in DLBCL Patients Based on In Vivo Radiomics and Clinical Parameters , 2022, Frontiers in Oncology.
[23] J. L. Herraiz,et al. Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions , 2022, Cancers.
[24] Semra Özdemir,et al. Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules , 2022, Molecular imaging and radionuclide therapy.
[25] Wei Han,et al. The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer , 2022, Frontiers in Oncology.
[26] Patrick E. Meyer,et al. Distinction of Lymphoma from Sarcoidosis on 18F-FDG PET/CT: Evaluation of Radiomics-Feature–Guided Machine Learning Versus Human Reader Performance , 2022, The Journal of Nuclear Medicine.
[27] Haipeng Tong,et al. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study , 2022, Frontiers in Immunology.
[28] E. Klang,et al. FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study , 2022, European Radiology.
[29] Alejandro F. Frangi,et al. Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma , 2022, Cancers.
[30] T. Yoshiura,et al. The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors. , 2022, The British journal of radiology.
[31] Zhengyang Zhou,et al. Radiomics signature from [18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma , 2022, European Radiology.
[32] Zhengyang Zhou,et al. Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma , 2022, European Journal of Nuclear Medicine and Molecular Imaging.
[33] Ji-gang Yang,et al. Prediction for Mitosis-Karyorrhexis Index Status of Pediatric Neuroblastoma via Machine Learning Based 18F-FDG PET/CT Radiomics , 2022, Diagnostics.
[34] K. Hirata,et al. Artificial intelligence for nuclear medicine in oncology , 2022, Annals of Nuclear Medicine.
[35] Shaoli Song,et al. Classification of solid pulmonary nodules using a machine-learning nomogram based on 18F-FDG PET/CT radiomics integrated clinicobiological features , 2022, Annals of translational medicine.
[36] Shaoli Song,et al. 18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma , 2022, Quantitative imaging in medicine and surgery.
[37] T. Kwee,et al. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [18F]FDG PET/CT features , 2021, European Journal of Nuclear Medicine and Molecular Imaging.
[38] T. Yoshiura,et al. Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients , 2021, Abdominal Radiology.
[39] M. Akyol,et al. IMPORTANCE of PRETREATMENT 18F-FDG PET/CT TEXTURE ANALYSIS in PREDICTING EGFR and ALK MUTATION in PATIENTS with NON-SMALL CELL LUNG CANCER , 2021, Nuklearmedizin - NuclearMedicine.
[40] Xiuying Wang,et al. Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT , 2021, Journal of translational medicine.
[41] Wei Chen,et al. Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning , 2021, Annals of Nuclear Medicine.
[42] E. Neri,et al. Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging , 2021, Frontiers in Psychology.
[43] Xiuying Wang,et al. 18F-FDG PET/CT Radiomics for Preoperative Prediction of Lymph Node Metastases and Nodal Staging in Gastric Cancer , 2021, Frontiers in Oncology.
[44] S. Jeong,et al. Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer , 2021, Diagnostics.
[45] Xiuying Wang,et al. Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics , 2021, Frontiers in Oncology.
[46] R. Gillies,et al. Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images , 2021, Journal for ImmunoTherapy of Cancer.
[47] Y. Tsushima,et al. Texture analysis of [18F]-fluorodeoxyglucose-positron emission tomography/computed tomography for predicting the treatment response of postoperative recurrent or metastatic oral squamous cell carcinoma treated with cetuximab , 2021, Annals of Nuclear Medicine.
[48] Kyle J. Lafata,et al. Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers. , 2021, Medical physics.
[49] M. Hatt,et al. Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer , 2021, Diagnostics.
[50] Patrick E. Meyer,et al. [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation , 2021, European Journal of Nuclear Medicine and Molecular Imaging.
[51] T. Yoshiura,et al. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer , 2021, Molecular Imaging and Biology.
[52] Quanshi Wang,et al. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning , 2021, Annals of Nuclear Medicine.
[53] Gang Wang,et al. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma , 2021, Frontiers in Oncology.
[54] Clemens P. Spielvogel,et al. Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics , 2021, Cancers.
[55] L. Huo,et al. Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics , 2021, EJNMMI Research.
[56] I. Castiglioni,et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer , 2021, European Journal of Nuclear Medicine and Molecular Imaging.
[57] Xuelei Ma,et al. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach , 2021, European Journal of Nuclear Medicine and Molecular Imaging.
[58] B. Song. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer , 2021, Breast Cancer.
[59] R. Gillies,et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics , 2020, Nature Communications.
[60] Chi Liu,et al. Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics , 2020, Translational oncology.
[61] Shaoli Song,et al. Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung , 2020, European Journal of Nuclear Medicine and Molecular Imaging.
[62] F. Motoi,et al. Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer , 2020, Scientific Reports.
[63] Hongzan Sun,et al. Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma , 2020, BMC Cancer.
[64] A. Scarsbrook,et al. Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma. , 2020, Clinical radiology.
[65] Tao Yu,et al. Prognostic value of the baseline 18F-FDG PET/CT metabolic tumour volume (MTV) and further stratification in low-intermediate (L-I) and high-intermediate (H-I) risk NCCNIPI subgroup by MTV in DLBCL MTV predict prognosis in DLBCL , 2020, Annals of Nuclear Medicine.
[66] Weidong Wang,et al. Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET , 2020, Frontiers in Oncology.
[67] F. Zhang,et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images , 2020, European Journal of Nuclear Medicine and Molecular Imaging.
[68] A. Aksu,et al. Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics , 2020, Nuclear Medicine and Molecular Imaging.
[69] Zheran Liu,et al. Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT , 2020, Aging.
[70] T. Frauenfelder,et al. FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients , 2020, EJNMMI Research.
[71] M. Prasad,et al. Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma , 2020, Cancers.
[72] D. Feng,et al. Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features , 2020, Translational lung cancer research.
[73] M. Prasad,et al. PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma , 2020, European Journal of Nuclear Medicine and Molecular Imaging.
[74] Wei-Chih Shen,et al. Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using 18F-fluorodeoxyglucose positron emission tomography and computed tomography radiomics , 2020, Annals of translational medicine.
[75] A. Sabri,et al. Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer , 2020, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.
[76] Xiuying Wang,et al. 18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients , 2020, European Journal of Nuclear Medicine and Molecular Imaging.
[77] R. Gillies,et al. Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy , 2019, European Journal of Nuclear Medicine and Molecular Imaging.
[78] Xuelei Ma,et al. Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study , 2019, Cancer medicine.
[79] Wengui Xu,et al. Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions. , 2019, European journal of radiology.
[80] Wenjuan Ma,et al. Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC , 2019, Front. Oncol..
[81] Xiaodong Yang,et al. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18 F-FDG PET/CT. , 2019, Medical physics.
[82] M. Gnant,et al. Breast cancer , 2019, Nature Reviews Disease Primers.
[83] A. Rahmim,et al. Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images , 2019, Molecular Imaging and Biology.
[84] R. Wahl,et al. 18F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia , 2019, Theranostics.
[85] Hassan Maleki,et al. Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches , 2019, ArXiv.
[86] S. Hyun,et al. Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. , 2019, Clinical radiology.
[87] M. Astaraki,et al. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.
[88] C. Fuller,et al. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma , 2019, Scientific Reports.
[89] G. Chong,et al. Tumor Budding is a Valuable Diagnostic Parameter in Prediction of Disease Progression of Endometrial Endometrioid Carcinoma , 2019, Pathology & Oncology Research.
[90] Peter Szolovits,et al. Artificial intelligence, machine learning and health systems , 2018, Journal of global health.
[91] Xiuhua Guo,et al. Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes , 2018, Physics in medicine and biology.
[92] A. Rahmim,et al. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal 18F-FDG PET/CT images , 2018 .
[93] S. Hohaus,et al. FDG-PET/CT at the end of immuno-chemotherapy in follicular lymphoma: the prognostic role of the ratio between target lesion and liver SUVmax (rPET) , 2018, Annals of Nuclear Medicine.
[94] A. Shaw,et al. Tumour heterogeneity and resistance to cancer therapies , 2018, Nature Reviews Clinical Oncology.
[95] Roy S. Herbst,et al. The biology and management of non-small cell lung cancer , 2018, Nature.
[96] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[97] H. Okazawa,et al. 18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer , 2017, Annals of Nuclear Medicine.
[98] Phil Quirke,et al. Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016 , 2017, Modern Pathology.
[99] B. Erickson,et al. Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.
[100] C. Thorns,et al. Expression of cyclooxygenase-2 in cervical cancer is associated with lymphovascular invasion , 2016, Oncology letters.
[101] S. Hohaus,et al. Interim FDG-PET/CT in Hodgkin lymphoma: the prognostic role of the ratio between target lesion and liver SUVmax (rPET) , 2016, Annals of Nuclear Medicine.
[102] J. Crowley,et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer , 2016, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.
[103] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[104] K. Kawaguchi,et al. Comparisons of the clinicopathological features and survival outcomes between lung cancer patients with adenocarcinoma and squamous cell carcinoma , 2015, General Thoracic and Cardiovascular Surgery.
[105] Martin L. Miller,et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer , 2015, Science.
[106] D. Eisele,et al. The clinical impact of HPV tumor status upon head and neck squamous cell carcinomas. , 2014, Oral oncology.
[107] S. Palaniswamy,et al. Diagnostic utility of PETCT in thyroid malignancies: an update , 2013, Annals of Nuclear Medicine.
[108] Hiroshi Honda,et al. Impact of FDG-PET/CT in the management of lymphoma , 2011, Annals of nuclear medicine.
[109] Yan-mei Yang,et al. COX-2 expression is correlated with VEGF-C, lymphangiogenesis and lymph node metastasis in human cervical cancer. , 2011, Microvascular research.
[110] Akbar K Waljee,et al. Machine Learning in Medicine: A Primer for Physicians , 2010, The American Journal of Gastroenterology.
[111] Thomas F Hany,et al. Integrated PET/CT: current applications and future directions. , 2006, Radiology.
[112] K. Togashi,et al. Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT , 2019, Annals of Nuclear Medicine.
[113] M. Pfreundschuh,et al. Diffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2012, Annals of oncology : official journal of the European Society for Medical Oncology.