Radiomics in medical imaging: pitfalls and challenges in clinical management
暂无分享,去创建一个
A. Giovagnoni | A. Borgheresi | R. Fusco | V. Granata | V. Miele | R. Grassi | S. Pradella | F. Bruno | P. Palumbo | A. Barile | R. Grassi | G. Grazzini | Alessandra Bruno
[1] L. Boldrini,et al. Delta radiomics: a systematic review , 2021, La radiologia medica.
[2] A. Giovagnoni,et al. Real-world clinical validity of cardiac magnetic resonance tissue tracking in primitive hypertrophic cardiomyopathy , 2021, La radiologia medica.
[3] F. Izzo,et al. Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features , 2021, La radiologia medica.
[4] Jie Tian,et al. A Radiomics-based Approach for Predicting Early Recurrence in Intrahepatic Cholangiocarcinoma after Surgical Resection: A Multicenter Study , 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
[5] M. Collado,et al. Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients , 2021, Cancers.
[6] R. Fusco,et al. Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography , 2021, Insights into Imaging.
[7] F. Izzo,et al. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment , 2021, Journal of personalized medicine.
[8] M. Scorsetti,et al. Palliative radiotherapy indications during the COVID-19 pandemic and in future complex logistic settings: the NORMALITY model , 2021, La radiologia medica.
[9] A. Barile. Correction to: Some thoughts and greetings from the new Editor‑in‑Chief , 2021, La radiologia medica.
[10] Ming-de Lu,et al. Preoperative Survival Prediction in Intrahepatic Cholangiocarcinoma Using an Ultrasound‐Based Radiographic‐Radiomics Signature , 2021, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.
[11] A. Revishvili,et al. Computed tomography-based radiomics approach in pancreatic tumors characterization , 2021, La radiologia medica.
[12] A. Izzo,et al. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients , 2021, La radiologia medica.
[13] B. Ganeshan,et al. Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia , 2021, La radiologia medica.
[14] F. Mazzoni,et al. Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients , 2021, Cancers.
[15] Xuelei Ma,et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma , 2021, World Journal of Surgical Oncology.
[16] Xingyu Liu,et al. CT radiomics nomogram for the preoperative prediction of severe post-hepatectomy liver failure in patients with huge (≥ 10 cm) hepatocellular carcinoma , 2021, World Journal of Surgical Oncology.
[17] F. Izzo,et al. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma , 2021, Infectious Agents and Cancer.
[18] Hong Yang,et al. Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery , 2021, La radiologia medica.
[19] E. Neri,et al. A deep look into radiomics , 2021, La radiologia medica.
[20] Yan Liu,et al. A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma , 2021, La radiologia medica.
[21] F. Garaci,et al. Qualitative and quantitative analysis of 3D T1 Silent imaging , 2021, La radiologia medica.
[22] C. Catalano,et al. Tocilizumab effects in COVID-19 pneumonia: role of CT texture analysis in quantitative assessment of response to therapy , 2021, La radiologia medica.
[23] F. Izzo,et al. Radiomics in hepatic metastasis by colorectal cancer , 2021, Infectious Agents and Cancer.
[24] C. Cavedon,et al. CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias , 2021, La radiologia medica.
[25] S. Rizzo,et al. An update in musculoskeletal tumors: from quantitative imaging to radiomics , 2021, La radiologia medica.
[26] M. G. Brizi,et al. The role of imaging in acute pancreatitis , 2021, La radiologia medica.
[27] K. Awai,et al. Advanced CT techniques for assessing hepatocellular carcinoma , 2021, La radiologia medica.
[28] F. Izzo,et al. Pancreatic cancer detection and characterization: state of the art and radiomics. , 2021, European review for medical and pharmacological sciences.
[29] P. Vallone,et al. Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification , 2021, Diagnostics.
[30] S. Park,et al. Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features , 2021, European Radiology.
[31] B. Wood,et al. Interventional Radiology ex-machina: impact of Artificial Intelligence on practice , 2021, La radiologia medica.
[32] Chunhong Hu,et al. Correlation of radiomic features on dynamic contrast-enhanced magnetic resonance with microvessel density in hepatocellular carcinoma based on different models , 2021, The Journal of international medical research.
[33] M. Manzoni,et al. CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors , 2021, La radiologia medica.
[34] W. Vermi,et al. CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer , 2021, La radiologia medica.
[35] F. Izzo,et al. Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases , 2021, Cancers.
[36] Francesco Mercaldo,et al. Radiomic features for prostate cancer grade detection through formal verification , 2021, La radiologia medica.
[37] M. Pirovano,et al. Radiomic analysis of the optic nerve at the first episode of acute optic neuritis: an indicator of optic nerve pathology and a predictor of visual recovery? , 2021, La radiologia medica.
[38] R. Fusco,et al. Correction to: Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis , 2021, La radiologia medica.
[39] A. Orlandi,et al. Automated breast volume scanner (ABVS) compared to handheld ultrasound (HHUS) and contrast-enhanced magnetic resonance imaging (CE-MRI) in the early assessment of breast cancer during neoadjuvant chemotherapy: an emerging role to monitoring tumor response? , 2021, La radiologia medica.
[40] E. Neri,et al. Quantitative imaging decision support (QIDSTM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan , 2021, Cancer control : journal of the Moffitt Cancer Center.
[41] F. Izzo,et al. Current status on response to treatment in locally advanced rectal cancer: what the radiologist should know. , 2020, European review for medical and pharmacological sciences.
[42] F. Izzo,et al. Abbreviated MRI protocol for colorectal liver metastases: How the radiologist could work in pre surgical setting , 2020, PloS one.
[43] M. Mazzei,et al. COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT) , 2020, La radiologia medica.
[44] A. Malek,et al. Performance of Radiomics derived morphological features for prediction of aneurysm rupture status , 2020, Journal of NeuroInterventional Surgery.
[45] D. Gu,et al. Deep Learning Based Radiomics Predicts Response to Chemotherapy in Colorectal Liver Metastases. , 2020, Medical Physics (Lancaster).
[46] A. Giovagnoni,et al. Third-generation iterative reconstruction on a dual-source, high-pitch, low-dose chest CT protocol with tin filter for spectral shaping at 100 kV: a study on a small series of COVID-19 patients , 2020, La radiologia medica.
[47] A. Laghi,et al. Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients , 2020, La radiologia medica.
[48] A. Gholamrezanezhad,et al. The lingering manifestations of COVID-19 during and after convalescence: update on long-term pulmonary consequences of coronavirus disease 2019 (COVID-19) , 2020, La radiologia medica.
[49] A. Laghi,et al. Artificial intelligence in cardiac radiology , 2020, La radiologia medica.
[50] R. Fusco,et al. Introduction to Special Issue of Radiology and Imaging of Cancer , 2020, Cancers.
[51] R. Fusco,et al. Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software , 2020, International journal of environmental research and public health.
[52] F. Izzo,et al. Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis , 2020, Cancers.
[53] L. Azario,et al. A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer , 2020, La radiologia medica.
[54] W. Rathmann,et al. MRI-Derived Radiomics Features of Hepatic Fat Predict Metabolic States in Individuals without Cardiovascular Disease. , 2020, Academic radiology.
[55] J. Babb,et al. Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features , 2020, Cancer Imaging.
[56] M. Bozkurt,et al. Factors affecting the response to Y-90 microsphere therapy in the cholangiocarcinoma patients , 2020, La radiologia medica.
[57] D. Gu,et al. Radiomics in liver diseases: Current progress and future opportunities , 2020, Liver international : official journal of the International Association for the Study of the Liver.
[58] F. Izzo,et al. Major and Ancillary Features According to LI-RADS in the Assessment of Combined Hepatocellular-cholangiocarcinoma , 2020, Radiology and oncology.
[59] Zhanlong Ma,et al. Combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging to predict neoadjuvant chemotherapy effect in FIGO stage IB2–IIA2 cervical cancers , 2020, La radiologia medica.
[60] P. Summers,et al. Dynamic contrast-enhanced MRI in oncology: how we do it , 2020, La radiologia medica.
[61] Jae-Joon Chung,et al. Sclerotic changes of cavernous hemangioma in the cirrhotic liver: long-term follow-up using dynamic contrast-enhanced computed tomography , 2020, La radiologia medica.
[62] G. Spolverato,et al. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) , 2020, La radiologia medica.
[63] F. Izzo,et al. Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging , 2020, Therapeutic advances in gastroenterology.
[64] E. Neri,et al. Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey , 2020, La radiologia medica.
[65] E. Neri,et al. Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology , 2020, La radiologia medica.
[66] L. Cozzi,et al. Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas , 2020, La radiologia medica.
[67] G. Low,et al. Hepatic hemangiomas: the various imaging avatars and its mimickers , 2020, La radiologia medica.
[68] Ming-de Lu,et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners , 2020, La radiologia medica.
[69] U. Maestroni,et al. Radiofrequency ablation (RFA) of T1a renal cancer with externally cooled multitined expandable electrodes , 2020, La radiologia medica.
[70] E. Samei,et al. Is regulatory compliance enough to ensure excellence in medicine? , 2020, La radiologia medica.
[71] R. Steenbakkers,et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.
[72] P. Vallone,et al. Evaluation of average glandular dose and investigation of the relationship with compressed breast thickness in dual energy contrast enhanced digital mammography and digital breast tomosynthesis. , 2020, European journal of radiology.
[73] J. Ren,et al. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors , 2020, La radiologia medica.
[74] C. Zoccali,et al. Evolution of the imaging features of osteoid osteoma treated with RFA or MRgFUS during a long-term follow-up: a pictorial review with clinical correlations , 2020, La radiologia medica.
[75] R. Fusco,et al. Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data , 2020, European Radiology Experimental.
[76] E. Neri,et al. Application of the ESR iGuide clinical decision support system to the imaging pathway of patients with hepatocellular carcinoma and cholangiocarcinoma: preliminary findings , 2020, La radiologia medica.
[77] M. Jacobs,et al. Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging , 2020, Breast Cancer Research and Treatment.
[78] Emanuele Neri,et al. Artificial intelligence: Who is responsible for the diagnosis? , 2020, La radiologia medica.
[79] H. Abdollahi,et al. CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm , 2019, La radiologia medica.
[80] G. Squadrito,et al. Dual-source dual-energy CT in the evaluation of hepatic fractional extracellular space in cirrhosis , 2019, La radiologia medica.
[81] P. Vallone,et al. Digital breast tomosynthesis and contrast‐enhanced dual‐energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification , 2019, The breast journal.
[82] F. Izzo,et al. Qualitative assessment of EOB-GD-DTPA and Gd-BT-DO3A MR contrast studies in HCC patients and colorectal liver metastases , 2019, Infectious Agents and Cancer.
[83] A. Tagliafico,et al. Radiomics of peripheral nerves MRI in mild carpal and cubital tunnel syndrome , 2019, La radiologia medica.
[84] J. Cui,et al. Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps , 2019, La radiologia medica.
[85] F. Izzo,et al. Liver radiologic findings of chemotherapy-induced toxicity in liver colorectal metastases patients. , 2019, European review for medical and pharmacological sciences.
[86] Xisheng Liu,et al. Histogram analysis of DCE-MRI for chemoradiotherapy response evaluation in locally advanced esophageal squamous cell carcinoma , 2019, La radiologia medica.
[87] Haidy Nasief,et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer , 2019, npj Precision Oncology.
[88] M. Deevband,et al. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning , 2019, La radiologia medica.
[89] L. Schwartz,et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules , 2019, European Radiology.
[90] F. Izzo,et al. Diagnostic performance of gadoxetic acid–enhanced liver MRI versus multidetector CT in the assessment of colorectal liver metastases compared to hepatic resection , 2019, BMC gastroenterology.
[91] S. Furui,et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach , 2019, Scientific Reports.
[92] M. Viergever,et al. Automatic classification of focal liver lesions based on MRI and risk factors , 2019, PloS one.
[93] F. Izzo,et al. Microvascular invasion and grading in hepatocellular carcinoma: correlation with major and ancillary features according to LIRADS , 2019, Abdominal Radiology.
[94] Xuwei Cai,et al. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling , 2019, European Radiology.
[95] F. Mortensen,et al. Texture in the monitoring of regorafenib therapy in patients with colorectal liver metastases , 2019, Acta radiologica.
[96] S. Alzubaidi,et al. Radiogenomics and Radiomics in Liver Cancers , 2018, Diagnostics.
[97] Massimo Bellomi,et al. Radiomics: the facts and the challenges of image analysis , 2018, European Radiology Experimental.
[98] P. Huang,et al. Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics. , 2018, International journal of radiation oncology, biology, physics.
[99] J R Fielding,et al. Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma. , 2018, Clinical radiology.
[100] F. Izzo,et al. A radiologist's point of view in the presurgical and intraoperative setting of colorectal liver metastases. , 2018, Future oncology.
[101] F. Izzo,et al. The current role and future prospectives of functional parameters by diffusion weighted imaging in the assessment of histologic grade of HCC , 2018, Infectious Agents and Cancer.
[102] Yiqun Sun,et al. MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases , 2018, Abdominal Radiology.
[103] R. Valicenti,et al. Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer , 2018, Technology in cancer research & treatment.
[104] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[105] Philippe Lambin,et al. Feature selection methodology for longitudinal cone-beam CT radiomics , 2017, Acta oncologica.
[106] John Quackenbush,et al. Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. , 2017, Cancer research.
[107] Jian Zhu,et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images , 2017, BMC Medical Imaging.
[108] Jinzhong Yang,et al. The Rise of Radiomics and Implications for Oncologic Management. , 2017, Journal of the National Cancer Institute.
[109] Roberta Fusco,et al. Diagnostic accuracy of magnetic resonance, computed tomography and contrast enhanced ultrasound in radiological multimodality assessment of peribiliary liver metastases , 2017, PloS one.
[110] N. Paragios,et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.
[111] F. Izzo,et al. Major and ancillary magnetic resonance features of LI-RADS to assess HCC: an overview and update , 2017, Infectious Agents and Cancer.
[112] P. Delrio,et al. Standardized Index of Shape (DCE-MRI) and Standardized Uptake Value (PET/CT): Two quantitative approaches to discriminate chemo-radiotherapy locally advanced rectal cancer responders under a functional profile , 2016, Oncotarget.
[113] R. Fusco,et al. Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review , 2016, Journal of medical and biological engineering.
[114] Ginu A. Thomas,et al. Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity , 2016, Scientific Reports.
[115] Laurence E Court,et al. The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer , 2016, The Journal of Nuclear Medicine.
[116] Hugo J. W. L. Aerts,et al. Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients , 2016, Front. Oncol..
[117] Stephen S F Yip,et al. Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction , 2016, Physics in medicine and biology.
[118] F. Izzo,et al. Early Assessment of Colorectal Cancer Patients with Liver Metastases Treated with Antiangiogenic Drugs: The Role of Intravoxel Incoherent Motion in Diffusion-Weighted Imaging , 2015, PloS one.
[119] Benjamin Haibe-Kains,et al. Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer , 2015, Scientific Reports.
[120] P. Lambin,et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[121] G. Collins,et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement , 2015, BMJ : British Medical Journal.
[122] M. Hatt,et al. 18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient Cohort , 2015, The Journal of Nuclear Medicine.
[123] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[124] Jesús Angulo,et al. Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification , 2014, IEEE Transactions on Biomedical Engineering.
[125] Ron Kikinis,et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer , 2013, Scientific Reports.
[126] Marek Ancukiewicz,et al. Magnetic resonance imaging biomarkers in hepatocellular carcinoma: association with response and circulating biomarkers after sunitinib therapy , 2013, Journal of Hematology & Oncology.
[127] S. Choi,et al. Blood oxygen level-dependent MRI for evaluation of early response of liver tumors to chemoembolization: an animal study. , 2013, Anticancer research.
[128] Jie Tian,et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach , 2013, Pattern Recognit..
[129] Vicky Goh,et al. Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? , 2013, The Journal of Nuclear Medicine.
[130] Alex Pentland,et al. Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[131] Mary M. Galloway,et al. Texture analysis using gray level run lengths , 1974 .
[132] P. Huang,et al. Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study. , 2018, Radiology.
[133] Ching-Han Hsu,et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer , 2014, European Journal of Nuclear Medicine and Molecular Imaging.
[134] Shan Tan,et al. Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics. , 2014, International journal of radiation oncology, biology, physics.
[135] Robert King,et al. Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..