| scholarly article | Q13442814 |
| P6179 | Dimensions Publication ID | 1092666377 |
| P356 | DOI | 10.1007/S00330-017-5118-Z |
| P698 | PubMed publication ID | 29134348 |
| P2093 | author name string | Wei Wang | |
| Peng Cao | |||
| Qingyu Zhou | |||
| Zhimin Yan | |||
| Qianyun Liu | |||
| Wenwei Zhu | |||
| Pengfei Rong | |||
| Zhichao Feng | |||
| P2860 | cites work | Hallmarks of Cancer: The Next Generation | Q22252312 |
| Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach | Q23760721 | ||
| Radiomics: the process and the challenges | Q26865378 | ||
| Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation | Q33778576 | ||
| Angiomyolipoma with minimal fat: differentiation from renal cell carcinoma at biphasic helical CT. | Q34545936 | ||
| Machine Learning methods for Quantitative Radiomic Biomarkers | Q35960448 | ||
| Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images | Q36306100 | ||
| Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? | Q36421023 | ||
| Incidence of benign pathologic findings at partial nephrectomy for solitary renal mass presumed to be renal cell carcinoma on preoperative imaging | Q36636996 | ||
| Lipid-poor renal angiomyolipoma: Differentiation from clear cell renal cell carcinoma using wash-in and washout characteristics on contrast-enhanced computed tomography | Q36640819 | ||
| Hyperattenuating renal masses: etiologies, pathogenesis, and imaging evaluation | Q36874532 | ||
| Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma | Q37687159 | ||
| How not to miss or mischaracterize a renal cell carcinoma: protocols, pearls, and pitfalls | Q37716379 | ||
| Angiomyolipoma with minimal fat: Differentiation from papillary renal cell carcinoma by helical CT | Q38074217 | ||
| Epithelioid angiomyolipoma of the kidney: Radiological imaging | Q38095627 | ||
| Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? | Q38438480 | ||
| Update on the Diagnosis and Management of Renal Angiomyolipoma | Q38649855 | ||
| Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features | Q38733841 | ||
| Texture analysis as a radiomic marker for differentiating renal tumors. | Q38831240 | ||
| Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer | Q38832957 | ||
| Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer | Q38857174 | ||
| Small (< 4 cm) Renal Masses: Differentiation of Angiomyolipoma Without Visible Fat From Renal Cell Carcinoma Using Unenhanced and Contrast-Enhanced CT. | Q40297704 | ||
| Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. | Q41110614 | ||
| "Textural analysis of multiparametric MRI detects transition zone prostate cancer". | Q42290321 | ||
| A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information | Q43459987 | ||
| Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. | Q45947834 | ||
| Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? | Q46252706 | ||
| Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging | Q47124309 | ||
| Metastatic potential of a renal mass according to original tumour size at presentation. | Q47377377 | ||
| Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps | Q47965890 | ||
| Are there useful CT features to differentiate renal cell carcinoma from lipid-poor renal angiomyolipoma? | Q48410918 | ||
| Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness. | Q50889358 | ||
| Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images | Q52939674 | ||
| T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. | Q52942612 | ||
| Angiomyolipoma (AML) without visible fat: Ultrasound, CT and MR imaging features with pathological correlation | Q53005820 | ||
| Incidence of benign pathologic lesions at partial nephrectomy for presumed RCC renal masses: Japanese dual-center experience with 176 consecutive patients | Q53402317 | ||
| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| Angiomyolipoma: imaging findings in lesions with minimal fat | Q73843601 | ||
| Characterization of renal cell carcinoma, oncocytoma, and lipid-poor angiomyolipoma by unenhanced, nephrographic, and delayed phase contrast-enhanced computed tomography | Q86133087 | ||
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | machine learning | Q2539 |
| P577 | publication date | 2017-11-13 | |
| P1433 | published in | European Radiology | Q5413071 |
| P1476 | title | Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. |
| Q98291804 | A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma |
| Q90046803 | A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma |
| Q60301871 | A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors |
| Q57493787 | Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma |
| Q99241263 | Applications of radiomics in genitourinary tumors |
| Q62494194 | Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade |
| Q98771173 | Computed Tomography-Based Differentiation of Benign and Malignant Craniofacial Lesions in Neurofibromatosis Type I Patients: A Machine Learning Approach |
| Q56517915 | Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole-tumor CT texture analysis as quantitative biomarkers |
| Q90318818 | Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective |
| Q132041983 | Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer |
| Q99411195 | Machine learning in the optimization of robotics in the operative field |
| Q92323573 | Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma |
| Q90414908 | Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas |
| Q92061634 | Role of Artificial Intelligence in Kidney Disease |
| Q92100248 | The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method |
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