☆ 4.6 Review

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

CANCERS (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Radiology, Nuclear Medicine & Medical Imaging

A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study

Xiaoli Li et al.

Summary: The study developed a CT-based radiomics nomogram integrating clinical factors and radiomics features to differentiate between renal oncocytoma and chromophobe renal cell carcinoma with high diagnostic efficacy. The radiomics nomogram performed better than clinical models and radiomics signatures, showing great potential in improving differential diagnosis and reducing unnecessary surgeries for renal tumors.

BRITISH JOURNAL OF RADIOLOGY (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma

Natalie L. Demirjian et al.

Summary: This study evaluated the utility of CT-based radiomics signatures in discriminating low-grade and high-grade, as well as low-stage and high-stage clear cell renal cell carcinomas (ccRCC). The results suggest that CT-based radiomics signatures have the potential to be valuable in distinguishing between different grades and stages of ccRCC.

EUROPEAN RADIOLOGY (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis

Shiwei Luo et al.

Summary: This study investigates the impact of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. The findings suggest that radiomics features extracted from the unenhanced phase (UP) and excretory phase (EP) outperform features from other phases in differentiating high vs low nuclear grade ccRCC. Shape features and first-order statistics features showed superior discrimination ability.

EUROPEAN RADIOLOGY (2022)

添加到收藏夹

Article Oncology

Development and Validation of a CT-Based Radiomics Nomogram for Predicting Postoperative Progression-Free Survival in Stage I-III Renal Cell Carcinoma

Haijie Zhang et al.

Summary: This study investigates the radiomics features associated with the progression-free survival of renal cell carcinoma (RCC) and develops a radiomics nomogram for individualized treatment. The results demonstrate the importance of the radiomics nomogram in assessing the individual risk of postoperative progression-free survival for patients with RCC.

FRONTIERS IN ONCOLOGY (2022)

添加到收藏夹

Article Health Care Sciences & Services

MRI in Pregnancy and Precision Medicine: A Review from Literature

Gianluca Gatta et al.

Summary: Magnetic resonance imaging (MRI) provides excellent diagnostic imaging results for evaluating pregnant women without exposing them to ionizing radiations. However, our knowledge about MRI safety during pregnancy, particularly regarding the administration of gadolinium-based contrast agents, is limited. Recent preclinical studies suggest that a long circulating liposomal nanoparticle-based blood-pool gadolinium contrast agent may be a safe option.

JOURNAL OF PERSONALIZED MEDICINE (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Differentiating Benign from Malignant Renal Tumors Using T2-and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists

Qing Xu et al.

Summary: The study demonstrated that MRI-based deep learning models were effective in differentiating between benign and malignant renal tumors, with the model based on T2WI + DWI showing the best performance.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2022)

添加到收藏夹

Article Oncology

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

Lifeng Xu et al.

Summary: This study explores a new method for grade assessment of clear cell renal cell carcinoma (ccRCC) based on CT images using deep learning (DL). The DL method accurately differentiated between different grades of ccRCC patients' CT scans. This non-invasive and labor-saving approach provides a valuable means for ccRCC grade stratification and individualized treatment.

CANCERS (2022)

添加到收藏夹

Article Urology & Nephrology

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

Nima Nassiri et al.

Summary: The study found that radiomic-based machine learning platforms can distinguish benign from malignant renal masses, and can to a certain extent substitute for the accuracy of clinical diagnosis. This method has the potential to improve patient selection before surgery and increase the utilization of active surveillance protocols.

EUROPEAN UROLOGY FOCUS (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Utility of radiomics features of diffusion-weighted magnetic resonance imaging for differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma: model development and external validation

Shunya Matsumoto et al.

Summary: This study investigates the utility of radiomics features of diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating fat-poor angiomyolipoma (fpAML) from clear cell renal cell carcinoma (ccRCC). The results suggest that radiomics features of ADC maps are useful for distinguishing fpAML from ccRCC. Certain radiomics features, including mean ADC value and higher-texture features, play an important role in the differentiation. This study provides valuable insights for the diagnosis of renal tumors.

ABDOMINAL RADIOLOGY (2022)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

The role of MRI texture analysis based on susceptibility-weighted imaging in predicting Fuhrman grade of clear cell renal cell carcinoma

Jun Sun et al.

Summary: The study explored the role of MRI texture analysis based on SWI in predicting the Fuhrman grade of ccRCC, with the developed SWI-based texture model showing reliable quantitative ability to differentiate high-grade from low-grade ccRCC. The SWI-based texture model had a high area under the ROC curve of 0.81, with 80% accuracy, and could potentially be used for preoperative assessment and decision-making in surgical treatment of ccRCC.

ACTA RADIOLOGICA (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses

Felix Y. Yap et al.

Summary: This study utilized a radiomics framework to analyze tumor shape and texture features, revealing that shape metrics alone can achieve high prediction performance and should be used in conjunction with texture metrics.

EUROPEAN RADIOLOGY (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Kidney tumor diffusion-weighted magnetic resonance imaging derived ADC histogram parameters combined with patient characteristics and tumor volume to discriminate oncocytoma from renal cell carcinoma

Tim J. van Oostenbrugge et al.

Summary: A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma.Intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used, although less accurate. This preliminary study stimulates further research using whole tumor ADC histogram parameters.

EUROPEAN JOURNAL OF RADIOLOGY (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma

Yuki Arita et al.

Summary: This study demonstrates that texture analysis of ADC maps can be used to differentiate fat-poor angiomyolipomas from non-clear-cell renal cell carcinomas, with diagnostic performance similar to that of radiologists. The results were consistent across different imaging device manufacturers and institutions, indicating the stability of the method.

EUROPEAN JOURNAL OF RADIOLOGY (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics

Ji Whae Choi et al.

Summary: This study demonstrates that preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its potential as a prognostic tool for renal cell carcinoma.

ABDOMINAL RADIOLOGY (2021)

添加到收藏夹

Article Oncology

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Xiaoping Yi et al.

Summary: A machine learning radiomic model was developed to effectively differentiate low-grade from high-grade ccRCC, providing a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

FRONTIERS IN ONCOLOGY (2021)

添加到收藏夹

Article Oncology

A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma

Yanqing Ma et al.

Summary: This study evaluated the role of tumor and mini-peritumor in CT-based radiomics analysis to differentiate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC). The results showed that the Ra-Pr in the nephrographic phase (NP) had higher efficiency in distinction compared to other models, indicating its potential for guiding individual therapeutic surgeries. The combination of tumoral and perirenal radiomics analysis was the most promising approach in distinguishing fp-AML and ccRCC.

CANCER MANAGEMENT AND RESEARCH (2021)

添加到收藏夹

Article Oncology

Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma

Shengsheng Lai et al.

Summary: This study investigates the predictive performance of different machine learning models for differentiating low and high nuclear grade ccRCC using radiomic features from multiphase CT images. Results show that features extracted from the unenhanced phase (UP) CT images outperform features from other phases, with the Bagging + CMIM model achieving the highest classification AUC. Top-ranked features from the UP include shape-based and first-order statistical features.

CANCER MANAGEMENT AND RESEARCH (2021)

添加到收藏夹

Article Oncology

MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier

Xin-Yuan Chen et al.

Summary: A machine learning-based grading model was developed using MRI textures to differentiate between low-grade and high-grade ccRCC, showing potential clinical application.

FRONTIERS IN ONCOLOGY (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI

Moozhan Nikpanah et al.

Summary: The study found that the deep convolutional neural network showed high accuracy and reliability in differentiating ccRCC from oncocytoma. The observer study showed moderate agreement between two radiologists and the AI algorithm.

CLINICAL IMAGING (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

Zahra Khodabakhshi et al.

Summary: By combining radiomic features with clinical information, it is possible to predict the overall survival of renal cell carcinoma patients. Clinical features such as tumor grade, tumor malignancy, and pathology t-stage, as well as radiomic features such as flatness, area density, and median, are strongly associated with overall survival time.

JOURNAL OF DIGITAL IMAGING (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

A deep look into radiomics

Camilla Scapicchio et al.

Summary: This review provides a detailed look into radiomics, covering its basis, technical applications, challenges, and potential in precision medicine. It discusses techniques used in the radiomics workflow and highlights promising results achieved in various applications, with a focus on limitations and possible solutions for clinical implementation. An in-depth and comprehensive understanding of current methods and applications is crucial in realizing the potential power of radiomics in improving patient care, especially in cancer detection, diagnosis, prognosis, and treatment evaluation.

RADIOLOGIA MEDICA (2021)

添加到收藏夹

Article Chemistry, Analytical

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Mohamed Shehata et al.

Summary: The study aimed to integrate various imaging features to develop a novel comprehensive renal cancer computer-assisted diagnosis system that can effectively distinguish malignant and benign renal tumors, improving accuracy. By integrating morphological, textural, and functional features, the diagnostic performance can be enhanced, making it a reliable noninvasive diagnostic tool.

SENSORS (2021)

添加到收藏夹

Article Oncology

CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma

Menglin Chen et al.

Summary: The study compared the diagnostic value of models based on CT texture and non-texture features for differentiating ccRCCs from non-ccRCCs, finding that combining these features significantly improved the predictive efficacy of ccRCC.

CANCER IMAGING (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

A CT-based radiomics nomogram for differentiation of small masses (<4 cm) of renal oncocytoma from clear cell renal cell carcinoma

Xiaoli Li et al.

Summary: This study developed a radiomics nomogram for preoperative differentiation of RO and ccRCC based on clinical data and radiomics signature, showing promising diagnostic performance. The nomogram could potentially reduce unnecessary surgeries and assist in tailoring precise therapy.

ABDOMINAL RADIOLOGY (2021)

添加到收藏夹

Article Oncology

Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

Kwang-Hyun Uhm et al.

Summary: This study proposed an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors on multi-phase CT, achieving good diagnostic performance that even outperformed radiologists, which can help improve the accuracy of diagnosing renal tumors.

NPJ PRECISION ONCOLOGY (2021)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images

Xiaopeng He et al.

ACADEMIC RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

Ruimeng Yang et al.

EUROPEAN RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma

Pei Nie et al.

EUROPEAN RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Usefulness of CT texture analysis in differentiating benign and malignant renal tumours

Y. Deng et al.

CLINICAL RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Yajuan Li et al.

ABDOMINAL RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

Zhenchao Tang et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning

Takashi Tanaka et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study

Shawn Haji-Momenian et al.

ABDOMINAL RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?

Yanqing Ma et al.

ABDOMINAL RADIOLOGY (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion

Fatemeh Zabihollahy et al.

EUROPEAN RADIOLOGY (2020)

添加到收藏夹

Article Medicine, General & Internal

Discriminating malignant and benign clinical T1 renal masses on computed tomography A pragmatic radiomics and machine learning approach

Johannes Uhlig et al.

MEDICINE (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma

Abdul Razik et al.

BRITISH JOURNAL OF RADIOLOGY (2020)

添加到收藏夹

Article Oncology

Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography

Johannes Uhlig et al.

CANCERS (2020)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers

Andrew T. Scrima et al.

ABDOMINAL RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations Between mRNA-Based Subtyping and CT Imaging Features

Lan Bowen et al.

ACADEMIC RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

Ceyda Turan Bektas et al.

EUROPEAN RADIOLOGY (2019)

添加到收藏夹

Article Oncology

A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors

Leilei Zhou et al.

TRANSLATIONAL ONCOLOGY (2019)

添加到收藏夹

Article Oncology

CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma

Zhan Feng et al.

CANCER IMAGING (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning and radiomics: the utility of Google TensorFlow Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

Heidi Coy et al.

ABDOMINAL RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

Fan Lin et al.

ABDOMINAL RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade

Yu Deng et al.

EUROPEAN RADIOLOGY (2019)

添加到收藏夹

Article Medicine, General & Internal

Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images

Xiaoqing Sun et al.

MEDICINE (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

The value of quantitative CT texture analysis in differentiation of angiomyolipoma without visible fat from clear cell renal cell carcinoma on four-phase contrast-enhanced CT images

M-W You et al.

CLINICAL RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

Ankur Goyal et al.

ABDOMINAL RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Utility of CT Texture Analysis in Differentiating Low-Attenuation Renal Cell Carcinoma From Cysts: A Bi-Institutional Retrospective Study

Nathan Y. Kim et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status

Burak Kocak et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?

G-M-Y Zhang et al.

CLINICAL RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade

Jun Shu et al.

EUROPEAN JOURNAL OF RADIOLOGY (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features

En-Ming Cui et al.

ACTA RADIOLOGICA (2019)

添加到收藏夹

Article Oncology

Radiogenomics in Clear Cell Renal Cell Carcinoma: Correlations Between Advanced CT Imaging (Texture Analysis) and MicroRNAs Expression

Chiara Marigliano et al.

TECHNOLOGY IN CANCER RESEARCH & TREATMENT (2019)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

Zhichao Feng et al.

EUROPEAN RADIOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Renal angiomyolipoma without visible fat: Can we make the diagnosis using CT and MRI?

Robert S. Lim et al.

EUROPEAN RADIOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Subtype Differentiation of Small (≤ 4 cm) Solid Renal Mass Using Volumetric Histogram Analysis of DWI at 3-T MRI

Anqin Li et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma

Q. Yin et al.

CLINICAL RADIOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT-based radiomic model predicts high grade of clear cell renal cell carcinoma

Jiule Ding et al.

EUROPEAN JOURNAL OF RADIOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

A Decision-Support Tool for Renal Mass Classification

Gautam Kunapuli et al.

JOURNAL OF DIGITAL IMAGING (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation

Hansang Lee et al.

MEDICAL PHYSICS (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of multiphasic contrast-enhanced MR textures in differentiating small renal mass subtypes

Uyen N. Hoang et al.

ABDOMINAL RADIOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping

Bino A. Varghese et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation

Burak Kocak et al.

EUROPEAN JOURNAL OF RADIOLOGY (2018)

添加到收藏夹

Article Multidisciplinary Sciences

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma

Hayley M. Reynolds et al.

PLOS ONE (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade

Jun Shu et al.

EUROPEAN JOURNAL OF RADIOLOGY (2018)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification

Han Sang Lee et al.

MEDICAL PHYSICS (2017)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges

Meghan G. Lubner et al.

RADIOGRAPHICS (2017)

添加到收藏夹

Article Oncology

CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib

Masoom A. Haider et al.

CANCER IMAGING (2017)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Texture analysis as a radiomic marker for differentiating renal tumors

HeiShun Yu et al.

ABDOMINAL RADIOLOGY (2017)

添加到收藏夹

Proceedings Paper Engineering, Biomedical

Texture analysis of common renal masses in multiple MR sequences for prediction of pathology

Uyen N. Hoang et al.

MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS (2017)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT Intensity Distribution Curve (Histogram) Analysis of Patients Undergoing Antiangiogenic Therapy for Metastatic Renal Cell Carcinoma

Johannes Boos et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2017)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes

Meghan G. Lubner et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2016)

添加到收藏夹

Article Oncology

Cancer Statistics in China, 2015

Wanqing Chen et al.

CA-A CANCER JOURNAL FOR CLINICIANS (2016)

添加到收藏夹

Article Urology & Nephrology

The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours

Holger Moch et al.

EUROPEAN UROLOGY (2016)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics: Images Are More than Pictures, They Are Data

Robert J. Gillies et al.

RADIOLOGY (2016)

添加到收藏夹

Article Oncology

Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study

Jacob Antunes et al.

TRANSLATIONAL ONCOLOGY (2016)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Angiomyolipoma with Minimal Fat: Differentiation From Clear Cell Renal Cell Carcinoma and Papillary Renal Cell Carcinoma by Texture Analysis on CT Images

Lifen Yan et al.

ACADEMIC RADIOLOGY (2015)

添加到收藏夹

Article Oncology

Cancer Statistics, 2015

Rebecca L. Siegel et al.

CA-A CANCER JOURNAL FOR CLINICIANS (2015)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Taryn Hodgdon et al.

RADIOLOGY (2015)

添加到收藏夹

Article Oncology

Diffusion-weighted and multiphase contrast-enhanced MRI as surrogate markers of response to neoadjuvant sunitinib in metastatic renal cell carcinoma

N. Bharwani et al.

BRITISH JOURNAL OF CANCER (2014)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Histogram Analysis of Whole-Lesion Enhancement in Differentiating Clear Cell from Papillary Subtype of Renal Cell Cancer

Hersh Chandarana et al.

RADIOLOGY (2012)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of Response to Tyrosine Kinase Inhibitors in Metastatic Renal Cell Cancer: CT Texture as a Predictive Biomarker

Vicky Goh et al.

RADIOLOGY (2011)

添加到收藏夹

Article Acoustics

CHARACTERIZATION AND DIAGNOSTIC CONFIDENCE OF CONTRAST-ENHANCED ULTRASOUND FOR SOLID RENAL TUMORS

Xiang Zhou et al.

ULTRASOUND IN MEDICINE AND BIOLOGY (2011)

添加到收藏夹

Editorial Material Urology & Nephrology

Active surveillance as the preferred management option for small renal masses

Ricardo A. Rendon

CUAJ-CANADIAN UROLOGICAL ASSOCIATION JOURNAL (2010)

添加到收藏夹

Review Urology & Nephrology

Small renal masses: current concepts regarding the natural history and reflections on the American Urological Association guidelines

Adam C. Mues et al.

CURRENT OPINION IN UROLOGY (2010)

添加到收藏夹

Review Urology & Nephrology

A Critical Analysis of the Actual Role of Minimally Invasive Surgery and Active Surveillance for Kidney Cancer

Roman Heuer et al.

EUROPEAN UROLOGY (2010)

添加到收藏夹

Article Medicine, General & Internal

Small Renal Mass

Inderbir S. Gill et al.

NEW ENGLAND JOURNAL OF MEDICINE (2010)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

Simplified imaging approach for evaluation of the solid renal mass in adults

Ray Dyer et al.

RADIOLOGY (2008)

添加到收藏夹

Article Radiology, Nuclear Medicine & Medical Imaging

Solid renal cortical tumors: Differentiation with CT

Jingbo Zhang et al.

RADIOLOGY (2007)

添加到收藏夹

Review Pathology

Outcome prediction for renal cell carcinoma: evaluation of prognostic factors for tumours divided according to histological subtype

Brett Delahunt et al.

PATHOLOGY (2007)

添加到收藏夹

Article Urology & Nephrology

The prevalence of renal cell carcinoma diagnosed at autopsy

SR Mindrup et al.

BJU INTERNATIONAL (2005)

添加到收藏夹

Article Pathology

Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma

JC Cheville et al.

AMERICAN JOURNAL OF SURGICAL PATHOLOGY (2003)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.