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

Haider, Masoom A.; Vosough, Alireza; Khalvati, Farzad; Kiss, Alexander; Ganeshan, Balaji; Bjarnason, Georg A.

doi:10.1186/s40644-017-0106-8

Cited by 81 publications

(53 citation statements)

References 17 publications

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“…Because chRCC and RO are relatively rare compared to renal clear cell and renal papillary cell carcinoma, radiomic studies of renal tumors are focused on relatively common renal tumors. Studies on the most frequently occurring renal clear cell carcinoma have focused on different aspects such as preoperative diagnosis [19][20][21][22], tumor grade [23], prognostic evaluation [24], and molecular analysis of the cancer genes [25][26][27]. Yu et al [20] extracted the texture features of four types of renal tumors, including renal clear cell carcinoma, renal papillary cell carcinoma, chRCC, and RO.…”

Section: Discussionmentioning

confidence: 99%

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

Huang

Xia³

2019

Abdom Radiol

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Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CTenhanced quantitative features and machine learning.

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Section: Discussionmentioning

confidence: 99%

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

Huang

Xia³

2019

Abdom Radiol

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“…Recent studies have found that radiomic features may have significant associations with clinical outcomes and gene-expression levels456789. These features can also be used to develop diagnosis or prognosis models that may serve as a tool for personalized diagnosis and clinical decision support systems.…”

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confidence: 99%

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

Zhang

Oikonomou

Wong

et al. 2017

Sci Rep

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216

199

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Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

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“…The prognostic features are usually determined using Cox proportional hazard model (CPH) [4]. In the past decade, several radiomics features have shown prognostic value in different diseases especially different types of cancer [5][6][7][8][9]. However, the high dimensionality nature of radiomics features makes the feature selection prone to multiple testing, leading to false positives and low performance in the validation cohorts.…”

Section: Introductionmentioning

confidence: 99%

CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging

et al. 2020

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Background: Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. Results: The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index and index of prediction accuracy, providing a better fit for patients' survival patterns. Conclusions: The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.

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

Cited by 81 publications

References 17 publications

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

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

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging

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