Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas

Koçak, Burak; Ateş, Ece; Durmaz, Emine Şebnem; Ulusan, Melis Baykara; Kılıçkesmez, Özgür

doi:10.1007/s00330-019-6003-8

Cited by 73 publications

(61 citation statements)

References 38 publications

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“…We used venous-phase images rather than non-enhanced or arterial-phase images because venous-phase images enable improved lesion visualization and accurate segmentation of anterior mediastinal neoplasms, which are surrounded by mediastinal fat tissue, large vessels, pleura, and lung parenchyma. Moreover, venous-phase images have previously been used to reveal enhancement heterogeneity for the radiomics analysis of soft-tissue neoplasms, such as gastric cancer, renal tumor, and hepatocellular cancer (30)(31)(32). We selected 3D radiomics features over 2D features because the former provide comprehensive information and improve the accuracy of radiomics-based predictions (33).…”

Section: Discussionmentioning

confidence: 99%

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

Chen

Bao

et al. 2020

Oncol Rep

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In the present study, we aimed to construct a radiomics model using contrast-enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low-and 97 high-risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast-enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non-zero coefficients were used to develop a radiomics score, which significantly differed between low-and high-risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high-risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627-0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874-0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887-0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision-curve analysis showed that the combined model added more net benefit than the single-parameter models. In conclusion, a radiomics signature based on contrast-enhanced CT has the potential to differentiate between low-and high-risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.

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Section: Discussionmentioning

confidence: 99%

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

Chen

Bao

et al. 2020

Oncol Rep

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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%

“…Importantly, to ensure the accuracy of the boundary delineation of the CT plain scans, it is still necessary to use the image of CT enhancement as a reference. Kocak et al [23] analyzed the influence of different edge segmentation methods on feature selection and classification performance, including contour focusing and edge contraction by 2 mm. The results show that the latter method can extract more texture features; however, the former method has better reproducibility of features and better classification performance for the nuclear grading systembased classification of renal clear cell carcinoma.…”

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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“…In fact, quanti cation of tumor delineation and tolerance assessment of the differences are likely more important in developing standardized research. Recently, Kocal et al [19] determined the in uence of segmentation with margin shrinkage of 2 mm on CT-based radiomics analysis for distinguishing low and high nuclear grade renal clear cell carcinomas (RcCCs). However, in most cases, delineation tends to overestimate the lesion volume to ensure that the entire lesion is identi ed [20].…”

Section: Introductionmentioning

confidence: 99%

The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups

Zhang

Zhong

Zhang

et al. 2019

Preprint

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Background Manual delineation of volume of interest (VOI) is widely used in current radiomics analysis, suffering from high variability. The tolerance of delineation differences and possible influence on each step of radiomics analysis are not clear, requiring quantitative assessment. The purpose of our study was to investigate the effects of delineation of VOIs on radiomics analysis for the preoperative prediction of metastasis in nasopharyngeal carcinoma (NPC) and sentinel lymph node (SLN) metastasis in breast cancer. Methods This study retrospectively enrolled two datasets (NPC group: 238 cases; SLN group: 146 cases). Three operations, namely, erosion, smoothing, and dilation, were implemented on the VOIs accurately delineated by radiologists to generate diverse VOI variations. Then, we extracted 2068 radiomics features and evaluated the effects of VOI differences on feature values by the intra-class correlation coefficient (ICC). Feature selection was conducted by Maximum Relevance Minimum Redundancy combined with 0.632+ bootstrap algorithms. The prediction performance of radiomics models with random forest classifier were tested on an independent validation cohort by the area under the receive operating characteristic curve (AUC). Results The larger the VOIs changed, the fewer features with high ICCs. Under any variation, SLN group showed fewer features with ICC ≥ 0.9 compared with NPC group. Not more than 15% top-predictive features identical to the accurate VOIs were observed across feature selection. The differences of AUCs of models derived from VOIs across smoothing or dilation with 3 pixels were not statistically significant compared with the accurate VOIs (p > 0.05) except for T2-weighted fat suppression images (smoothing: 0.845 vs. 0.725, p = 0.001; dilation: 0.800 vs. 0.725, p = 0.042). Dilation with 5 and 7 pixels contributed to remarkable AUCs in SLN group but the opposite in NPC group. The radiomics models did not perform well when tested by data from other delineations. Conclusions Differences in delineation of VOIs affected radiomics analysis, related to specific disease and MRI sequences. Differences from smooth delineation or expansion with 3 pixels width around the tumors or lesions were acceptable. The delineation for radiomics analysis should follow a predefined and unified standard.

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Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas

Cited by 73 publications

References 38 publications

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

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

The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups

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