Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies

Koçak, Burak; Durmaz, Emine Şebnem; Erdim, Çağrı; Ateş, Ece; Kaya, Ozlem Korkmaz; Kılıçkesmez, Özgür

doi:10.2214/ajr.19.21709

Cited by 43 publications

(28 citation statements)

References 48 publications

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“…22,25,26 Only two previous investigations included the UP, eg, Lin et al 21 reported that they employed a machine learning model based on threephase [precontrast phase (PCP, equal to UP in our study), CMP and NP] CT images and claimed to achieve superior diagnostic performance to those based on single-phase CT images in differentiating low-from high-grade ccRCC. Kocak's study 24 acknowledged the role of the UP, and they stated that machine learning-based unenhanced CT texture analysis could be a promising noninvasive method with favorable accuracy. Our finding is consistent with the previous study, such that the UP phase seems to be more suitable for texture analysis in differentiating the nuclear grade.…”

Section: Discussionmentioning

confidence: 99%

“…Several investigations have been reported in quantitatively analyzing CT-based radiomic features in an attempt to differentiate low and high Fuhrman nuclear grades. [20][21][22][23][24][25][26][27] However, these reviewed studies are confined by the limitations that either texture features were usually extracted from a single CT phase or classification modeling was built upon a randomly selected classifier. To the best of our knowledge, no comprehensive investigations have been reported in determining which phase(s), classifier(s) or their possible combinations could be more discriminative.…”

Section: Introductionmentioning

confidence: 99%

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

Lai¹,

Sun²,

Wu³

et al. 2021

CMAR

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Objective To investigate the predictive performance of different machine learning models for the discrimination of low and high nuclear grade clear cell renal cell carcinoma (ccRCC) by using multiphase computed tomography (CT)-based radiomic features. Materials and Methods A total of 137 consecutive patients with pathologically proven ccRCC (including 96 low-grade [grade 1 or 2] and 41 high-grade [grade 3 or 4] ccRCC) from January 2011 to January 2019 were enrolled in this retrospective study. Target region of interest (ROI) delineation followed by texture extraction was performed on a representative slice with the largest section of the tumor on the four-phase (unenhanced phase [UP], corticomedullary phase [CMP], nephrographic phase [NP] and excretory phase [EP]) CT images. Fifteen concatenations of the four-phase features were fed into 176 classification models (built with 8 classifiers and 22 feature selection methods), the classification performances of the 2640 resultant discriminative models were compared, and the top-ranked features were analyzed. Results Image features extracted from the unenhanced phase (UP) CT images demonstrated a dominant classification performance over features from the other three phases. The discriminative model “Bagging + CMIM” achieved the highest classification AUC of 0.75. The top-ranked features from the UP included one shape-based feature and five first-order statistical features. Conclusion Image features extracted from the UP are more effective than other CT phases in differentiating low and high nuclear grade ccRCC based on machine learning–based classification modeling.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lai¹,

Sun²,

Wu³

et al. 2021

CMAR

View full text Add to dashboard Cite

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“…Firstly, the variability in study design, radiomic methods employed, texture features extracted, and recorded endpoints make it difficult to compare any two techniques and to perform quantitative analysis. Secondly, most ML and DL algorithms utilized in these studies were validated with their own dataset; therefore, without external validation, result generalizability and reproducibility cannot be applied to other datasets and populations [62]. Thirdly, repeatability, reproducibility, sample size, statistical power, and standardization are still vital factors to be considered in future investigations [63].…”

Section: Limitations/challenges Of Radiomicsmentioning

confidence: 99%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.

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“…The challenges of reproducibility and validation strategies in radiomics have been recently addressed in a review focusing on renal masses [ 10 ]. The aim of our study is to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas.…”

Section: Introductionmentioning

confidence: 99%

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

et al. 2021

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Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

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Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies

Cited by 43 publications

References 48 publications

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

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

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

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