Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients

Kakino, Ryo; Nakamura, Mitsuhiro; Mitsuyoshi, Takamasa; Satō, Takashi; Hirashima, Hideaki

doi:10.1016/j.ejmp.2019.12.019

Cited by 37 publications

(25 citation statements)

References 37 publications

(43 reference statements)

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“…Note: Modelling does not have a letter associated with since there is no consensus on the best statistical modelling strategies. Problem area Potential problems Potential solutions Image acquisition A Different scanners and acquisition protocols affect feature reproducibility [ [79] , [80] , [81] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [90] , [91] ] Image phantoms on different scanners to provide baseline [ 79 ], establish credibility of scanners and protocols [ 84 ], catalogue reproducible features [ 86 , 90 ], model a correction algorithm [ 89 ], harmonize data [ 91 ]. B Patient motion affects feature reproducibility [ 80 , 92 , 93 ] Set motion tolerances, reduce ROI boundaries [ 80 ], use single phase from 4D images [ 92 ], find robust features using 4DCT data [ 93 ].…”

Section: Reported Methodological Limitations Of Ct Based Radiomics Stmentioning

confidence: 99%

“…Image acquisition A Different scanners and acquisition protocols affect feature reproducibility [79][80][81][82][83][84][85][86][87][88][89][90][91] Image phantoms on different scanners to provide baseline [79], establish credibility of scanners and protocols [84], catalogue reproducible features [86,90], model a correction algorithm [89] [98,[110][111][112] Normalization of features to volume [98], bit depth resampling [110], feature redesign [110], more robust statistics to check added value of radiomics signatures [111]. Test re-test H Radiomic features may not be repeatable over multiple measurements [113][114][115], repeatable features are not generalizable to other disease sites [116].…”

Section: Problem Area Potential Problems Potential Solutionsmentioning

confidence: 99%

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Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

et al. 2020

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Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

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Section: Reported Methodological Limitations Of Ct Based Radiomics Stmentioning

confidence: 99%

Section: Problem Area Potential Problems Potential Solutionsmentioning

confidence: 99%

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

et al. 2020

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“…In a recent study, radiomic feature values from diagnostic contrast-enhanced and non-contrast enhanced CT in NSCLC patients were shown to be affected by the contrast enhancement and in particular may depend largely on the patient characteristics, for example, patient weight, tumor volume or performance status. 39 Therefore, additional analysis is needed to define robust radiomic features for different levels of contrast-agent uptake, which to our current knowledge has not been performed. To overcome IOV, standardized contouring protocols or alternatively semi-or full-automatized segmentation software solutions can be helpful.…”

Section: Discussionmentioning

confidence: 99%

Comparison of robust to standardized CT radiomics models to predict overall survival for non‐small cell lung cancer patients

et al. 2020

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Background: Radiomics is a promising tool for the identification of new prognostic biomarkers. Radiomic features can be affected by different scanning protocols, often present in retrospective and prospective clinical data. We compared a computed tomography (CT) radiomics model based on a large but highly heterogeneous multicentric image dataset with robust feature pre-selection to a model based on a smaller but standardized image dataset without pre-selection. Materials and methods: Primary tumor radiomics was extracted from pre-treatment CTs of IIIA/N2/IIIB NSCLC patients from a prospective Swiss multicentric randomized trial (n patient = 124, n institution = 14, SAKK 16/00) and a validation dataset (n patient = 31, n institution = 1). Four robustness studies investigating inter-observer delineation variation, motion, convolution kernel, and contrast were conducted to identify robust features using an intraclass correlation coefficient threshold >0.9. Two 12-months overall survival (OS) logistic regression models were trained: (a) on the entire multicentric heterogeneous dataset but with robust feature pre-selection (MCR) and (b) on a smaller standardized subset using all features (STD). Both models were validated on the validation dataset acquired with similar reconstruction parameters as the STD dataset. The model performances were compared using the DeLong test. Results: In total, 113 stable features were identified (n shape = 8, n intensity = 0, n texture = 7, n wavelet = 98). The convolution kernel had the strongest influence on the feature robustness (<20% stable features). The final models of MCR and STD consisted of one and two features respectively. Both features of the STD model were identified as non-robust. MCR did not show performance significantly different from STD on the validation cohort (AUC [95%CI] = 0.72 [0.48-0.95] and 0.79 [0.63-0.95], p = 0.59). Conclusion: Prognostic OS CT radiomics model for NSCLC based on a heterogeneous multicentric imaging dataset with robust feature pre-selection performed equally well as a model on a standardized dataset.

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“…However, in clinical practice, a 25 second delay, which could be too early for lung lesion enhancement to show up, is not the standard timing for lung contrast enhancement. Kakino et al 41 claimed that contrast enhancement in the delayed phase (120 s) of CT image for NSCLC patients affected some of the radiomics features and the variability of radiomics features, compared with non-contrast CT images. Of note, the performance of this radiogenomics classier was improved by replacing pre-contrast CT images with clinical standard post 38-48 s contrast CT images.…”

Section: Discussionmentioning

confidence: 99%

<p>Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers</p>

Ma¹,

Gao²,

Dan³

et al. 2020

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To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively. Materials and Methods: This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 precontrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPVof pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538-0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630-0.9567), 76.92%, 83.33%, 71.43%, and 86.96%. Conclusion: Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard postcontrast CT radiomics biomarkers produced superior performance compared with that of precontrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.

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Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients

Cited by 37 publications

References 37 publications

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

Comparison of robust to standardized CT radiomics models to predict overall survival for non‐small cell lung cancer patients

<p>Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers</p>

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