Effects of simulated dose variation on contrast-enhanced CT-based radiomic analysis for Non-Small Cell Lung Cancer

Hepp, Tobias; Othman, Ahmed E.; Liebgott, Annika; Kim, Jong Hyo; Pfannenberg, Christina; Gatidis, Sergios

doi:10.1016/j.ejrad.2019.108804

Cited by 11 publications

(20 citation statements)

References 23 publications

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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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“…These studies inspired intensive investigations in feature variability and reproducibility, which have confirmed the initial findings and extended them to broader research areas. Investigations on the sources of variation in CT image acquisition have mainly focused on one or combinations of the following factors: test-retest (28,31,32), vendors' scanner (30,(33)(34)(35)(36), tube voltage and current (37)(38)(39)(40)(41), pitch (36), field of view/pixel spacing (42)(43)(44), reconstruction kernel and slice thickness (we do not here distinguish between slice thickness and slice interval, the real physical distance between any two adjacent images) (29,31,38,39,(45)(46)(47), contrast administration (48)(49)(50), and 4D phases (51,52). In the following subsections, a number of studies exploring sources of variation in image acquisition is reviewed, followed by a discussion on potential strategies and practical considerations to reduce variability in image acquisition.…”

Section: Image Acquisitionmentioning

confidence: 99%

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Zhao

2021

Front. Oncol.

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Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.

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“…However, it is necessary to point out that all SR sections are independent from each other, so that the Patient Clinical Data and Clinical Evaluation sections are optional and may be filled in or not upon user choice, although they were conceived with the aim of creating databases. In fact, the possibility of collecting all these data could allow the creation of a large database, not only for epidemiological studies, but also in the highest conception of radiology, to lay the foundations for radiomics studies [34][35][36][37]. Radiology reports should be rich in data that could potentially be pooled, analyzed and correlated with patient outcomes, thereby assisting future clinical and imaging guidelines.…”

Section: Discussionmentioning

confidence: 99%

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

et al. 2021

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Background: Structured reporting (SR) in radiology is becoming necessary and has recently been recognized by major scientific societies. This study aimed to build CT-based structured reports for lung cancer during the staging phase, in order to improve communication between radiologists, members of the multidisciplinary team and patients. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi exercise was used to build the structural report and to assess the level of agreement for all the report sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to perform a quality analysis according to the average inter-item correlation. Results: The final SR version was built by including 16 items in the “Patient Clinical Data” section, 4 items in the “Clinical Evaluation” section, 8 items in the “Exam Technique” section, 22 items in the “Report” section, and 5 items in the “Conclusion” section. Overall, 55 items were included in the final version of the SR. The overall mean of the scores of the experts and the sum of scores for the structured report were 4.5 (range 1–5) and 631 (mean value 67.54, STD 7.53), respectively, in the first round. The items of the structured report with higher accordance in the first round were primary lesion features, lymph nodes, metastasis and conclusions. The overall mean of the scores of the experts and the sum of scores for staging in the structured report were 4.7 (range 4–5) and 807 (mean value 70.11, STD 4.81), respectively, in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.89 in the first round and 0.92 in the second round for staging in the structured report. Conclusions: The wide implementation of SR is critical for providing referring physicians and patients with the best quality of service, and for providing researchers with the best quality of data in the context of the big data exploitation of the available clinical data. Implementation is complex, requiring mature technology to successfully address pending user-friendliness, organizational and interoperability challenges.

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Effects of simulated dose variation on contrast-enhanced CT-based radiomic analysis for Non-Small Cell Lung Cancer

Cited by 11 publications

References 23 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

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Structured Reporting of Lung Cancer Staging: A Consensus Proposal

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