CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review

Espinasse, Mathilde; Pitre-Champagnat, Stéphanie; Charmettant, Benoit; Boyer, François; Volk, Andreas; Balleyguier, Corinne; Lassau, Nathalie; Caramella, Caroline

doi:10.3390/diagnostics10050258

Cited by 32 publications

(27 citation statements)

References 33 publications

(47 reference statements)

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“…Variations in feature computation are caused by possible differences in feature definition, parameter setting, and implementation. Variations also come from the previous steps of image acquisition, lesion segmentation, and image preprocessing, which exaggerate variability in radiomics features and models built using these features ( 12 , 115 , 116 ).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Indeed, a fast-growing literature shows the great promise of radiomics signatures (radiomics features and models) as a "virtual biopsy" to assist in cancer diagnosis and prognosis, treatment plan, patient stratification, and assessment of tumor response to therapy. The current status of CT-based radiomics in lung cancer has been well summarized in a recent collection of review articles [e.g., (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Zhao

2021

Front. Oncol.

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“…For instance, differences on imaging acquisition modalities could greatly influence radiomics features ( 100 ). Thus, harmonizing acquisition parameters between studies is a crucial step for future texture analysis ( 101 ). There is a real need for the harmonization of features to allow consistent findings in radiomics multicenter studies.…”

Section: Discussionmentioning

confidence: 99%

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

Ayachy

Giraud

et al. 2021

Front. Oncol.

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PurposeLung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients’ care.MethodsA literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020ResultsWe identified several studies at each point of patient’s care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.ConclusionPromising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.

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“…As already stated, various factors can influence image quality and therefore have an impact on the results of a radiomics model on CT as well as on FDG PET images [25][26][27][28][29][30][31]. Acquisition of FDG PET/CT was done on different machines with different parameter settings, which can affect the robustness of radiomic features [32].…”

Section: Discussionmentioning

confidence: 99%

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

et al. 2020

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Background: Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated. Materials and methods: This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS. Results: For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS. Conclusion: We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.

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CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review

Cited by 32 publications

References 33 publications

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

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