MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

Leporq, Benjamin; Bouhamama, Amine; Pilleul, F.; Lame, Fabrice; Bihane, Catherine; Sdika, Michaël; Blay, Jean‐Yves; Beuf, Olivier

doi:10.1186/s40644-020-00354-7

Cited by 23 publications

(16 citation statements)

References 22 publications

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“…Improvement in differential diagnosis of malignancy have been reported by several authors [ 97 , 98 ]. In particular radiomics-based differentiation between soft-tissue lipoma and well-differentiated liposarcoma [ 99 , 100 ] was demonstrated despite similar radiologic and pathologic presentation, often requiring molecular analysis of MDM2 amplification status; interestingly, superior performance of a machine-learning classifier as compared to trained radiologists has been shown [ 101 ]. Similarly, radiomic features allowed to distinguish myxoma from myxofibrosarcoma [ 102 ] and atypical leiomyoma from uterine sarcoma [ 103 , 104 , 105 ].…”

Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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“…ICC threshold ranged between 0.6 [ 13 ] and 0.9 [ 22 ] for reproducible features. The following statistical methods were used less commonly: analysis of variance [ 30 , 31 ]; Cronbach alpha statistic [ 26 ]; Pearson correlation coefficient [ 19 ], and Spearman correlation coefficient [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

“…Eighteen (37%) of the 49 studies included a reproducibility analysis of the radiomic features in their workflow. In 16 (33%) investigations [13,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], the reproducibility of radiomic features was assessed on the basis of repeated segmentations performed by different readers and/or the same reader at different time points. Two (4%) studies presented an analysis to assess the reproducibility based on different acquisition [30] or post-processing [31] techniques.…”

Section: Reproducibility Strategiesmentioning

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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“…AI has also enabled the construction of predictive radiomics models for a multitude of MSK applications. Differentiation between benign and malignant osseous [19][20][21] and soft tissue lesions 21 has been performed with the use of radiomics data based on computed tomography (CT), MRI, and nuclear imaging, 22 used to build high accuracy machine learning models. Such models (with or without the addition of radiomics) have also been used to diagnose osteoarthritis [23][24][25][26] and osteoporosis 27 and to identify factors responsible for the development of early-onset osteoarthritis.…”

Section: Current Research Landscape In Msk Radiologymentioning

confidence: 99%

Research in Musculoskeletal Radiology: Setting Goals and Strategic Directions

Klontzas

Karantanas

2022

Semin Musculoskelet Radiol

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The future of musculoskeletal (MSK) radiology is being built on research developments in the field. Over the past decade, MSK imaging research has been dominated by advancements in molecular imaging biomarkers, artificial intelligence, radiomics, and novel high-resolution equipment. Adequate preparation of trainees and specialists will ensure that current and future leaders will be prepared to embrace and critically appraise technological developments, will be up to date on clinical developments, such as the use of artificial tissues, will define research directions, and will actively participate and lead multidisciplinary research. This review presents an overview of the current MSK research landscape and proposes tangible future goals and strategic directions that will fortify the future of MSK radiology.

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MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

Cited by 23 publications

References 22 publications

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

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

Research in Musculoskeletal Radiology: Setting Goals and Strategic Directions

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