Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest

Yang, Yuhan; Ma, Xuelei; Wang, Yixi; Ding, Xinyan

doi:10.1007/s13304-021-01074-8

Cited by 14 publications

(11 citation statements)

References 38 publications

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“…7 articles assessed the role of radiomics models for predicting lung metastasis [ 9 , 13 – 18 ]; 3 articles analysed radiomics models for prediction of distant metastasis or metastatic relapse-free survival [ 19 – 21 ]. The ability to predict overall survival or free survival was evaluated in 6 studies [ 22 – 27 ]; in particular, according to Spraker et al [ 26 ], texture features related to histogram_skewness, histogram_kurtosis, GLZSM_Small zone/low grey emphasis and GLZSM_Zone, obtained from T1-weighted contrast-enhanced images, were selected in the models for predicting overall survival. Fadli et al [ 28 ] found that increase in heterogeneity (visually evaluated) and logarithmic change in radiomics features clusters, in contrast enhancement MRI T1-weighted images, were independent predictors for metastatic relapse-free and local relapse-free survival.…”

Section: Resultsmentioning

confidence: 99%

The impact of radiomics in the management of soft tissue sarcoma

De Angelis,

Casale,

Coquelet

et al. 2024

Discov Onc

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Introduction Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation. Materials and methods We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: ‘radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])’. Only original articles, referring to humans, were included. Results A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response. Conclusions With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.

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Section: Resultsmentioning

confidence: 99%

The impact of radiomics in the management of soft tissue sarcoma

De Angelis,

Casale,

Coquelet

et al. 2024

Discov Onc

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“…The following segmentation styles were identified: 3D in 45 (82%) studies, 2D without multiple sampling in 7 (13%) studies, 2D with multiple sampling in 1 (2%) study, and multiple segmentation styles such as 3D and 2D without multiple sampling in 1 (2%) study. In the remaining study, the segmentation style was not specified [ 29 ]. Of note, a single slice showing maximum tumor extension was chosen in all studies employing 2D segmentation without multiple sampling, except in one case where it was chosen based on tumor characteristics [ 30 ] and another study where the criteria for slice selection were not specified [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

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

Gitto,

Cuocolo,

Huisman

et al. 2024

Insights Imaging

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Objective To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. Methods A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. Results Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. Conclusions Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. Critical relevance statement An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. Key points • 2021–2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation. Graphical Abstract

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“…Clinicians have applied radiomics to predict patient prognosis, predominantly metastatic relapse-free survival (MFS), local relapse-free survival (LFS), overall survival (OS), and the risk of presenting lung metastases following initial treatments (either curative surgery alone or neoadjuvant radiotherapy/chemotherapy followed by curative surgery). The resulting radiomics prognostic models demonstrated good to strong performances (c-index: 0.77 for LFS, 0.84-0.93 for MFS, and 0.73-0.80 for OS) [36,[58][59][60][61][62][63][64][65]. Eventually, radiomics has demonstrated the ability to predict the histologic response following neoadjuvant treatments using baseline RFs and delta-radiomics, which correspond to a quantitative change in RFs between two radiological evaluations.…”

Section: F I G U R Ementioning

confidence: 93%

The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy

Crombé

Roulleau‐Dugage

Italiano

2022

Cancer Communications

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Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring.Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings.Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides

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Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest

Cited by 14 publications

References 38 publications

The impact of radiomics in the management of soft tissue sarcoma

The impact of radiomics in the management of soft tissue sarcoma

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

The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy

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