A contrast‐enhanced MRI‐based nomogram to identify lung metastasis in soft‐tissue sarcoma: A multi‐centre study

Hu, Yue; Wang, Hongbo; Yue, Zhibin; Wang, Xiaoyu; Wang, Yan; Luo, Yahong; Jiang, Wenyan

doi:10.1002/mp.16136

Cited by 8 publications

(4 citation statements)

References 58 publications

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“…In 14 (25%) studies [ 26 , 36 , 38 , 39 , 43 , 44 , 48 – 51 , 56 , 63 , 64 , 67 ], clinical validation was performed on an independent set of data from an external institution, namely the external test dataset. In 2 (4%) studies [ 22 , 33 ], both internal and external test datasets were used for clinical validation. The distribution of the employed clinical validation strategies among the included studies is shown in the bar plot in Fig.…”

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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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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“…Notably, although the fully unsupervised h-RFs, the CAE clustering, and the HSCAE clustering exhibited c-indices lower than some previously published supervised models, they remained significantly better than Article random in both univariable and multivariable analyses accounting for cofounding covariables underscoring their intrinsic prognostic value for STS patients 7,14,18,[20][21][22] . Transcriptomics groups, while exhibiting weaker but significant associations with patient survivals, showed a substantial increase in prognostic performance (c-index = 0.709) when combined with HSCAE groups.…”

Section: Discussionmentioning

confidence: 99%

Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis

Crombé,

Lucchesi,

Bertolo

et al. 2024

npj Precis. Onc.

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Our objective was to capture subgroups of soft-tissue sarcoma (STS) using handcraft and deep radiomics approaches to understand their relationship with histopathology, gene-expression profiles, and metastatic relapse-free survival (MFS). We included all consecutive adults with newly diagnosed locally advanced STS (N = 225, 120 men, median age: 62 years) managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI postprocessing, segmentation, and reproducibility assessment, 175 handcrafted radiomics features (h-RFs) were calculated. Convolutional autoencoder neural network (CAE) and half-supervised CAE (HSCAE) were trained in repeated cross-validation on representative contrast-enhanced slices to extract 1024 deep radiomics features (d-RFs). Gene-expression levels were calculated following RNA sequencing (RNAseq) of 110 untreated samples from the same cohort. Unsupervised classifications based on h-RFs, CAE, HSCAE, and RNAseq were built. The h-RFs, CAE, and HSCAE grouping were not associated with the transcriptomics groups but with prognostic radiological features known to correlate with lower survivals and higher grade and SARCULATOR groups (a validated prognostic clinical-histological nomogram). HSCAE and h-RF groups were also associated with MFS in multivariable Cox regressions. Combining HSCAE and transcriptomics groups significantly improved the prognostic performances compared to each group alone, according to the concordance index. The combined radiomic-transcriptomic group with worse MFS was characterized by the up-regulation of 707 genes and 292 genesets related to inflammation, hypoxia, apoptosis, and cell differentiation. Overall, subgroups of STS identified on pre-treatment MRI using handcrafted and deep radiomics were associated with meaningful clinical, histological, and radiological characteristics, and could strengthen the prognostic value of transcriptomics signatures.

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“…In this investigation, a machine learning algorithm was built to predict the presence of lung metastasis at the time of soft tissue sarcoma tumor imaging. 52 Using radiomic features derived from contrast-enhanced fat-suppressed T1-weighted images of the soft tissue sarcoma and one semantic factor ("margin" ¼ well-or ill-defined) incorporated into a nomogram, an AUC of 0.843 (95% CI, 0.696-0.990) was demonstrated in the prediction of lung metastases in their external validation patient cohort. The authors concluded that their developed nomogram had potential value for preoperative prediction of lung metastasis status in soft tissue sarcomas, and it could offer essential information for clinicians in individualized treatment decision support for soft tissue sarcoma patients.…”

Section: Systemic Spread Of Diseasementioning

confidence: 99%

Radiomics in Musculoskeletal Tumors

Brandenberger,

White

2024

Semin Musculoskelet Radiol

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Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

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A contrast‐enhanced MRI‐based nomogram to identify lung metastasis in soft‐tissue sarcoma: A multi‐centre study

Cited by 8 publications

References 58 publications

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

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

Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis

Radiomics in Musculoskeletal Tumors

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