An Update in Imaging Evaluation of Histopathological Grade of Soft Tissue Sarcomas Using Structural and Quantitative Imaging and Radiomics

Fang, Shaobo; Yang, Yanyu; Xu, Nan; Tu, Yun; Yin, Zhenzhen; Liu, Yajie; Duan, Zhiqing; Liu, Wenyu; Wang, Shaowu

doi:10.1002/jmri.27954

Cited by 6 publications

(5 citation statements)

References 71 publications

(166 reference statements)

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“…Recent advantages in regards of quantitative imaging may offer more precise and reliable information, in all the phases of the disease. Several quantitative tools applicable to MRI such as diffusion (DWI) sequences, dynamic contrast-enhanced, and radiomics analyses are increasing in use to obtain a solid and more reliable evaluation [ 25 , 26 ]. In the near future, the radiomic features extrapolated from MRI studies will permit to build artificial intelligence algorithms, in order to help in diagnosis, to predict patients’ prognosis and for tumor grade prediction, and to evaluate responses to treatments.…”

Section: Discussionmentioning

confidence: 99%

Vascular Proximity Increases the Risk of Local Recurrence in Soft-Tissue Sarcomas of the Thigh—A Retrospective MRI Study

Sambri

Caldari

Montanari

et al. 2021

Cancers

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The aim of this study was to establish the prognostic effects of the proximity of the tumor to the main vessels in patients affected by soft tissue sarcomas (STS) of the thigh. A total of 529 adult patients with deeply seated STS of the thigh and popliteal fossa were included. Vascular proximity was defined on MRI: type 1 > 5 mm; type 2 ≤ 5 mm and >0 mm; type 3 close to the tumor; type 4 enclosed by the tumor. Proximity to major vessels type 1–2 had a local recurrence (LR) rate lower than type 3–4 (p < 0.001). In type 4, vascular by-pass reduced LR risk. On multivariate analysis infiltrative histotypes, high FNLCC grade, radiotherapy administration, and type 3–4 of proximity to major vessels were found to be independent prognostic factors for LR. We observed an augmented risk of recurrence, but not of survival as the tumor was near to the major vessels. When major vessels were found to be surrounded by the tumor on preoperative MRI, vascular resection and bypass reconstruction offered a better local control.

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Section: Discussionmentioning

confidence: 99%

Vascular Proximity Increases the Risk of Local Recurrence in Soft-Tissue Sarcomas of the Thigh—A Retrospective MRI Study

Sambri

Caldari

Montanari

et al. 2021

Cancers

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“…The investigation and application of ML to STS has lagged bone sarcoma because of a more heterogeneous appearance and fewer diagnostic characteristics. A significant proportion of the literature of ML in STS focuses on the use of radiomics for predicting tumor grade or biologic behavior 43-45 . Lee et al created an ensemble ML algorithm that used multiple sequence inputs comparisons (T1 + T2, T1 + T2 +contrast T1, T1 + T2 + diffusion weighted imaging, and all of the above), for differentiation of benign vs. soft tissue differentiation with AUC of 0.752, 0.756, 0.750, 0.749, respectively 46 .…”

Section: Applications Of Ai In Orthopaedic Oncologymentioning

confidence: 99%

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

Rizk,

Gonzalez,

Galoaa

et al. 2024

JBJS Reviews

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» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

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“…Dynamic contrast-enhanced MRI (DCE-MRI) is being increasingly applied to obtain temporal information about tumour microvascularity and there have been a wealth of studies in the past few decades (90)(91)(92)(93)(94). It is well recognised that the tumour vascular system has an altered pathophysiology and role in tumourigenesis, supporting the diagnostic clinical application of DCE-MRI (95,96). DCE-MRI utilises serial images capturing the arrival, duration and exit of a contrast agent in a tissue of interest, generating a signal intensity curve that may provide detailed information on both the physical and physiological properties of tissue in response to the agent (91,(97)(98)(99).…”

Section: Quantitative Mrimentioning

confidence: 99%

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

Arthur

Johnston²,

Winfield³

et al. 2022

Front. Oncol.

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A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

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An Update in Imaging Evaluation of Histopathological Grade of Soft Tissue Sarcomas Using Structural and Quantitative Imaging and Radiomics

Cited by 6 publications

References 71 publications

Vascular Proximity Increases the Risk of Local Recurrence in Soft-Tissue Sarcomas of the Thigh—A Retrospective MRI Study

Vascular Proximity Increases the Risk of Local Recurrence in Soft-Tissue Sarcomas of the Thigh—A Retrospective MRI Study

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

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