Soft tissue sarcoma: correlation of dynamic contrast-enhanced magnetic resonance imaging features with HIF-1α expression and patient outcomes

Li, Xiangwen; Xie, Yuxue; Hu, Yu-Hen; Lü, Rong; Li, Qing; Xiong, Bo; Tao, Hongyue; Chen, Shuang

doi:10.21037/qims-22-75

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…This is consistent with previous studies and highlights the importance of a multiparametric approach to identify differences in histological and biological characteristics of heterogeneous STS tumor parts. [39,40] Our study has several limitations. First, this pilot study reports the initial results of a newly developed co-registration procedure for STS with only a small sample size of 9 tumor sections from 3 patients.…”

Section: Discussionmentioning

confidence: 94%

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Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm

Jung¹,

Bogner²,

Diallo³

et al. 2023

Theranostics

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Rationale: To establish a spatially exact co-registration procedure between in vivo multiparametric magnetic resonance imaging (mpMRI) and (immuno)histopathology of soft tissue sarcomas (STS) to identify imaging parameters that reflect radiation therapy response of STS. Methods: The mpMRI-Protocol included diffusion-weighted (DWI), intravoxel-incoherent motion (IVIM), and dynamic contrast-enhancing (DCE) imaging. The resection specimen was embedded in 6.5% agarose after initial fixation in formalin. To ensure identical alignment of histopathological sectioning and in vivo imaging, an ex vivo MRI scan of the specimen was rigidly co-registered with the in vivo mpMRI. The deviating angulation of the specimen to the in vivo location of the tumor was determined. The agarose block was trimmed accordingly. A second ex vivo MRI in a dedicated localizer with a 4 mm grid was performed, which was matched to a custom-built sectioning machine. Microtomy sections were stained with hematoxylin and eosin. Immunohistochemical staining was performed with anti-ALDH1A1 antibodies as a radioresistance and anti-MIB1 antibodies as a proliferation marker. Fusion of the digitized microtomy sections with the in vivo mpMRI was accomplished through nonrigid co-registration to the in vivo mpMRI. Co-registration accuracy was qualitatively assessed by visual assessment and quantitatively evaluated by computing target registration errors (TRE). Results: The study sample comprised nine tumor sections from three STS patients. Visual assessment after nonrigid co-registration showed a strong morphological correlation of the histopathological specimens with ex vivo MRI and in vivo mpMRI after neoadjuvant radiation therapy. Quantitative assessment of the co-registration procedure using TRE analysis of different pairs of pathology and MRI sections revealed highly accurate structural alignment, with a total median TRE of 2.25 mm (histology - ex vivo MRI), 2.22 mm (histology - in vivo mpMRI), and 2.02 mm ( ex vivo MRI - in vivo mpMRI). There was no significant difference between TREs of the different pairs of sections or caudal, middle, and cranial tumor parts, respectively. Conclusion: Our initial results show a promising approach to obtaining accurate co-registration between histopathology and in vivo MRI for STS. In a larger cohort of patients, the method established here will enable the prospective identification and validation of in vivo imaging biomarkers for radiation therapy response prediction and monitoring in STS patients via precise molecular and cellular correlation.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm

Jung¹,

Bogner²,

Diallo³

et al. 2023

Theranostics

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“…Recent studies described STS configurations as suitable for predicting tumour grades of primary STS or the con figuration of recurrences [27,28] the configuration of STS as a significant predictor of the hypoxiainducible factor 1α (HIF1α) expression, which determines patients' outcomes. Accordingly, patients with multilobulated STS are at a higher risk of a worse outcome than those with nonmultilobulated STS [29].…”

Section: Discussionmentioning

confidence: 99%

Primary synovial sarcoma on MRI – a case series and review of the literature

Sedaghat¹,

Sedaghat²

2023

Pol J Radiol

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PurposeSimple MRI features are mandatory to facilitate the diagnostics of synovial sarcomas, especially for radiologists outside multidisciplinary sarcoma centres. In this case-series and review, we investigate the main appearance of synovial sarcoma on MRI.Material and methodsFifteen histologically proven primary synovial sarcomas who underwent MRI at 2 different sarcoma centres were included in this case series. Patients and their primary synovial sarcomas were examined for age, localization, mean tumour size (in mm), histological grade (G – according to the Fédération Nationale des Centres de Lutte Contre Le Cancer [FNCLCC]), configuration, T2 signal intensity, presence/absence of “triple sign”, heterogeneity/homogeneity, borders (well-defined or infiltrative), and intensity of contrast enhancement on MRI. Additionally, a comprehensive literature review to identify observational studies, reviews, and case-reports assessing MRI features of primary synovial sarcoma was performed.ResultsThe mean age of the patients was 47.6 years (SD: 17.2). The mean size of primary synovial sarcoma was 59.3 mm (SD: 42). Primary synovial sarcomas were significantly most often multilobulated (n = 12, p < 0.01), heterogeneous (n = 10), and infiltrative (n = 9). Additionally, 3 other primary synovial sarcomas showed the following configurations: ovoid/nodular (n = 2) and fascicular (n = 1). Ovoid/nodular synovial sarcomas were solely depicted as homogeneous with well-defined borders. All tumours showed T2 hyperintense signal and presented with marked contrast enhancement.ConclusionsPrimary synovial sarcomas are mainly multilobulated, heterogeneous, and infiltrative tumours. In minor cases, ovoid/nodular or fascicular configurations occur. Knowing the main appearance of synovial sarcoma can help facilitate the diagnostics of primary synovial sarcomas.

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“…We included time-signal intensity curve (TIC) types and quantitative evaluations in the DCE-MRI data analysis. Based on the previously described approach, the TIC types were characterized as having no evident upward trend or consistently increasing (type I), rapidly increasing then flattening (type II), or rapidly increasing and dropping (type III) [ 22 ]. The region of interest (ROI) of the DCE-MRI image was positioned in the solid portion of the lesion based on regular MRI findings and enhancing features of the lesion.…”

Section: Methodsmentioning

confidence: 99%

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

Gao,

Wang,

Zhang

et al. 2024

Cancer Imaging

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Background To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs). Methods This research included 92 patients (41females, 51 males; age range 16–86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve. Results TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884–0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899–0.915) and the traditional imaging model (AUC, 0.802, 0.712–0.891) alone. Conclusions Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

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Soft tissue sarcoma: correlation of dynamic contrast-enhanced magnetic resonance imaging features with HIF-1α expression and patient outcomes

Cited by 6 publications

References 37 publications

Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm

Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm

Primary synovial sarcoma on MRI – a case series and review of the literature

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

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