Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high‐grade lesions

Corino, Valentina D. A.; Montin, Eros; Messina, A.; Casali, Paolo G.; Gronchi, Alessandro; Marchianò, Alfonso; Mainardi, Luca

doi:10.1002/jmri.25791

Cited by 103 publications

(107 citation statements)

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“…Interestingly, our best models relied on a limited set of features from noncontrast‐enhanced sequences. Corino et al also found that only three features from diffusion imaging provided the highest accuracy to predict the histological grade of STS . The best model to predict lung metastatic relapse of STS according to Vallières et al relied on four texture features .…”

Section: Discussionmentioning

confidence: 99%

“…Because of their complex morphology, architecture, and changes during treatments, STS may be particularly appropriate to the radiomics approach. Indeed, radiomics on diffusion‐weighted imaging (DWI) may help to improve STS grading on microbiopsy . In addition, Hayano et al have demonstrated that texture parameters on a computed tomography (CT) scan were associated with neoangiogenesis and overall survival for STS treated with radiotherapy and bevacizumab .…”

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T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

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Kind

et al. 2018

Magnetic Resonance Imaging

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Background Standard of care for patients with high‐grade soft‐tissue sarcoma (STS) are being redefined since neoadjuvant chemotherapy (NAC) has demonstrated a positive effect on patients' outcome. Yet response evaluation in clinical trials still relies on RECIST criteria. Purpose To investigate the added value of a Delta‐radiomics approach for early response prediction in patients with STS undergoing NAC. Study Type Retrospective. Population Sixty‐five adult patients with newly‐diagnosed, locally‐advanced, histologically proven high‐grade STS of trunk and extremities. All were treated by anthracycline‐based NAC followed by surgery and had available MRI at baseline and after two chemotherapy cycles. Field Strength/Sequence Pre‐ and postcontrast enhanced T1‐weighted imaging (T1‐WI), turbo spin echo T2‐WI at 1.5 T. Assessment A threshold of <10% viable cells on surgical specimens defined good response (Good‐HR). Two senior radiologists performed a semantic analysis of the MRI. After 3D manual segmentation of tumors at baseline and early evaluation, and standardization of voxel‐sizes and intensities, absolute changes in 33 texture and shape features were calculated. Statistical Tests Classification models based on logistic regression, support vector machine, k‐nearest neighbors, and random forests were elaborated using crossvalidation (training and validation) on 50 patients ("training cohort") and was validated on 15 other patients ("test cohort"). Results Sixteen patients were good‐HR. Neither RECIST status (P = 0.112) nor semantic radiological variables were associated with response (range of P‐values: 0.134–0.490) except an edema decrease (P = 0.003), although 14 shape and texture features were (range of P‐values: 0.002–0.037). On the training cohort, the highest diagnostic performances were obtained with random forests built on three features: Δ_Histogram_Entropy, Δ_Elongation, Δ_Surrounding_Edema, which provided: area under the curve the receiver operating characteristic = 0.86, accuracy = 88.1%, sensitivity = 94.1%, and specificity = 66.3%. On the test cohort, this model provided an accuracy of 74.6% but 3/5 good‐HR were systematically ill‐classified. Data Conclusion A T2‐based Delta‐radiomics approach might improve early response assessment in STS patients with a limited number of features. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:497–510.

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Section: Discussionmentioning

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T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

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et al. 2018

Magnetic Resonance Imaging

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“…The advent of hybrid positron emission tomography/magnetic resonance (PET/MR) using either 2‐deoxy‐2‐[fluorine‐18]‐fluoro‐D‐glucose (18F‐FDG) or 68gallium‐labeled‐prostate specific membrane antigen (68Ga‐PSMA) systems provide an opportunity to characterize a soft tissue mass of interest as benign or malignant using its anatomic and metabolic imaging characteristics, while simultaneously performing local as well as whole‐body staging. At this time, there is limited clinical experience on the utility of PET/MR on the evaluation of a musculoskeletal soft tissue mass at presentation, although quantitative PET/MR systems performed using DWI/ADC mapping enable quantitative voxel‐based measurement of standard uptake values (SUV) and ADC values synchronously . In 29 patients with sarcoma, 18F‐FDG PET/MR was comparable to conventional imaging (CT and MR) for initial sarcoma staging .…”

Section: Future Directionsmentioning

confidence: 99%

Magnetic resonance imaging biomarkers in musculoskeletal soft tissue tumors: Review of conventional features and focus on nonmorphologic imaging

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Fritz

Morris

et al. 2019

Magnetic Resonance Imaging

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The majority of peripheral musculoskeletal soft tissue masses encountered in routine clinical practice are benign and associated with an excellent prognosis, whereas a small proportion of soft tissue masses are malignant and are associated with higher morbidity and mortality. Owing to the excellent tissue contrast resolution provided, magnetic resonance imaging (MRI) is a primary modality for the diagnostic evaluation of soft tissue masses, particularly for defining the anatomic extent. MRI is also useful for the characterization of soft tissue masses; however, after the exclusion of cysts and lipomas, it is challenging to accurately categorize neoplastic lesions as benign or malignant with conventional anatomic MRI. Quantitative MRI biomarkers obtained with nonconventional pulse sequences are becoming increasingly available, and can improve the accuracy of determining the character of soft tissue masses. In this article we discuss established qualitative and emerging quantitative MRI‐based biomarkers available with dynamic contrast‐enhanced MRI, diffusion‐weighted imaging, and MR spectroscopy and their utility for the characterization of untreated soft tissue masses. Level of Evidence: 5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:11–27.

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“…All of the previous advantages make radiomics a technique of interest for tumor characterization. As a matter of fact, radiomics has already found a wide range of possible applications [4–14] such as prediction of clinical outcomes and response to treatment, tumor staging, discrimination of different types of tumor tissues, and assessment of cancer genetics.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images

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The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard’s index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard’s index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.Electronic supplementary materialThe online version of this article (10.1007/s10278-018-0092-9) contains supplementary material, which is available to authorized users.

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Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high‐grade lesions

Abstract: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Cited by 103 publications

References 31 publications

T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

Magnetic resonance imaging biomarkers in musculoskeletal soft tissue tumors: Review of conventional features and focus on nonmorphologic imaging

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images

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Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high‐grade lesions

Abstract: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Cited by 103 publications

References 31 publications

T2‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

T2‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

Magnetic resonance imaging biomarkers in musculoskeletal soft tissue tumors: Review of conventional features and focus on nonmorphologic imaging

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images

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T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.

T₂‐based MRI Delta‐radiomics improve response prediction in soft‐tissue sarcomas treated by neoadjuvant chemotherapy.