Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade

Xu, Wenjian; Hao, Dapeng; Hou, Feng; Zhang, Dejing; Wang, Hexiang

doi:10.2214/ajr.19.22147

Cited by 38 publications

(37 citation statements)

References 34 publications

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“…At least one machine learning validation technique was used in 25 (51%) of the 49 papers. K-fold cross-validation was used in most of the studies [ 13 , 25 , 28 , 31 – 33 , 37 , 38 , 40 , 43 , 44 , 46 – 50 ]. The following machine learning validation techniques were used less commonly: bootstrapping [ 42 , 51 ]; leave-one-out cross-validation [ 34 , 35 , 41 ]; leave-p-out cross-validation [ 52 ]; Monte Carlo cross-validation [ 23 ]; nested cross-validation [ 25 , 27 ]; random-split cross-validation [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

“…A clinical validation of the radiomics-based prediction model was reported in 19 (39%) of the 49 papers. It was performed on a separate set of data from the primary institution, i.e., internal test set, in 14 (29%) studies [ 15 , 16 , 22 , 24 , 28 , 31 , 32 , 35 , 37 , 38 , 41 , 46 , 47 , 52 ]. It was performed on an independent set of data from the primary institution (related to a different scanner) or from an external institution, i.e., external test set, in 5 (10%) studies [ 25 , 27 , 29 , 43 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Two (4%) studies presented an analysis to assess the reproducibility based on different acquisition [30] or post-processing [31] techniques. Of note, segmentations were validated by a second experienced reader in 15 studies [12,[32][33][34][35][36][37][38][39][40][41][42][43][44][45] without, however, addressing the issue of radiomic feature reproducibility.…”

Section: Reproducibility Strategiesmentioning

confidence: 99%

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

et al. 2021

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Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Reproducibility Strategiesmentioning

confidence: 99%

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

et al. 2021

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“…As previously mentioned, other authors have evaluated tumor grading prediction using MRI-based radiomics [38][39][40][41][42]. However, only one study validated their models in an external testing cohort [40].…”

Section: Discussionmentioning

confidence: 99%

“…Wang et al developed radiomic models to differentiate malignant and benign soft-tissue lesions [36]. Further research studies evaluated the differentiation of high-grade from low-grade STS based on MRI and CT imaging scans using radiomic analysis [37][38][39][40][41][42]. No study has yet analyzed the possibility of DL-based tumor grading prediction.…”

Section: Introductionmentioning

confidence: 99%

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Navarro

Dapper

Asadpour

et al. 2021

Cancers

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Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

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

Fang

Yang

et al. 2021

Magnetic Resonance Imaging

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Over the past two decades, considerable efforts have been made to develop non‐invasive methods for determining tumor grade or surrogates for predicting the biological behavior, aiding early treatment decisions, and providing prognostic information. The development of new imaging tools, such as diffusion‐weighted imaging, diffusion kurtosis imaging, perfusion imaging, and magnetic resonance spectroscopy have provided leverage in the diagnosis of soft tissue sarcomas. Artificial intelligence is a new technology used to study and simulate human thinking and abilities, which can extract and analyze advanced and quantitative image features from medical images with high throughput for an in‐depth characterization of the spatial heterogeneity of tumor tissues. This article reviews the current imaging modalities used to predict the histopathological grade of soft tissue sarcomas and highlights the advantages and limitations of each modality. Level of Evidence 5 Technical Efficacy Stage 2

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Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade

Cited by 38 publications

References 34 publications

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

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

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

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

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