Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI

Nie

Magnetic Resonance Imaging

et al. 2019

Background: Preoperative differentiation between malignant and benign tumors is important for treatment decisions. Purpose/Hypothesis: To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses. Study Type: Retrospective. Population: Imaging data of 91 patients. Field Strength/Sequence: T 1 -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T 2 -weighted images (T 2 WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm. Assessment: Fat-suppressed FSE T 2 WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T 2 WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared. Statistical Tests: 1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ 2 test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results. Results: The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone. Data Conclusion: This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model. Level of Evidence: 3 Technical Efficacy: Stage 2

Section: Discussionmentioning

confidence: 99%

Radiomics nomogram for differentiating between benign and malignant soft‐tissue masses of the extremities

Nie

Magnetic Resonance Imaging

et al. 2019

“…Importantly, the notion that texture analysis can identify otherwise invisible and more detailed tumor information expands beyond radiology to the histopathology aspect. Thus, texture analysis is a potentially useful method for predicting tumor grade in STSs …”

Section: Discussionmentioning

confidence: 99%

“…Radiomics extract a great number of image features, the analysis of which can reflect vital information about tumor physiology . Past studies have shown that biomarkers based on quantitative MRI radiomics features were associated with the histopathological grades of STSs …”

mentioning

confidence: 99%

Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas

Magnetic Resonance Imaging

Chen

Duan

et al. 2019

Background Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning. Purpose To assess the value of radiomics features in distinguishing histological grades of STSs. Study Type Retrospective. Population In all, 113 patients with pathology‐confirmed low‐grade (grade I), intermediate‐grade (grade II), or high‐grade (grade III) soft tissue sarcoma were collected. Field Strength/Sequence The 3.0T axial T1‐weighted imaging (T1WI) with 550 msec repetition time (TR); 18 msec echo time (TE), 312 × 312 matrix, fat‐suppressed fast spin‐echo T2WI with 4291 msec TR, 85 msec TE, 312 × 312 matrix. Assessment Multiple machine‐learning methods were trained to establish classification models for predicting STS grades. Eighty STS patients (18 low‐grade [grade I]; 62 high‐grade [grades II–III]) were enrolled in the primary set and we tested the model with a validation set with 33 patients (7 low‐grade, 26 high‐grade). Statistical Tests 1) Student's t‐tests were applied for continuous variables and the χ2 test were applied for categorical variables between low‐grade STS and high‐grade STS groups. 2) For feature subset selection, either no subset selection or recursive feature elimination was performed. This technology was combined with random forest and support vector machine‐learning methods. Finally, to overcome the disparity in the frequencies of the STS grades, each machine‐learning model was trained i) without subsampling, ii) with the synthetic minority oversampling technique, and iii) with random oversampling examples, for a total of 12 combinations of machine‐learning algorithms that were assessed, trained, and tested in the validation cohort. Results The best classification model for the prediction of STS grade was a combination of features selected by recursive feature elimination and random forest classification algorithms with a synthetic minority oversampling technique, which had an area under the curve of 0.9615 (95% confidence interval 0.8944–1.0) in the validation set. Data Conclusion Radiomics feature‐based machine‐learning methods are useful for distinguishing STS grades. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:791–797.

Semin Musculoskelet Radiol

“…More than a thousand features were extracted from T2w FS sequences in 35 MRI examinations included retrospectively. 89 Tumors were manually segmented (but the interobserver agreement was not assessed). Feature selection was made using LASSO.…”

Section: Radiomics Literature: General Considerations and Musculoskelmentioning

confidence: 99%

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Cuadra

Favre

Omoumi

2020

Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.