Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors.

Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

Fields BKK ，Demirjian NL ，Hwang DH ，Varghese BA ，Cen SY ，Lei X ，Desai B ，Duddalwar V ，Matcuk GR Jr ... -  《-》

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.

To evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses. Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively. Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

Fields BKK ，Demirjian NL ，Cen SY ，Varghese BA ，Hwang DH ，Lei X ，Desai B ，Duddalwar V ，Matcuk GR Jr ... -  《-》

Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study.

Preoperative differentiation between malignant and benign soft-tissue masses is important for treatment decisions. To construct/validate a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses. Retrospective. In all, 206 cases. The T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352-550/2.75-19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700-6370/40-120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort. Twelve machine-learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively). 1) Demographic characteristics: a one-way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) values. The LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2. A machine-learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft-tissue masses. 3 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:873-882.

Wang H ，Zhang J ，Bao S ，Liu J ，Hou F ，Huang Y ，Chen H ，Duan S ，Hao D ，Liu J ... -  《-》

Ensemble learning-based radiomics with multi-sequence magnetic resonance imaging for benign and malignant soft tissue tumor differentiation.

Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs.

Lee S ，Lee SY ，Jung JY ，Nam Y ，Jeon HJ ，Jung CK ，Shin SH ，Chung YG ... -  《PLoS One》

Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.

Wang X ，Wan Q ，Chen H ，Li Y ，Li X ... -  《-》