2019
DOI: 10.1002/jmri.26818
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Radiomics nomogram for differentiating between benign and malignant soft‐tissue masses of the extremities

Abstract: 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… Show more

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Cited by 45 publications
(41 citation statements)
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“…We established a radiomics nomogram that combined independent clinical factors and the RS-Combined model, showing the best AUC in each dataset, better calibration, and highest net bene t in a range of threshold probabilities. This is consistent with recent reports of strati cation of patients with glioblastoma and soft tissue tumors [37,38]. The nomogram graphically creates a clinical statistical predictive model, is easy to use, and enables accurate prediction of an individual patient's probability of preoperative strati cation.…”
Section: Discussionsupporting
confidence: 89%
See 1 more Smart Citation
“…We established a radiomics nomogram that combined independent clinical factors and the RS-Combined model, showing the best AUC in each dataset, better calibration, and highest net bene t in a range of threshold probabilities. This is consistent with recent reports of strati cation of patients with glioblastoma and soft tissue tumors [37,38]. The nomogram graphically creates a clinical statistical predictive model, is easy to use, and enables accurate prediction of an individual patient's probability of preoperative strati cation.…”
Section: Discussionsupporting
confidence: 89%
“…In addition, radiomics has been successfully applied in prediction of the histologic grade, local recurrence or distant metastasis, overall survival, and response to neoadjuvant therapy in STS patients 17–24 . However, MRI‐based radiomics nomogram that combined radiomics and clinical‐MRI morphological factors applied for soft tissue masses were relatively limited 25,26 …”
mentioning
confidence: 99%
“…Similarly, Lang et al (16) found that the accuracy of radiomics analysis and convolutional neural network (CNN) was similar in the identification of spinal metastases originated from the lung and other tumors. LR is one of the most commonly used algorithms in radiomics analysis and has been proved to be effective (27)(28)(29)(30). Despite nomogram's visualization, it has limited power for future big data era.…”
Section: Discussionmentioning
confidence: 99%
“…Zhang et al (34) reported that the model based on radiomics features from TWI, DKI, and quantitative DCE pharmacokinetic parameter maps was a good tool to differentiate malignant and benign breast lesions, with an AUC of 0.92 in the test set. Wang et al (35) reported that the radiomics nomogram can be used to classify between malignant and benign soft-tissue masses in the extremities with an AUC of 0.94 in the test set. This study analyzed all 1,224 MRI features to establish the radiomics using the mRMR and LASSO algorithm.…”
Section: Discussionmentioning
confidence: 99%
“…Wang et al. ( 35 ) reported that the radiomics nomogram can be used to classify between malignant and benign soft-tissue masses in the extremities with an AUC of 0.94 in the test set. This study analyzed all 1,224 MRI features to establish the radiomics using the mRMR and LASSO algorithm.…”
Section: Discussionmentioning
confidence: 99%