Radiomics Nomograms Based on Non‐enhanced <scp>MRI</scp> and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma

Pan, Jielin; Zhang, Ke; Le, Hongbo; Jiang, Yunping; Li, Wenjuan; Geng, Yayuan; Li, Shaolin; Hong, Guobin

doi:10.1002/jmri.27690

Cited by 30 publications

(31 citation statements)

References 32 publications

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“…However, histogram analysis extracts the distribution of densities only. It cannot fully explore the potential value of imaging, for instance, the shape features and the spatial heterogeneity of the lesions [27,28]. Herein, we mined high-throughput quantitative features, including intensity-based, structural, texture-based, and wavelet transform-based features, from ADC maps.…”

Section: Discussionmentioning

confidence: 99%

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values

Fan

Sun

Min

et al. 2022

European Journal of Radiology

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To develop a machine-learning-based radiomics signature of ADC for discriminating between benign and malignant testicular masses and compare its classification performance with that of minimum and mean ADC. Methods: A total of ninety-seven patients with 101 histopathologically confirmed testicular masses (70 malignancies, 31 benignities) were evaluated in this retrospective study. Eight hundred fifty-one radiomics features were extracted from the preoperative ADC map of each lesion. The mean and minimum ADC values are part of the radiomics features. Thirty lesions were randomly selected to estimate the reliability of the features. The redundant features were eliminated using univariate analysis (independent t test and Mann-Whitney U test, where appropriate) and Spearman's rank correlation. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection and radiomics signature generation. The classification performance of the radiomics signature and minimum and mean ADC values were evaluated by receiver operating characteristic (ROC) curve analysis and compared by DeLong's test. Results: The whole lesion-based mean ADC showed no difference between benign and malignant testicular masses (P = 0.070, training cohort; P = 0.418, validation cohort). Compared with the minimum ADC, the ADC-based radiomics signature yielded a higher area under the curve (AUC) in both the training (AUC: 0.904, 95% confidence interval [CI]: 0.832-0.975) and validation cohorts (AUC: 0.868, 95% CI: 0.728-1.00). Conclusions: Conventional mean ADC values are not always helpful in discriminating between testicular benignities and malignancies. The minimum ADC and radiomics signature might be better alternatives, with the radiomics signature performing better than the minimum ADC.

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Section: Discussionmentioning

confidence: 99%

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values

Fan

Sun

Min

et al. 2022

European Journal of Radiology

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“…Radiomic studies to date have focused on the classification of cartilaginous bone tumours, such as enchondroma, ACT and high-grade CS, using radiomics alone 27 , 28 , 29 or combined with machine learning. 13 14 Particularly, in a recent study we focused on CT radiomics-based machine learning and the distinction between ACT and high-grade CS of long bones, including CS2, grade III and dedifferentiated CS in the latter group.…”

Section: Discussionmentioning

confidence: 99%

MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones

et al. 2022

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Summary Background Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones. Methods One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test. Findings After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134). Interpretation Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features. Funding ESSR Young Researchers Grant.

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“…The biomarkers included in the model cover different biological scales from molecular to phenotypic [ 15 ]. Radiomics in the application of skeletal muscle system is usually in terms of bone tumours, such as bone disease diagnosis and differential diagnosis of tumour prediction of tumour complications, the prognosis of tumour treatment pathologic grading [ 16 – 19 ] and tumour [ 20 ], a small study applies beside the osteoporosis [ 21 ], Alzheimer's disease [ 22 ], temporo-mandibular joint osteoarthritis [ 23 ], postoperative infection and inflammation [ 24 ], and so on. Few radiomics studies have been conducted on LHD.…”

Section: Discussionmentioning

confidence: 99%

Application of a nomogram to radiomics labels in the treatment prediction scheme for lumbar disc herniation

Yang²,

Zhang³

et al. 2022

BMC Med Imaging

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Objective To investigate and verify the efficiency and effectiveness of a nomogram based on radiomics labels in predicting the treatment of lumbar disc herniation (LDH). Methods By reviewing medical records that were analysed over the past three years, clinical and imaging data of 200 lumbar disc patients at the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were obtained. The collected cases were randomly divided into a training group (n = 140) and a testing group (n = 60) at a ratio of 7:3. Two radiologists with experience in reading orthopaedics images independently segmented the ROIs. The whole intervertebral disc with the most obvious protrusion in the sagittal plane T2WI lumbar MRI as a mask (ROI) is sketched. The LASSO (Least Absolute Shrinkage And Selection Operator) algorithm was used to filter the features after extracting the radiomics features. The multivariate logistic regression model was used to construct a quantitative imaging Rad‑Score for the selected features with nonzero coefficients. The radiomics labels and nomogram were evaluated using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The calibration curve was used to evaluate the consistency between the nomogram prediction and the actual treatment plan. The DCA decision curve was used to evaluate the clinical applicability of the nomogram. Result Following feature extraction, 11 radiomics features were used to construct the radiomics label for predicting the treatment plan of LDH. A nomogram was then constructed. The AUC was 0.93 (95% CI: 0.90–0.97), with a sensitivity of 89%, a specificity of 91%, a positive predictive value of 92.7%, a negative predictive value of 89.4%, and an accuracy of 91%. The calibration curve showed that there was good consistency between the prediction and the actual observation. The DCA decision curve analysis showed that the nomogram of the imaging group has great potential for clinical application when the risk threshold is between 5 and 72%. Conclusion A nomogram based on radiomics labels has good predictive value for the treatment of LDH and can be used as a reference for clinical decision-making.

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Radiomics Nomograms Based on Non‐enhanced MRI and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma

Cited by 30 publications

References 32 publications

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values

Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values

MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones

Application of a nomogram to radiomics labels in the treatment prediction scheme for lumbar disc herniation

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