CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas

Gitto, Salvatore; Cuocolo, Renato; Annovazzi, Alessio; Anelli, Vincenzo; Acquasanta, Marzia; Cincotta, Antonino; Albano, Domenico; Chianca, Vito; Ferraresi, Virginia; Messina, Carmelo; Zoccali, Carmine; Armiraglio, Elisabetta; Parafioriti, Antonina; Sciuto, Rosa; Luzzati, Alessandro; Biagini, R.; Imbriaco, Massimo; Sconfienza, Luca Maria

doi:10.1016/j.ebiom.2021.103407

Cited by 46 publications

(30 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One major concern should be noted that there were too many features involved in their modeling compared to ours (60 vs. 14). The most convincing method to identify whether the trained model is overfitted is externally testing it on unseen data obtained from another institution [ 29 , 30 ]. The results of our external test suggest there was a moderate overfitting in our models, even if only 14 features were used as classifier inputs.…”

Section: Discussionmentioning

confidence: 99%

Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

Zhao

Chen

et al. 2022

Cancers

View full text Add to dashboard Cite

Background/aim This study aimed to explore the value of radiological and radiomic features retrieved from magnetic resonance imaging in the prediction of a Ki-67 proliferative index in meningioma patients using a machine learning model. Methods This multicenter, retrospective study included 371 patients collected from two centers. The Ki-67 expression was classified into low-expressed and high-expressed groups with a threshold of 5%. Clinical features and radiological features were collected and analyzed by using univariate and multivariate statistical analyses. Radiomic features were extracted from contrast-enhanced images, followed by three independent feature selections. Six predictive models were constructed with different combinations of features by using linear discriminant analysis (LDA) classifier. Results The multivariate analysis suggested that the presence of intratumoral necrosis (p = 0.032) and maximum diameter (p < 0.001) were independently correlated with a high Ki-67 status. The predictive models showed good performance with AUC of 0.837, accuracy of 0.810, sensitivity of 0.857, and specificity of 0.771 in the internal test and with AUC of 0.700, accuracy of 0.557, sensitivity of 0.314, and specificity of 0.885 in the external test. Conclusion The results of this study suggest that the predictive model can efficiently predict the Ki-67 index of meningioma patients to facilitate the therapeutic management.

show abstract

Section: Discussionmentioning

confidence: 99%

Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

Zhao

Chen

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…Accurate prediction for chondrosarcoma survival is crucial for the counseling, follow-up, and treatment planning of patients. Previous studies have revealed various prognostic factors influencing the survival times of patients with chondrosarcoma, including patient age, tumor size, histological type, tumor grade, and metastasis ( 6 , 19 – 21 ).. At the same time, increasing amounts of imaging ( 22 , 23 ) and genetic data ( 2 , 24 ) are being mined for survival analysis of chondrosarcoma patients. In the face of high-dimensional data, the limitations of the linear relationship between variables assumed by the classical CoxPH model are evident ( 11 ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis

Yan

Gao

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

BackgroundAccurate prediction of prognosis is critical for therapeutic decisions in chondrosarcoma patients. Several prognostic models have been created utilizing multivariate Cox regression or binary classification-based machine learning approaches to predict the 3- and 5-year survival of patients with chondrosarcoma, but few studies have investigated the results of combining deep learning with time-to-event prediction. Compared with simplifying the prediction as a binary classification problem, modeling the probability of an event as a function of time by combining it with deep learning can provide better accuracy and flexibility.Materials and methodsPatients with the diagnosis of chondrosarcoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three algorithms—two based on neural networks (DeepSurv, neural multi-task logistic regression [NMTLR]) and one on ensemble learning (random survival forest [RSF])—were selected for training. Meanwhile, a multivariate Cox proportional hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into training and testing datasets at a ratio of 7:3. Hyperparameter tuning was conducted through a 1000-repeated random search with 5-fold cross-validation on the training dataset. The model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-, 3-, 5- and 10-year survival was evaluated using receiver operating characteristic curves (ROC), calibration curves, and the area under the ROC curves (AUC).ResultsA total of 3145 patients were finally enrolled in our study. The mean age at diagnosis was 52 ± 18 years, 1662 of the 3145 patients were male (53%), and mean survival time was 83 ± 67 months. Two deep learning models outperformed the RSF and classical CoxPH models, with the C-index on test datasets achieving values of 0.832 (DeepSurv) and 0.821 (NMTLR). The DeepSurv model produced better accuracy and calibrated survival estimates in predicting 1-, 3- 5- and 10-year survival (AUC:0.895-0.937). We deployed the DeepSurv model as a web application for use in clinical practice; it can be accessed through https://share.streamlit.io/whuh-ml/chondrosarcoma/Predict/app.py.ConclusionsTime-to-event prediction models based on deep learning algorithms are successful in predicting chondrosarcoma prognosis, with DeepSurv producing the best discriminative performance and calibration.

show abstract

“…Afterwards, the performance of a locally weighted ensemble classifier was evaluated on the test cohort, and was showed to be as good as an experienced musculoskeletal radiologist (AUC = 0.78). In agreement with these results, more recently they attempted to develop a machine-learning classifier based on preoperative CT radiomic features to discriminate between atypical cartilaginous tumors and high-grade chondrosarcomas of long bones [62]. The CT radiomics-based machine-learning classifier achieved 75% accuracy overall, 81% accuracy in identifying atypical cartilaginous tumors, and 70% accuracy in identifying higher-grade chondrosarcomas, and still there was no difference in comparison with an experienced radiologist (p = 0.75).…”

Section: Bone Tumorsmentioning

confidence: 91%

Radiomics of Musculoskeletal Sarcomas: A Narrative Review

et al. 2022

Self Cite

View full text Add to dashboard Cite

Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.

show abstract

CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas

Cited by 46 publications

References 35 publications

Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis

Radiomics of Musculoskeletal Sarcomas: A Narrative Review

Contact Info

Product

Resources

About