Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas

Tian, Lijun; Zhang, Defei; Bao, Shan; Nie, Pei; Hao, Dapeng; Liu, Y.; Zhang, Ji-gang; Wang, H.

doi:10.1016/j.crad.2020.08.038

Cited by 25 publications

(22 citation statements)

References 35 publications

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“…After careful pre-processing steps (including feature robustness and redundancy analyses), we used the remaining features to build radiomic signatures, investigating three feature-ranking methods (UDFS, DGUFS and UFSOL) and one classifier (SVM). Good performance for both ranking methods and classifiers used in our work were reported in previous studies (54,55). 3 radiomic signatures were defined, with the best performance in terms of AUROC and specificity achieved by the FS method UDFS coupled with the SVM classifier (0.725 §0.091 and 0.741 § 0.114, respectively), while and the highest sensitivity and NPV values was obtained by UFSOL+SVM (0.796 §0.128 and 0.777 §0.125, respectively).…”

Section: Discussionsupporting

confidence: 80%

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

et al. 2022

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Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An indepth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data.Results: The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725 §0.091, sensitivity = 0.709 §0.176, specificity = 0.741 §0.114, PPV = 0.72 §0.093, and NPV = 0.75 §0.114. Conclusion:In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

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Section: Discussionsupporting

confidence: 80%

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

et al. 2022

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“…A radiomic approach may be used to improve prediction of patients’ outcome. RM texture analysis alone [ 113 , 114 ] or combined with PET/CT metabolic data [ 115 , 116 ] was associated with metastatic relapse and specific signatures were identified for prediction of survival [ 117 , 118 ]. Radiomic analysis was also applied on surveillance MRI in patients undergoing follow-up after surgical resection [ 119 ], resulting in improved detection and characterization of local recurrence [ 120 ].…”

Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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“…K-fold cross-validation was used in most of the studies [ 13 , 25 , 28 , 31 – 33 , 37 , 38 , 40 , 43 , 44 , 46 – 50 ]. The following machine learning validation techniques were used less commonly: bootstrapping [ 42 , 51 ]; leave-one-out cross-validation [ 34 , 35 , 41 ]; leave-p-out cross-validation [ 52 ]; Monte Carlo cross-validation [ 23 ]; nested cross-validation [ 25 , 27 ]; random-split cross-validation [ 20 ]. Figure 2 provides an overview of machine learning validation techniques.…”

Section: Resultsmentioning

confidence: 99%

“…A clinical validation of the radiomics-based prediction model was reported in 19 (39%) of the 49 papers. It was performed on a separate set of data from the primary institution, i.e., internal test set, in 14 (29%) studies [ 15 , 16 , 22 , 24 , 28 , 31 , 32 , 35 , 37 , 38 , 41 , 46 , 47 , 52 ]. It was performed on an independent set of data from the primary institution (related to a different scanner) or from an external institution, i.e., external test set, in 5 (10%) studies [ 25 , 27 , 29 , 43 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Two (4%) studies presented an analysis to assess the reproducibility based on different acquisition [30] or post-processing [31] techniques. Of note, segmentations were validated by a second experienced reader in 15 studies [12,[32][33][34][35][36][37][38][39][40][41][42][43][44][45] without, however, addressing the issue of radiomic feature reproducibility.…”

Section: Reproducibility Strategiesmentioning

confidence: 99%

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CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

et al. 2021

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Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

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Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas

Cited by 25 publications

References 35 publications

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

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