ArticleFrontiers in oncology2022
Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 37 citations in OpenAlex.
- Clinical-Radiomics Signature Predicts Pathologic Complete Response After Neoadjuvant Therapy in Oesophageal Squamous Cell Carcinoma.Interdisciplinary cardiovascular and thoracic surgery · 2026Article
- Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.Translational cancer research · 2026Article
- MRI-derived radiomics model for predicting intratumoral tertiary lymphoid structures in soft tissue sarcoma.Insights into imaging · 2025Article
- Diagnostic utility of MRI-based convolutional neural networks in soft tissue sarcomas: a mini-review.Frontiers in oncology · 2025Review
- Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- The impact of radiomics in the management of soft tissue sarcoma.Discover oncology · 2024Review
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients.Frontiers in oncology · 2024Article
- Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma.BMC medical imaging · 2023Article
- New trends in the surgical management of soft tissue sarcoma: The role of preoperative biopsy.World journal of clinical oncology · 2023Review
- Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.Frontiers in oncology · 2022Article
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10 authors at 5 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: To build and evaluate a deep learning radiomics nomogram (DLRN) for preoperative prediction of lung metastasis (LM) status in patients with soft tissue sarcoma (STS). Methods: In total, 242 patients with STS (training set, n=116; external validation set, n=126) who underwent magnetic resonance imaging were retrospectively enrolled in this study. We identified independent predictors for LM-status and evaluated their performance. The minimum redundancy maximum relevance (mRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm were adopted to screen radiomics features. Logistic regression, decision tree, random forest, support vector machine (SVM), and adaptive boosting classifiers were compared for their ability to predict LM. To overcome the imbalanced distribution of the LM data, we retrained each machine-learning classifier using the synthetic minority over-sampling technique (SMOTE). A DLRN combining the independent clinical predictors with the best performing radiomics prediction signature (mRMR+LASSO+SVM+SMOTE) was established. Area under the receiver operating characteristics curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the performance and clinical applicability of the models. Result: Comparisons of the AUC values applied to the external validation set revealed that the DLRN model (AUC=0.833) showed better prediction performance than the clinical model (AUC=0.664) and radiomics model (AUC=0.799). The calibration curves indicated good calibration efficiency and the DCA showed the DLRN model to have greater clinical applicability than the other two models. Conclusion: The DLRN was shown to be an accurate and efficient tool for LM-status prediction in STS.
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