scholarly journals Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jin Deng ◽  
Weiming Zeng ◽  
Yuhu Shi ◽  
Wei Kong ◽  
Shunjie Guo
Keyword(s):  
Soft Tissue ◽  
Clinical Features ◽  
Lung Metastasis ◽  
Feature Fusion ◽  
Back Propagation ◽  
Soft Tissue Sarcomas ◽  
Single Mode ◽  
Standard Uptake Value ◽  
Clinical Feature ◽  
Prediction Ability

Extracting massive features from images to quantify tumors provides a new insight to solve the problem that tumor heterogeneity is difficult to assess quantitatively. However, quantification of tumors by single-mode methods often has defects such as difficulty in features extraction and high computational complexity. The multimodal approach has shown effective application prospects in solving these problems. In this paper, we propose a feature fusion method based on positron emission tomography (PET) images and clinical information, which is used to obtain features for lung metastasis prediction of soft tissue sarcomas (STSs). Random forest method was adopted to select effective features by eliminating irrelevant or redundant features, and then they were used for the prediction of the lung metastasis combined with back propagation (BP) neural network. The results show that the prediction ability of the proposed model using fusion features is better than that of the model using an image or clinical feature alone. Furthermore, a good performance can be obtained using 3 standard uptake value (SUV) features of PET image and 7 clinical features, and its average accuracy, sensitivity, and specificity on all the sets can reach 92%, 91%, and 92%, respectively. Therefore, the fusing features have the potential to predict lung metastasis for STSs.

scholarly journals DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

2020 ◽  
Author(s):  
Zhengyuan Wu ◽  
Miao Yu ◽  
Jing-yuan Fan ◽  
Zhen-pei Wen ◽  
Tian-yu Ren ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Soft Tissue ◽  
Clinical Features ◽  
Soft Tissue Sarcomas ◽  
Survival Prediction ◽  
Biological Functions ◽  
Promoter Regions ◽  
Patient Prognosis ◽  
Selection Operator ◽  
Methylation Patterns

Abstract Background: Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. Methods: In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database.Results: Eventually, samples were clustered into four subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above‐described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer‑associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. Conclusion: In conclusion, this study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

scholarly journals A Novel Nomogram Model Based on Cone-Beam CT Radiomics Analysis Technology for Predicting Radiation Pneumonitis in Esophageal Cancer Patients Undergoing Radiotherapy

2020 ◽  
Vol 10 ◽  
Author(s):  
Feng Du ◽  
Ning Tang ◽  
Yuzhong Cui ◽  
Wei Wang ◽  
Yingjie Zhang ◽  
...  
Keyword(s):  
Clinical Features ◽  
Roc Curve ◽  
Validation Cohort ◽  
Radiation Pneumonitis ◽  
Tumor Stage ◽  
Cone Beam ◽  
Clinical Feature ◽  
Prediction Ability ◽  
Model Based ◽  
Calibration Curves

PurposeWe quantitatively analyzed the characteristics of cone-beam computed tomography (CBCT) radiomics in different periods during radiotherapy (RT) and then built a novel nomogram model integrating clinical features and dosimetric parameters for predicting radiation pneumonitis (RP) in patients with esophageal squamous cell carcinoma (ESCC).MethodsAt our institute, a retrospective study was conducted on 96 ESCC patients for whom we had complete clinical feature and dosimetric parameter data. CBCT images of each patient in three different periods of RT were obtained, the images were segmented using both lungs as the region of interest (ROI), and 851 image features were extracted. The least absolute shrinkage selection operator (LASSO) was applied to identify candidate radiomics features, and logistic regression analyses were applied to construct the rad-score. The optimal period for the rad-score, clinical features, and dosimetric parameters were selected to construct the nomogram model and then the receiver operating characteristic (ROC) curve was used to evaluate the prediction capacity of the model. Calibration curves and decision curves were used to demonstrate the discriminatory and clinical benefit ratios, respectively.ResultsThe relative volume of total lung treated with ≥5 Gy (V5), mean lung dose (MLD), and tumor stage were independent predictors of RP and were finally incorporated into the nomogram. When the three time periods were modeled, the first period was better than the others. In the primary cohort, the area under the ROC curve (AUC) was 0.700 (95% confidence interval (CI) 0.568–0.832), and in the independent validation cohort, the AUC was 0.765 (95% CI 0.588–0.941). In the nomogram model that integrates clinical features and dosimetric parameters, the AUC in the primary cohort was 0.836 (95% CI 0.700–0.918), and the AUC in the validation cohort was 0.905 (95% CI 0.799–1.000). The nomogram model exhibits excellent performance. Calibration curves indicate a favorable consistency between the nomogram prediction and the actual outcomes. The decision curve exhibits satisfactory clinical utility.ConclusionThe radiomics model based on early lung CBCT is a potentially valuable tool for predicting RP. V5, MLD, and tumor stage have certain predictive effects for RP. The developed nomogram model has a better prediction ability than any of the other predictors and can be used as a quantitative model to predict RP.

Clinical features and outcomes of young patients with head and neck non-rhabdomyosarcoma soft tissue sarcomas

Head & Neck ◽  
2014 ◽  
Vol 37 (1) ◽  
pp. 76-83 ◽  
Author(s):  
Sara M. Federico ◽  
David Gilpin ◽  
Sandeep Samant ◽  
Catherine A. Billups ◽  
Sheri L. Spunt
Keyword(s):  
Soft Tissue ◽  
Head And Neck ◽  
Clinical Features ◽  
Young Patients ◽  
Soft Tissue Sarcomas

Sarcoma

2021 ◽  
pp. 95-104
Author(s):  
Ian M. Smith ◽  
Vinay Itte
Keyword(s):  
Soft Tissue ◽  
Clinical Features ◽  
Multidisciplinary Team ◽  
Systematic Approach ◽  
Soft Tissues ◽  
Soft Tissue Sarcomas ◽  
Malignant Tumours ◽  
Staging Classification

Sarcomas are malignant tumours of the soft tissues or bone. Epidemiology, aetiology, pathology, clinical features, investigations, diagnosis, staging, classification, and management of soft tissue sarcomas are described in this chapter. These tumours are relatively uncommon but require a systematic approach to treatment involving a multidisciplinary team.

scholarly journals Surgical Treatment of Synchronous Lung Metastasis in Patient with Osteosarcomas and Soft Tissue Sarcomas

2015 ◽  
Vol 26 ◽  
pp. i45
Author(s):  
E. Smolenov ◽  
Y. Ragulin ◽  
A. Starodubtcev ◽  
A. Kurilchik ◽  
V. Usachev ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Soft Tissue ◽  
Lung Metastasis ◽  
Soft Tissue Sarcomas

scholarly journals Angiosarcoma Presenting with Spontaneous Hydropneumothorax: Report of a Case and Review of the Literature

2014 ◽  
Vol 8 (1) ◽  
pp. 48-54 ◽  
Author(s):  
Jung H Chang ◽  
Ji H Kim ◽  
So-Hyeon Hong ◽  
Myung E Song ◽  
Yon J Ryu ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Soft Tissue ◽  
Clinical Features ◽  
Lung Metastasis ◽  
Malignant Tumor ◽  
Cutaneous Lesion ◽  
Review Of The Literature ◽  
Pleural Invasion ◽  
Immunohistochemistry Analysis

Angiosarcoma is a rare malignant tumor of soft tissue. Because angiosarcoma originates from endothelial cells, it can occur in any organ and shows aggressive clinical features. Most commonly, angiosarcoma initially presents as a cutaneous lesion. Lung metastasis from scalp angiosarcoma can develop pneumothorax. We report a case of multiorgan involvement of an angiosarcoma, including the scalp, initially presenting with hydropneumothorax. Immunohistochemistry analysis of the cells obtained from the study confirmed the pleural invasion of the angiosarcoma.

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