Skeletal muscle measurements predict surgical wound complications but not overall survival in patients with soft tissue sarcoma

Abstract Background It is unknown whether sarcopenia influences treatment outcome in patients with soft tissue sarcoma. Herein, we aimed to elucidate the impact of sarcopenia on sarcoma treatment. Methods A total of 163 soft tissue sarcoma patients were included. Skeletal muscle measures were calculated using computed tomography images. Skeletal muscle area (SMA) and density (SMD) at the L3 level were extracted, and SMA was normalized by height as skeletal muscle index (SMI). The skeletal muscle gauge (SMG) was calculated by multiplying SMD × SMI. The relationship of skeletal muscle measures and clinical factors to wound complications and prognosis was evaluated, and classification and regression tree (CART) analysis was used to develop classification models for risk groups of surgical wound complications. Results Thirty-three patients developed wound complications. In univariate analysis, age (P = 0.0022), tumour location of adductor compartment of the thigh (P = 0.0019), operating time (P = 0.010), blood loss (P = 0.030), SMD (P = 0.0004) and SMG (P = 0.0001) were significantly correlated with complications. In multivariate analysis, lower SMG was an independent risk factor (P = 0.031, OR = 3.27). CART analysis classified three risk groups of surgical wound complications by SMG, age, tumour location and operating time, and area under the receiver operating characteristic curve (AUROCC) was 0.75. SMG was not associated with prognosis in univariate analysis (P = 0.15). Conclusions The SMG does not affect overall survival but predicts surgical wound complications.

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Prognosis and surgical outcome of soft tissue sarcoma with malignant fungating wounds

Japanese Journal of Clinical Oncology ◽

10.1093/jjco/hyaa176 ◽

2020 ◽

Vol 51 (1) ◽

pp. 78-84

Author(s):

Koichi Okajima ◽

Hiroshi Kobayashi ◽

Tomotake Okuma ◽

Sho Arai ◽

Liuzhe Zhang ◽

...

Keyword(s):

Overall Survival ◽

Soft Tissue ◽

Local Recurrence ◽

Soft Tissue Sarcoma ◽

Limb Salvage ◽

Salvage Surgery ◽

Wound Complications ◽

Limb Salvage Surgery ◽

High Grade ◽

Surgical Wound

Abstract Objective Malignant fungating wounds are ulcerating tumors that infiltrate the overlying skin. Little evidence exists regarding the prognosis or treatment of malignant fungating wound in soft tissue sarcoma. This study aimed to reveal the prognosis and outcome of surgical treatment of malignant fungating wound in soft tissue sarcoma. Methods We retrospectively reviewed 26 patients with malignant fungating wound in high-grade soft tissue sarcoma between 2005 and 2018. The patients’ characteristics, treatments, surgical wound complications, local recurrences and prognoses were analyzed. Overall survival was analyzed using the Kaplan–Meier method and compared with that of the control cohort, consisting of 236 consecutive patients with non-malignant fungating wound high-grade soft tissue sarcoma treated during the same period. Results Among the 26 patients, undifferentiated pleomorphic sarcoma was the most common subtype. Twenty-three patients, including 20 (87%) and 3 (13%), underwent limb-salvage surgery and amputation, respectively. Among the 20 patients who underwent limb-salvage surgery, 4 (20%) had surgical wound complications, which required additional surgical procedures. Excluding the patients who underwent palliative surgery, local recurrence occurred in 2 patients (11%). The 5-year overall survival rate for all high-grade malignant fungating wound and non-malignant fungating wound patients was 26.0 and 67.3% (P < 0.0001), respectively. Conclusions Malignant fungating wounds in soft tissue sarcoma were significantly associated with a poor prognosis; however, the incidence of surgical complications and local recurrence after limb-salvage surgery was comparable to that of general soft tissue sarcoma cases. Limb-salvage surgery should be considered, if possible, to preserve the patient’s quality of life because of the dismal prognosis of patients with malignant fungating wound in soft tissue sarcoma.

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Identification of predictors for wound complications following preoperative or postoperative radiotherapy in extremity soft tissue sarcoma

European Journal of Surgical Oncology ◽

10.1016/j.ejso.2018.02.002 ◽

2018 ◽

Vol 44 (6) ◽

pp. 816-822 ◽

Cited By ~ 7

Author(s):

Marc G. Stevenson ◽

Jan F. Ubbels ◽

Jelena Slump ◽

Marijn A. Huijing ◽

Esther Bastiaannet ◽

...

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Postoperative Radiotherapy ◽

Wound Complications ◽

Extremity Soft Tissue Sarcoma

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Identification of Novel Prognostic Risk Signatures of Soft Tissue Sarcoma Based on Ferroptosis-Related Genes

Frontiers in Oncology ◽

10.3389/fonc.2021.629868 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wenjing Huang ◽

Yuhe Duan ◽

Xiuwei Yang ◽

Cong Shang ◽

Xin Chen ◽

...

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Disease Free Survival ◽

Risk Groups ◽

Gene Expression Omnibus ◽

Comprehensive Analysis ◽

Related Gene ◽

Free Survival ◽

Disease Free

BackgroundThe role of ferroptosis in tumorigenesis has been confirmed in previous studies. However, the comprehensive analysis of ferroptosis-related gene (FRG) to study the role of FRG in soft tissue sarcoma (STS) is lacking.MethodsRNA sequencing profile of TCGA-SARC cohort and GTEx were used to select differentially expressed FRGs (DEFRGs). Univariate, LASSO, and multivariate Cox analyses were selected to determine overall survival (OS)- and disease-free survival (PFS)-related FRGs. Two prognostic signatures were established and validated in two independent sets from Gene Expression Omnibus (GEO). Finally, the expression of key FRGs were validated with RT-qPCR.ResultsIn total, 198 FRGs (90.4%) were abnormally expressed in STS. Twelve DEFRGs were incorporated in the final signatures and showed favorable discrimination in both training and validation cohorts. Patients in the different risk groups not only showed different prognosis, but also showed different infiltration of immune cells. Two nomograms combining signature and clinical variables were established and the C-indexes were 0.852 and 0.752 for the OS and DFS nomograms, respectively. Finally, the expression of NOX5, HELLS, and RPL8 were validated with RT-qPCR.ConclusionThis comprehensive analysis of the FRG landscape in STS revealed novel FRGs related to carcinogenesis and prognosis. These findings have implications for prognosis and therapeutic responses, which revealed potential prognostic biomarkers and promote precision medicine.

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PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma

Cancer Imaging ◽

10.1186/s40644-021-00438-y ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Wenzhe Zhao ◽

Xin Huang ◽

Geliang Wang ◽

Jianxin Guo

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Univariate Analysis ◽

Multivariable Analysis ◽

Classification Performance ◽

Fusion Method ◽

Mr Images ◽

Feature Level Fusion ◽

Optimal Classification ◽

Level Fusion

Abstract Background Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). Methods The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. Results By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413–0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356–0.9999)). Conclusions For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.

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