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Thirty-Day Outcomes following Pediatric Bone and Soft Tissue Sarcoma Surgery: A NSQIP Pediatrics Analysis
Background. Pediatric bone and soft tissue sarcomas are rare; therefore, national registries are essential tools for orthopedic oncology research. Past studies provide excellent data on long-term prognosis and survival trends but fail to examine treatment-specific morbidity. The aim of this study is to use a national registry to describe patient demographics, comorbidities, and adverse events in the first thirty days following surgical management of pediatric bone and soft tissue sarcomas. Methods. A retrospective review of patients in the American College of Surgeons National Surgical Quality Improvement Program—Pediatrics database (NSQIP-P) was performed. The cohort was partitioned by tumor origin (bone versus soft tissue) and tumor location (axial versus appendicular). Results. One-hundred ninety-two patients were identified. Bone sarcomas were more common (71.9%) and predominately appendicular (62.3%), while soft tissue sarcomas were predominately axial (77.8%). The overall complication rate was 8.9%. The most frequent etiologies were wound dehiscence (3.6%) and infectious complications such as surgical site infections (2.6%), pneumonia (1.6%), urinary tract infections (1.6%), and C. diff colitis (1.0%). Twenty-four percent of patients experienced bleeding requiring transfusion. The unplanned readmission rate was 12.5% (3.6% related to principle procedure), and the unplanned reoperation rate was 4.7% (4.2% related to principle procedure). The mortality rate was 1.0%. Neoadjuvant chemotherapy was associated with higher rates of wound dehiscence and infectious complications. There were no differences in adverse events with respect to tumor origin or location. Conclusion. Approximately 1 in 11 pediatric patients will experience a complication in the first thirty days following surgery. However, perioperative mortality remains low. This study represents the first comprehensive review of pediatric bone and soft tissue sarcoma surgery in the NSQIP-P database. As the case volume of NSQIP-P continues to grow, NSQIP-P has the potential to become a powerful tool for pediatric orthopedic oncology research.
Targeting Refractory Sarcomas and Malignant Peripheral Nerve Sheath Tumors in a Phase I/II Study of Sirolimus in Combination with Ganetespib (SARC023)
Purpose. Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft tissue sarcomas. Combining Hsp90 inhibitors to enhance endoplasmic reticulum stress with mTOR inhibition results in dramatic MPNST shrinkage in a genetically engineered MPNST mouse model. Ganetespib is an injectable potent small molecule inhibitor of Hsp90. Sirolimus is an oral mTOR inhibitor. We sought to determine the safety, tolerability, and recommended dose of ganetespib and sirolimus in patients with refractory sarcomas and assess clinical benefits in patients with unresectable/refractory MPNSTs. Patients and Methods. In this multi-institutional, open-label, phase 1/2 study of ganetespib and sirolimus, patients ≥16 years with histologically confirmed refractory sarcoma (phase 1) or MPNST (phase 2) were eligible. A conventional 3 + 3 dose escalation design was used for phase 1. Pharmacokinetic and pharmacodynamic measures were evaluated. Primary objectives of phase 2 were to determine the clinical benefit rate (CBR) of this combination in MPNSTs. Patient-reported outcomes assessed pain. Results. Twenty patients were enrolled (10 per phase). Toxicities were manageable; most frequent non-DLTs were diarrhea, elevated liver transaminases, and fatigue. The recommended dose of ganetespib was 200 mg/m2 intravenously on days 1, 8, and 15 with sirolimus 4 mg orally once daily with day 1 loading dose of 12 mg. In phase 1, one patient with leiomyosarcoma achieved a sustained partial response. In phase 2, no responses were observed. The median number of cycles treated was 2 (1–4). Patients did not meet the criteria for clinical benefit as defined per protocol. Pain ratings decreased or were stable. Conclusion. Despite promising preclinical rationale and tolerability of the combination therapy, no responses were observed, and the study did not meet parameters for further evaluation in MPNSTs. This trial was registered with (NCT02008877).
Diagnostic Value of TLE1 in Synovial Sarcoma: A Systematic Review and Meta-Analysis
Background. Synovial sarcoma can present morphologically in multiple forms, including biphasic and monophasic subtypes. As a result, the histological diagnosis can sometimes be challenging. Transducin-Like Enhancer 1 (TLE1) is a transcriptional corepressor that normally is involved in embryogenesis and hematopoiesis but is also expressed in certain tumors. This systematic review examines the potential role of TLE1 as a diagnostic biomarker for the synovial sarcoma. Materials and Methods. A literature review and meta-analysis were conducted using the electronic databases Pubmed, the Cochrane Library, and Google Scholar. Thirteen studies met our eligibility criteria and were selected for in-depth analysis. Results. The mean sensitivity and specificity of TLE1 in detecting synovial sarcoma were 94% (95% CI 91%–97%) and 81% (95% CI 72%–91%), respectively, when all studies were aggregated together. The mean positive predictive value (PPV) of TLE1 was 75% (95% CI 62%–87%), whereas the negative predictive value (NPV) was 96% (95% CI 93%–98%). Conclusion. TLE1 is a sensitive and specific marker for synovial sarcoma that can aid in its diagnosis. Due to its involvement in several relevant signaling pathways, TLE1 might have direct relevance to the pathophysiology of the disease.
Does Reconstruction with Reimplantation of Sterilized Tumor Bone Provide Survival Benefit in Diaphyseal Osteosarcoma?
Does reimplantation of sterilized tumor bone for reconstruction provide outcome benefits in intercalary osteosarcoma based on the potential immunogenic effect of reimplanted sterilized tumor tissue? Of 720 cases of surgically treated high-grade osteosarcoma patients treated at our institute from 2006 to 2013, 61 had predominantly diaphyseal disease. All patients were nonmetastatic at presentation. Patient and tumor characteristics, treatment details, and local recurrence-free, metastasis-free, and overall survival were compared for 24 patients who had reconstruction with sterilized tumor bone reimplantation vs 37 who did not. Both the groups were well matched in terms of baseline characteristics. Means were compared with the t-test, proportions with the chi-square test, and survival with the log-rank test. The Kaplan‐Meier method was used to construct time to event curves. Cox proportional hazard regression modeling was employed for multivariate time to event analysis. Twenty-two had extracorporeal radiation and reimplantation (ECRT) with or without the vascularised fibula. Fifty-gray single dose was used in all cases. Two had pasteurization and reimplantation. Thirty seven had non-reimplantation reconstructions (including intercalary or osteoarticular endoprosthesis, pedicled bone grafts, rotation-plasty, and amputations). Five-year local recurrence-free survival was 85% for reimplantation and 97% for non-reimplantation groups (). Five-year metastasis-free survival was 63% and 54%, respectively (). Five-year overall survival was 70% and 58%, respectively (). The data from this study did not demonstrate significantly better local recurrence-free, distant relapse-free, or overall survival benefit in the tumor bone reimplantation group.
Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists
Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine learning allow for several types of pathologies encountered on radiological images to be automatically and reliably distinguished. The aim of the study was to assess the contribution of radiomics and machine learning in the differentiation between soft-tissue lipoma and liposarcoma on preoperative MRI and to assess the diagnostic accuracy of a machine-learning model compared to musculoskeletal radiologists. 86 radiomics features were retrospectively extracted from volume-of-interest on T1-weighted spin-echo 1.5 and 3.0 Tesla MRI of 38 soft-tissue tumors (24 lipomas and 14 liposarcomas, based on histopathological diagnosis). These radiomics features were then used to train a machine-learning classifier to distinguish lipoma and liposarcoma. The generalization performance of the machine-learning model was assessed using Monte-Carlo cross-validation and receiver operating characteristic curve analysis (ROC-AUC). Finally, the performance of the machine-learning model was compared to the accuracy of three specialized musculoskeletal radiologists using the McNemar test. Machine-learning classifier accurately distinguished lipoma and liposarcoma, with a ROC-AUC of 0.926. Notably, it performed better than the three specialized musculoskeletal radiologists reviewing the same patients, who achieved ROC-AUC of 0.685, 0.805, and 0.785. Despite being developed on few cases, the trained machine-learning classifier accurately distinguishes lipoma and liposarcoma on preoperative MRI, with better performance than specialized musculoskeletal radiologists.
Optimization of the Therapeutic Approach to Patients with Sarcoma: Delphi Consensus
Soft tissue sarcomas (STS) constitute a heterogeneous group of rare solid tumors associated with significant morbidity and mortality. The evaluation and treatment of STS require a multidisciplinary team with extensive experience in the management of these types of tumors. National and international clinical practice guidelines for STS do not always provide answers to a great many situations that specialists have to contend with in their everyday practice. This consensus provides a series of specific recommendations based on available scientific evidence and the experience of a group of experts to assist in decision-making by all the specialists involved in the management of STS.