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Background/Objectives: The relationship between pathologic findings in soft tissue sarcoma (STS) after neoadjuvant treatment and oncological outcomes remains uncertain due to varying evaluation methods and cut-off values. This study aims to assess pathologic findings after neoadjuvant radiotherapy in STS using the EORTC-STBSG response score and evaluate its prognostic value. Methods: Clinical and outcome data from 44 patients were reviewed. Resected specimens were re-evaluated to measure viable cells, necrosis, fibrosis, and hyalinization. Local recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), and overall survival (OS) were analyzed using Kaplan–Meier survival analysis. Cox proportional hazards regression was used for univariate and multivariate analyses to correlate outcomes with pathologic response. Results: The median percentages of viable cells, necrosis, and fibrosis/hyalinization were 20%, 11%, and 40%, respectively. A pathologic complete response (pCR), defined as ≤5% viable cells, was achieved in 25% of cases. Local recurrence occurred in 33% of cases, with a significantly higher rate of 64% after R1 resection compared to 22% after R0 resection. Distant metastases were observed in 42% of patients, primarily in the lungs. The 3-year rates for LRFS, DMFS, and OS were 65%, 54%, and 67%, respectively. A correlation between outcomes and tumor size, grade and histological subtype was observed. Classifying pathologic response by the EORTC-STBSG score failed to show an association with outcomes. Patients achieving pCR showed lower risk of LR and improved OS. Conclusions: While the EORTC-STBSG score did not show a prognostic value, resection specimens with ≤5% viable cells were linked to improved LRFS and OS.
Background/Objectives: The relationship between pathologic findings in soft tissue sarcoma (STS) after neoadjuvant treatment and oncological outcomes remains uncertain due to varying evaluation methods and cut-off values. This study aims to assess pathologic findings after neoadjuvant radiotherapy in STS using the EORTC-STBSG response score and evaluate its prognostic value. Methods: Clinical and outcome data from 44 patients were reviewed. Resected specimens were re-evaluated to measure viable cells, necrosis, fibrosis, and hyalinization. Local recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), and overall survival (OS) were analyzed using Kaplan–Meier survival analysis. Cox proportional hazards regression was used for univariate and multivariate analyses to correlate outcomes with pathologic response. Results: The median percentages of viable cells, necrosis, and fibrosis/hyalinization were 20%, 11%, and 40%, respectively. A pathologic complete response (pCR), defined as ≤5% viable cells, was achieved in 25% of cases. Local recurrence occurred in 33% of cases, with a significantly higher rate of 64% after R1 resection compared to 22% after R0 resection. Distant metastases were observed in 42% of patients, primarily in the lungs. The 3-year rates for LRFS, DMFS, and OS were 65%, 54%, and 67%, respectively. A correlation between outcomes and tumor size, grade and histological subtype was observed. Classifying pathologic response by the EORTC-STBSG score failed to show an association with outcomes. Patients achieving pCR showed lower risk of LR and improved OS. Conclusions: While the EORTC-STBSG score did not show a prognostic value, resection specimens with ≤5% viable cells were linked to improved LRFS and OS.
The transformation of healthcare from a fee-for-service model to value-based care is particularly crucial in managing complex and rare diseases like sarcoma, where data fragmentation and variability present significant challenges. This manuscript reviews strategies for structured and harmonized data integration—a critical precursor to precision medicine in sarcoma care. We demonstrate how standardizing data formats, ontologies, and coding systems enable seamless integration of clinical, economic, and patient-reported outcomes across institutions, paving the way for comprehensive predictive analytics. By establishing robust value-based healthcare (VBHC) frameworks through digital transformation and predictive models, including digital twins, we create the foundation for personalized sarcoma treatment and real-world-time clinical decision-making. The manuscript also addresses practical challenges, including the need for system standardization, overcoming regulatory and privacy concerns, and managing high costs. We propose actionable strategies to overcome these barriers and discuss the role of advanced analytics and future research directions that further enhance VBHC and precision medicine. This work outlines the necessary steps to build a cohesive, data-driven approach that supports the transition to precision medicine, fundamentally improving outcomes for sarcoma patients.
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