Combining Targeted Agents With Modern Radiotherapy in Soft Tissue Sarcomas
Bibliographic record
Abstract
Improved understanding of soft-tissue sarcoma (STS) biology has led to better distinction and subtyping of these diseases with the hope of exploiting the molecular characteristics of each subtype to develop appropriately targeted treatment regimens. In the care of patients with extremity STS, adjunctive radiation therapy (RT) is used to facilitate limb and function, preserving surgeries while maintaining five-year local control above 85%. In contrast, for STS originating from nonextremity anatomical sites, the rate of local recurrence is much higher (five-year local control is approximately 50%) and a major cause of death and morbidity in these patients. Incorporating novel technological advancements to administer accurate RT in combination with novel radiosensitizing agents could potentially improve local control and overall survival. RT efficacy in STS can be increased by modulating biological pathways such as angiogenesis, cell cycle regulation, cell survival signaling, and cancer-host immune interactions. Previous experiences, advancements, ongoing research, and current clinical trials combining RT with agents modulating one or more of the above pathways are reviewed. The standard clinical management of patients with STS with pretreatment biopsy, neoadjuvant treatment, and primary surgery provides an opportune disease model for interrogating translational hypotheses. The purpose of this review is to outline a strategic vision for clinical translation of preclinical findings and to identify appropriate targeted agents to combine with radiotherapy in the treatment of STS from different sites and/or different histology subtypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".