Pre- and Postoperative Chemotherapy in Localized Extremity Soft Tissue Sarcoma: A European Organization for Research and Treatment of Cancer Expert Survey.

BACKGROUND: The management of localized extremity soft tissue sarcomas (STS) is challenging and the role of pre- and postoperative chemotherapy is unclear and debated among experts. MATERIALS AND METHODS: Medical oncology experts of the European Organization for Research and Treatment of Cancer Soft Tissue and Bone Sarcoma Group were asked to participate in this survey on the use of pre- and postoperative chemotherapy in STS. Experts from 12 centers in Belgium, France, Germany, Great Britain, Italy, Switzerland, and The Netherlands agreed to participate and provided their treatment algorithm. Answers were converted into decision trees based on the objective consensus methodology. The decision trees were used as a basis to identify consensus and discrepancies. RESULTS: Several criteria used for decision-making in extremity STS were identified: chemosensitivity, fitness, grading, location, and size. In addition, resectability and resection status were relevant in the pre- and postoperative setting, respectively. Preoperative chemotherapy is considered in most centers for marginally resectable tumors only. Yet, in some centers, neoadjuvant chemotherapy is used routinely and partially combined with hyperthermia. Although most centers do not recommend postoperative chemotherapy, some offer this treatment on a regular basis. Radiotherapy is an undisputed treatment modality in extremity STS. CONCLUSION: Due to lacking evidence on the utility of pre- and postoperative chemotherapy in localized extremity STS, treatment strategies vary considerably among European experts. The majority recommended neoadjuvant chemotherapy for marginally resectable grade 2-3 tumors; the majority did not recommend postoperative chemotherapy in any setting. IMPLICATIONS FOR PRACTICE: The management of localized extremity soft tissue sarcomas (STS) is challenging and the role of pre- and postoperative chemotherapy is unclear and debated among experts. This study analyzed the decision-making process among 12 European experts on systemic therapy for STS. A wide range of recommendations among experts regarding the use of perioperative chemotherapy was discovered. Discrepancies in the use of decision criteria were also uncovered, including the definition of what constitutes high-risk cancer, which is a basis for many to recommend chemotherapy. Before any standardization is possible, a common use of decision criteria is necessary.

Exploring Capabilities of Large Language Models such as ChatGPT in Radiation Oncology.

Purpose: Technological progress of machine learning and natural language processing has led to the development of large language models (LLMs), capable of producing well-formed text responses and providing natural language access to knowledge. Modern conversational LLMs such as ChatGPT have shown remarkable capabilities across a variety of fields, including medicine. These models may assess even highly specialized medical knowledge within specific disciplines, such as radiation therapy. We conducted an exploratory study to examine the capabilities of ChatGPT to answer questions in radiation therapy. Methods and Materials: A set of multiple-choice questions about clinical, physics, and biology general knowledge in radiation oncology as well as a set of open-ended questions were created. These were given as prompts to the LLM ChatGPT, and the answers were collected and analyzed. For the multiple-choice questions, it was checked how many of the answers of the model could be clearly assigned to one of the allowed multiple-choice-answers, and the proportion of correct answers was determined. For the open-ended questions, independent blinded radiation oncologists evaluated the quality of the answers regarding correctness and usefulness on a 5-point Likert scale. Furthermore, the evaluators were asked to provide suggestions for improving the quality of the answers. Results: For 70 multiple-choice questions, ChatGPT gave valid answers in 66 cases (94.3%). In 60.61% of the valid answers, the selected answer was correct (50.0% of clinical questions, 78.6% of physics questions, and 58.3% of biology questions). For 25 open-ended questions, 12 answers of ChatGPT were considered as "acceptable," "good," or "very good" regarding both correctness and helpfulness by all 6 participating radiation oncologists. Overall, the answers were considered "very good" in 29.3% and 28%, "good" in 28% and 29.3%, "acceptable" in 19.3% and 19.3%, "bad" in 9.3% and 9.3%, and "very bad" in 14% and 14% regarding correctness/helpfulness. Conclusions: Modern conversational LLMs such as ChatGPT can provide satisfying answers to many relevant questions in radiation therapy. As they still fall short of consistently providing correct information, it is problematic to use them for obtaining medical information. As LLMs will further improve in the future, they are expected to have an increasing impact not only on general society, but also on clinical practice, including radiation oncology.