RESUMEN
Thrombosis may be included in the profile of side effects associated with CDK4/6 inhibitors. Its significance might be greater than reported in randomized clinical trials. To investigate this, a retrospective, multicenter study was conducted. The primary objective was to calculate the incidence of thrombosis associated with CDK4/6 inhibitors. Secondary objectives included examining the impact of thrombosis on survival and identifying predictor variables for the development of venous thromboembolism (VTE) or arterial thrombosis (AT). A total of 986 patients were recruited. The incidence of VTE/AT associated with CDK4/6 inhibitor treatment during the follow-up period was 5.48 %. Survival analysis did not indicate that the development of VTE/AT during CDK4/6 inhibitor treatment significantly impacted patient survival (p = 0.133). In our analysis, two variables were found to be statistically significant (p < 0.05) as predictors of VTE/AT in breast cancer patients receiving CDK4/6 inhibitor therapy. These variables were the presence of central nervous system (CNS) metastasis with an odds ratio (OR) of 3.68 (95 % CI 1.18 - 11.49) and the use of abemaciclib with an OR of 2.3 (95 % CI 1.16 - 4.57).
RESUMEN
INTRODUCTION: Large Language Models (LLMs), such as the GPT model family from OpenAI, have demonstrated transformative potential across various fields, especially in medicine. These models can understand and generate contextual text, adapting to new tasks without specific training. This versatility can revolutionize clinical practices by enhancing documentation, patient interaction, and decision-making processes. In oncology, LLMs offer the potential to significantly improve patient care through the continuous monitoring of chemotherapy-induced toxicities, which is a task that is often unmanageable for human resources alone. However, existing research has not sufficiently explored the accuracy of LLMs in identifying and assessing subjective toxicities based on patient descriptions. This study aims to fill this gap by evaluating the ability of LLMs to accurately classify these toxicities, facilitating personalized and continuous patient care. METHODS: This comparative pilot study assessed the ability of an LLM to classify subjective toxicities from chemotherapy. Thirteen oncologists evaluated 30 fictitious cases created using expert knowledge and OpenAI's GPT-4. These evaluations, based on the CTCAE v.5 criteria, were compared to those of a contextualized LLM model. Metrics such as mode and mean of responses were used to gauge consensus. The accuracy of the LLM was analyzed in both general and specific toxicity categories, considering types of errors and false alarms. The study's results are intended to justify further research involving real patients. RESULTS: The study revealed significant variability in oncologists' evaluations due to the lack of interaction with fictitious patients. The LLM model achieved an accuracy of 85.7% in general categories and 64.6% in specific categories using mean evaluations with mild errors at 96.4% and severe errors at 3.6%. False alarms occurred in 3% of cases. When comparing the LLM's performance to that of expert oncologists, individual accuracy ranged from 66.7% to 89.2% for general categories and 57.0% to 76.0% for specific categories. The 95% confidence intervals for the median accuracy of oncologists were 81.9% to 86.9% for general categories and 67.6% to 75.6% for specific categories. These benchmarks highlight the LLM's potential to achieve expert-level performance in classifying chemotherapy-induced toxicities. DISCUSSION: The findings demonstrate that LLMs can classify subjective toxicities from chemotherapy with accuracy comparable to expert oncologists. The LLM achieved 85.7% accuracy in general categories and 64.6% in specific categories. While the model's general category performance falls within expert ranges, specific category accuracy requires improvement. The study's limitations include the use of fictitious cases, lack of patient interaction, and reliance on audio transcriptions. Nevertheless, LLMs show significant potential for enhancing patient monitoring and reducing oncologists' workload. Future research should focus on the specific training of LLMs for medical tasks, conducting studies with real patients, implementing interactive evaluations, expanding sample sizes, and ensuring robustness and generalization in diverse clinical settings. CONCLUSIONS: This study concludes that LLMs can classify subjective toxicities from chemotherapy with accuracy comparable to expert oncologists. The LLM's performance in general toxicity categories is within the expert range, but there is room for improvement in specific categories. LLMs have the potential to enhance patient monitoring, enable early interventions, and reduce severe complications, improving care quality and efficiency. Future research should involve specific training of LLMs, validation with real patients, and the incorporation of interactive capabilities for real-time patient interactions. Ethical considerations, including data accuracy, transparency, and privacy, are crucial for the safe integration of LLMs into clinical practice.
RESUMEN
BACKGROUND: Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a complex family of tumors of widely variable clinical behavior. The World Health Organization (WHO) 2010 classification provided a valuable tool to stratify neuroendocrine neoplasms (NENs) in three prognostic subgroups based on the proliferation index. However, substantial heterogeneity remains within these subgroups, and simplicity sometimes entails an ambiguous and imprecise prognostic stratification. The purpose of our study was to evaluate the prognostic impact of histological differentiation within the WHO 2010 grade (G) 1/G2/G3 categories, and explore additional Ki-67 cutoff values in GEP-NENs. SUBJECTS, MATERIALS, AND METHODS: A total of 2,813 patients from the Spanish National Tumor Registry (RGETNE) were analyzed. Cases were classified by histological differentiation as NETs (neuroendocrine tumors [well differentiated]) or NECs (neuroendocrine carcinomas [poorly differentiated]), and by Ki-67 index as G1 (Ki-67 <2%), G2 (Ki-67 3%-20%), or G3 (Ki-67 >20%). Patients were stratified into five cohorts: NET-G1, NET-G2, NET-G3, NEC-G2, and NEC-G3. RESULTS: Five-year survival was 72%. Age, gender, tumor site, grade, differentiation, and stage were all independent prognostic factors for survival. Further subdivision of the WHO 2010 grading improved prognostic stratification, both within G2 (5-year survival: 81% [Ki-67 3%-5%], 72% [Ki-67 6%-10%], 52% [Ki-67 11%-20%]) and G3 NENs (5-year survival: 35% [Ki-67 21%-50%], 22% [Ki-67 51%-100%]). Five-year survival was significantly greater for NET-G2 versus NEC-G2 (75.5% vs. 58.2%) and NET-G3 versus NEC-G3 (43.7% vs. 25.4%). CONCLUSION: Substantial clinical heterogeneity is observed within G2 and G3 GEP-NENs. The WHO 2010 classification can be improved by including the additive effect of histological differentiation and the proliferation index. IMPLICATIONS FOR PRACTICE: Gastroenteropancreatic neuroendocrine neoplasms are tumors of widely variable clinical behavior, roughly stratified by the World Health Organization (WHO) 2010 classification into three subgroups based on proliferation index. Real-world data from 2,813 patients of the Spanish Registry RGETNE demonstrated substantial clinical heterogeneity within grade (G) 2 and G3 neuroendocrine neoplasms. Tumor morphology and further subdivision of grading substantially improves prognostic stratification of these patients and may help individualize therapy. This combined, additive effect shall be considered in future classifications of neuroendocrine tumors and incorporated for stratification purposes in clinical trials.