End-to-end breast cancer radiotherapy planning via LMMs with consistency embedding

초록

Recent advances in AI foundation models have significant potential for lightening the clinical workload mimicking the comprehensive and multi-faceted approaches used by medical professionals. In the field radiation oncology, the integration of multiple modalities holds great importance, so the opportunity foundational model is abundant. Inspired by this, here we present RO-LMM, a multi-purpose, comprehensive large multimodal model (LMM) tailored for the field of radiation oncology. This model effectively manages a series of tasks within the clinical workflow, including clinical context summarization, radiotherapy strategy suggestion, and plan-guided target volume segmentation by leveraging the capabilities of LMM. In particular, perform consecutive clinical tasks without error accumulation, we present a novel Consistency Embedding Tuning (CEFTune) technique, which boosts LMM's robustness to noisy inputs while preserving the consistency of handling clean inputs. We further extend this concept to LMM-driven segmentation framework, leading novel Consistency Embedding Segmentation (CESEG) techniques. Experimental results including multi-center validation confirm that our RO-LMM with CEFTune and CESEG results in promising performance for multiple clinical tasks with generalization capabilities.

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