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End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding (2311.15876v4)

Published 27 Nov 2023 in cs.CV, cs.AI, and cs.LG

Abstract: Recent advances in AI foundation models have significant potential for lightening the clinical workload by mimicking the comprehensive and multi-faceted approaches used by medical professionals. In the field of radiation oncology, the integration of multiple modalities holds great importance, so the opportunity of 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, radiation treatment plan suggestion, and plan-guided target volume segmentation by leveraging the capabilities of LMM. In particular, to perform consecutive clinical tasks without error accumulation, we present a novel Consistency Embedding Fine-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 to a novel Consistency Embedding Segmentation (CESEG) techniques. Experimental results including multi-centre 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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Summary

  • The paper introduces RO-LLaMA, a flexible large language model that streamlines breast cancer radiotherapy planning by integrating clinical note summarization, treatment planning, and 3D volume segmentation.
  • It employs innovative techniques like Noisy Embedding Fine-Tuning and Consistency Embedding Fine-Tuning to enhance robustness across multi-modal tasks.
  • Experimental results on multi-centre cohorts show RO-LLaMA outperforms baselines, validating its potential to improve clinical workflows in radiation oncology.

Introduction

AI has dramatically impacted the medical field by providing tools to assist in clinical decisions and reducing workloads. Despite that, most AI models are designed to handle single tasks with uni-modal data, which does not align well with the multifaceted nature of medical professional responsibilities. This paper introduces a novel AI model, RO-LLaMA, which operates as a generalist LLM specifically for the clinical workflow in radiation oncology.

Methodology

RO-LLaMA exhibits capabilities in three crucial areas: (1) efficiently summarizing comprehensive patient histories into concise clinical notes, (2) proposing treatment plans from a clinical expert perspective, and (3) delineating radiation target volumes directly from clinical reports. To enhance robustness against inevitable errors during sequential tasks, two pioneering techniques are introduced: Noisy Embedding Fine-Tuning (NEFTune), which injects noise into embeddings during training, and Consistency Embedding Fine-Tuning (CEFTune), which enforces prediction consistency between noisy and clean inputs. These techniques, when applied to 3D segmentation tasks, lead to Noisy Embedding Segmentation (NESEG) and Consistency Embedding Segmentation (CESEG), thereby boosting the model's generalization abilities.

Experiments and Results

A comprehensive set of experiments conducted on multi-centre cohorts established RO-LLaMA's promise. For text-related tasks like clinical report summarization and treatment plan suggestion, the model—augmented with NEFTune and CEFTune—outperformed baseline methods on both internal and external datasets. When assessing the 3D target volume segmentation task, RO-LLaMA, combined with NESEG and CESEG, advanced beyond traditional methods, validating its adeptness in multi-modal reasoning.

Discussion and Conclusion

RO-LLaMA is poised as a versatile, multifunctional tool that could revolutionize the integration of AI into routine medical workflows. It extends beyond current AI solutions, which are often constrained to uni-modal, single-task applications. This model's innovations in noise augmentation and consistency regularization may lead to the development of fully generalist medical AI models, capable of holistically grasping clinical workflows in departments such as radiation oncology.

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