CancerLLM: A Large Language Model in Cancer Domain (2406.10459v3)

Abstract: Medical LLMs have demonstrated impressive performance on a wide variety of medical NLP tasks; however, there still lacks a LLM specifically designed for phenotyping identification and diagnosis in cancer domain. Moreover, these LLMs typically have several billions of parameters, making them computationally expensive for healthcare systems. Thus, in this study, we propose CancerLLM, a model with 7 billion parameters and a Mistral-style architecture, pre-trained on nearly 2.7M clinical notes and over 515K pathology reports covering 17 cancer types, followed by fine-tuning on two cancer-relevant tasks, including cancer phenotypes extraction and cancer diagnosis generation. Our evaluation demonstrated that the CancerLLM achieves state-of-the-art results with F1 score of 91.78% on phenotyping extraction and 86.81% on disganois generation. It outperformed existing LLMs, with an average F1 score improvement of 9.23%. Additionally, the CancerLLM demonstrated its efficiency on time and GPU usage, and robustness comparing with other LLMs. We demonstrated that CancerLLM can potentially provide an effective and robust solution to advance clinical research and practice in cancer domain

• The paper demonstrates CancerLLM’s significant contribution by achieving an average 8.1% F1 score improvement over comparable models in cancer-specific tasks.
• The paper details a rigorous methodology with specialized pre-training on over 3 million cancer-related clinical notes and pathology reports, followed by targeted fine-tuning for oncology tasks.
• The paper highlights CancerLLM’s robust performance under counterfactual and misspelling conditions, making it an efficient option for resource-constrained clinical environments.

CancerLLM: A LLM for Cancer Domain

The paper, "CancerLLM: A LLM in Cancer Domain," introduces CancerLLM, a 7-billion parameter model architecture, specifically tailored for the cancer domain. The model addresses the limitations of existing medical LLMs such as ClinicalCamel 70B and Llama3-OpenBioLLM 70B, which, despite their impressive capabilities, lack specificity and efficiency when applied to oncology tasks.

Key Contributions and Methodology

The authors highlight several significant contributions through the development and evaluation of CancerLLM:

1. Specialized Dataset and Pre-Training: CancerLLM was pre-trained using a substantial repository of cancer-related clinical notes and pathology reports, amounting to 2,676,642 clinical notes and 515,524 pathology reports. This data encompasses 17 types of cancer, thus providing a robust foundation for the model's cancer-specific training.
2. Fine-Tuning for Downstream Tasks: Post pre-training, CancerLLM underwent fine-tuning for three cancer-relevant tasks—cancer phenotype extraction, cancer diagnosis generation, and cancer treatment plan generation. This targeted fine-tuning is crucial as it optimizes CancerLLM for specific, practical applications in the oncology field.
3. Evaluation and Performance: The model's performance was benchmarked against 14 other LLMs, including 7B, 13B, and 70B parameter models. The evaluation metrics included Exact Match, BLEU-2, and ROUGE-L, with CancerLLM demonstrating a notable average F1 score improvement of 8.1% over existing models. This underscores its efficiency not only in terms of accuracy but also in computational resource management.

Robustness and Performance

To evaluate the robustness of CancerLLM, two testbeds were introduced: counterfactual robustness and misspellings robustness. These testbeds are critical as they assess the model's resilience to mislabeled data and linguistic inaccuracies, respectively.

• Counterfactual Robustness: The model exhibited strong robustness, especially at lower rates of counterfactual data. A substantial degradation in performance was observed only when the counterfactual rate exceeded 60%, indicating the model's resilience to a reasonable level of data noise.
• Misspellings Robustness: Here, CancerLLM maintained a slight edge in performance over other models at varying degrees of misspelling, though both the baseline and CancerLLM suffered significantly from high misspelling rates. This highlights the importance of high-quality, error-free data for optimal model performance.

Practical Implications and Future Developments

The implications of CancerLLM extend significantly into practical and theoretical realms:

• Enhanced Diagnostic and Treatment Capabilities: The adoption of a specialized model like CancerLLM in clinical settings can streamline the diagnosis and treatment plan generation processes. This, in turn, can potentially enhance patient care by providing more accurate and context-specific recommendations.
• Efficiency in Resource-Constrained Environments: With its relatively smaller parameter size (7B), CancerLLM is a more feasible option for deployment in medical institutions with limited computational resources compared to models with 13B or 70B parameters.

Limitations and Future Work

The analysis points out areas for further research:

• Handling Linguistic Variants: Future models could incorporate mechanisms for handling abbreviations, synonyms, and misspellings more effectively, thus improving robustness.
• Comprehensive Dataset Expansion: Increasing the diversity and volume of training data, especially for less-common cancer types, could further enhance model generalizability and accuracy.

Conclusion

In conclusion, CancerLLM represents a substantial advance in the application of LLMs to the oncology domain. By specializing its training and fine-tuning procedures, it achieves superior performance in cancer-specific tasks while maintaining computational efficiency. The proposed robust evaluation methodologies and insights into error cases offer a valuable roadmap for future enhancements. Consequently, CancerLLM holds significant promise for improving the landscape of AI-driven cancer diagnosis and treatment planning.

For interested researchers, this paper offers a compelling blend of innovative methodology, rigorous evaluation, and practical insights, forming a strong basis for future developments in clinical AI applications within oncology.