The rapidly expanding field of precision oncology relies heavily on the accurate and timely interpretation of complex clinical genetics reports to identify actionable genomic variants, which guide targeted therapies. However, these reports are typically presented as unstructured text, making manual extraction and interpretation a labor-intensive, error-prone, and unsustainable bottleneck in clinical workflows. Automating this process using advanced Natural Language Processing (NLP) and deep learning can transform precision oncology by ensuring scalability, reducing delays, and improving the consistency and accuracy of identifying patients who could benefit from specific therapies. This research addresses a critical unmet need in personalized cancer treatment.
The aim of the study.
To develop and validate a comprehensive artificial intelligence pipeline, integrating natural language processing and deep learning, for the automated and accurate extraction of actionable genomic variants and associated therapeutic implications from unstructured clinical oncology genetics reports.
Material and methods.
This retrospective study utilized a dataset of 15,000 de-identified clinical oncology genetics reports generated over five years from a large cancer center. These reports described genomic sequencing results, variant interpretations, and therapeutic recommendations for various solid tumors and hematological malignancies. The AI pipeline comprised several stages: 1) Text preprocessing (tokenization, sentence segmentation), 2) Named Entity Recognition (NER) for genes (e.g., EGFR, BRCA1), variant types (e.g., SNV, indel, fusion), pathogenicity (e.g., 'pathogenic', 'VUS'), and therapeutic contexts (e.g., 'responsive to osimertinib'). This was achieved using a fine-tuned ClinicalBERT model augmented with a custom dictionary and rule-based heuristics. 3) Relation Extraction to link variants to genes and therapeutic implications. A randomly selected subset of 1,500 reports was independently annotated by two expert molecular pathologists to serve as the gold standard for training and evaluating the NLP model. Performance metrics included precision, recall, and F1-score for each entity and relation type, as well as time efficiency comparison against manual review.
The AI pipeline demonstrated high accuracy and efficiency in extracting critical information. It achieved a combined precision of 93.5% and a recall of 91.2% (F1-score 0.92) for the overarching task of identifying actionable genomic variants with direct therapeutic implications. For specific entity types, the system achieved: gene name extraction (precision 95.0%, recall 94.5%, F1 0.94), variant type identification (precision 91.8%, recall 90.5%, F1 0.91), and pathogenicity assertion extraction (precision 89.2%, recall 88.0%, F1 0.89). Manual review of the same reports typically took an average of 10-15 minutes per report by a trained pathologist; in contrast, the automated AI system processed each report in under 30 seconds, demonstrating a >20-fold increase in processing speed. The most common errors involved distinguishing between prognostic and predictive variants and resolving complex nested negations, though these were infrequent (less than 2% of actionable variants).
This study successfully developed and validated an AI-driven system that accurately and efficiently interprets complex clinical oncology genetics reports to identify actionable genomic variants. Its high precision and recall, coupled with a dramatic reduction in processing time, underscore its potential to significantly streamline precision oncology workflows, reduce diagnostic bottlenecks, and ensure that cancer patients receive personalized, targeted therapies more rapidly. This technology can enhance the scalability and equity of precision oncology programs by automating a critical, labor-intensive step. Future work will focus on integrating this pipeline into clinical decision support systems and conducting prospective validation in real-time clinical settings.
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