ArticleRadiology2021
Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.
Article in Radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 52 citations in OpenAlex.
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- The Infectious Diseases Orchestrator: Embracing AI Literacy in the Agentic Era.Open forum infectious diseases · 2026Article
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 2025Article
- AI cancer driver mutation predictions are valid in real-world data.Nature communications · 2025Article
- Large language model trained on clinical oncology data predicts cancer progression.NPJ digital medicine · 2025Article
- Large Language Models in Cancer Imaging: Applications and Future Perspectives.Journal of clinical medicine · 2025Review
- Natural Language Processing of Radiology Reports to Assess Survival in Patients with Advanced Melanoma.Cancers · 2025Article
- Automated MRI pituitary structured reporting from free-text using a fine-tuned Llama model: a feasibility study.Japanese journal of radiology · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Incidence and Prevalence of Bone Metastases in Different Solid Tumors Determined by Natural Language Processing of CT Reports.Cancers · 2025Article
- Targeted generative data augmentation for automatic metastases detection from free-text radiology reports.Frontiers in artificial intelligence · 2025Article
- The clinical application potential assessment of the Deepseek-R1 large language model in lung cancer.Frontiers in oncology · 2025Article
- Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Imaging of Peritoneal Surface Malignancies.Journal of surgical oncology · 2024Review
- DNA liquid biopsy-based prediction of cancer-associated venous thromboembolism.Nature medicine · 2024Article
- Based on Medicine, The Now and Future of Large Language Models.Cellular and molecular bioengineering · 2024Review
- Distinct CT imaging features of new liver metastases from primary genitourinary cancers.Abdominal radiology (New York) · 2024Article
- Use of Natural Language Processing to Infer Sites of Metastatic Disease From Radiology Reports at Scale.JCO clinical cancer informatics · 2024Article
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18 authors at 1 institution in 1 country.
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Abstract
Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. Purpose To show the feasibility of using natural language processing (NLP) to gather accurate data from radiology reports for assessing spatial and temporal patterns of metastatic spread in a large patient cohort. Materials and Methods In this retrospective longitudinal study, consecutive patients who underwent CT from July 2009 to April 2019 and whose CT reports followed a departmental structured template were included. Three radiologists manually curated a sample of 2219 reports for the presence or absence of metastases across 13 organs; these manually curated reports were used to develop three NLP models with an 80%-20% split for training and test sets. A separate random sample of 448 manually curated reports was used for validation. Model performance was measured by accuracy, precision, and recall for each organ. The best-performing NLP model was used to generate a final database of metastatic disease across all patients. For each cancer type, statistical descriptive reports were provided by analyzing the frequencies of metastatic disease at the report and patient levels. Results In 91 665 patients (mean age ± standard deviation, 61 years ± 15; 46 939 women), 387 359 reports were labeled. The best-performing NLP model achieved accuracies from 90% to 99% across all organs. Metastases were most frequently reported in abdominopelvic (23.6% of all reports) and thoracic (17.6%) nodes, followed by lungs (14.7%), liver (13.7%), and bones (9.9%). Metastatic disease tropism is distinct among common cancers, with the most common first site being bones in prostate and breast cancers and liver among pancreatic and colorectal cancers. Conclusion Natural language processing may be applied to cancer patients' CT reports to generate a large database of metastatic phenotypes. Such a database could be combined with genomic studies and used to explore prognostic imaging phenotypes with relevance to treatment planning. © RSNA, 2021
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