ArticleEuropean radiology2024
Natural language processing pipeline to extract prostate cancer-related information from clinical notes.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects.Abdominal radiology (New York) · 2026Article
- Development and validation of a natural language processing system to assess quality of physician communication in prostate cancer consultations.Prostate cancer and prostatic diseases · 2026Article
- Bias in deep learning-based image quality assessments of T2-weighted imaging in prostate MRI.Abdominal radiology (New York) · 2026Article
- Natural Language Processing Methods Automate Molecular Marker Extraction From Glioma Pathology Reports.Neurosurgery · 2026Article
- Prostate MRI cancer detection rate by deep learning-assisted image quality categorization: gas-induced susceptibility artifacts in diffusion-weighted imaging.Insights into imaging · 2025Article
- Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.JCO clinical cancer informatics · 2025Article
- A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes.Head & face medicine · 2025Article
- Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes.JCO clinical cancer informatics · 2025Article
- Large language models for efficient whole-organ MRI score-based reports and categorization in knee osteoarthritis.Insights into imaging · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Estimated diagnostic performance of prostate MRI performed with clinical suspicion of prostate cancer.Insights into imaging · 2024Article
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Authors and funding
18 authors.
Funding
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Abstract
objectivesTo develop an automated pipeline for extracting prostate cancer-related information from clinical notes. MATERIALS AND
methodsThis retrospective study included 23,225 patients who underwent prostate MRI between 2017 and 2022. Cancer risk factors (family history of cancer and digital rectal exam findings), pre-MRI prostate pathology, and treatment history of prostate cancer were extracted from free-text clinical notes in English as binary or multi-class classification tasks. Any sentence containing pre-defined keywords was extracted from clinical notes within one year before the MRI. After manually creating sentence-level datasets with ground truth, Bidirectional Encoder Representations from Transformers (BERT)-based sentence-level models were fine-tuned using the extracted sentence as input and the category as output. The patient-level output was determined by compilation of multiple sentence-level outputs using tree-based models. Sentence-level classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) on 15% of the sentence-level dataset (sentence-level test set). The patient-level classification performance was evaluated on the patient-level test set created by radiologists by reviewing the clinical notes of 603 patients. Accuracy and sensitivity were compared between the pipeline and radiologists.
resultsSentence-level AUCs were ≥ 0.94. The pipeline showed higher patient-level sensitivity for extracting cancer risk factors (e.g., family history of prostate cancer, 96.5% vs. 77.9%, p < 0.001), but lower accuracy in classifying pre-MRI prostate pathology (92.5% vs. 95.9%, p = 0.002) and treatment history of prostate cancer (95.5% vs. 97.7%, p = 0.03) than radiologists, respectively.
conclusionThe proposed pipeline showed promising performance, especially for extracting cancer risk factors from patient's clinical notes. CLINICAL RELEVANCE STATEMENT: The natural language processing pipeline showed a higher sensitivity for extracting prostate cancer risk factors than radiologists and may help efficiently gather relevant text information when interpreting prostate MRI. KEY POINTS: When interpreting prostate MRI, it is necessary to extract prostate cancer-related information from clinical notes. This pipeline extracted the presence of prostate cancer risk factors with higher sensitivity than radiologists. Natural language processing may help radiologists efficiently gather relevant prostate cancer-related text information.
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