Natural language processing for automated breast cancer recurrence detection and classification in computed tomography reports.
Bibliographic record
Abstract
e13591 Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".