Automated Extraction of Postoperative Cancer Recurrence and Metastasis From Computed Tomography (CT) Reports: Semisupervised Deep Learning Study

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Abstract Background Perioperative computed tomography (CT) imaging is essential for detecting postoperative recurrence and metastasis in cancer patients. However, large-scale automated extraction of oncological outcomes from CT reports remains limited by the unstructured nature of report text and wide variability in reporting styles. Radiology reports frequently contain linguistic ambiguities, including negations, hedging, and expressions conveying diagnostic uncertainty (eg, “cannot exclude recurrence” or “possibly metastatic”). Manual review is labor-intensive and constrains consistent extraction at scale. The inability to systematically account for diagnostic uncertainty represents a major barrier to reliable automated surveillance systems. Objective This study aimed to develop a semisupervised deep learning (DL) classification framework that explicitly captures diagnostic uncertainty by classifying postoperative recurrence and metastasis into 3 categories (positive, negative, and uncertain). Methods This retrospective study analyzed 288,076 postoperative CT reports from 86,083 cancer surgery patients at Asan Medical Center (2014‐2021). After exact-match deduplication, model training and evaluation used 17,846 unique reports for recurrence and 63,766 for metastasis. Preprocessing identified presumed negatives through keyword filtering and unsupervised clustering. The semisupervised framework incorporated human-in-the-loop validation across 3 cycles—with clinicians reviewing approximately 2000 samples per cycle (