Talom, Benjamin K.

PURPOSE Cancer recurrence is a critical outcome for patients and physicians. Retrospective cancer recurrence data can evaluate recurrence-directed treatment and generate novel interventions targeting recurrent cancers. However, large cancer databases do not provide recurrence-related information, stymying study at scale, and consequently require significant manual record review. Automated evaluation of records may allow for the rapid generation of easily analyzed data sets, accelerating the evaluation of recurrence altogether. METHODS Patients treated with radiation therapy at one tertiary referral center from 2010 to 2018 with a verified cancer status (cancer recurrence v no cancer recurrence) were identified. Patients with recurrent disease were initially identified through manual record review, and the associated pathology report was collected. Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro were used to generate a model for binary classification, with comparison to the gold-standard manually developed data set. RESULTS A total of 7,054 patients were identified. 3,431 (48.6%) were female, with a median age of 64 years. Head and neck (1,482, 21%), breast (1,480, 21%), upper GI (1,307, 18.5%), and lung/thorax (1,107, 15.7%) were the most common disease sites. Recurrence was verified for 1,546 patients (21.9%) using pathology reports, of which 1,249 positive cases were paired with 651 negative pathology reports for model development. Google Gemini 1.5 Pro consistently outperformed AutoNLP across all measurements of accuracy, generating a greater absolute difference in precision, recall, negative predictive value, and specificity, and a higher likelihood of correct classification at the individual level, rendering Gemini superior in recurrence status extraction. CONCLUSION AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.

424 Background: Advances in prostate cancer (PC) care have led to a 98% 10-year survival rate. Prostate Specific Antigen (PSA) is the primary method of follow-up after definitive radiotherapy (RT), with benign PSA ‘bounce’ occurring in up to 30% of patients, causing uncertainty and anxiety. Distinguishing between PSA bounce and biochemical recurrence (BCR) remains challenging. The Phoenix Criteria is the standard definition of BCR since 2006, defined as PSA nadir + 2 ng/dl, but does not consider patient specific information. Our study aims to explore factors associated with bounce and BCR and develop a predictive tool to differentiate between these two events. Methods: PC patients who received RT at our institution between 2006 and 2022 were identified, including only patients with a PSA increase during follow-up. Exclusion criteria included palliative RT, prostatectomy, or insufficient follow-up information. Patients’ clinical data was collected, and PSA increase was classified as bounce or BCR. Variables were compared using Kruskal Wallis or Chi-square. Multivariate logistic regression was performed in the subset of patients with absolute PSA value >2 (n= 581). Results: Of 1783 patients reviewed due to PSA rise after treatment, 694 met the inclusion criteria. There were 213 (30.7%) with PSA bounce and 481 (69.3%) with recurrence. Prior to meeting the Phoenix Criteria, 20 (4.2%) had biopsy-proven recurrence and 18 (3.7%) imaging-proven recurrence. The median delta PSA was 0.8 ng/mL for bounce vs 5 ng/mL for recurrence (p<0.001). The median time from treatment to event was 14.4 months for bounce vs 45.3 months for BCR (p<0.001). Recurrence was significantly associated (p<0.001) with older age at diagnosis, N1 stage, higher risk category, Gleason score, and higher baseline PSA. In the subgroup of patients without ADT, recurrence was associated with a lower median first PSA post-RT (1.9 vs. 2.4, p=0.046) and a lower median nadir before event (0.6 vs. 1.8, p<0.001). Multivariate logistic regression achieved an accuracy of 0.94, with a sensitivity of 0.83, specificity of 0.98, and an area under the curve (AUC) of 0.97. Conclusions: Our results highlight significant differences in clinical factors between bounce and recurrence in post-RT patients. The multivariate logistic regression differentiated the events with 94% accuracy for patients with a rising PSA value >2 subgroup. We highlight the possible improvement that could be accomplished by personalizing a PSA threshold for recurrence versus the Phoenix definition. This tool offers patients and providers a highly specific tool to differentiate between PSA recurrence and bounce, and may reduce stress and unnecessary testing, or help diagnosis recurrence faster. Next steps include prospective validation of the model to assess the impact on clinical practice costs, patient experience, and cancer outcomes.