Browsing by Author "Quan, May L."

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    New method for determining breast cancer recurrence-free survival using routinely collected real-world health data
    (2022-03-16) Jung, Hyunmin; Lu, Mingshan; Quan, May L.; Cheung, Winson Y.; Kong, Shiying; Lupichuk, Sasha; Feng, Yuanchao; Xu, Yuan
    Abstract Background In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence. Methods Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review. Results In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan–Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar. Conclusion The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis.
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    Prevention of persistent pain with lidocaine infusions in breast cancer surgery (PLAN): study protocol for a multicenter randomized controlled trial
    (2024-05-22) Khan, James S.; Gilron, Ian; Devereaux, P. J.; Clarke, Hance; Ayach, Nour; Tomlinson, George; Quan, May L.; Ladha, Karim S.; Choi, Stephen; Munro, Allana; Brull, Richard; Lim, David W.; Avramescu, Sinziana; Richebé, Philippe; Hodgson, Nicole; Paul, James; McIsaac, Daniel I.; Derzi, Simone; Zbitnew, Geoff L.; Easson, Alexandra M.; Siddiqui, Naveed T.; Miles, Sarah J.; Karkouti, Keyvan
    Abstract Background Persistent pain is a common yet debilitating complication after breast cancer surgery. Given the pervasive effects of this pain disorder on the patient and healthcare system, post-mastectomy pain syndrome (PMPS) is becoming a larger population health problem, especially as the prognosis and survivorship of breast cancer increases. Interventions that prevent persistent pain after breast surgery are needed to improve the quality of life of breast cancer survivors. An intraoperative intravenous lidocaine infusion has emerged as a potential intervention to decrease the incidence of PMPS. We aim to determine the definitive effects of this intervention in patients undergoing breast cancer surgery. Methods PLAN will be a multicenter, parallel-group, blinded, 1:1 randomized, placebo-controlled trial of 1,602 patients undergoing breast cancer surgery. Adult patients scheduled for a lumpectomy or mastectomy will be randomized to receive an intravenous 2% lidocaine bolus of 1.5 mg/kg with induction of anesthesia, followed by a 2.0 mg/kg/h infusion until the end of surgery, or placebo solution (normal saline) at the same volume. The primary outcome will be the incidence of persistent pain at 3 months. Secondary outcomes include the incidence of pain and opioid consumption at 1 h, 1–3 days, and 12 months after surgery, as well as emotional, physical, and functional parameters, and cost-effectiveness. Discussion This trial aims to provide definitive evidence on an intervention that could potentially prevent persistent pain after breast cancer surgery. If this trial is successful, lidocaine infusion would be integrated as standard of care in breast cancer management. This inexpensive, widely available, and easily administered intervention has the potential to reduce pain and suffering in an already afflicted patient population, decrease the substantial costs of chronic pain management, potentially decrease opioid use, and improve the quality of life in patients. Trial registration This trial has been registered on clinicaltrials.gov (NCT04874038, Dr. James Khan. Date of registration: May 5, 2021).
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    Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports
    (2024-10-03) Cheligeer, Ken; Wu, Guosong; Laws, Alison; Quan, May L.; Li, Andrea; Brisson, Anne-Marie; Xie, Jason; Xu, Yuan
    Abstract Aims The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies. Methods The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system’s computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports. Results In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0–100.0%), a positive predictive value of 90.9% (95%CI: 76.5–100.0%), and an F1 score of 93.0% (95%CI: 83.7–100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction. Conclusions The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.