Payling, Miles

560 Background: Early diagnosis remains a critical challenge in oncology, particularly for the five deadliest cancers: lung, breast, prostate, colorectal, and pancreatic. These cancers often present with nonspecific symptoms, leading to repeated healthcare visits without a timely suspicion of malignancy. Artificial intelligence (AI) offers an opportunity to augment clinical decision-making and support earlier identification of cancer. C the Signs , an AI cancer prediction platform, has been widely adopted across the UK with substantial evidence supporting its ability to identify cancer at earlier stages. Methods: This retrospective study assessed the performance of C the Signs in intercepting cancer earlier than physician diagnosis. De-identified electronic medical records were accessed via the Mayo Data Platform, spanning a 20-year period from January 1, 2002 to December 31, 2021. The study cohort included 55,375 patients diagnosed with lung, breast, prostate, colorectal or pancreatic cancer. The primary outcome was the proportion of cases identified earlier by the AI model, measured in annual increments up to five years prior to physician diagnosis. Additional analyses were conducted across the five target cancer types. Results: The C the Signs platform identified 14,528 patients (26.2%) with cancer earlier than the original physician diagnosis. Early identification occurred across multiple time points: 8.9% in year 1, 4.7% in year 2, 4.4% in year 3, 4.3% in year 4, and 3.9% in year 5. Across cancer types, the highest early interception rate was observed in prostate cancer (31.7%), while breast cancer showed the lowest impact (21.0%). Conclusions: Use of the C the Signs AI cancer prediction platform demonstrated meaningful improvements in early cancer interception, particularly for lung and prostate cancers. These findings highlight the potential of AI tools to enhance early diagnosis and support timely clinical interventions, offering a scalable solution to improve cancer outcomes across populations.

8053 Background: In the US, only 27.4% of lung cancer cases are diagnosed early, with 5-year survival rates of 63% for localized and 27% for late-stage cancers. Despite recommendations since 2012 for screening high-risk individuals with low-dose CT, uptake has been limited, and most lung cancer diagnoses occur after symptoms appear. Similarly, chest x-rays, while commonly used as an initial test to investigate patients with symptomatic suspected lung cancer, have demonstrated limited sensitivities between 50-70% and specificities over 80%. Symptoms often overlap with common conditions, making detection challenging, and studies have identified median delays of 187 days from symptom onset to diagnosis. This prolonged interval presents an opportunity for improvement. This study examines the use of the AI cancer prediction platform, C the Signs, to passively screen for lung cancer by leveraging electronic medical records (EMRs) for early lung cancer detection. Methods: Utilizing the Mayo data platform, we conducted a retrospective analysis of EMR data from 894,409 patients, including 7,395 individuals diagnosed with lung cancer. We assessed the sensitivity and specificity of the AI cancer prediction platform, in identifying patients at risk of lung cancer. Additionally, we compared the timing of lung cancer risk identification by the AI cancer prediction platform with the timing of diagnoses made by physicians to determine whether the platform enabled earlier detection. Results: The AI cancer prediction platform detected 6,749 cases of lung cancer among the 7,395 individuals diagnosed, resulting in an early detection sensitivity of 91.5%. The platform identified 423,249 false positives among the 887,014 patients who did not have lung cancer, leading to a specificity of 52.3%. Additionally, it identified the risk of a lung cancer diagnosis in 26.6% of patients up to five years earlier than the diagnoses made by physicians. Conclusions: This study highlights the potential of leveraging EMR data and AI platforms like C the Signs to enhance early lung cancer detection. Chest X-rays, with their reduced sensitivity for early-stage lesions and reliance on symptom-driven use, remain limited as a screening tool. In contrast, the AI platform achieved a sensitivity of 91.5% and identified 26.6% of cases up to five years earlier than traditional diagnoses. These findings underscore the promise of AI-based platforms as supplementary tools for improving early detection and facilitating timely intervention to enhance patient outcomes.

371 Background: Prostate cancer is the most common malignancy in men globally and remains among the top five leading causes of cancer-related deaths. Emerging evidence suggests that early identification of symptoms can detect localized disease thus enabling opportunities for curative intent treatments. This study evaluates the use of the AI-powered prediction model, C the Signs, to passively screen for prostate cancer by leveraging data from electronic medical records (EMRs), offering a novel pathway for identifying high-risk individuals. Methods: A retrospective analysis was conducted using the Mayo Data Platform, encompassing 418,477 male patient records, of which 16,835 were diagnosed with prostate cancer. C the Signs identified patients at risk based solely on their EMR data, utilizing AI-driven pattern recognition. Sensitivity and specificity analyses were performed to assess the prediction model’s accuracy. Additionally, we examined the time-to-diagnosis advantage for patients flagged by the model compared to traditional physician clinical diagnoses. Results: C the Signs demonstrated a sensitivity of 83.3% and a specificity of 52.5% for identifying patients at risk of prostate cancer. Notably, 31.8% of prostate cancer cases were identified at risk up to five years earlier by the model compared to traditional physician clinical diagnosis. Conclusions: The integration of AI-driven prediction models like C the Signs into prostate cancer screening pathways provides an opportunity to enhance early detection, particularly in symptomatic individuals. Compared to prostate-specific antigen (PSA) testing, the model achieved equivalent sensitivity and 38% higher specificity, positioning it as a valuable companion tool for improving patient outcomes.

54 Background: The incidence of colorectal cancer is increasing rapidly, particularly amongst younger people. Despite a screening program being widely available, most patients are diagnosed with cancer through symptomatic presentation. Leveraging electronic medical records (EMRs) through clinical decision support platforms offers potential for identifying high-risk individuals before clinical suspicion develops. This study aims to explore the challenges associated with early colorectal cancer diagnosis and evaluate the effectiveness of the cancer case finding feature of a cancer management platform called C the Signs. Methods: A retrospective analysis was conducted using data from the Mayo Data Platform, comprising records of 894,275 patients, including 7,348 diagnosed with colorectal cancer. C the Signs was employed to identify patients at risk of colorectal cancer based on EMR data. Sensitivity and specificity analyses were performed to assess the platform's performance in identifying high-risk patients. Additionally, the study evaluated the temporal discrepancy in colorectal cancer diagnoses between those flagged by C the Signs and those detected by primary care physicians. Results: The C the Signs screening platform had a sensitivity of 93.8% and a specificity of 19.7% in identifying patients at risk of colorectal cancer. Notably, 29.4% of patients with colorectal cancer were identified as being at risk up to 5 years earlier by C the Signs compared to diagnoses made by primary care physicians, highlighting the platform's potential for early detection. Conclusions: Early identification of high-risk individuals holds promise for improving patient outcomes and reducing the burden of colorectal cancer. Whilst the specificity of the C the Signs platform for colorectal cancer is relatively low, the sensitivity is comparable to the most favorable rates reported for colonoscopy. In addition, if C the Signs was used in conjunction with a relatively cheap screening test such as Faecal Immunochemical Testing, this would significantly drive up the specificity whilst maintaining the sensitivity. Further studies are needed to assess its efficacy in conjunction with tests available in primary care.