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Introducing the AI Prediction Model: C the Signs
Colorectal cancer (CRC) detection may soon see a significant improvement with the advent of an AI-prediction model, “C the Signs”. A recent study presented at the ASCO Gastrointestinal Cancers Symposium underscores the potential of this AI-powered tool in enhancing early CRC detection, offering a fresh approach to early diagnosis.
Understanding the Role of C the Signs
Seema Dadhania, a consultant clinical oncologist from Imperial College London Department of Surgery & Cancer, explained the motive behind the study. The team aimed to discern if the C the Signs platform could detect signs or risks of CRC earlier than the patient’s actual diagnosis. They sought to understand if they could identify early signals of CRC by aggregating symptoms or other data present within the Mayo Data Platform.
The Rising Challenge of CRC
CRC is being diagnosed increasingly, especially among younger populations, presenting a growing public health challenge. Despite advancements in screening, many CRC cases are still detected only after symptoms manifest, often at later stages when outcomes are less favorable. C the Signs could address these challenges by identifying high-risk individuals earlier, even before clinical suspicion arises.
How Does C the Signs Work?
In under 30 seconds, C the Signs can identify which cancers a patient is at risk of and recommend the appropriate test or specialist to diagnose their cancer. The tool has the potential to significantly reduce diagnostic delays associated with symptom clusters that are often linked with benign conditions.
Assessing the Performance of C the Signs
A retrospective study using data from primary care settings evaluated the performance of C the Signs. Researchers analyzed a comprehensive dataset of electronic medical records (EMRs) spanning 20 years, from January 1, 2002, to December 31, 2021. They used the AI model to assess its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), comparing its performance against other screening methods like colonoscopy and Fecal Immunochemical Testing (FIT).
Results Show Promising Potential for Early Detection
The study analyzed 894,275 patients, among whom 7348 were diagnosed with CRC. The model achieved a sensitivity of 93.8% and a specificity of 19.7% in identifying patients at risk of CRC. Interestingly, the model identified 29.4% of these patients as high-risk up to 5 years earlier than primary care physicians, highlighting its potential for early detection. Despite its lower specificity, the model’s high sensitivity and early identification ability underline its value in facilitating timely interventions and improving patient outcomes in CRC care.
Implications for Future CRC Diagnosis and Treatment
According to Dadhania, this tool not only captures patients who would already get through the system, but it also identifies a proportion of patients who wouldn’t ordinarily be flagged for a colonoscopy under the current system. This significant development could pave the way for more effective CRC diagnosis and treatment in the future.