Objectives: Accurate prediction of risks such as extracapsular spread, seminal vesicle invasion and lymph node involvement is critical for treatment planning and patient prognosis in prostate cancer. Traditional nomograms are widely used for this risk stratification. In recent years, artificial intelligence (AI)-based Chabot’s have shown potential in this field. The aim of this study was to evaluate the correlation between AI chatbot (ChatGPT-4o) predictions and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram predictions in prostate cancer patients according to risk groups.
Materials and Methods: 40 synthetic patient scenarios representing low, intermediate, high and locally advanced risk groups were created. These scenarios were entered into both ChatGPT-4o and MSKCC nomogram and predictions of “Organ-Confined Disease”, “Extracapsular Extension”, “Seminal Vesicle Invasion” and “Lymph Node Involvement” were obtained. The obtained data were analyzed using Spearman Correlation Coefficient.
Results: In general, there was a significant positive correlation between ChatGPT-4o and MSKCC nomogram in all prediction topics (p < 0.001). However, no significant correlation was found between the predictions of “Organ-Confined Disease” (r = 0.521, p = 0.123), “Seminal Vesicle Invasion” (r = 0.382, p = 0.276) and “Lymph Node Involvement” (r = 0.218, p = 0.546) in the high-risk patient group. Similarly, no significant correlation was found between the estimates of “Organ-Confined Disease” (r = 0.522, p = 0.122) and “Extracapsular Extension” (r = 0.524, p = 0.120) in the locally advanced patient group.
Conclusion: An overall high correlation between an AI-based chatbot (ChatGPT-4o) and the MSKCC nomogram was demonstrated for prostate cancer risk prediction. However, no significant correlation was observed especially in high-risk and locally advanced patient groups. These findings suggest that while AI chatbots are a potential tool for prostate cancer risk stratification, they require extensive validation and development studies before they can be put into clinical use, especially in more complex and advanced cases.
Abstract
Objectives: Accurate prediction of risks such as extracapsular spread, seminal vesicle invasion and lymph node involvement is critical for treatment planning and patient prognosis in prostate cancer. Traditional nomograms are widely used for this risk stratification. In recent years, artificial intelligence (AI)-based Chabot’s have shown potential in this field. The aim of this study was to evaluate the correlation between AI chatbot (ChatGPT-4o) predictions and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram predictions in prostate cancer patients according to risk groups.
Materials and Methods: 40 synthetic patient scenarios representing low, intermediate, high and locally advanced risk groups were created. These scenarios were entered into both ChatGPT-4o and MSKCC nomogram and predictions of “Organ-Confined Disease”, “Extracapsular Extension”, “Seminal Vesicle Invasion” and “Lymph Node Involvement” were obtained. The obtained data were analyzed using Spearman Correlation Coefficient.
Results: In general, there was a significant positive correlation between ChatGPT-4o and MSKCC nomogram in all prediction topics (p < 0.001). However, no significant correlation was found between the predictions of “Organ-Confined Disease” (r = 0.521, p = 0.123), “Seminal Vesicle Invasion” (r = 0.382, p = 0.276) and “Lymph Node Involvement” (r = 0.218, p = 0.546) in the high-risk patient group. Similarly, no significant correlation was found between the estimates of “Organ-Confined Disease” (r = 0.522, p = 0.122) and “Extracapsular Extension” (r = 0.524, p = 0.120) in the locally advanced patient group.
Conclusion: An overall high correlation between an AI-based chatbot (ChatGPT-4o) and the MSKCC nomogram was demonstrated for prostate cancer risk prediction. However, no significant correlation was observed especially in high-risk and locally advanced patient groups. These findings suggest that while AI chatbots are a potential tool for prostate cancer risk stratification, they require extensive validation and development studies before they can be put into clinical use, especially in more complex and advanced cases.