TY - JOUR
T1 - Bayesian network structure for predicting local tumor recurrence in rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery
AU - Osong, Biche
AU - Masciocchi, Carlotta
AU - Damiani, Andrea
AU - Bermejo, Inigo
AU - Meldolesi, Elisa
AU - Chiloiro, Giuditta
AU - Berbee, Maaike
AU - Lee, Seok Ho
AU - Dekker, Andre
AU - Valentini, Vincenzo
AU - Gerard, Jean-Pierre
AU - Rödel, Claus
AU - Bujko, Krzysztof
AU - van de Velde, Cornelis
AU - Folkesson, Joakim
AU - Sainato, Aldo
AU - Glynne-Jones, Robert
AU - Ngan, Samuel
AU - Brændengen, Morten
AU - Sebag-Montefiore, David
AU - van Soest, Johan
N1 - © 2022 The Author(s).
PY - 2022/4
Y1 - 2022/4
N2 - Background and Purpose: Tumor recurrence, a characteristic of malignant tumors, is the biggest concern for rectal cancer survivors. The epidemiology of the disease calls for a pressing need to improve healthcare quality and patient outcomes. Prediction models such as Bayesian networks, which can probabilistically reason under uncertainty, could assist caregivers with patient management. However, some concerns are associated with the standard approaches to developing these structures in medicine. Therefore, this study aims to compare Bayesian network structures that stem from these two techniques.Patients and Methods: A retrospective analysis was performed on 6754 locally advanced rectal cancer (LARC) patients enrolled in 14 international clinical trials. Local tumor recurrence at 2, 3, and 5-years was defined as the endpoints of interest. Five rectal cancer treating physicians from three countries elicited the expert structure. The algorithmic structure was inferred from the data with the hill-climbing algorithm. Structural performance was assessed with calibration plots and area under the curve values.Results: The area under the curve for the expert structure on the training and validation data was above 0.9 and 0.8, respectively, for all the time points. However, the algorithmic structure had superior predictive performance over the expert structure for all time points of interest.Conclusion: We have developed and internally validated a Bayesian networks structure from experts' opinions, which can predict the risk of a LARC patient developing a tumor recurrence at 2, 3, and 5 years. Our result shows that the algorithmic-based structures are more performant and less interpretable than expert-based structures.
AB - Background and Purpose: Tumor recurrence, a characteristic of malignant tumors, is the biggest concern for rectal cancer survivors. The epidemiology of the disease calls for a pressing need to improve healthcare quality and patient outcomes. Prediction models such as Bayesian networks, which can probabilistically reason under uncertainty, could assist caregivers with patient management. However, some concerns are associated with the standard approaches to developing these structures in medicine. Therefore, this study aims to compare Bayesian network structures that stem from these two techniques.Patients and Methods: A retrospective analysis was performed on 6754 locally advanced rectal cancer (LARC) patients enrolled in 14 international clinical trials. Local tumor recurrence at 2, 3, and 5-years was defined as the endpoints of interest. Five rectal cancer treating physicians from three countries elicited the expert structure. The algorithmic structure was inferred from the data with the hill-climbing algorithm. Structural performance was assessed with calibration plots and area under the curve values.Results: The area under the curve for the expert structure on the training and validation data was above 0.9 and 0.8, respectively, for all the time points. However, the algorithmic structure had superior predictive performance over the expert structure for all time points of interest.Conclusion: We have developed and internally validated a Bayesian networks structure from experts' opinions, which can predict the risk of a LARC patient developing a tumor recurrence at 2, 3, and 5 years. Our result shows that the algorithmic-based structures are more performant and less interpretable than expert-based structures.
KW - ABSTRACT
KW - MODELS
KW - NOMOGRAMS
KW - Netherlands
KW - PREOPERATIVE RADIOTHERAPY
KW - RESECTION
KW - RISK-FACTORS
KW - SURVIVAL PREDICTION
U2 - 10.1016/j.phro.2022.03.002
DO - 10.1016/j.phro.2022.03.002
M3 - Article
C2 - 35372704
SN - 2405-6316
VL - 22
SP - 1
EP - 7
JO - Physics & Imaging in Radiation Oncology
JF - Physics & Imaging in Radiation Oncology
ER -