Bayesian network structure for predicting local tumor recurrence in rectal cancer patients treated with neoadjuvant chemoradiation followed by surgery.
Journal
Physics and imaging in radiation oncology
ISSN: 2405-6316
Titre abrégé: Phys Imaging Radiat Oncol
Pays: Netherlands
ID NLM: 101704276
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
19
10
2021
revised:
07
03
2022
accepted:
11
03
2022
entrez:
4
4
2022
pubmed:
5
4
2022
medline:
5
4
2022
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
Background and Purpose
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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.
Identifiants
pubmed: 35372704
doi: 10.1016/j.phro.2022.03.002
pii: S2405-6316(22)00026-4
pmc: PMC8968052
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1-7Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Références
PLoS One. 2014 Aug 29;9(8):e106344
pubmed: 25171093
Lancet. 2009 Mar 7;373(9666):811-20
pubmed: 19269519
Int J Radiat Oncol Biol Phys. 2017 Oct 1;99(2):344-352
pubmed: 28871984
Dis Colon Rectum. 2018 Apr;61(4):433-440
pubmed: 29521824
PLoS One. 2013 Dec 06;8(12):e82349
pubmed: 24324773
J Clin Oncol. 2011 Aug 10;29(23):3163-72
pubmed: 21747092
Dis Colon Rectum. 2017 Nov;60(11):1168-1174
pubmed: 28991081
World J Surg Oncol. 2019 Oct 28;17(1):173
pubmed: 31660992
Med Phys. 2010 Apr;37(4):1401-7
pubmed: 20443461
J Natl Compr Canc Netw. 2009 Sep;7(8):883-93; quiz 894
pubmed: 19755048
Asian Pac J Cancer Prev. ;18(9):2465-2470
pubmed: 28952277
N Engl J Med. 2001 Aug 30;345(9):638-46
pubmed: 11547717
J Clin Oncol. 2010 Sep 20;28(27):4268-74
pubmed: 20585094
Radiother Oncol. 2014 Jul;112(1):37-43
pubmed: 24846083
Cancer. 2005 Dec 1;104(11 Suppl):2565-76
pubmed: 16258929
Cancer Med. 2018 Aug;7(8):3673-3681
pubmed: 29992773
S Afr J Surg. 2017 Mar;55(1):29-34
pubmed: 28876555
J Clin Oncol. 2017 Aug 10;35(23):2631-2638
pubmed: 28657814
Ann Oncol. 2015 May;26(5):928-935
pubmed: 25609247