Volume 5 • Issue 2 • PP: 01–08 • 2025
Recurrence Shadow Mapping under Neutrosophic Clinical Evidence: An Uncertainty-Oriented Model for Post-Treatment Healthcare Decision Support
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Post-treatment follow-up in differentiated thyroid cancer requires a decision model that is not limited to binary recurrence prediction. Patients may present with partially reassuring anatomical findings, incomplete biochemical response, hetero-geneous pathological subtype, or contradictory clinical history. These situations are better described as a triadic state composed of support for recurrence, support against recurrence, and unresolved indeterminacy. This paper proposes a recurrence shadow mapping model based on single-valued neutrosophic clinical evidence. The model transforms clinico-pathologic descriptors into truth, indeterminacy, and falsity memberships; aggregates evidence through entropy-contrast weighting; and produces a recurrence-shadow index that separates stable, observation, alert, and high-alert follow-up states. The proposed method is designed for healthcare decision support rather than automatic replacement of clinical judgment. Its mathematical contribution is a bounded neutrosophic score that penalizes inconsistent evidence without suppressing clinically meaningful warning signals. Experimental evaluation demonstrates that recurrence-oriented evidence sources can be expressed in a transparent mathematical form, and that indeterminacy itself becomes an interpretable clinical quantity. The findings support the use of neutrosophic information fusion for medical cases where uncertainty is structural rather than merely statistical.
Keywords
References
[1] S. Borzooei and A. Tarokhian, “Differentiated thyroid cancer recurrence,” UCI Machine Learning Repository, 2023.
[2] S.-W. Jang, J.-H. Park, H.-R. Kim, H. J. Kwon, Y.-M. Lee, S. W. Hong, and J.-H. Yoon, “Recurrence risk evaluation in patients with papillary thyroid carcinoma: Multicenter machine learning evaluation of lymph node variables,” Cancers, vol. 15, no. 2, p. 550, 2023.
[3] Y. Li, J. Tian, K. Jiang, Z. Wang, S. Gao, K. Wei, A. Yang, and Q. Li, “Risk factors and predictive model for recurrence in papillary thyroid carcinoma: A singlecenter retrospective cohort study based on 955 cases,” Frontiers in Endocrinology, vol. 14, p. 1268282, 2023.
[4] A. Coca-Pelaz, J. P. Rodrigo, J. P. Shah, I. J. Nixon, D. M. Hartl, K. T. Robbins, L. P. Kowalski, A. A. Mäkitie, M. Hamoir, F. López, N. F. Saba, S. Nuyts, A. Rinaldo, and A. Ferlito, “Recurrent differentiated thyroid cancer: The current treatment options,” Cancers, vol. 15, no. 10, p. 2692, 2023.
[5] R. I. Haddad, L. Bischoff, D. Ball et al., “Thyroid carcinoma, version 2.2022, NCCN clinical practice guidelines in oncology,” Journal of the National Comprehensive Cancer Network, vol. 20, no. 8, pp. 925–951, 2022.
[6] H. M. A. Farid and M. Riaz, “Single-valued neutrosophic einstein interactive aggregation operators with applications for material selection in engineering design: Case study of cryogenic storage tank,” Complex & Intelligent Systems, 2022.
[7] H. M. A. Farid and M. Riaz, “Single-valued neutrosophic dynamic aggregation information with time sequence preference for IoT technology in supply chain management,” Engineering Applications of Artificial Intelligence, vol. 126, p. 106940, 2023.
[8] P. Liu et al., “Novel EDAS methodology based on single-valued neutrosophic aczel–alsina aggregation information,” Mathematical Problems in Engineering, p. 2394472, 2022.
[9] A. G. Lara Jácome, E. Mayorga Aldaz, M. Ramos Argilagos, and D. M. Ramírez Guerra, “Neutrosophic perspectives in healthcare decision making: Navigating complexity with ethics, information, and collaboration,” Neutrosophic Sets and Systems, vol. 62, pp. 121–128, 2023.
[10] A. Pathak, Z. Yu, D. Paredes, E. P. Monsour, A. Ortiz Rocha, J. P. Brito, N. S. Ospina, and Y. Wu, “Extracting thyroid nodules characteristics from ultrasound reports using transformer-based natural language processing methods,” AMIA Annual Symposium Proceedings, vol. 2023, pp. 1193–1200, 2023.
[11] A. Naseem, M. Akram, K. Ullah, and Z. Ali, “Aczel– alsina aggregation operators based on complex singlevalued neutrosophic information and their application in decision-making problems,” Decision Making Advances, vol. 1, no. 1, pp. 86–114, 2023.
[12] F. Taher, “Neutrosophic multi-criteria decision-making methodology to identify key barriers in education,” International Journal of Neutrosophic Science, vol. 22, no. 4, pp. 111–120, 2023.
[13] Y. Habchi, Y. Himeur, H. Kheddar, A. Boukabou, S. Atalla, A. Chouchane, A. Ouamane, andW. Mansoor, “AI in thyroid cancer diagnosis: Techniques, trends, and future directions,” arXiv preprint arXiv:2308.13592, 2023.
[14] T. F. Lee, S. H. Lee, C. D. Tseng et al., “Using machine learning algorithm to analyse the hypothyroidism complications caused by radiotherapy in patients with head and neck cancer,” Scientific Reports, vol. 13, p. 19185, 2023.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.