# Evaluating Pancreatic Cancer Treatment Strategies Using a Novel Polytopic Fuzzy Tensor Approach

## TL;DR

This paper introduces a new fuzzy tensor framework to evaluate pancreatic cancer treatment strategies, handling uncertainty and complex decision-making more effectively than traditional methods.

## Contribution

The novel polytopic fuzzy tensor (PFT) model unifies geometric structures with fuzzy tensors for multi-criteria decision-making under uncertainty.

## Key findings

- The PFT-based approach outperforms traditional fuzzy methods in consistency and reliability for evaluating cancer treatments.
- The model effectively handles multidimensional expert evaluations and conflicting information.
- The framework is validated through a real-world application assessing six treatment strategies against five criteria.

## Abstract

In response to the growing complexity and uncertainty in real-world decision-making, this study introduces a novel framework based on the polytopic fuzzy tensor (PFT) model, which unifies the geometric structure of polytopes with the representational power of fuzzy tensors. The PFT framework is specifically designed to handle high-dimensional, imprecise, and ambiguous information commonly encountered in multi-criteria group decision-making scenarios. To support this framework, we define a suite of algebraic operations, aggregation mechanisms, and theoretical properties tailored to the PFT environment, with comprehensive mathematical formulations and illustrative validations. The effectiveness of the proposed method is demonstrated through a real-world application involving the evaluation of six pancreatic cancer treatment strategies. These alternatives are assessed against five key criteria: quality of life, side effects, treatment accessibility, cost, and duration. Our results reveal that the PFT-based approach outperforms traditional fuzzy decision-making techniques by delivering more consistent, interpretable, and reliable outcomes under uncertainty. Moreover, comparative analysis confirms the model’s superior ability to handle multidimensional expert evaluations and integrate conflicting information. This research contributes a significant advancement in the field of fuzzy decision science by offering a flexible, theoretically sound, and practically applicable tool for complex decision problems. Future work will focus on improving computational performance, adapting the model for real-time data, and exploring broader interdisciplinary applications.