The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

被引:0
|
作者
Paydar, Shahram [1 ]
Pourahmad, Saeedeh [2 ]
Azad, Mohsen [2 ]
Bolandparvaz, Shahram [1 ]
Taheri, Reza [3 ]
Ghahramani, Zahra [1 ]
Zamani, Ali [4 ]
Jeddi, Marjan [4 ]
Karimi, Fariba [4 ]
Dabbaghmanesh, Mohammad Hossein [4 ]
Shams, Mesbah [4 ]
Abbasi, Hamid Reza [1 ]
机构
[1] Shiraz Univ Med Sci, Shahid Rajaee Emtiaz Trauma Hosp, Trauma Res Ctr, Shiraz, Iran
[2] Shiraz Univ Med Sci, Dept Biostat, Shiraz, Iran
[3] Shiraz Univ Med Sci, Dept Neurosurg, Shiraz, Iran
[4] Shiraz Univ Med Sci, Endocrinol & Metab Res Ctr, Shiraz, Iran
关键词
Malignancy; Risk prediction model; Thyroid nodules; Artificial neural network;
D O I
暂无
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: Clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the United States. In the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. A considerable fact is that sensitivity and specificity of the fine needle aspiration varies significantly as it remains highly dependent on the operator as well as the cytologist's skills. Practically, in the above group of patients, thyroid lobectomy/isthmusectomy becomes mandatory for attaining a definitive diagnosis where the majority (70%-80%) have a benign surgical pathology. The scattered nature of clinically gathered data and analysis of their relevant variables need a compliant statistical method. The artificial neural network is a branch of artificial intelligence. We have hypothesized that conduction of an artificial neural network applied to certain clinical attributes could develop a malignancy risk assessment tool to help physicians interpret the fine needle aspiration biopsy results of thyroid nodules in a context composed of patient's clinical variables, known as malignancy related risk factors. Methods: We designed and trained an artificial neural network on a prospectively formed cohort gathered over a four year period (2007-2011). The study population comprised 345 subjects who underwent thyroid resection at Nemazee and Rajaee hospitals, tertiary care centers of Shiraz University of Medical Sciences, and Rajaee Hospital as a level I trauma center in Shiraz, Iran after having undergone thyroid fine needle aspiration. Histopathological results of the fine needle aspirations and surgical specimens were analyzed and compared by experienced, board-certified pathologists who lacked knowledge of the fine needle aspiration results for thyroid malignancy. Results: We compared the preoperative fine needle aspiration and surgical histopathology results. The results matched in 63.5% of subjects. On the other hand, fine needle aspiration biopsy results falsely predicted malignant thyroid nodules in 16% of cases (false-negative). In 20.5% of subjects, fine needle aspiration was falsely positive for thyroid malignancy. The Resilient back Propagation (RP) training algorithm lead to acceptable accuracy in prediction for the designed artificial neural network (64.66%) by the cross-validation method. Under the cross-validation method, a back propagation algorithm that used the resilient back propagation protocol - the accuracy in prediction for the trained artificial neural network was 64.66%. Conclusion: An extensive bio-statistically validated artificial neural network of certain clinical, paraclinical and individual given inputs (predictors) has the capability to stratify the malignancy risk of a thyroid nodule in order to individualize patient care. This risk assessment model (tool) can virtually minimize unnecessary diagnostic thyroid surgeries as well as FNA misleading.
引用
收藏
页码:47 / 52
页数:6
相关论文
共 50 条
  • [41] PVT Properties Prediction Using Artificial Neural Network
    Rashidi, F.
    Rasouli, I.
    Khamehchi, E.
    PROCEEDINGS OF THE NINTH ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON COMBUSTION AND ENERGY UTILIZATION, 2008, : 78 - 81
  • [42] The Prediction of Permeability Using an Artificial Neural Network System
    Pazuki, G. R.
    Nikookar, M.
    Dehnavi, M.
    Al-Anazi, B.
    PETROLEUM SCIENCE AND TECHNOLOGY, 2012, 30 (20) : 2108 - 2113
  • [43] Prediction of Dissolved Oxygen Using Artificial Neural Network
    Areerachakul, Sirilak
    Junsawang, Prem
    Pomsathit, Auttapon
    COMPUTER COMMUNICATION AND MANAGEMENT, 2011, 5 : 524 - 528
  • [44] Establishment of an Ultrasound Malignancy Risk Stratification Model for Thyroid Nodules Larger Than 4 cm
    Xi, Xuehua
    Wang, Ying
    Gao, Luying
    Jiang, Yuxin
    Liang, Zhiyong
    Ren, Xinyu
    Gao, Qing
    Lai, Xingjian
    Yang, Xiao
    Zhu, Shenling
    Zhao, Ruina
    Zhang, Xiaoyan
    Zhang, Bo
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [45] An integrated risk prediction model for corrosion-induced pipeline incidents using artificial neural network and Bayesian analysis
    Kumari, Pallavi
    Halim, Syeda Zohra
    Kwon, Joseph Sang-Il
    Quddus, Noor
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2022, 167 : 34 - 44
  • [46] Risk prediction of pleural effusion in lung malignancy patients treated with CT-guided percutaneous microwave ablation: a nomogram and artificial neural network model
    Xu, Sheng
    Qi, Jing
    Li, Bin
    Bie, Zhi-Xin
    Li, Yuan-Ming
    Li, Xiao-Guang
    INTERNATIONAL JOURNAL OF HYPERTHERMIA, 2021, 38 (01) : 220 - 228
  • [47] Prediction of Exhaustion Threshold Based on ECG Features Using The Artificial Neural Network Model
    Ahmad, Zulkifli
    Jamaludin, Mohd Najeb
    Soeed, Kamaruzaman
    2018 IEEE-EMBS CONFERENCE ON BIOMEDICAL ENGINEERING AND SCIENCES (IECBES), 2018, : 523 - 528
  • [48] Performance prediction of PV module using electrical equivalent model and artificial neural network
    Mittal, Manan
    Bora, Birinchi
    Saxena, Sahaj
    Gaur, Anshu Mli
    SOLAR ENERGY, 2018, 176 : 104 - 117
  • [49] A Hybrid Model for Runoff Prediction Using Variational Mode Decomposition and Artificial Neural Network
    Sibtain, Muhammad
    Li, Xianshan
    Bashir, Hassan
    Azam, Muhammad Imran
    WATER RESOURCES, 2021, 48 (05) : 701 - 712
  • [50] Tensile Strength Prediction of Fiberglass Polymer Composites Using Artificial Neural Network Model
    Spanu, Paulina
    Abaza, Bogdan Felician
    MATERIALE PLASTICE, 2022, 59 (02) : 111 - 118