Neuro-Fuzzy Networks and Their Applications in Medical Fields

Vlamou, Elena; Papadopoulos, Basil K.

doi:10.1007/978-3-030-32622-7_41

Cited by 1 publication

(4 citation statements)

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“…98 It is therefore crucial to identify EGFR mutations pretherapy. 99 The 18 F-labeled small-molecule PET imaging molecular probe 18 F-MPG has been used by some scholars to identify mutated EGFR to screen patients who would benefit from TKI therapy, but as the imaging agents used were too difficult to obtain, it has not been extensively studied. 100 Later, some scholars 101 used PET/CT images to establish a DL model, such as small-residual-convolutional neural network (SResCNN) to predict the mutation state of EGFR.…”

Section: Molecular Subtypingmentioning

confidence: 99%

“…AI refers to the use of computers and advanced technologies to simulate human intelligent behavior and critical thinking, 12 which has achieved great success in research and application. 13 AI includes many different technologies: intelligent agent, 14 symbolic and subsymbolic reasoning, 15 plan, 16 case-based reasoning, 17 fuzzy systems, 18 expert systems, 19 and so on. The tool that enables most of these techniques is machine learning (ML).…”

Section: Introductionmentioning

confidence: 99%

“…As a non-invasive examination method, it can provide anatomical and functional information of multiple organs and tissues in the whole body, so it has been routinely used for the early identification, staging, and efficacy evaluation of tumors such as lung cancer, rectal cancer, ovarian cancer, breast cancer, and so on, which provides comprehensive diagnosis and treatment information. 7,8 The most widely used imaging agent for PET/CT examination in clinical practice is 18 F-FDG, which is a glucose analog. By combining the degree of tumor uptake with some semi-quantitative parameters provided by the instrument, it provides most characteristics of the tumor such as size, shape, internal structure, and relationship with surrounding tissues, so it can provide an important reference for tumor treatment and prognosis.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is to summarize the application of PET/CT-based ML methods in lung cancer in recent years (as shown in Figure 1). At present, 18 F-FDG is mostly used as a PET/CT imaging agent. There are few studies on other imaging agents, so the former is generally introduced without special explanation in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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