Artificial intelligence applications for oncological positron emission tomography imaging

Abstract

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PETbased AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

“…This high-throughput strategy to mine quantitative data from medical images searching for novel biomarkers and to generate decision-support models is deemed a feasible approach to overcome the limitations of conventional image interpretation, particularly in oncology [3][4][5]. The potential applications of radiomics are seemingly endless across all imaging modalities, and according to a survey study, the future physicians are confident that advanced computer-aided image analyses will revolutionize radiology for the best [6][7][8][9].…”

Staging and response assessment of lymphoma: a brief review of the Lugano classification and the role of FDG-PET/CT

“…Most recently, research on PET-based quantitative evaluation of cancer using artificial intelligence (AI) and deep learning has been actively conducted [ 27 , 28 ]. Multiple studies suggested AI could enhance the characterization and quantification of tumors and predict treatment response and risk stratification of recurrence [ 29 ]. Although it is still challenging to apply AI-based procedures routinely in clinical practice, it is expected that experiences and data will be gradually accumulated, and more effective clinical application of FDG-PET/CT on lymphoma will be achieved.…”

Artificial intelligence technologies in nuclear medicine

“…For the interpretation of images, studies on an AI-based triage system for identifying artifacts have been published recently[ 15 ]. In the near future, similar systems will be able to detect directly using raw data, such as sinograms, and issue alarms throughout the scanning process, even before reconstruction, so that technicians can adjust or prolong the scheduled scan procedure to accommodate an unexpected discovery[ 16 ].…”