Potentials and caveats of AI in hybrid imaging

Sundar, Lalith Kumar Shiyam; Muzik, Otto; Buvat, Irène; Bidaut, Luc; Beyer, Thomas

doi:10.1016/j.ymeth.2020.10.004

Cited by 15 publications

(7 citation statements)

References 151 publications

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“…These systems allow leveraging of the compositional nature of images by an end-to-end approach integrating imagebased features [22]. In contrast to traditional ML approaches, deep learning (DL) models based on convolutional neural networks (CNN) do not require a predefined definition of image features but are able to learn relevant features directly from imaging data [6,9,23]. Beyond the prediction of patient outcome, DL is particularly useful for object detection, e. g. localization of lung nodules, or image segmentation for the assessment of tumors or organs.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

“…Applicability of AI for imaging data optimization AI applications were recently successfully evaluated for attenuation correction, pre-and postprocessing, co-registration of data, and PET or MRI/CT-based motion correction. A detailed review of potential applications can be found in [6].…”

Section: Hybrid Imaging and Machine Learningmentioning

confidence: 99%

“…At this point, integrated AI applications for the structured analysis of hybrid imaging data remain scarce, particularly due to data privacy and the lack of publicly available, expert-annotated hybrid imaging data sets [6]. Structured data repositories, such as The Cancer Imaging Archive (TCIA), and large-scale, privacy-preserving initiatives, such as the Radiology Cooperative Network (RACOON), promise to increase sample sizes for the development of AI models, possibly with federated learning approaches [27].…”

Section: Limitations Challenges and Perspectivesmentioning

confidence: 99%

“…Due to the level of digitalization and natural accruement of big data, AI applications might add value to patient care by assisting physicians in simple tasks [4,5]. With disproportionate growth of imaging data in a single examination, measures to increase productivity and to leverage data are desired to assist physicians and technicians with prescreened data, optimizable raw images/post-processing tools, and quantitative features along the whole imaging workflow [6]. Hybrid imaging is the combination of morphological (CT/MRI) and functional imaging using a variety of radiotracers such as 18 F-FDG, 68 Ga/ 18 F-PSMA, or 18 F-SIFA-TATE, and provides complementary information regarding, e. g., tumor characteristics and metabolism.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in Oncological Hybrid Imaging

Feuerecker

Heimer

Geyer

et al. 2022

Rofo

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Background Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. Methods and Results The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. Conclusion AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. Key Points: Citation Format

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

Section: Hybrid Imaging and Machine Learningmentioning

confidence: 99%

Section: Limitations Challenges and Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence in Oncological Hybrid Imaging

Feuerecker

Heimer

Geyer

et al. 2022

Rofo

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“…Artificial intelligence (AI), with its ability to identify patterns within a massive dataset, is highly useful in this setting. This term covers several interrelated categories, including machine learning, which refers to all modeling and prediction applications based on training data (e.g., logistic regression), and deep learning, a sub-category of machine learning based on a neural network that is supposed to reproduce—on a smaller scale—the functioning of a human brain [ 4 ]. One of the key points of AI is the training base, which must be large enough to avoid overfitting issues [ 5 ], or, in other words, to avoid that the model becomes too attuned to the training data and loses its applicability to any other dataset.…”

Section: Introductionmentioning

confidence: 99%

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

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