Comparison of characteristics of 18F-fluorodeoxyglucose and 18F-fluorothymidine PET during staging of esophageal squamous cell carcinoma. Nucl Med Commun. 2015;36:1181-1186. [PMID: 26367213 DOI: 10.1097/mnm.0000000000000378]

Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers Part II. [18F]F-FLT, [18F]F-FET, [11C]C-MET and Other Less-Commonly Used Radiotracers

Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than 18F-fluorodeoxyglucose ([18F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, 18F-O-(2-fluoroethyl)-L-tyrosine ([18F]F-FET), 18F-Fluorothymidine ([18F]F-FLT) and 11C-Methionine ([11C]C-MET). [18F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [18F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [18F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [18F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [18F]F-FET, the other amino acid tracer which reflects cellular proliferation, [11C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [18F]F-DOPA, [18F]F-FACBC, [18F]F-NaF, [68Ga]Ga-CXCR-4 and [18F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [18F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.

Radiomics-based T-staging of hollow organ cancers

Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm.

Comparison of 18F-fluorothymidine Positron Emission Tomography/Computed Tomography and 18F-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Patients with Breast Cancer

The uptake of ¹⁸F-fluorothymidine (¹⁸F-FLT) depends on cells' proliferative rates. We compared the characteristics of ¹⁸F-FLT positron emission tomography/computed tomography (PET/CT) with those of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT for breast cancer. We prospectively diagnosed patients with breast cancer who underwent ¹⁸F-FLT PET/CT and ¹⁸F-FDG PET/CT. Subsequently, significant differences and correlation coefficients of the maximum standardized uptake value (SUVmax) in primary breast cancer and axillary lymph nodes were statistically evaluated. We enrolled eight patients with breast cancer. In six treatment-naive patients, the SUVmax for primary lesions showed a significant difference (mean, 2.1 vs. 4.1, p = 0.031) and a strong correlation (r = 0.969) between ¹⁸F-FLT and ¹⁸F-FDG. Further, although the SUVmax for the axillary lymph nodes did not show a significant difference between ¹⁸F-FLT and ¹⁸F-FDG (P = 0.246), there was a strong correlation between the two (r = 0.999). In a patient-by-patient study, there were cases in which only ¹⁸F-FDG uptake was observed in lymph nodes and normal breasts. Bone metastases demonstrated lower accumulation than bone marrow on the ¹⁸F-FLT PET/CT. In conclusion, a strong correlation was observed between the ¹⁸F-FLT PET/CT and ¹⁸F-FDG PET/CT uptake. Differences in the biochemical characteristics of ¹⁸F-FLT and ¹⁸F-FDG were reflected in the accumulation differences for breast cancer, metastatic lesions, and normal organs.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Diagnostic advances of artificial intelligence and radiomics in gastroenterology

Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.

Current clinical status of 18F-FLT PET or PET/CT in digestive and abdominal organ oncology

Positron emission tomography (PET) or PET/computed tomography (CT) using ¹⁸F-3'-fluoro-3'-deoxythymidine (¹⁸F-FLT) offers noninvasive assessment of cell proliferation in human cancers in vivo. The present review discusses the current status on clinical applications of ¹⁸F-FLT-PET (or PET/CT) in digestive and abdominal oncology by comparing with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-PET (or PET/CT). The results of this review show that although ¹⁸F-FLT uptake is lower in most cases of digestive and abdominal malignancies compared with ¹⁸F-FDG uptake, ¹⁸F-FLT-PET can be used to detect primary tumors. ¹⁸F-FLT-PET has shown greater specificity for N staging than ¹⁸F-FDG-PET which can show false-positive uptake in areas of inflammation. However, because of the high background uptake in the liver and bone marrow, it has a limited role of assessing liver and bone metastases. Instead, ¹⁸F-FLT-PET will be a powerful tool for monitoring response to treatment and provide prognostic information in digestive and abdominal oncology.

The emerging field of radiomics in esophageal cancer: current evidence and future potential

'Radiomics' is the name given to the emerging field of extracting additional information from standard medical images using advanced feature analysis. This innovative form of quantitative image analysis appears to have future potential for clinical practice in patients with esophageal cancer by providing an additional layer of information to the standard imaging assessment. There is a growing body of evidence suggesting that radiomics may provide incremental value for staging, predicting treatment response, and predicting survival in esophageal cancer, for which the current work-up has substantial limitations. This review outlines the available evidence and future potential for the application of radiomics in the management of patients with esophageal cancer. In addition, an overview of the current evidence on the importance of reproducibility of image features and the substantial influence of varying smoothing scales, quantization levels, and segmentation methods is provided.