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Special Issue "Role of PET/CT in the Era of Personalized Medicine"

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Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
Interests: PET/CT; molecular imaging; nuclear medicine; radiotracer; machine learning

Special Issue Information

Dear Colleagues,

In the era of precision medicine in oncology, medical imaging is critical for a wide range of indications, including detection, staging and restaging, detecting recurrent or residual disease, monitoring response to therapy and prognosis. PET/CT combines molecular and high-resolution anatomical structure information in a unique, synergistic approach that provides greater sensitivity and specificity in detecting malignancies.

This Special Issue of the Journal of Personalized Medicine highlights the use of PET/CT in a variety of tumors. We are soliciting papers focusing on, but not limited to, clinical studies, new PET tracers, and novel methods of post processing, such as machine learning, biomarkers, and clinical trials.

Dr. Shengming Deng
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

medical imaging
PET/CT
machine learning
biomarker
personalized medicine
precision medicine

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Research

Open AccessArticle

Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on ¹⁸F-FDG PET

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J. Pers. Med. 2023, 13(3), 539; https://doi.org/10.3390/jpm13030539 - 17 Mar 2023

Cited by 2 | Viewed by 906

Abstract

Background: The characteristic magnetic resonance imaging (MRI) and the positron emission tomography (PET) findings of PCNSL often overlap with other intracranial tumors, making definitive diagnosis challenging. PCNSL typically shows iso-hypointense to grey matter on T2-weighted imaging. However, a particular part of PCNSL can demonstrate T2-weighted hyperintensity as other intracranial tumors. Moreover, normal high uptake of FDG in the basal ganglia, thalamus, and grey matter can mask underlying PCNSL in ¹⁸F-FDG PET. In order to promote the efficiency of diagnosis, the MRI-based or PET/CT-based radiomics models combining histograms with texture features in diagnosing glioma and brain metastases have been widely established. However, the diagnosing model for PCNSL has not been widely reported. The study was designed to investigate a machine-learning (ML) model based on multiple parameters of 2-deoxy-2-[18F]-floor-D-glucose (¹⁸F-FDG) PET for differential diagnosis of PCNSL and metastases in the brain. Methods: Patients who underwent an ¹⁸F-FDG PET scan with untreated PCNSL or metastases in the brain were included between May 2016 and May 2022. A total of 126 lesions from 51 patients (43 patients with untreated brain metastases and eight patients with untreated PCNSL), including 14 lesions of PCNSL, and 112 metastatic lesions in the brain, met the inclusion criteria. PCNSL or brain metastasis was confirmed after pathology or clinical history. Principal component analysis (PCA) was used to decompose the datasets. Logistic regression (LR), support vector machine (SVM), and random forest classification (RFC) models were trained by two different groups of datasets, the group of multi-class features and the group of density features, respectively. The model with the highest mean precision score was selected. The testing sets and original data were used to examine the efficacy of models separately by using the weighted average F1 score and area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results: The multi-class features-based RFC and SVM models reached identical weighted-average F1 scores in the testing set, and the score was 0.98. The AUCs of RFC and SVM models calculated from the testing set were 1.00 equally. Evaluated by the original dataset, the RFC model based on multi-class features performs better than the SVM model, whose weighted-average F1 scores of the RFC model calculated from the original data were 0.85 with an AUC of 0.93. Conclusions: The ML based on multi-class features of ¹⁸F-FDG PET exhibited the potential to distinguish PCNSL from brain metastases. The RFC models based on multi-class features provided comparatively high efficiency in our study. Full article

(This article belongs to the Special Issue Role of PET/CT in the Era of Personalized Medicine)

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Open AccessArticle

Association Analysis of Maximum Standardized Uptake Values Based on ¹⁸F-FDG PET/CT and EGFR Mutation Status in Lung Adenocarcinoma

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J. Pers. Med. 2023, 13(3), 396; https://doi.org/10.3390/jpm13030396 - 23 Feb 2023

Viewed by 635

Abstract

(1) Background: To investigate the association between maximum standardized uptake value (SUV_max) based on ¹⁸F-FDG PET/CT and EGFR mutation status in lung adenocarcinoma. (2) Methods: A total of 366 patients were retrospectively collected and divided into the EGFR mutation group (n = 228) and EGFR wild-type group (n = 138) according to their EGFR mutation status. The two groups’ general information and PET/CT imaging parameters were compared. A hierarchical binary logistic regression model was used to assess the interaction effect on the relationship between SUV_max and EGFR mutation in different subgroups. Univariate and multivariate logistic regression was used to analyze the association between SUV_max and EGFR mutation. After adjusting for confounding factors, a generalized additive model and smooth curve fitting were applied to address possible non-linearities. (3) Results: Smoking status significantly affected the relationship between SUV_max and EGFR mutation (p for interaction = 0.012), with an interaction effect. After adjusting for age, gender, nodule type, bronchial sign, and CEA grouping, in the smoking subgroup, curve fitting results showed that the relationship between SUV_max and EGFR mutation was approximately linear (df = 1.000, c² = 3.897, p = 0.048); with the increase in SUV_max, the probability of EGFR mutation gradually decreased, and the OR value was 0.952 (95%CI: 0.908–0.999; p = 0.045). (4) Conclusions: Smoking status can affect the relationship between SUV_max and EGFR mutation status in lung adenocarcinoma, especially in the positive smoking history subgroup. Fully understanding the effect of smoking status will help to improve the accuracy of SUV_max in predicting EGFR mutations. Full article

(This article belongs to the Special Issue Role of PET/CT in the Era of Personalized Medicine)

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Special Issue "Role of PET/CT in the Era of Personalized Medicine"

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