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Artificial Intelligence for Advanced Analysis in PET Imaging

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 7073

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Prof. Dr. Salvatore Annunziata

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Unità di Medicina Nucleare, TracerGLab, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico A. Gemelli IRCCS, 00168 Roma, Italy
Interests: PET; radiomics; AI; lymphoma; radiopharmaceuticals

Special Issue Information

Dear Colleagues,

In recent years Positron Emission Tomography (PET) have changed clinical and scientific scenarios in several diagnostic areas, such as oncology, neurology, endocrinology, internal medicine, and cardiology. Hybrid imaging such as PET/CT or PET/MR have helped to merge functional and morphological information. Other than 18F-Fluorodeoxiglucose (18F-FDG), several other tracers have been studied and used in clinical practice.

More recently, Artificial Intelligence applications have been proposed in several context of PET imaging, such as acquisition, reconstruction, lesion recognition, contouring, radiomics, data analysis, and management, to further help clinicians for scientific purpose or in daily routine.

In the Special Issue called "Artificial Intelligence for Advanced Analysis in PET Imaging", we invite all original or review papers, letters, and commentaries about this topic, with no limitations about clinical or technical context.

Applications by senior/young authors, large cohorts or pilot studies are appreciated.

Dr. Salvatore Annunziata
Guest Editor

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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. Diagnostics is an international peer-reviewed open access semimonthly 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

PET
imaging
AI
radiomics
deep learning

Published Papers (3 papers)

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14 pages, 3246 KiB

Open AccessArticle

Baseline ¹⁸F-FDG PET/CT Radiomics in Classical Hodgkin’s Lymphoma: The Predictive Role of the Largest and the Hottest Lesions

by Elizabeth Katherine Anna Triumbari, Roberto Gatta, Elena Maiolo, Marco De Summa, Luca Boldrini, Marius E. Mayerhoefer, Stefan Hohaus, Lorenzo Nardo, David Morland and Salvatore Annunziata

Diagnostics 2023, 13(8), 1391; https://doi.org/10.3390/diagnostics13081391 - 11 Apr 2023

Cited by 1 | Viewed by 1465

Abstract

This study investigated the predictive role of baseline ¹⁸F-FDG PET/CT (bPET/CT) radiomics from two distinct target lesions in patients with classical Hodgkin’s lymphoma (cHL). cHL patients examined with bPET/CT and interim PET/CT between 2010 and 2019 were retrospectively included. Two bPET/CT target lesions were selected for radiomic feature extraction: Lesion_A, with the largest axial diameter, and Lesion_B, with the highest SUV_max. Deauville score at interim PET/CT (DS) and 24-month progression-free-survival (PFS) were recorded. Mann–Whitney test identified the most promising image features (p < 0.05) from both lesions with regards to DS and PFS; all possible radiomic bivariate models were then built through a logistic regression analysis and trained/tested with a cross-fold validation test. The best bivariate models were selected based on their mean area under curve (mAUC). A total of 227 cHL patients were included. The best models for DS prediction had 0.78 ± 0.05 maximum mAUC, with a predominant contribution of Lesion_A features to the combinations. The best models for 24-month PFS prediction reached 0.74 ± 0.12 mAUC and mainly depended on Lesion_B features. bFDG-PET/CT radiomic features from the largest and hottest lesions in patients with cHL may provide relevant information in terms of early response-to-treatment and prognosis, thus representing an earlier and stronger decision-making support for therapeutic strategies. External validations of the proposed model are planned. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Analysis in PET Imaging)

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13 pages, 943 KiB

Open AccessArticle

Prognostic Value of Axillary Lymph Node Texture Parameters Measured by Pretreatment ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Locally Advanced Breast Cancer with Neoadjuvant Chemotherapy

by Jae Pil Hwang, Joon Young Choi, Joon Ho Choi, Young Seok Cho, Sung Mo Hur, Zisun Kim, Cheol Wan Lim, Seongho Seo, Ji Eun Moon, Sang-Keun Woo and Jung Mi Park

Diagnostics 2022, 12(10), 2285; https://doi.org/10.3390/diagnostics12102285 - 22 Sep 2022

Cited by 1 | Viewed by 1569

Abstract

Background: This study investigated the prognostic value of axillary lymph node (ALN) heterogeneity texture features through ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in patients with locally advanced breast cancer (LABC). Methods: We retrospectively analyzed 158 LABC patients with FDG-avid, pathology-proven, metastatic ALN who underwent neoadjuvant chemotherapy (NAC) and curative surgery. Tumor and ALN texture parameters were extracted from pretreatment ¹⁸F-FDG PET/CT using Chang-Gung Image Texture Analysis software. The least absolute shrinkage and selection operator regression was performed to select the most significant predictive texture parameters. The predictive impact of texture parameters was evaluated for both progression-free survival and pathologic NAC response. Results: The median follow-up period of 36.8 months and progression of disease (PD) was observed in 36 patients. In the univariate analysis, ALN textures (minimum standardized uptake value (SUV) (p = 0.026), SUV skewness (p = 0.038), SUV bias-corrected Kurtosis (p = 0.034), total lesion glycolysis (p = 0.011)), tumor textures (low-intensity size zone emphasis (p = 0.045), minimum SUV (p = 0.047), and homogeneity (p = 0.041)) were significant texture predictors. On the Cox regression analysis, ALN SUV skewness was an independent texture predictor of PD (p = 0.016, hazard ratio 2.3, 95% confidence interval 1.16–4.58). Conclusions: ALN texture feature from pretreatment ¹⁸F-FDG PET/CT is useful for the prediction of LABC progression. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Analysis in PET Imaging)

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11 pages, 1602 KiB

Open AccessArticle

Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [¹⁸F]-PSMA-1007 PET-CT

by Elin Trägårdh, Olof Enqvist, Johannes Ulén, Jonas Jögi, Ulrika Bitzén, Fredrik Hedeer, Kristian Valind, Sabine Garpered, Erland Hvittfeldt, Pablo Borrelli and Lars Edenbrandt

Diagnostics 2022, 12(9), 2101; https://doi.org/10.3390/diagnostics12092101 - 30 Aug 2022

Cited by 12 | Viewed by 2532

Abstract

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [¹⁸F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians’ corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Analysis in PET Imaging)

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