Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research

Computers in Biology and Medicine, Год журнала: 2024, Номер 179, С. 108827 - 108827

Опубликована: Июль 3, 2024

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) offers unique insights into tumor biology treatment response, it is imperative elucidate challenges constraints inherent this domain facilitate their translation clinical practice. This review examines limitations applying PET imaging, synthesizing findings last five years (2019-2023) highlights significance addressing these realize full molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, Web Science, using keywords relevant issues Only studies published peer-reviewed journals were eligible inclusion review. Although many have highlighted predicting assessing heterogeneity, enabling risk stratification, personalized therapy selection, various regarding practical implementation proposed models still need be addressed. illustrates cancer types, encompassing both phantom investigations. The analyzed highlight importance reproducible segmentation methods, standardized pre-processing post-processing methodologies, create large multicenter registered a centralized database promote continuous validation integration

Язык: Английский

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Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Diagnostics, Год журнала: 2024, Номер 14(2), С. 181 - 181

Опубликована: Янв. 14, 2024

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with therapeutic compounds that deliver ionizing radiation. Given introduction very promising radiopharmaceuticals, radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used diagnose treat patients metastatic neuroendocrine tumors (NETs) prostate cancer. In parallel, radiomics artificial intelligence (AI), important areas quantitative image analysis, paving way for significantly enhanced workflows diagnostic theranostic fields, from data processing clinical decision support, improving patient selection, personalized treatment strategies, response prediction, prognostication. Furthermore, AI has potential tremendous effectiveness dosimetry which copes complex time-consuming tasks RPT workflow. The present work provides comprehensive overview application radiotheranostics, focusing on SSTR- or PSMA-targeting radioligands, describing fundamental concepts specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, 225Ac. Specifically, contributions via towards improved acquisition, reconstruction, response, segmentation, restaging, lesion classification, dose estimation well ongoing developments future directions discussed.

Язык: Английский

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Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images

Mathematics, Год журнала: 2024, Номер 12(9), С. 1296 - 1296

Опубликована: Апрель 25, 2024

For decades, wavelet theory has attracted interest in several fields dealing with signals. Nowadays, it is acknowledged that not very suitable to face aspects of multidimensional data like singularities and this led the development other mathematical tools. A recent application radiomics, an emerging field aiming improve diagnostic, prognostic predictive analysis various cancer types through features extracted from medical images. In paper, for a radiomics study prostate magnetic resonance (MR) images, we apply similar but more sophisticated tool, namely shearlet transform which, contrast transform, allows us examine variations along orientations. particular, conduct parallel based on two different transformations highlight better performance (evaluated terms statistical measures) use (in absolute value). The results achieved suggest taking into consideration studies contexts.

Язык: Английский

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Theranostic Approaches for Gastric Cancer: An Overview of In Vitro and In Vivo Investigations

Cancers, Год журнала: 2024, Номер 16(19), С. 3323 - 3323

Опубликована: Сен. 28, 2024

Gastric cancer (GC) is the second most common cause of cancer-related death worldwide and a serious public health concern. This high rate mostly caused by late-stage diagnoses, which lead to poor treatment outcomes. Radiation immunotherapy targeted therapies are becoming increasingly popular in GC treatment, addition surgery systemic chemotherapy. In this review, we have focused on both vitro vivo research, presents summary recent developments for gastric cancer. We explore therapy approaches, including integrin receptors, HER2, Claudin 18, glutathione-responsive systems. For instance, targeting receptors such as αvβ3 αvβ5 integrins shown promise enhancing diagnostic precision efficacy. Furthermore, nanotechnology provides novel approaches drug delivery imaging. These include nanoplatforms cyclic RGD peptide-conjugated nanoparticles. strategies seek reduce toxicity while increasing specificity To sum up, review addresses significance personalized medicine advancements cancer-targeted therapies. It explores potential methods prognosis future.

Язык: Английский

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matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model

Journal of Imaging, Год журнала: 2022, Номер 8(8), С. 221 - 221

Опубликована: Авг. 18, 2022

Radiomics aims to support clinical decisions through its workflow, which is divided into: (i) target identification and segmentation, (ii) feature extraction, (iii) selection, (iv) model fitting. Many radiomics tools were developed fulfill the steps mentioned above. However, date, users must switch different software complete workflow. To address this issue, we a new free user-friendly framework, namely matRadiomics, allows user: import inspect biomedical images, identify segment target, extract features, reduce select them, (v) build predictive using machine learning algorithms. As result, images can be visualized segmented and, integration of Pyradiomics into radiomic features extracted. These selected hybrid descriptive-inferential method, consequently, used train three classifiers: linear discriminant analysis, k-nearest neighbors, vector machines. Model validation performed k-fold cross-Validation stratified cross-validation. Finally, performance metrics each are shown in graphical interface matRadiomics. In study, discuss architecture, application, future development demonstrate working principles real case study with aim establishing reference standard for whole starting from image visualization up implementation.

Язык: Английский

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A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models

Journal of Imaging, Год журнала: 2022, Номер 8(4), С. 92 - 92

Опубликована: Март 30, 2022

The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET three time points after injection. Specifically, the were divided into group 1 (acquisition h [64Cu] administration, n = 3 mice), 2 4 [64Cu]chelator and 24 mice). Successively, all PET studies segmented means of registration with standard template space (3D whole-body Digimouse atlas), 108 radiomics features extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, lung) investigate possible changes over biodistribution. one-way analysis variance post hoc Tukey Honestly Significant Difference test revealed that, while lung districts showed very low percentage significant variations (p-value < 0.05) among groups mice, large number (greater than 60% 50%, respectively) that varied significantly between observed bladder indicating uptake time. proposed methodology may improve method calculating open way towards decision support system field new radiopharmaceuticals used preclinical trials.

Язык: Английский

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Peptide Receptor Radionuclide Therapy of Neuroendocrine Tumors: Agonist, Antagonist and Alternatives

Seminars in Nuclear Medicine, Год журнала: 2024, Номер 54(4), С. 557 - 569

Опубликована: Март 15, 2024

Peptide receptor radionuclide therapy (PRRT) today is a well-established treatment strategy for patients with neuroendocrine tumors (NET). First performed already more than 30 years ago, PRRT was incorporated only in recent into the major oncology guidelines, based on its proven efficacy and safety clinical trials. Following phase 3 NETTER-1 trial, which led to final registration of radiopharmaceutical Luthatera® G1/G2 NET 2017, long-term results NETTER-2 trial may pave way new option also advanced G2/G3 as first-line therapy. The growing knowledge about synergistic effect combined therapies could allow alternative (re)treatment options patients, order create tailored strategy. evolving thera(g)nostic concept be applied identification who might benefit from different image-guided strategies. In this scenario, use dual tracer PET/CT using both [18F]F-FDG/[68Ga]Ga-DOTA-somatostatin analog (SSA) diagnosis follow-up, under discussion result powerful prognostic tool. addition, strategies metabolic pathways, radioisotopes, or combinations medical approaches applied. A number promising "doors" thus open near future - "key" will thera(g)nostic!

Язык: Английский

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Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis

Academic Radiology, Год журнала: 2024, Номер 31(10), С. 3956 - 3967

Опубликована: Апрель 13, 2024

Язык: Английский

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Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers

Seminars in Nuclear Medicine, Год журнала: 2023, Номер 54(1), С. 171 - 180

Опубликована: Сен. 24, 2023

Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend also been seen medicine recent years, particularly field of imaging. Machine learning (ML), deep (DL), and their methods (eg, SVM, CNN), as well radiomics, are terminologies that have introduced to this and, some extent, become familiar expert clinicians. PET is one modalities enhanced via these state-of-the-art algorithms. robust imaging technique further merged with anatomical modalities, such computed tomography (CT) magnetic resonance (MRI), provide reliable hybrid PET/CT PET/MRI. Applying AI-based algorithms on different components (PET, CT, MRI) resulted promising results, maximizing value However, [

Язык: Английский

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Improvements and future perspective in diagnostic tools for neuroendocrine neoplasms

Expert Review of Endocrinology & Metabolism, Год журнала: 2024, Номер 19(4), С. 349 - 366

Опубликована: Июнь 3, 2024

Neuroendocrine neoplasms (NENs) represent a complex group of tumors arising from neuroendocrine cells, characterized by heterogeneous behavior and challenging diagnostics. Despite advancements in medical technology, NENs present major challenge early detection, often leading to delayed diagnosis variable outcomes. This review aims provide an in-depth analysis current diagnostic methods as well the evolving future directions strategies for NENs.

Язык: Английский

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