The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer

Journal of Personalized Medicine, Journal Year: 2024, Volume and Issue: 14(3), P. 287 - 287

Published: March 7, 2024

Molecular imaging is a key tool in the diagnosis and treatment of prostate cancer (PCa). Magnetic Resonance (MR) plays major role this respect with nuclear medicine imaging, particularly, Prostate-Specific Membrane Antigen-based, (PSMA-based) positron emission tomography computed (PET/CT) also playing rapidly increasing importance. Another technology finding growing application across specifically molecular use machine learning (ML) artificial intelligence (AI). Several authoritative reviews are available MR-based sparsity PET/CT. This review will focus on AI for PCa. It aim to achieve two goals: firstly, give reader an introduction technologies available, secondly, provide overview applied PET/CT The clinical applications include diagnosis, staging, target volume definition planning, outcome prediction monitoring. ML AL techniques discussed radiomics, convolutional neural networks (CNN), generative adversarial (GAN) training methods: supervised, unsupervised semi-supervised learning.

Language: Английский

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Prostate-specific Membrane Antigen Reporting and Data System Version 2.0

European Urology, Journal Year: 2023, Volume and Issue: 84(5), P. 491 - 502

Published: July 4, 2023

Prostate-specific Membrane Antigen Reporting and Data System (PSMA-RADS) was introduced for standardized reporting, PSMA-RADS version 1.0 allows classification of lesions based on their likelihood representing a site prostate cancer PSMA-targeted positron emission tomography (PET). In recent years, this system has extensively been investigated. Increasing evidence accumulated that the different categories reflect actual meanings, such as true positivity in 4 5 lesions. Interobserver agreement studies demonstrated high concordance among broad spectrum 68Ga- or 18F-labeled, PSMA-directed radiotracers, even less experienced readers. Moreover, also applied to challenging clinical scenarios assist decision-making, example, avoid overtreatment oligometastatic disease. Nonetheless, with an increasing use 1.0, framework shown not only benefits, but limitations, follow-up assessment locally treated Thus, we aimed update include refined set order optimize lesion-level characterization best decision-making (PSMA-RADS 2.0).

Language: Английский

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Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT

Journal of Nuclear Medicine, Journal Year: 2024, Volume and Issue: 65(4), P. 643 - 650

Published: Feb. 29, 2024

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation prognosis on PET/CT. Methods: This retrospective study consisted 611 ¹⁸F-FDG PET/CT scans patients with lung cancer, melanoma, lymphoma, head neck breast 408 prostate-specific membrane antigen (PSMA) prostate cancer. The had nnU-net backbone learned the task PSMA images using limited annotations radiomics analysis. True-positive rate Dice similarity coefficient were assessed evaluate performance. Prognostic models imaging measures extracted from predicted segmentations perform risk stratification based follow-up levels, survival estimation by Kaplan–Meier method Cox regression analysis, pathologic complete response prediction after neoadjuvant chemotherapy. Overall accuracy area under receiver-operating-characteristic (AUC) curve assessed. Results: Our yielded median true-positive rates 0.75, 0.85, 0.87, 0.75 coefficients 0.81, 0.76, 0.83, 0.73 respectively, task. model an overall 0.83 AUC 0.86. Patients classified as low- intermediate- high-risk mean levels 18.61 727.46 ng/mL, respectively (P < 0.05). score was significantly associated univariable multivariable analyses Predictive only pretherapy both pre- posttherapy accuracies 0.72 0.84 AUCs respectively. Conclusion: proposed demonstrated accurate in across 6 types scans.

Language: Английский

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A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans

Cancers, Journal Year: 2024, Volume and Issue: 16(3), P. 486 - 486

Published: Jan. 23, 2024

Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) aims to evaluate AI’s ability enhance reporting, diagnostics, predictive capabilities for mPCa PSMA scans. Inclusion criteria covered studies using AI PET, excluding non-PSMA tracers. A search conducted Medline, Embase, Scopus inception July 2023. After screening 249 studies, 11 remained eligible inclusion. Due heterogeneity meta-analysis precluded. The prediction model risk bias assessment tool (PROBAST) indicated a low overall in ten though only one incorporated clinical parameters (such as age, Gleason score). demonstrated accuracy (98%) identifying lymph node involvement disease, albeit with sensitivity variation (62–97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, automating tasks accurately. In conclusion, showcases promising enhancing potential scans mPCa, addressing current limitations efficiency variability.

Language: Английский

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Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging

Seminars in Nuclear Medicine, Journal Year: 2023, Volume and Issue: 54(1), P. 141 - 149

Published: June 24, 2023

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number nuclear medicine physicians and radiologists to interpret these scans limited. Additionally, there variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning deep algorithms, are being used address challenges provide additional insights from images. aim this scoping review was summarize available research on development applications AI cancer imaging. A systematic literature search performed PubMed, Embase Cinahl according Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. total 26 publications were included synthesis. studies focus different aspects artificial PET/CT, detection primary tumor, local recurrence metastatic lesions, lesion classification, tumor quantification prediction/prognostication. Several show similar performances algorithms compared human interpretation. Few tools approved clinical practice. Major limitations include lack external validation prospective design. Demonstrating impact utility crucial their adoption healthcare settings. To take next step towards a clinically valuable tool that provides quantitative data, independent needed across institutions equipment ensure robustness.

Language: Английский

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Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy

Cancers, Journal Year: 2023, Volume and Issue: 15(13), P. 3416 - 3416

Published: June 29, 2023

Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains first sign aggressive disease; hence, better assessment potential long-term post-RP BCR-free survival crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting in PCa. A total 437 patients with PCa who underwent mpMRI followed by between 2008 and 2009 were enrolled; radiomics features extracted from T2-weighted imaging, apparent diffusion coefficient maps, contrast-enhanced sequences manually delineating index tumors. Deep same set deep neural network based on pretrained EfficentNet-B0. Here, we present clinical (six variables), model, DL (DLM-Deep feature), clinical–radiomics (CRM-Multi), clinical–DL (CDLM-Deep feature) that built Cox models regularized least absolute shrinkage selection operator. We compared their prognostic performances stratified fivefold cross-validation. In median follow-up 61 months, 110/437 experienced BCR. CDLM-Deep feature achieved best performance (hazard ratio [HR] = 7.72), DLM-Deep (HR 4.37) or RM-Multi 2.67). CRM-Multi performed moderately. results confirm superior our mpMRI-derived algorithm over conventional radiomics.

Language: Английский

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Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives

Cancers, Journal Year: 2023, Volume and Issue: 15(19), P. 4746 - 4746

Published: Sept. 27, 2023

Prostate cancer is the most frequent epithelial neoplasia after skin in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. imaging includes several radiological modalities, ranging ultrasonography, computed tomography (CT), magnetic resonance to nuclear medicine hybrid techniques such single-photon emission (SPECT)/CT positron (PET)/CT. Innovation radiopharmaceutical compounds has introduced specific tracers with diagnostic therapeutic indications, opening horizons targeted very effective clinical care for patients prostate cancer. The aim of present review illustrate current knowledge future perspectives medicine, including stand-alone theragnostic approaches, management initial staging advanced disease.

Language: Английский

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Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management

Cancers, Journal Year: 2024, Volume and Issue: 16(10), P. 1809 - 1809

Published: May 9, 2024

Background: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); biopsy; associated challenges and their clinical implications. Methods: A search PubMed database conducted based inclusion exclusion criteria for use DL methods within abovementioned areas. Results: total 784 articles were found, which, 64 included. Reconstruction prostate, detection stratification cancer, reconstruction PET/CT, ADT, biopsy analyzed in 21, 22, 6, 7, 2, 6 studies, respectively. Among studies describing MR-based purposes, datasets field power 3 T, 1.5 3/1.5 T used 18/19/5, 0/1/0, 3/2/1 respectively, 6/7 analyzing PET/CT which data from single institution. radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, [18F]PSMA-1007 5, 1, 1 study, Only two that context DT met criteria. Both performed single-institution dataset only manual labeling training data. Three each biopsy, single- multi-institutional datasets. TeUS, TRUS, MRI as input modalities two, three, one Conclusion: models show promise but are not yet ready due variability methods, labels, evaluation Conducting additional research while acknowledging all limitations outlined is crucial reinforcing utility effectiveness DL-based settings.

Language: Английский

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Deep learning based on 68Ga-PSMA-11 PET/CT for predicting pathological upgrading in patients with prostate cancer

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 13

Published: Jan. 8, 2024

To explore the feasibility and importance of deep learning (DL) based on 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT in predicting pathological upgrading from biopsy to radical prostatectomy (RP) patients with prostate cancer (PCa).

Language: Английский

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The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions

Medical Review, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Abstract The diagnosis and prognosis of Prostate cancer (PCa) have undergone a significant transformation with the advent prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET) imaging. PSMA-PET imaging has demonstrated superior performance compared to conventional methods by detecting PCa, its biochemical recurrence, sites metastasis higher sensitivity specificity. That now intersects rapid advances in artificial intelligence (AI) – including emergence generative AI. However, there are unique clinical challenges associated that still need be addressed ensure continued widespread integration into care research trials. Some those very wide dynamic range lesion uptake, benign uptake organs may adjacent disease, insufficient large datasets for training AI models, as well artifacts images. Generative e.g., adversarial networks, variational autoencoders, diffusion language models played crucial roles overcoming many such across various modalities, PET, computed tomography, magnetic resonance imaging, ultrasound, etc. In this review article, we delve potential role enhancing robustness utilization image analysis, drawing insights from existing literature while also exploring current limitations future directions domain.

Language: Английский

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