Position dependence of recovery coefficients in 177Lu-SPECT/CT reconstructions – phantom simulations and measurements

EJNMMI Physics, Год журнала: 2024, Номер 11(1)

Опубликована: Июнь 28, 2024

Although the importance of quantitative SPECT has increased tremendously due to newly developed therapeutic radiopharmaceuticals, there are still no accreditation programs harmonize imaging. Work is currently underway develop an for

Язык: Английский

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Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology

European Radiology, Год журнала: 2023, Номер 33(11), С. 7556 - 7560

Опубликована: Июнь 26, 2023

Язык: Английский

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Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET

EJNMMI Research, Год журнала: 2022, Номер 12(1)

Опубликована: Дек. 29, 2022

Accurate classification of sites interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation prostate cancer (PCa) from foci physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level patient-level PSMA PET patients with PCa. This was IRB-approved, HIPAA-compliant, retrospective study. Lesions [18F]DCFPyL PET/CT scans were assigned reporting data system (PSMA-RADS) categories randomly partitioned into training, validation, test sets. The extracted image features, radiomic tissue type information cropped slice containing lesion performed PSMA-RADS PCa classification. Performance evaluated by assessing area under receiver operating characteristic curve (AUROC). A t-distributed stochastic neighbor embedding (t-SNE) analysis performed. Confidence probability scores measured. Statistical significance determined using two-tailed t test. 267 men had 3794 lesions categories. yielded AUROC values 0.87 0.90 classification, respectively, set. 0.92 0.85 t-SNE revealed learned relationships between disease findings. Mean confidence reflected expected accuracy significantly higher correct predictions than incorrect (P < 0.05). Measured likelihood consistent framework. provided images. interpretable that may assist physicians in making more informed clinical decisions.

Язык: Английский

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The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation

Seminars in Radiation Oncology, Год журнала: 2023, Номер 33(3), С. 252 - 261

Опубликована: Июнь 16, 2023

Язык: Английский

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Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer

British Journal of Radiology, Год журнала: 2023, Номер 96(1146)

Опубликована: Март 3, 2023

Complete pathological response to neoadjuvant systemic treatment (NAST) in some subtypes of breast cancer (BC) has been used as a surrogate long-term outcome. The possibility predicting BC NAST based on the baseline 18F-Fluorodeoxyglucose positron emission tomography (FDG PET), without need an interim study, is focus recent discussion. This review summarises characteristics and results available studies regarding potential impact heterogeneity features primary tumour burden FDG PET patients. Literature search was conducted PubMed database relevant data from each selected study were collected. A total 13 eligible for inclusion, all them published over last 5 years. Eight out analysed indicated association between PET-based uptake prediction NAST. When associated with derived, these varied studies. Therefore, definitive reproducible findings across series difficult establish. lack consensus may reflect low number included series. clinical relevance this topic justifies further investigation about predictive role PET.

Язык: Английский

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Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Фев. 8, 2024

Abstract This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[ 18 F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: first during treatment and second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated 16 different models trained by combining various feature selection methods time-to-event algorithms. Performance was assessed cross-validation. To evaluate robustness, an additional dataset including 35 with single PET scan therapy discontinuation used. Model performance compared strategy extracting informative set applying them 2 scans. Delta-absolute achieved highest when pipeline directly applied 18-patient subset (support vector machine (SVM) recursive elimination (RFE): C-index = 0.783 [0.744–0.818]). result remained consistent transferring (SVM + RFE: 0.751 [0.716–0.784], p 0.06). In addition, it significantly outperformed delta-absolute (C-index 0.584 [0.548–0.620], < 0.001) single-time-point 0.546 [0.512–0.580], 0.001), highlighting considerable potential delta cancer cohorts.

Язык: Английский

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PET radiomics for histologic subtype classification of non-small cell lung cancer: a systematic review and meta-analysis

European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

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Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies

Cancer Imaging, Год журнала: 2025, Номер 25(1)

Опубликована: Март 12, 2025

Abstract Purpose This study aimed to select robust features against lung motion in a phantom and use them as input feature selection algorithms machine learning classifiers clinical predict the lymphovascular invasion (LVI) of non-small cell cancer (NSCLC). The results were also compared with conventional techniques without considering robustness radiomic features. Methods An in-house developed was two 22mm lesion sizes based on study. A specific motor built simulate orthogonal directions. Lesions both studies segmented using Fuzzy C-means-based segmentation algorithm. After inducing extracting 105 4 sets, including shape, first-, second-, higher-order statistics from each region interest (ROI) image, statistical analyses performed motion. Subsequently, these total extracted 126 data. Various (FS) multiple (ML) implemented LVI NSCLC, followed by comparing predicting common not Results Our demonstrated that selecting FS ML surges sensitivity, which has gentle negative effect accuracy area under curve (AUC) predictions commonly used methods 12 15 outcomes. top performance prediction achieved NB classifier RFE 95% AUC, 67% accuracy, 100% sensitivity. Moreover, belonged Boruta 92% 86% Conclusion Robustness over various influential factors is critical should be considered Selecting solution overcome low reproducibility Although setting minor impact AUC prediction, it boosts sensitivity large extent.

Язык: Английский

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Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation

European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2023, Номер 50(6), С. 1720 - 1734

Опубликована: Янв. 24, 2023

Язык: Английский

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A radiomic‐ and dosiomic‐based machine learning regression model for pretreatment planning in ¹⁷⁷Lu‐DOTATATE therapy

Medical Physics, Год журнала: 2023, Номер 50(11), С. 7222 - 7235

Опубликована: Сен. 18, 2023

Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements final therapeutic outcome. Only a comprehensive definition dosage window encompassing range absorbed doses, that is, helpful without being detrimental can therapy individualization and improved outcomes. As result, setting dose safety limits for organs at risk (OARs) requires knowledge dose-effect relationship. Data sets consistent reliable inter-center findings are required characterize this relationship.We developed standardized new model consisting predictive procedure OARs patients with neuroendocrine tumors (NETs) treated 177 Lu-DOTATATE (Lutathera). In retrospective study described herein, we used machine learning (ML) regression algorithms predict doses by exploiting combination radiomic dosiomic features extracted from patients' imaging data.Pretreatment posttreatment data 20 NETs were collected two clinical centers. A total 3412 computed tomography (CT) scans maps, respectively. All maps generated using Monte Carlo simulations. An ML was designed based on predicting every OAR (liver, left kidney, right spleen) before after between each session, thus any possible radiotoxic effects.We evaluated nine algorithms. Our achieved mean absolute error (MAE, Gy) 0.61 liver, 1.58 spleen, 1.30 1.35 kidney pretherapy 68 Ga-DOTATOC positron emission (PET)/CT posttherapy single photon (SPECT)/CT scans. Τhe best performance observed gradient boost extra tree regressor spleen. Evaluation model's according its ability PET/CT treatment cycle SPECT/CT as well consequent revealed differences ranges -0.55 0.68 Gy. Incorporating radiodosiomics first further precision minimized standard deviation predictions out 12 instances. average improvement 57.34% (range: 17.53%-96.12%). However, it's important note three instances (i.e., Ga,C.1 → C3 spleen C2 kidney) did not observe an (absolute 0.17, 0.08, 0.05 Gy, respectively). Wavelet-based proved have high correlated value, whereas non-linear-based be more capable than linear-based producing precise prediction our case.The radiomics dosiomics has potential utility personalized radiotherapy (PMR) response evaluation prediction. These radiodosiomic potentially provide information disease recurrence highly useful decision-making, especially regarding escalation issues.

Язык: Английский

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