A Systematic Review of the Applications of Deep Learning for the Interpretation of Positron Emission Tomography Images of Patients with Lymphoma DOI Open Access
Theofilos Kanavos, Effrosyni Birbas, Theodoros P. Zanos

et al.

Cancers, Journal Year: 2024, Volume and Issue: 17(1), P. 69 - 69

Published: Dec. 29, 2024

Background: Positron emission tomography (PET) is a valuable tool for the assessment of lymphoma, while artificial intelligence (AI) holds promise as reliable resource analysis medical images. In this context, we systematically reviewed applications deep learning (DL) interpretation lymphoma PET Methods: We searched PubMed until 11 September 2024 studies developing DL models evaluation images patients with lymphoma. The risk bias and applicability concerns were assessed using prediction model (PROBAST). articles included categorized presented based on task performed by proposed models. Our study was registered international prospective register systematic reviews, PROSPERO, CRD42024600026. Results: From 71 papers initially retrieved, 21 total 9402 participants ultimately in our review. achieved promising performance diverse tasks, namely, detection histological classification lesions, differential diagnosis from other conditions, quantification metabolic tumor volume, treatment response survival areas under curve, F1-scores, R2 values up to 0.963, 87.49%, 0.94, respectively. Discussion: primary limitations several small number absence external validation. conclusion, can reliably be aided models, which are not designed replace physicians but assist them managing large volumes scans through rapid accurate calculations, alleviate their workload, provide decision support tools precise care improved outcomes.

Language: Английский

The Evolution of Artificial Intelligence in Nuclear Medicine DOI Creative Commons
Leonor Lopes, Alejandro López-Montes, Yizhou Chen

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Nuclear medicine has continuously evolved since its beginnings, constantly improving the diagnosis and treatment of various diseases. The integration artificial intelligence (AI) is one latest revolutionizing chapters, promising significant advancements in diagnosis, prognosis, segmentation, image quality enhancement, theranostics. Early AI applications nuclear focused on diagnostic accuracy, leveraging machine learning algorithms for disease classification outcome prediction. Advances deep learning, including convolutional more recently transformer-based neural networks, have further enabled precise segmentation as well low-dose imaging, patient-specific dosimetry personalized treatment. Generative AI, driven by large language models diffusion techniques, now allowing process, interpretation, generation complex medical images. Despite these achievements, challenges such data scarcity, heterogeneity, ethical concerns remain barriers to clinical translation. Addressing issues through interdisciplinary collaboration will pave way a broader adoption medicine, potentially enhancing patient care optimizing therapeutic outcomes.

Language: Английский

Citations

1
Comparing the clinical value of baseline [68 Ga]Ga-FAPI-04 PET/CT and [18F]F-FDG PET/CT in pancreatic ductal adenocarcinoma: additional prognostic value of the distal pancreatitis DOI
Jie Ding, Jiangdong Qiu,

Zhixin Hao

et al.

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2023, Volume and Issue: 50(13), P. 4036 - 4050

Published: July 26, 2023

Language: Английский

Citations

13
[18F] AlF-NOTA-FAPI-04 PET/CT can predict treatment response and survival in patients receiving chemotherapy for inoperable pancreatic ductal adenocarcinoma DOI

Ziyuan Zhu,

Kai Cheng, Yun Zhang

et al.

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2023, Volume and Issue: 50(11), P. 3425 - 3438

Published: June 17, 2023

Language: Английский

Citations

12
Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke DOI

Tzu-Hsien Yang,

Yingying Su, Chia-Ling Tsai

et al.

European Journal of Radiology, Journal Year: 2024, Volume and Issue: 174, P. 111405 - 111405

Published: March 3, 2024

Language: Английский

Citations

4
Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis DOI Open Access
Saeed Shafiee Hasanabadi, Seyed Mahmud Reza Aghamiri, Ahmad Ali Abin

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(20), P. 3511 - 3511

Published: Oct. 17, 2024

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature lymphoma makes challenging to definitively pinpoint valuable biomarkers for predicting tumor biology selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically

Language: Английский

Citations

4
Integrating AI Into PET/CT and PET/MRI DOI

R. T. Subhalakshmi,

M. Nivaashini,

Gowrishankar Ganesh

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 311 - 332

Published: Feb. 28, 2025

Positron emission tomography combined with artificial intelligence is becoming a powerful tool for drug discovery. By analyzing PET imaging data AI algorithms, researchers can find new targets, improve treatment plans, and better understand diseases. PET/CT leading cancer method used in clinical practice, while combining MRI's anatomical PET's functional offers exciting research opportunities. PET/MRI applications cardiology, neurology, oncology, inflammation are also expanding. Advances like Total-Body could revolutionize therapeutic imaging, providing deeper insights into human physiology Integrating AI, machine learning, deep learning imaging—from image capture to interpretation—has further improved hybrid techniques PET/MRI, enhancing their diagnostic capabilities.

Language: Английский

Citations

0
The Role of CT Radiomics Analysis in Predicting Overall Survival Following initial Chemotherapy for Diffuse Large B-cell Lymphoma DOI Creative Commons

Manxin Yin,

Chunhai Yu,

Jianxin Zhang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: April 19, 2024

Abstract Objectives: The current study sought to determine the potential use of CT radiomics model in predicting overall survival DLBCL patients. Methods: images and clinical data patients receiving chemotherapy from January 2013 May 2018 were retrospectively analyzed, 130 included categorized as training cohort (n=91) validation (n=39) at a 7:3 ratio. The features extracted, Rad-score was calculated using LASSO (least absolute shrinkage selection operator) algorithm. Univariate multivariate Cox regression used screen independent risk factors, then nomogram developed jointly with Rad-score. ROC(operating characteristic curve), calibration curve, decision curve assessments utilized assess model's effectiveness, accuracy, significance OS. Results: In total, 878 obtained each patient, 15 highly correlated OS screened calculate predict Patients <-0.51 had shorter time, those >-0.51 longer time. A constructed by combining factors (Ann Arbor staging, IPI score, PS, effectiveness) based on analysis cohorts, AUC values for 3 5 years 0.860 0.810, respectively, 0.838 0.816 which higher than (0.744 0.763, 0.787 0.563). Furthermore, evaluations revealed that strongly agrees has high value Conclusion: characteristics have better prediction efficacy following first-line treatment patients, it exceeds model.

Language: Английский

Citations

0
Comparison of Cox regression and generalized Cox regression models to machine learning in predicting survival of children with diffuse large B-cell lymphoma DOI Open Access

Jia-Jia Qin,

Xiao-Xiao Zhu,

Xi Chen

et al.

Translational Cancer Research, Journal Year: 2024, Volume and Issue: 13(7), P. 3370 - 3381

Published: July 1, 2024

Background: The incidence of diffuse large B-cell lymphoma (DLBCL) in children is increasing globally. Due to the immature immune system children, prognosis DLBCL quite different from that adults. We aim use multicenter retrospective analysis for study disease. Methods: For our analysis, we retrieved data Surveillance, Epidemiology and End Results (SEER) database included 836 patients under 18 years old who were treated at 22 central institutions between 2000 2019. randomly divided into a modeling group validation based on ratio 7:3. Cox stepwise regression, generalized regression eXtreme Gradient Boosting (XGBoost) used screen all variables. selected prognostic variables construct nomogram through regression. importance was ranked using XGBoost. predictive performance model assessed by C-index, area curve (AUC) receiver operating characteristic (ROC) curve, sensitivity specificity. consistency evaluated calibration curve. clinical practicality verified decision (DCA). Results: ROC demonstrated models except non-proportional hazards non-log linearity (NPHNLL) model, achieved AUC values above 0.7, indicating high accuracy. DCA further confirmed strong practicability. Conclusions: In this study, successfully constructed machine learning combining XGBoost with models. This integrated approach accurately predicts multiple dimensions. These findings provide scientific basis accurate prediction.

Language: Английский

Citations

0
Follicular Lymphoma Grading Based on 3D-DDcGAN and Bayesian CNN Using PET-CT Images DOI
Lulu He, Chunjun Qian, Yue Teng

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 21 - 30

Published: Oct. 4, 2024

Language: Английский

Citations

0
Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning DOI Creative Commons
Jianxin Chen, Fengyi Lin,

Zhaoyan Dai

et al.

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(10)

Published: Oct. 9, 2024

We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on multimodal PET-CT deep features radiomics signature (DFR-signature).

Language: Английский

Citations

0