Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics DOI Creative Commons

Xinchao Zhang,

Fenglian Jing, Yujing Hu

et al.

EJNMMI Research, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 13, 2024

A considerable portion of patients with fever unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as most prevalent etiology associated Distinguishing between benign malignant lymph node lesions is a major challenge for physicians an urgent clinical concern patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing lesions. This study utilizes PET/CT radiomics to differentiate in FUO, aiming improve diagnostic accuracy. Data were collected from 204 who underwent 18F-FDG examinations 114 90 Patients randomly divided into training testing groups at ratio 7:3. total 15 effective features obtained least absolute shrinkage selection operator (LASSO) algorithm. Machine learning models constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) algorithms. In group, area under curve (AUC) values predicting cases LR, SVM, RF, KNN 0.936, 0.930, 0.998, 0.938, respectively. There statistically significant differences AUC RF other (all P < 0.001). four 0.860, 0.866, 0.915, 0.891, respectively, no observed among them > 0.05). The decision analysis (DCA) curves model outperformed those three both groups. demonstrated promising performance discriminating showing best performance.

Language: Английский

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
Semiquantitative 2-[18F]FDG PET/CT-based parameters role in lymphoma DOI Creative Commons
Domenico Albano, Marco Ravanelli,

Rexhep Durmo

et al.

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 11

Published: Dec. 18, 2024

2-deoxy-2-[

Language: Английский

Citations

3
Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics DOI Creative Commons

Xinchao Zhang,

Fenglian Jing, Yujing Hu

et al.

EJNMMI Research, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 13, 2024

A considerable portion of patients with fever unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as most prevalent etiology associated Distinguishing between benign malignant lymph node lesions is a major challenge for physicians an urgent clinical concern patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing lesions. This study utilizes PET/CT radiomics to differentiate in FUO, aiming improve diagnostic accuracy. Data were collected from 204 who underwent 18F-FDG examinations 114 90 Patients randomly divided into training testing groups at ratio 7:3. total 15 effective features obtained least absolute shrinkage selection operator (LASSO) algorithm. Machine learning models constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) algorithms. In group, area under curve (AUC) values predicting cases LR, SVM, RF, KNN 0.936, 0.930, 0.998, 0.938, respectively. There statistically significant differences AUC RF other (all P < 0.001). four 0.860, 0.866, 0.915, 0.891, respectively, no observed among them > 0.05). The decision analysis (DCA) curves model outperformed those three both groups. demonstrated promising performance discriminating showing best performance.

Language: Английский

Citations

0