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New Technologies Used to Diagnose and Treat Lymphomas

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 2116

Special Issue Editor


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Guest Editor
Nuclear Medicine Division, Santa Croce and Carle Hospital, 12100 Cuneo, Italy
Interests: nuclear medicine; FDG-PET/CT; sarcopenia; cancer Imaging

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit original articles or reviews on lymphomas, one of the most common cancers in the world. There are different types of lymphomas with varied patterns of clinical behavior, prognosis, and responses to treatment. In recent years, new therapeutic strategies have been used and new diagnostic tools have been found, especially using artificial intelligence for diagnoses and treatment responses. However many studies are still needed to optimize diagnostic and therapeutic tools. In the era of immunotherapy, new diagnostic tools are needed to determine the correct response to therapy using artificial intelligence.

This Special Issue aims to showcase new therapeutic strategies increasingly targeted towards the individual patient. In addition, new tools are being used for diagnosis and response to treatments, both from a molecular and imaging perspective, with particular reference to PET/CT and MRI. In this setting, we must not forget artificial intelligence, a new and increasingly fundamental tool used in the diagnostic and therapeutic pathway of lymphomas.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: nuclear medicine, radiology, hematology, immunotherapy, and radiotherapy.

I look forward to receiving your contributions.

Dr. Ambra Buschiazzo
Guest Editor

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Keywords

  • lymphoma
  • PET/CT
  • immunotherapy
  • sarcopenia
  • artificial intelligence

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Published Papers (1 paper)

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Research

13 pages, 1721 KiB  
Article
The Role of [18F]FDG PET and Clinicopathologic Factors in Detecting and Predicting Bone Marrow Involvement in Non-Hodgkin Lymphoma
by Akram Al-Ibraheem, Ahmad Saad Abdlkadir, Nabil Hasasna, Hasan Alalawi, Ali Mohamedkhair, Salem Al-Yazjeen, Shahed Obeidat, Obayda Rabei, Dua’a Al-Jarrah, Omar Shahin, Kamal Al-Rabi, Mohammad Makoseh and Nidal Almasri
Cancers 2025, 17(2), 231; https://doi.org/10.3390/cancers17020231 - 13 Jan 2025
Cited by 1 | Viewed by 1752
Abstract
Background/Objectives: This study evaluates the diagnostic accuracy of [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) using bone marrow biopsy (BMB) and clinical follow-up as reference standards. It further identifies predictive factors for bone marrow involvement (BMI) in non-Hodgkin lymphoma (NHL) patients. [...] Read more.
Background/Objectives: This study evaluates the diagnostic accuracy of [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) using bone marrow biopsy (BMB) and clinical follow-up as reference standards. It further identifies predictive factors for bone marrow involvement (BMI) in non-Hodgkin lymphoma (NHL) patients. Methods: NHL patients who underwent [18F]FDG PET and BMB at diagnosis in a tertiary cancer center were included in this study. Diagnostic accuracy was analyzed, and logistic regression was performed to identify BMI predictors using Stata software version 17. A retrospective analysis of 262 NHL patients was conducted. Results: Concordance rates between [18F]FDG PET and BMB and between [18F]FDG PET and clinical follow-up were 75.6% and 88.1%, respectively. The primary cause of discordance between [18F]FDG PET and BMB was the detection of extra-iliac focal hypermetabolic bone marrow lesions by [18F]FDG PET, which were negative on BMB. The sensitivity, specificity, and accuracy of [18F]FDG PET were 62.9%, 80%, and 75.6%, respectively, with BMB as a reference, and 74.1%, 97.5%, and 88.2%, respectively, with clinical follow-up as a reference. The focal bone marrow [18F]FDG pattern was the most reliable indicator of BMI. Univariate logistic regression showed that advanced NHL stage, elevated alkaline phosphatase, thrombocytopenia, leukopenia, and elevated lactate dehydrogenase were significant predictors of BMI. Multivariate analysis revealed advanced NHL stage and thrombocytopenia as clinical predictors. Conclusions: [18F]FDG PET is a reliable tool for assessing BMI, providing comprehensive total-body evaluation and identifying extra-iliac involvement beyond the scope of BMB. The collective interpretation of molecular imaging, clinical, and biochemical factors is crucial for predicting BMI. Full article
(This article belongs to the Special Issue New Technologies Used to Diagnose and Treat Lymphomas)
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