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Chua BJG, Low CE, Yau CE, Tan YH, Chiang J, Chang EWY, Chan JY, Poon EYL, Somasundaram N, Rashid MFBH, Tao M, Lim ST, Yang VS. Recent updates on central nervous system prophylaxis in patients with high-risk diffuse large B-cell lymphoma. Exp Hematol Oncol 2024; 13:1. [PMID: 38173015 PMCID: PMC10765685 DOI: 10.1186/s40164-023-00467-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
The use of central nervous system (CNS) prophylaxis for patients with diffuse large B-cell lymphoma (DLBCL) remains controversial. Although uncommon, CNS relapses are invariably fatal in this otherwise curable disease. Accurate identification of patients at risk and the optimal approach to CNS prophylaxis therefore remains an area of unmet need. The existing literature, largely retrospective in nature, provides mixed conclusions regarding the efficacy of CNS prophylaxis. The utility of CNS prophylaxis has itself been challenged. In this review, we dissect the issues which render the value of CNS prophylaxis uncertain. We first compare international clinical guidelines for CNS prophylaxis. We then interrogate the factors that should be used to identify high-risk patients accurately. We also explore how clinical patterns of CNS relapse have changed in the pre-rituximab and rituximab era. We then discuss the efficacy of CNS-directed approaches, intensification of systemic treatment and other novel approaches in CNS prophylaxis. Improved diagnostics for early detection of CNS relapses and newer therapeutics for CNS prophylaxis are areas of active investigation. In an area where prospective, randomized studies are impracticable and lacking, guidance for the use of CNS prophylaxis will depend on rigorous statistical review of retrospective data.
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Affiliation(s)
- Bernard Ji Guang Chua
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
| | - Chen Ee Low
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore, 117597, Singapore
| | - Chun En Yau
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore, 117597, Singapore
| | - Ya Hwee Tan
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
| | - Jianbang Chiang
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
| | - Esther Wei Yin Chang
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
| | - Jason Yongsheng Chan
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
- Duke-NUS Medical School, Oncology Academic Clinical Program, 8 College Road, Singapore, 169857, Singapore
| | - Eileen Yi Ling Poon
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
| | - Nagavalli Somasundaram
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
- Duke-NUS Medical School, Oncology Academic Clinical Program, 8 College Road, Singapore, 169857, Singapore
| | - Mohamed Farid Bin Harunal Rashid
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
- Duke-NUS Medical School, Oncology Academic Clinical Program, 8 College Road, Singapore, 169857, Singapore
| | - Miriam Tao
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
- Duke-NUS Medical School, Oncology Academic Clinical Program, 8 College Road, Singapore, 169857, Singapore
| | - Soon Thye Lim
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore
- Duke-NUS Medical School, Oncology Academic Clinical Program, 8 College Road, Singapore, 169857, Singapore
| | - Valerie Shiwen Yang
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore, 169610, Singapore.
- Duke-NUS Medical School, Oncology Academic Clinical Program, 8 College Road, Singapore, 169857, Singapore.
- Translational Precision Oncology Lab, Institute of Molecular and Cell Biology (IMCB), 61 Biopolis Dr Proteos, Singapore, 138673, A*STAR, Singapore.
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Xu C, Feng J, Yue Y, Cheng W, He D, Qi S, Zhang G. A hybrid few-shot multiple-instance learning model predicting the aggressiveness of lymphoma in PET/CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107872. [PMID: 37922655 DOI: 10.1016/j.cmpb.2023.107872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 09/29/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Patients with aggressive non-Hodgkin lymphoma (NHL) undergo distinct therapy strategies compared with indolent NHL patients. However, it is challenging to estimate NHL aggressiveness based on visual inspection of positron emission tomography (PET) or computed tomography (CT) images. Since diffuse large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL) are the most typical and dominant aggressive and indolent NHL, respectively, this study aims to develop an artificial-intelligence-enabled model to distinguish DLBCL from FL in PET/CT images as the first step to tackle this challenge. METHODS We propose a hybrid few-shot multiple-instance learning model to predict the aggressiveness of the NHL. First, rotation-based self-supervision learning (SSL) has been employed to train the encoder on a large-scale, publicly available CT image dataset. Second, hybrid instance-level features are obtained for each NHL lesion by combining deep features with the radiomics features from both PET and CT modalities. Third, instance-level features are transformed into bag-level (or patient-level) representations. Finally, bag-level representations are fed into a distance-based classifier through few-shot learning to predict NHL aggressiveness. RESULTS Our model achieves an accuracy of 0.751 ± 0.008, a sensitivity of 0.787 ± 0.012, a specificity of 0.715 ± 0.013, an F1-score of 0.753 ± 0.009, and an area under the curve (AUC) of 0.795 ± 0.009 at the bag level. It outperforms the typical counterparts that use the radiomic features, random forest for feature selection, and support vector machines (SVMs) as classifiers. The three counterparts yield accuracies of 0.714 ± 0.023, 0.705 ± 0.008, and 0.698 ± 0.008, respectively. Moreover, settings of the SSL training dataset (Deep lesion) and task (rotation), hybrid CT and radiomic PET features, the pool-layer strategy of maximum, and distance-based classifier generate the best model. CONCLUSIONS A hybrid few-shot multiple-instance learning model can predict lymphoma aggressiveness in PET/CT images and could be a potential tool for determining therapy strategies. Hybrid features and the combination of SSL, few-shot learning, and weakly supervised learning are the two powerful pillars of the model, and these can be expanded to other medical applications with limited samples and incomplete annotations.
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Affiliation(s)
- Caiwen Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Jie Feng
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wanjun Cheng
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Guojun Zhang
- Department of Hematology, Shengjing Hospital of China Medical University, Shenyang, China.
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3
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Tavakkoli M, Barta SK. 2024 Update: Advances in the risk stratification and management of large B-cell lymphoma. Am J Hematol 2023; 98:1791-1805. [PMID: 37647158 DOI: 10.1002/ajh.27075] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 09/01/2023]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with varying clinical outcomes. Our understanding of its molecular makeup continues to improve risk stratification, and artificial-intelligence and ctDNA-based analyses have the potential to enhance risk assessment and disease monitoring. R-CHOP and Pola-R-CHP are used in the frontline setting; chimeric antigen receptor therapy (CART) is now the new standard-of-care for most with primary refractory disease; both CART and autologous stem cell transplantation are utilized in the relapsed and refractory setting. In this review, we summarize the classification and management of DLBCL with an emphasis on recent advances in the field.
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Affiliation(s)
- Montreh Tavakkoli
- Department of Hematology Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stefan K Barta
- Department of Hematology Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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4
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Ferrández MC, Golla SSV, Eertink JJ, de Vries BM, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Schilder L, Heymans MW, Zijlstra JM, Boellaard R. Sensitivity of an AI method for [ 18F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols. EJNMMI Res 2023; 13:88. [PMID: 37758869 PMCID: PMC10533444 DOI: 10.1186/s13550-023-01036-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Convolutional neural networks (CNNs), applied to baseline [18F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols. Baseline [18F]FDG PET/CT scans were collected from 20 DLBCL patients. EARL1, EARL2 and high-resolution (HR) protocols were applied per scan, generating three images with different image qualities. Image-based transformation was applied by blurring EARL2 and HR images to generate EARL1 compliant images using a Gaussian filter of 5 and 7 mm, respectively. MIPs were generated for each of the reconstructions, before and after image transformation. An in-house developed CNN predicted the probability of tumor progression within 2 years for each MIP. The difference in probabilities per patient was then calculated between both EARL2 and HR with respect to EARL1 (delta probabilities or ΔP). We compared these to the probabilities obtained after aligning the data with ComBat using the difference in median and interquartile range (IQR). RESULTS CNN probabilities were found to be sensitive to different reconstruction protocols (EARL2 ΔP: median = 0.09, interquartile range (IQR) = [0.06, 0.10] and HR ΔP: median = 0.1, IQR = [0.08, 0.16]). Moreover, higher resolution images (EARL2 and HR) led to higher probability values. After image-based and ComBat transformation, an improved agreement of CNN probabilities among reconstructions was found for all patients. This agreement was slightly better after image-based transformation (transformed EARL2 ΔP: median = 0.022, IQR = [0.01, 0.02] and transformed HR ΔP: median = 0.029, IQR = [0.01, 0.03]). CONCLUSION Our CNN-based outcome predictions are affected by the applied reconstruction protocols, yet in a predictable manner. Image-based harmonization is a suitable approach to harmonize CNN predictions across image reconstruction protocols.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart M de Vries
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Louise Schilder
- Department of Internal Medicine, Amstelland Hospital, Amstelveen, The Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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5
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Alderuccio JP, Kuker RA, Yang F, Moskowitz CH. Quantitative PET-based biomarkers in lymphoma: getting ready for primetime. Nat Rev Clin Oncol 2023; 20:640-657. [PMID: 37460635 DOI: 10.1038/s41571-023-00799-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2023] [Indexed: 08/20/2023]
Abstract
The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.
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Affiliation(s)
- Juan Pablo Alderuccio
- Department of Medicine, Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Russ A Kuker
- Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Fei Yang
- Department of Radiation Oncology, Division of Medical Physics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Craig H Moskowitz
- Department of Medicine, Division of Hematology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
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6
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Ferrández MC, Golla SSV, Eertink JJ, de Vries BM, Lugtenburg PJ, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Kurch L, Hüttmann A, Hanoun C, Dührsen U, de Vet HCW, Zijlstra JM, Boellaard R. An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients. Sci Rep 2023; 13:13111. [PMID: 37573446 PMCID: PMC10423266 DOI: 10.1038/s41598-023-40218-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023] Open
Abstract
Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart M de Vries
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lars Kurch
- Department of Nuclear Medicine, Clinic and Polyclinic for Nuclear Medicine, University of Leipzig, Leipzig, Germany
| | - Andreas Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christine Hanoun
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Ulrich Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Henrica C W de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Methodology, Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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7
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Barrington SF. Advances in positron emission tomography and radiomics. Hematol Oncol 2023; 41 Suppl 1:11-19. [PMID: 37294959 PMCID: PMC10775708 DOI: 10.1002/hon.3137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 06/11/2023]
Abstract
Positron emission tomography is established for staging and response evaluation in lymphoma using visual evaluation and semi-quantitative analysis. Radiomic analysis involving quantitative imaging features at baseline, such as metabolic tumor volume and markers of disease dissemination and changes in the standardized uptake value during treatment are emerging as powerful biomarkers. The combination of radiomic features with clinical risk factors and genomic analysis offers the potential to improve clinical risk prediction. This review discusses the state of current knowledge, progress toward standardization of tumor delineation for radiomic analysis and argues that radiomic features, molecular markers and circulating tumor DNA should be included in clinical trial designs to enable the development of baseline and dynamic risk scores that could further advance the field to facilitate testing of novel treatments and personalized therapy in aggressive lymphomas.
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Affiliation(s)
- Sally F. Barrington
- School of Biomedical Engineering and Imaging SciencesSt Thomas' Campus, Kings College LondonLondonUK
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8
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Veziroglu EM, Farhadi F, Hasani N, Nikpanah M, Roschewski M, Summers RM, Saboury B. Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma. Semin Nucl Med 2023; 53:426-448. [PMID: 36870800 DOI: 10.1053/j.semnuclmed.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 03/06/2023]
Abstract
Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.
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Affiliation(s)
| | - Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Geisel School of Medicine at Dartmouth, Hanover, NH
| | - Navid Hasani
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Moozhan Nikpanah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Department of Radiology, University of Alabama at Birmingham, AL
| | - Mark Roschewski
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - Ronald M Summers
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
| | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD.
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9
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Kuker RA, Lehmkuhl D, Kwon D, Zhao W, Lossos IS, Moskowitz CH, Alderuccio JP, Yang F. A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma. Cancers (Basel) 2022; 14:5221. [PMID: 36358642 PMCID: PMC9653575 DOI: 10.3390/cancers14215221] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 08/20/2023] Open
Abstract
Metabolic tumor volume (MTV) is a robust prognostic biomarker in diffuse large B-cell lymphoma (DLBCL). The available semiautomatic software for calculating MTV requires manual input limiting its routine application in clinical research. Our objective was to develop a fully automated method (AM) for calculating MTV and to validate the method by comparing its results with those from two nuclear medicine (NM) readers. The automated method designed for this study employed a deep convolutional neural network to segment normal physiologic structures from the computed tomography (CT) scans that demonstrate intense avidity on positron emission tomography (PET) scans. The study cohort consisted of 100 patients with newly diagnosed DLBCL who were randomly selected from the Alliance/CALGB 50,303 (NCT00118209) trial. We observed high concordance in MTV calculations between the AM and readers with Pearson's correlation coefficients and interclass correlations comparing reader 1 to AM of 0.9814 (p < 0.0001) and 0.98 (p < 0.001; 95%CI = 0.96 to 0.99), respectively; and comparing reader 2 to AM of 0.9818 (p < 0.0001) and 0.98 (p < 0.0001; 95%CI = 0.96 to 0.99), respectively. The Bland-Altman plots showed only relatively small systematic errors between the proposed method and readers for both MTV and maximum standardized uptake value (SUVmax). This approach may possess the potential to integrate PET-based biomarkers in clinical trials.
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Affiliation(s)
- Russ A. Kuker
- Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - David Lehmkuhl
- Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Deukwoo Kwon
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Weizhao Zhao
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Izidore S. Lossos
- Sylvester Comprehensive Cancer Center, Department of Medicine, Division of Hematology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Craig H. Moskowitz
- Sylvester Comprehensive Cancer Center, Department of Medicine, Division of Hematology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Juan Pablo Alderuccio
- Sylvester Comprehensive Cancer Center, Department of Medicine, Division of Hematology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Fei Yang
- Sylvester Comprehensive Cancer Center, Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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