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Mingels C, Nalbant H, Sari H, Godinez F, Sen F, Spencer B, Esteghamat NS, Tuscano JM, Nardo L. Long-Axial Field-of-View PET Imaging in Patients with Lymphoma: Challenges and Opportunities. PET Clin 2024:S1556-8598(24)00051-8. [PMID: 38969563 DOI: 10.1016/j.cpet.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024]
Abstract
[18F]fluoro-2-deoxy-d-glucose PET/computed tomography has been implemented in the management of patients with lymphoma, offering real-time metabolic information on lymphoma with the promise of more accurate staging, treatment response assessment, prognostication, and early detection of disease recurrence. The clinical management of lymphoproliferative disease has recently, rapidly evolved from initial chemotherapeutic to the use of immunotherapy, targeted agents, and to the use of chimeric antigen receptor T-cell therapies. The implementation of these new systems and imaging protocols together with new tracer development creates, in the field of lymphoproliferative disease, both opportunities and challenges that will be detailed in this comprehensive literature review.
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Affiliation(s)
- Clemens Mingels
- Department of Radiology, University of California Davis, Sacramento, CA, USA; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Hande Nalbant
- Department of Radiology, University of California Davis, Sacramento, CA, USA
| | - Hasan Sari
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Siemens Healthineers International AG, Zurich, Switzerland
| | - Felipe Godinez
- Department of Radiology, University of California Davis, Sacramento, CA, USA; UC Cavis Comprehensive Cancer Center, University of California Davis, Sacramento, CA, USA
| | - Fatma Sen
- Department of Radiology, University of California Davis, Sacramento, CA, USA
| | - Benjamin Spencer
- Department of Radiology, University of California Davis, Sacramento, CA, USA
| | - Naseem S Esteghamat
- Division of Malignant Hematology, Cellular Therapy & Transplantation, Department of Internal Medicine, University of California Davis, Sacramento, CA, USA
| | - Joseph M Tuscano
- Division of Malignant Hematology, Cellular Therapy & Transplantation, Department of Internal Medicine, University of California Davis, Sacramento, CA, USA
| | - Lorenzo Nardo
- Department of Radiology, University of California Davis, Sacramento, CA, USA
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2
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Marchal E, Palard-Novello X, Lhomme F, Meyer ME, Manson G, Devillers A, Marolleau JP, Houot R, Girard A. Baseline [ 18F]FDG PET features are associated with survival and toxicity in patients treated with CAR T cells for large B cell lymphoma. Eur J Nucl Med Mol Imaging 2024; 51:481-489. [PMID: 37721580 DOI: 10.1007/s00259-023-06427-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/04/2023] [Indexed: 09/19/2023]
Abstract
PURPOSE Chimeric antigen receptor (CAR) T cells have established themselves as an effective treatment for refractory or relapsed large B cell lymphoma (LBCL). Recently, the sDmax, which corresponds to the distance separating the two farthest lesions standardized by the patient's body surface area, has appeared as a prognostic factor in LBCL. This study aimed to identify [18F]FDG-PET biomarkers associated with prognosis and predictive of adverse events in patients treated with CAR T cells. METHODS Patients were retrospectively included from two different university hospitals. They were being treated with CAR T cells for LBCL and underwent [18F]FDG-PET just before CAR T cell infusion. Lesions were segmented semi-automatically with a threshold of 41% of the maximal uptake. In addition to clinico-biological features, sDmax, total metabolic tumor volume (TMTV), SUVmax, and uptake intensity of healthy lymphoid organs and liver were collected. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. The occurrence of adverse events, such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), was reported. RESULTS Fifty-six patients were included. The median follow-up was 9.7 months. Multivariate analysis showed that TMTV (cut-off of 36 mL) was an independent prognostic factor for PFS (p < 0.001) and that sDmax (cut-off of 0.15 m-1) was an independent prognostic factor for OS (p = 0.008). Concerning the occurrence of adverse events, a C-reactive protein level > 35 mg/L (p = 0.006) and a liver SUVmean > 2.5 (p = 0.027) before CAR T cells were associated with grade 2 to 4 CRS and a spleen SUVmean > 1.9 with grade 2 to 4 ICANS. CONCLUSION TMTV and sDmax had independent prognostic values, respectively, on PFS and OS. Regarding adverse events, the mean liver and spleen uptakes were associated with the occurrence of grade 2 to 4 CRS and ICANS, respectively. Integrating these biomarkers into the clinical workflow could be useful for early adaptation of patients management.
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Affiliation(s)
- E Marchal
- Department of Nuclear Medicine, Amiens-Picardie University Hospital, Amiens, France.
| | - X Palard-Novello
- Department of Nuclear Medicine, University Rennes, CLCC Eugène Marquis, INSERM, LTSI-UMR 1099, Rennes, France
| | - F Lhomme
- Department of Clinical Hematology, Rennes University Hospital, Rennes, France
| | - M E Meyer
- Department of Nuclear Medicine, Amiens-Picardie University Hospital, Amiens, France
| | - G Manson
- Department of Clinical Hematology, Rennes University Hospital, Rennes, France
| | - A Devillers
- Department of Nuclear Medicine, CLCC Eugène Marquis, Rennes, France
| | - J P Marolleau
- Department of Hematology, Amiens-Picardie University Hospital, Amiens, France
| | - R Houot
- Department of Clinical Hematology, Rennes University Hospital, Rennes, France
| | - A Girard
- Department of Nuclear Medicine, Amiens-Picardie University Hospital, Amiens, France
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Yuan J, Zhang Y, Wang X. Application of machine learning in the management of lymphoma: Current practice and future prospects. Digit Health 2024; 10:20552076241247963. [PMID: 38628632 PMCID: PMC11020711 DOI: 10.1177/20552076241247963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.
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Affiliation(s)
- Junyun Yuan
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ya Zhang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
- Branch of National Clinical Research Center for Hematologic Diseases, Jinan, Shandong, China
- National Clinical Research Center for Hematologic Diseases, Hospital of Soochow University, Suzhou, China
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4
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Wang P, Chen K, Wang J, Ni Z, Shang N, Meng W. A new nomogram for assessing complete response (CR) in gastric diffuse large B-cell lymphoma (DLBCL) patients after chemotherapy. J Cancer Res Clin Oncol 2023; 149:9757-9765. [PMID: 37247082 PMCID: PMC10423136 DOI: 10.1007/s00432-023-04862-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/13/2023] [Indexed: 05/30/2023]
Abstract
PURPOSE Achieving complete response (CR) after first-line chemotherapy in gastric DLBCL patients often results in longer disease-free survival. We explored whether a model based on imaging features combined with clinicopathological factors could assess the CR to chemotherapy in patients with gastric DLBCL. METHODS Univariate (P < 0.10) and multivariate (P < 0.05) analyses were used to identify factors associated with a CR to treatment. As a result, a system was developed to evaluate whether gastric DLBCL patients had a CR to chemotherapy. Evidence was found to support the model's ability to predict outcomes and demonstrate clinical value. RESULTS We retrospectively analysed 108 people who had been diagnosed gastric DLBCL; 53 were in CR. Patients were divided at random into a 5:4 training/testing dataset split. β2 microglobulin before and after chemotherapy and lesion length after chemotherapy were independent predictors of the CR of gastric DLBCL patients after chemotherapy. These factors were used in the predictive model construction. In the training dataset, the area under the curve (AUC) of the model was 0.929, the specificity was 0.806, and the sensitivity was 0.862. In the testing dataset, the model had an AUC of 0.957, specificity of 0.792, and sensitivity of 0.958. The AUC did not differ significantly between the training and testing dates (P > 0.05). CONCLUSION A model constructed using imaging features combined with clinicopathological factors could effectively evaluate the CR to chemotherapy in gastric DLBCL patients. The predictive model can facilitate the monitoring of patients and be used to adjust individualised treatment plans.
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Affiliation(s)
- Ping Wang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081 Heilongjiang China
| | - Kaige Chen
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081 Heilongjiang China
| | - Jiayang Wang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081 Heilongjiang China
| | - Zihao Ni
- Department of Ultrasound, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081 Heilongjiang China
| | - Naijian Shang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081 Heilongjiang China
| | - Wei Meng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150081 Heilongjiang China
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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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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: 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: 03/28/2023] [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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Tutino F, Giovannini E, Chiola S, Giovacchini G, Ciarmiello A. Assessment of Response to Immunotherapy in Patients with Hodgkin Lymphoma: Towards Quantifying Changes in Tumor Burden Using FDG-PET/CT. J Clin Med 2023; 12:jcm12103498. [PMID: 37240602 DOI: 10.3390/jcm12103498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/25/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Immune checkpoint inhibitors are currently the standard of care for many advanced solid tumors, and they have been recently approved for the treatment of relapsed/refractory Hodgkin lymphoma and primary mediastinal B cell lymphoma. Assessments of the response to immunotherapy may be complicated by the occurrence of the flare/pseudoprogression phenomenon, consisting of initial tumor enlargement and even the appearance of new lesions, followed by a response, which may initially be indistinguishable from true progression. There have been efforts to characterize and capture the new patterns of response observed during immunotherapy, namely, pseudoprogression and delayed response, and several immune-related response criteria have been proposed. Confirming progression on a subsequent scan and measuring the total tumor burden are both common in immune-related criteria. Due to the peculiarity of hematologic malignancies, lymphoma-specific immune-related criteria have been developed (LYRIC), and they have been evaluated in research studies in comparison to the Lugano Classification. In this review work, we illustrate the evolution of the response criteria in lymphomas from the first CT-based criteria to the development of the PET-based Lugano Classification, further refined to take into account the flare phenomenon encountered during immunotherapy. We also describe the additional contribution of PET-derived volumetric parameters to the interpretation of responses during immunotherapy.
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Affiliation(s)
- Francesca Tutino
- Nuclear Medicine Unit, Ospedale Civile Sant'Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy
| | - Elisabetta Giovannini
- Nuclear Medicine Unit, Ospedale Civile Sant'Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy
| | - Silvia Chiola
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Giampiero Giovacchini
- Nuclear Medicine Unit, Ospedale Civile Sant'Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy
| | - Andrea Ciarmiello
- Nuclear Medicine Unit, Ospedale Civile Sant'Andrea, Via Vittorio Veneto 170, 19124 La Spezia, Italy
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Zanoni L, Bezzi D, Nanni C, Paccagnella A, Farina A, Broccoli A, Casadei B, Zinzani PL, Fanti S. PET/CT in Non-Hodgkin Lymphoma: An Update. Semin Nucl Med 2023; 53:320-351. [PMID: 36522191 DOI: 10.1053/j.semnuclmed.2022.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 12/15/2022]
Abstract
Non-Hodgkin lymphomas represents a heterogeneous group of lymphoproliferative disorders characterized by different clinical courses, varying from indolent to highly aggressive. 18F-FDG-PET/CT is the current state-of-the-art diagnostic imaging, for the staging, restaging and evaluation of response to treatment in lymphomas with avidity for 18F-FDG, despite it is not routinely recommended for surveillance. PET-based response criteria (using five-point Deauville Score) are nowadays uniformly applied in FDG-avid lymphomas. In this review, a comprehensive overview of the role of 18F-FDG-PET in Non-Hodgkin lymphomas is provided, at each relevant point of patient management, particularly focusing on recent advances on diffuse large B-cell lymphoma and follicular lymphoma, with brief updates also on other histotypes (such as marginal zone, mantle cell, primary mediastinal- B cell lymphoma and T cell lymphoma). PET-derived semiquantitative factors useful for patient stratification and prognostication and emerging radiomics research are also presented.
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Affiliation(s)
- Lucia Zanoni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
| | - Davide Bezzi
- Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Andrea Paccagnella
- Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy; Nuclear Medicine Unit, AUSL Romagna, Cesena, Italy
| | - Arianna Farina
- Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Alessandro Broccoli
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy; Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Beatrice Casadei
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy; Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Pier Luigi Zinzani
- IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia "Seràgnoli," Bologna, Italy; Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale, Università di Bologna, Bologna, Italy
| | - Stefano Fanti
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; Nuclear Medicine, Alma Mater Studiorum University of Bologna, Bologna, Italy
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Albano D, Treglia G, Dondi F, Calabrò A, Rizzo A, Annunziata S, Guerra L, Morbelli S, Tucci A, Bertagna F. 18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor? Cancers (Basel) 2023; 15:cancers15092494. [PMID: 37173962 PMCID: PMC10177347 DOI: 10.3390/cancers15092494] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Recently, several studies introduced the potential prognostic usefulness of maximum tumor dissemination (Dmax) measured by 2-deoxy-2-fluorine-18-fluoro-D-glucose positron-emission tomography/computed tomography (18F-FDG PET/CT). Dmax is a simple three-dimensional feature that represents the maximal distance between the two farthest hypermetabolic PET lesions. A comprehensive computer literature search of PubMed/MEDLINE, Embase, and Cochrane libraries was conducted, including articles indexed up to 28 February 2023. Ultimately, 19 studies analyzing the value of 18F-FDG PET/CT Dmax in patients with lymphomas were included. Despite their heterogeneity, most studies showed a significant prognostic role of Dmax in predicting progression-free survival (PFS) and overall survival (OS). Some articles showed that the combination of Dmax with other metabolic features, such as MTV and interim PET response, proved to better stratify the risk of relapse or death. However, some methodological open questions need to be clarified before introducing Dmax into clinical practice.
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Affiliation(s)
- Domenico Albano
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Giorgio Treglia
- Clinic of Nuclear Medicine, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6501 Bellinzona, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1011 Lausanne, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Francesco Dondi
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Anna Calabrò
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
| | - Alessio Rizzo
- Department of Nuclear Medicine, Candiolo Cancer Institute, FPO-IRCCS, 10060 Turin, Italy
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy
| | - Luca Guerra
- Nuclear Medicine Division, Ospedale San Gerardo, 20900 Monza, Italy
| | - Silvia Morbelli
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | | | - Francesco Bertagna
- Division of Nuclear Medicine, Università degli Studi di Brescia, ASST Spedali Civili di Brescia, 25123 Brescia, Italy
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Girum KB, Rebaud L, Cottereau AS, Meignan M, Clerc J, Vercellino L, Casasnovas O, Morschhauser F, Thieblemont C, Buvat I. 18F-FDG PET Maximum-Intensity Projections and Artificial Intelligence: A Win-Win Combination to Easily Measure Prognostic Biomarkers in DLBCL Patients. J Nucl Med 2022; 63:1925-1932. [PMID: 35710733 PMCID: PMC9730929 DOI: 10.2967/jnumed.121.263501] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/27/2022] [Indexed: 01/11/2023] Open
Abstract
Total metabolic tumor volume (TMTV) and tumor dissemination (Dmax) calculated from baseline 18F-FDG PET/CT images are prognostic biomarkers in diffuse large B-cell lymphoma (DLBCL) patients. Yet, their automated calculation remains challenging. The purpose of this study was to investigate whether TMTV and Dmax features could be replaced by surrogate features automatically calculated using an artificial intelligence (AI) algorithm from only 2 maximum-intensity projections (MIPs) of the whole-body 18F-FDG PET images. Methods: Two cohorts of DLBCL patients from the REMARC (NCT01122472) and LNH073B (NCT00498043) trials were retrospectively analyzed. Experts delineated lymphoma lesions from the baseline whole-body 18F-FDG PET/CT images, from which TMTV and Dmax were measured. Coronal and sagittal MIP images and associated 2-dimensional reference lesion masks were calculated. An AI algorithm was trained on the REMARC MIP data to segment lymphoma regions. The AI algorithm was then used to estimate surrogate TMTV (sTMTV) and surrogate Dmax (sDmax) on both datasets. The ability of the original and surrogate TMTV and Dmax to stratify patients was compared. Results: Three hundred eighty-two patients (mean age ± SD, 62.1 y ± 13.4 y; 207 men) were evaluated. sTMTV was highly correlated with TMTV for REMARC and LNH073B datasets (Spearman r = 0.878 and 0.752, respectively), and so were sDmax and Dmax (r = 0.709 and 0.714, respectively). The hazard ratios for progression free survival of volume and MIP-based features derived using AI were similar, for example, TMTV: 11.24 (95% CI: 2.10-46.20), sTMTV: 11.81 (95% CI: 3.29-31.77), and Dmax: 9.0 (95% CI: 2.53-23.63), sDmax: 12.49 (95% CI: 3.42-34.50). Conclusion: Surrogate TMTV and Dmax calculated from only 2 PET MIP images are prognostic biomarkers in DLBCL patients and can be automatically estimated using an AI algorithm.
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Affiliation(s)
- Kibrom B. Girum
- LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France
| | - Louis Rebaud
- LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France;,Research and Clinical Collaborations, Siemens Medical Solutions, Knoxville, Tennessee
| | - Anne-Ségolène Cottereau
- LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France;,Department of Nuclear Medicine, Cochin Hospital, AP-HP, Paris Descartes University, Paris, France
| | - Michel Meignan
- Lysa Imaging, Henri Mondor University Hospital, AP-HP, University Paris East, Créteil, France
| | - Jérôme Clerc
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, Paris Descartes University, Paris, France
| | | | | | - Franck Morschhauser
- Department of Hematology, Claude Huriez Hospital, University Lille, EA 7365, Research Group on Injectable Forms and Associated Technologies, Lille, France; and
| | | | - Irène Buvat
- LITO Laboratory, U1288 Inserm, Institut Curie, University Paris-Saclay, Orsay, France
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11
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Gong H, Tang B, Li T, Li J, Tang L, Ding C. The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T-cell lymphoma. EJHAEM 2022; 4:67-77. [PMID: 36819177 PMCID: PMC9928789 DOI: 10.1002/jha2.610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/13/2022] [Accepted: 10/20/2022] [Indexed: 11/30/2022]
Abstract
To explore the prognostic values of baseline 2-deoxy-2-[18F] fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) dissemination parameter in angioimmunoblastic T-cell lymphoma (AITL) and its added values to total metabolic tumour volume (TMTV). Eighty-one AITL patients with at least two FDG-avid lesions in baseline PET/CT were retrospectively included. PET parameters including TMTV and the distance between the two lesions that are the furthest apart (Dmax) were obtained. Univariate Cox analysis showed that both Dmax and TMTV were risk factors for progression-free survival (PFS) and overall survival (OS). Multivariate Cox analysis models of different combinations showed that high Dmax (> 65.7 cm) could independently predict both PFS and OS, while high TMTV (>456.6 cm3) was only significant for OS. A concise PET model based on TMTV and Dmax can effectively risk-stratify patients. PFS and OS rates were significantly lower in patients with high Dmax and high TMTV than in patients with low Dmax and low TMTV (3-year PFS rate: 15.0% vs. 48.7%, p = 0.001; 3-year OS rate: 27.6% vs. 79.0%, p < 0.001). Dmax can directly reflect the disease dissemination characteristic and has a significant prognostic value for FDG-avid AITL patients. It has the potential to be introduced into new risk stratification models for tailored treatment.
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Affiliation(s)
- Huanyu Gong
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Bo Tang
- Department of RadiologyShuyang Hospital of Traditional Chinese MedicineSuqianChina
| | - Tiannv Li
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Jianyong Li
- Department of HematologyJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Lijun Tang
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Chongyang Ding
- Department of Nuclear MedicineJiangsu Province HospitalThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
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Hasani N, Paravastu SS, Farhadi F, Yousefirizi F, Morris MA, Rahmim A, Roschewski M, Summers RM, Saboury B. Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions). PET Clin 2022; 17:145-174. [PMID: 34809864 PMCID: PMC8735853 DOI: 10.1016/j.cpet.2021.09.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.
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Affiliation(s)
- Navid Hasani
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA
| | - Sriram S Paravastu
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA
| | - Faraz Farhadi
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Michael A Morris
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore Country, Baltimore, MD, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Mark Roschewski
- Lymphoid Malignancies Branch, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Ronald M Summers
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore Country, Baltimore, MD, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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