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Xia Y, Zhou J, Xun X, Zhang J, Wei T, Gao R, Reddy B, Liu C, Kim G, Yu Z. CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy. Insights Imaging 2024; 15:214. [PMID: 39186192 PMCID: PMC11347550 DOI: 10.1186/s13244-024-01784-8] [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: 05/23/2024] [Accepted: 07/23/2024] [Indexed: 08/27/2024] Open
Abstract
OBJECTIVES To develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC). METHODS This retrospective study included immunotherapy-treated advanced HCC patients from 52 multi-national in-house centers between 2018 and 2022. A multi-modal prognostic model using baseline and the first follow-up CT images and 7 clinical variables was proposed. A convolutional-recurrent neural network (CRNN) was developed to extract spatial-temporal information from automatically selected representative 2D CT slices to provide a radiological score, then fused with a Cox-based clinical score to provide the survival risk. The model's effectiveness was assessed using a time-dependent area under the receiver operating curve (AUC), and risk group stratification using the log-rank test. Prognostic performances of multi-modal inputs were compared to models of missing modality, and the size-based RECIST criteria. RESULTS Two-hundred seven patients (mean age, 61 years ± 12 [SD], 180 men) were included. The multi-modal CRNN model reached the AUC of 0.777 and 0.704 of 1-year overall survival predictions in the validation and test sets. The model achieved significant risk stratification in validation (hazard ratio [HR] = 3.330, p = 0.008), and test sets (HR = 2.024, p = 0.047) based on the median risk score of the training set. Models with missing modalities (the single-modal imaging-based model and the model incorporating only baseline scans) can still achieve favorable risk stratification performance (all p < 0.05, except for one, p = 0.053). Moreover, results proved the superiority of the deep learning-based model to the RECIST criteria. CONCLUSION Deep learning analysis of CT scans and clinical data can offer significant prognostic insights for patients with advanced HCC. CRITICAL RELEVANCE STATEMENT The established model can help monitor patients' disease statuses and identify those with poor prognosis at the time of first follow-up, helping clinicians make informed treatment decisions, as well as early and timely interventions. KEY POINTS An AI-based prognostic model was developed for advanced HCC using multi-national patients. The model extracts spatial-temporal information from CT scans and integrates it with clinical variables to prognosticate. The model demonstrated superior prognostic ability compared to the conventional size-based RECIST method.
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Affiliation(s)
- Yujia Xia
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Zhou
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolei Xun
- Statistics in Global Statistics and Data Science, Beigene, Shanghai, China
| | | | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | | | - Chao Liu
- PiHealth USA, Cambridge, MA, USA
| | | | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Sadik M, Barrington SF, Trägårdh E, Saboury B, Nielsen AL, Jakobsen AL, Gongora JLL, Urdaneta JL, Kumar R, Edenbrandt L. Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis. Clin Physiol Funct Imaging 2024; 44:220-227. [PMID: 38011940 DOI: 10.1111/cpf.12868] [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: 06/07/2023] [Revised: 11/10/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
AIM To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference. METHODS Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7-75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists. RESULTS The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79-568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10-86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively. CONCLUSION The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.
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Affiliation(s)
- May Sadik
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Sally F Barrington
- School of Biomedical Engineering and Imaging Sciences Kings College, King's College London and Guy's and St Thomas' PET Centre, London, UK
| | - Elin Trägårdh
- Departmet of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Anne L Nielsen
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Annika L Jakobsen
- Department of Clinical Physiology, Nuclear Medicine and PET, Centre of Diagnostic Investigations, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jose L L Gongora
- Department of Diagnostic Imaging, Akershus University Hospital, Oslo, Norway
| | - Jesus L Urdaneta
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Rajender Kumar
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
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Tricarico P, Chardin D, Martin N, Contu S, Hugonnet F, Otto J, Humbert O. Total metabolic tumor volume on 18F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy. J Immunother Cancer 2024; 12:e007628. [PMID: 38649279 PMCID: PMC11043703 DOI: 10.1136/jitc-2023-007628] [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] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Because of atypical response imaging patterns in patients with metastatic non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs), new biomarkers are needed for a better monitoring of treatment efficacy. The aim of this prospective study was to evaluate the prognostic value of volume-derived positron-emission tomography (PET) parameters on baseline and follow-up 18F-fluoro-deoxy-glucose PET (18F-FDG-PET) scans and compare it with the conventional PET Response Criteria in Solid Tumors (PERCIST). METHODS Patients with metastatic NSCLC were included in two different single-center prospective trials. 18F-FDG-PET studies were performed before the start of immunotherapy (PETbaseline), after 6-8 weeks (PETinterim1) and after 12-16 weeks (PETinterim2) of treatment, using PERCIST criteria for tumor response assessment. Different metabolic parameters were evaluated: absolute values of maximum standardized uptake value (SUVmax) of the most intense lesion, total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), but also their percentage changes between PET studies (ΔSUVmax, ΔTMTV and ΔTLG). The median follow-up of patients was 31 (7.3-31.8) months. Prognostic values and optimal thresholds of PET parameters were estimated by ROC (Receiver Operating Characteristic) curve analysis of 12-month overall survival (12M-OS) and 6-month progression-free survival (6M-PFS). Tumor progression needed to be confirmed by a multidisciplinary tumor board, considering atypical response patterns on imaging. RESULTS 110 patients were prospectively included. On PETbaseline, TMTV was predictive of 12M-OS [AUC (Area Under Curve) =0.64; 95% CI: 0.61 to 0.66] whereas SUVmax and TLG were not. On PETinterim1 and PETinterim2, all metabolic parameters were predictive for 12M-OS and 6M-PFS, the residual TMTV on PETinterim1 (TMTV1) being the strongest prognostic biomarker (AUC=0.83 and 0.82; 95% CI: 0.74 to 0.91, for 12M-OS and 6M-PFS, respectively). Using the optimal threshold by ROC curve to classify patients into three TMTV1 subgroups (0 cm3; 0-57 cm3; >57 cm3), TMTV1 prognostic stratification was independent of PERCIST criteria on both PFS and OS, and significantly outperformed them. Subgroup analysis demonstrated that TMTV1 remained a strong prognostic biomarker of 12M-OS for non-responding patients (p=0.0003) according to PERCIST criteria. In the specific group of patients with PERCIST progression on PETinterim1, low residual tumor volume (<57 cm3) was still associated with a very favorable patients' outcome (6M-PFS=73%; 24M-OS=55%). CONCLUSION The absolute value of residual metabolic tumor volume, assessed 6-8 weeks after the start of ICPI, is an optimal and independent prognostic measure, exceeding and complementing conventional PERCIST criteria. Oncologists should consider it in patients with first tumor progression according to PERCIST criteria, as it helps identify patients who benefit from continued treatment. TRIAL REGISTRATION NUMBER 2018-A02116-49; NCT03584334.
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Affiliation(s)
- Pierre Tricarico
- Department of Nuclear Medicine, Centre Antoine-Lacassagne, Nice, France
| | - David Chardin
- Department of Nuclear Medicine, Centre Antoine-Lacassagne, Nice, France
- IBV, Université Côte d'Azur, CNRS, Inserm, Nice, France
| | - Nicolas Martin
- Department of Medical Oncology, Centre Antoine-Lacassagne, Nice, France
| | - Sara Contu
- Department of Biostatistics, Centre Antoine-Lacassagne, Nice, France
| | - Florent Hugonnet
- Department of Nuclear Medicine, Centre Hospitalier Princesse Grâce, Monaco
| | - Josiane Otto
- Department of Medical Oncology, Centre Antoine-Lacassagne, Nice, France
| | - Olivier Humbert
- Department of Nuclear Medicine, Centre Antoine-Lacassagne, Nice, France
- IBV, Université Côte d'Azur, CNRS, Inserm, Nice, France
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Häggström I, Leithner D, Alvén J, Campanella G, Abusamra M, Zhang H, Chhabra S, Beer L, Haug A, Salles G, Raderer M, Staber PB, Becker A, Hricak H, Fuchs TJ, Schöder H, Mayerhoefer ME. Deep learning for [ 18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis. Lancet Digit Health 2024; 6:e114-e125. [PMID: 38135556 DOI: 10.1016/s2589-7500(23)00203-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/29/2023] [Accepted: 09/26/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. METHODS In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. FINDINGS In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937). INTERPRETATION Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. FUNDING National Institutes of Health-National Cancer Institute Cancer Center Support Grant.
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Affiliation(s)
- Ida Häggström
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Doris Leithner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, NYU Langone Health, Grossman School of Medicine, New York, NY, USA
| | - Jennifer Alvén
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Gabriele Campanella
- Hasso Plattner Institute for Digital Health, Mount Sinai Medical School, New York, NY, USA; Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Murad Abusamra
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Honglei Zhang
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shalini Chhabra
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucian Beer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexander Haug
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gilles Salles
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Markus Raderer
- Department of Medicine I, Medical University of Vienna, Vienna, Austria
| | - Philipp B Staber
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anton Becker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA; Department of Radiology, NYU Langone Health, Grossman School of Medicine, New York, NY, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Thomas J Fuchs
- Hasso Plattner Institute for Digital Health, Mount Sinai Medical School, New York, NY, USA; Department of AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Marius E Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Weill Cornell Medical College, Cornell University, New York, NY, USA; Department of Radiology, NYU Langone Health, Grossman School of Medicine, New York, NY, USA.
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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: 6] [Impact Index Per Article: 6.0] [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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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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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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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: 7] [Impact Index Per Article: 7.0] [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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Huang Z, Guo Y, Zhang N, Huang X, Decazes P, Becker S, Ruan S. Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images. Comput Biol Med 2022; 151:106230. [PMID: 36306574 DOI: 10.1016/j.compbiomed.2022.106230] [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: 07/04/2022] [Revised: 09/28/2022] [Accepted: 10/16/2022] [Indexed: 12/27/2022]
Abstract
Accurate lymphoma segmentation in PET/CT images is important for evaluating Diffuse Large B-Cell Lymphoma (DLBCL) prognosis. As systemic multiple lymphomas, DLBCL lesions vary in number and size for different patients, which makes DLBCL labeling labor-intensive and time-consuming. To reduce the reliance on accurately labeled datasets, a weakly supervised deep learning method based on multi-scale feature similarity is proposed for automatic lymphoma segmentation. Weak labeling was performed by randomly dawning a small and salient lymphoma volume for the patient without accurate labels. A 3D V-Net is used as the backbone of the segmentation network and image features extracted in different convolutional layers are fused with the Atrous Spatial Pyramid Pooling (ASPP) module to generate multi-scale feature representations of input images. By imposing multi-scale feature consistency constraints on the predicted tumor regions as well as the labeled tumor regions, weakly labeled data can also be effectively used for network training. The cosine similarity, which has strong generalization, is exploited here to measure feature distances. The proposed method is evaluated with a PET/CT dataset of 147 lymphoma patients. Experimental results show that when using data, half of which have accurate labels and the other half have weak labels, the proposed method performed similarly to a fully supervised segmentation network and achieved an average Dice Similarity Coefficient (DSC) of 71.47%. The proposed method is able to reduce the requirement for expert annotations in deep learning-based lymphoma segmentation.
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Affiliation(s)
- Zhengshan Huang
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Yu Guo
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China.
| | - Ning Zhang
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Xian Huang
- Department of Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Pierre Decazes
- LITIS, University of Rouen Normandy, Rouen, France; Department of Nuclear Medicine, Henri Becquerel Cancer Centre, Rouen, France
| | - Stephanie Becker
- LITIS, University of Rouen Normandy, Rouen, France; Department of Nuclear Medicine, Henri Becquerel Cancer Centre, Rouen, France
| | - Su Ruan
- LITIS, University of Rouen Normandy, Rouen, France
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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: 6] [Impact Index Per Article: 3.0] [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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