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Schroers-Martin JG, Advani RH. The role of response adapted therapy in the era of novel agents. Semin Hematol 2024:S0037-1963(24)00072-6. [PMID: 39004520 DOI: 10.1053/j.seminhematol.2024.06.002] [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: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/16/2024]
Abstract
The optimal treatment of classic Hodgkin Lymphoma (cHL) requires an individualized approach, with therapy guided by pretreatment clinical risk stratification and interim response assessment with positron emission tomography (PET). The overall goal is to achieve high cure rates while minimizing acute toxicity and late therapy-related effects. Interim PET-adapted strategies (iPET) were initially developed with traditional chemotherapy, reducing intensity after interim complete response and escalating treatment for patients with iPET+ disease. Recently, novel agents including brentuximab vedotin and the checkpoint inhibitor immunotherapies (CPIs) pembrolizumab and nivolumab have been adopted into the front-line treatment of cHL, and PET-adapted approaches may be relevant for these drugs as well. In this review we discuss response-adapted strategies utilizing novel agents, consider challenges including indeterminate radiographic findings with CPIs, and address emerging techniques for response assessment including new PET-based imaging metrics and the role of circulating tumor DNA.
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Affiliation(s)
| | - Ranjana H Advani
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA.
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2
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He J, Zhang Y, Chung M, Wang M, Wang K, Ma Y, Ding X, Li Q, Pu Y. Whole-body tumor segmentation from PET/CT images using a two-stage cascaded neural network with camouflaged object detection mechanisms. Med Phys 2023; 50:6151-6162. [PMID: 37134002 DOI: 10.1002/mp.16438] [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: 06/06/2022] [Revised: 03/25/2023] [Accepted: 04/12/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Whole-body Metabolic Tumor Volume (MTVwb) is an independent prognostic factor for overall survival in lung cancer patients. Automatic segmentation methods have been proposed for MTV calculation. Nevertheless, most of existing methods for patients with lung cancer only segment tumors in the thoracic region. PURPOSE In this paper, we present a Two-Stage cascaded neural network integrated with Camouflaged Object Detection mEchanisms (TS-Code-Net) for automatic segmenting tumors from whole-body PET/CT images. METHODS Firstly, tumors are detected from the Maximum Intensity Projection (MIP) images of PET/CT scans, and tumors' approximate localizations along z-axis are identified. Secondly, the segmentations are performed on PET/CT slices that contain tumors identified by the first step. Camouflaged object detection mechanisms are utilized to distinguish the tumors from their surrounding regions that have similar Standard Uptake Values (SUV) and texture appearance. Finally, the TS-Code-Net is trained by minimizing the total loss that incorporates the segmentation accuracy loss and the class imbalance loss. RESULTS The performance of the TS-Code-Net is tested on a whole-body PET/CT image data-set including 480 Non-Small Cell Lung Cancer (NSCLC) patients with five-fold cross-validation using image segmentation metrics. Our method achieves 0.70, 0.76, and 0.70, for Dice, Sensitivity and Precision, respectively, which demonstrates the superiority of the TS-Code-Net over several existing methods related to metastatic lung cancer segmentation from whole-body PET/CT images. CONCLUSIONS The proposed TS-Code-Net is effective for whole-body tumor segmentation of PET/CT images. Codes for TS-Code-Net are available at: https://github.com/zyj19/TS-Code-Net.
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Affiliation(s)
- Jiangping He
- Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Yangjie Zhang
- Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Maggie Chung
- Department of Radiology, University of California, San Francisco, California, USA
| | - Michael Wang
- Department of Pathology, University of California, San Francisco, California, USA
| | - Kun Wang
- Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Yan Ma
- Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Xiaoyang Ding
- Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Qiang Li
- Department of Electronic Engineering, Lanzhou University of Finance and Economics, Lanzhou, Gansu, China
| | - Yonglin Pu
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
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3
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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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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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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: 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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8
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Yuan C, Shi Q, Huang X, Wang L, He Y, Li B, Zhao W, Qian D. Multimodal deep learning model on interim [ 18F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma. Eur Radiol 2022; 33:77-88. [PMID: 36029345 DOI: 10.1007/s00330-022-09031-8] [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: 03/14/2022] [Revised: 05/30/2022] [Accepted: 07/13/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES The prediction of primary treatment failure (PTF) is necessary for patients with diffuse large B-cell lymphoma (DLBCL) since it serves as a prominent means for improving front-line outcomes. Using interim 18F-fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) imaging data, we aimed to construct multimodal deep learning (MDL) models to predict possible PTF in low-risk DLBCL. METHODS Initially, 205 DLBCL patients undergoing interim [18F]FDG PET/CT scans and the front-line standard of care were included in the primary dataset for model development. Then, 44 other patients were included in the external dataset for generalization evaluation. Based on the powerful backbone of the Conv-LSTM network, we incorporated five different multimodal fusion strategies (pixel intermixing, separate channel, separate branch, quantitative weighting, and hybrid learning) to make full use of PET/CT features and built five corresponding MDL models. Moreover, we found the best model, that is, the hybrid learning model, and optimized it by integrating the contrastive training objective to further improve its prediction performance. RESULTS The final model with contrastive objective optimization, named the contrastive hybrid learning model, performed best, with an accuracy of 91.22% and an area under the receiver operating characteristic curve (AUC) of 0.926, in the primary dataset. In the external dataset, its accuracy and AUC remained at 88.64% and 0.925, respectively, indicating its good generalization ability. CONCLUSIONS The proposed model achieved good performance, validated the predictive value of interim PET/CT, and holds promise for directing individualized clinical treatment. KEY POINTS • The proposed multimodal models achieved accurate prediction of primary treatment failure in DLBCL patients. • Using an appropriate feature-level fusion strategy can make the same class close to each other regardless of the modal heterogeneity of the data source domain and positively impact the prediction performance. • Deep learning validated the predictive value of interim PET/CT in a way that exceeded human capabilities.
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Affiliation(s)
- Cheng Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200040, China
| | - Qing Shi
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Li Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yang He
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weili Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200040, China.
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