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Brosch-Lenz J, Kurkowska S, Frey E, Dewaraja YK, Sunderland J, Uribe C. An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 3: Contribution from Calculating Absorbed Dose from Time-Integrated Activity. J Nucl Med 2024; 65:1166-1172. [PMID: 38960715 PMCID: PMC11294060 DOI: 10.2967/jnumed.123.267293] [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: 12/17/2023] [Accepted: 05/21/2024] [Indexed: 07/05/2024] Open
Abstract
Image-based dosimetry-guided radiopharmaceutical therapy has the potential to personalize treatment by limiting toxicity to organs at risk and maximizing the therapeutic effect. The 177Lu dosimetry challenge of the Society of Nuclear Medicine and Molecular Imaging consisted of 5 tasks assessing the variability in the dosimetry workflow. The fifth task investigated the variability associated with the last step, dose conversion, of the dosimetry workflow on which this study is based. Methods: Reference variability was assessed by 2 medical physicists using different software, methods, and all possible combinations of input segmentation formats and time points as provided in the challenge. General descriptive statistics for absorbed dose values from the global submissions from participants were calculated, and variability was measured using the quartile coefficient of dispersion. Results: For the liver, which included lesions with high uptake, variabilities of up to 36% were found. The baseline analysis showed a variability of 29% in absorbed dose results for the liver from datasets where lesions included and excluded were grouped, indicating that variation in how lesions in normal liver were treated was a significant source of variability. For other organs and lesions, variability was within 7%, independently of software used except for the local deposition method. Conclusion: The choice of dosimetry method or software had a small contribution to the overall variability of dose estimates.
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Affiliation(s)
- Julia Brosch-Lenz
- Department of Nuclear Medicine, Rechts der Isar Medical Center, Technical University of Munich, Munich, Germany
| | - Sara Kurkowska
- Department of Nuclear Medicine, Pomeranian Medical University, Szczecin, Poland
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Eric Frey
- Rapid, LLC, Baltimore, Maryland
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland
| | - Yuni K Dewaraja
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - John Sunderland
- Department of Radiology, University of Iowa, Iowa City, Iowa
| | - Carlos Uribe
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada;
- Molecular Imaging and Therapy, BC Cancer, Vancouver, British Columbia, Canada; and
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
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Moraitis A, Küper A, Tran-Gia J, Eberlein U, Chen Y, Seifert R, Shi K, Kim M, Herrmann K, Fragoso Costa P, Kersting D. Future Perspectives of Artificial Intelligence in Bone Marrow Dosimetry and Individualized Radioligand Therapy. Semin Nucl Med 2024; 54:460-469. [PMID: 39013673 DOI: 10.1053/j.semnuclmed.2024.06.003] [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: 06/15/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
Radioligand therapy is an emerging and effective treatment option for various types of malignancies, but may be intricately linked to hematological side effects such as anemia, lymphopenia or thrombocytopenia. The safety and efficacy of novel theranostic agents, targeting increasingly complex targets, can be well served by comprehensive dosimetry. However, optimization in patient management and patient selection based on risk-factors predicting adverse events and built upon reliable dose-response relations is still an open demand. In this context, artificial intelligence methods, especially machine learning and deep learning algorithms, may play a crucial role. This review provides an overview of upcoming opportunities for integrating artificial intelligence methods into the field of dosimetry in nuclear medicine by improving bone marrow and blood dosimetry accuracy, enabling early identification of potential hematological risk-factors, and allowing for adaptive treatment planning. It will further exemplify inspirational success stories from neighboring disciplines that may be translated to nuclear medicine practices, and will provide conceptual suggestions for future directions. In the future, we expect artificial intelligence-assisted (predictive) dosimetry combined with clinical parameters to pave the way towards truly personalized theranostics in radioligand therapy.
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Affiliation(s)
- Alexandros Moraitis
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
| | - Alina Küper
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Uta Eberlein
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Yizhou Chen
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Robert Seifert
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Moon Kim
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Pedro Fragoso Costa
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - David Kersting
- Department of Nuclear Medicine, West German Cancer Center (WTZ), University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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Mansouri Z, Salimi Y, Akhavanallaf A, Shiri I, Teixeira EPA, Hou X, Beauregard JM, Rahmim A, Zaidi H. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [ 177Lu]Lu-DOTATATE radiopharmaceutical therapy. Eur J Nucl Med Mol Imaging 2024; 51:1516-1529. [PMID: 38267686 PMCID: PMC11043201 DOI: 10.1007/s00259-024-06618-9] [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: 11/13/2023] [Accepted: 01/15/2024] [Indexed: 01/26/2024]
Abstract
PURPOSE Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study. METHODS We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions). RESULTS The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses. CONCLUSION A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.
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Affiliation(s)
- Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Azadeh Akhavanallaf
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Eliluane Pirazzo Andrade Teixeira
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Xinchi Hou
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Jean-Mathieu Beauregard
- Cancer Research Centre and Department of Radiology and Nuclear Medicine, Université Laval, Quebec City, QC, Canada
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Belge Bilgin G, Bilgin C, Burkett BJ, Orme JJ, Childs DS, Thorpe MP, Halfdanarson TR, Johnson GB, Kendi AT, Sartor O. Theranostics and artificial intelligence: new frontiers in personalized medicine. Theranostics 2024; 14:2367-2378. [PMID: 38646652 PMCID: PMC11024845 DOI: 10.7150/thno.94788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/17/2024] [Indexed: 04/23/2024] Open
Abstract
The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.
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Affiliation(s)
| | - Cem Bilgin
- Department of Radiology, Mayo Clinic Rochester, MN, USA
| | | | - Jacob J. Orme
- Department of Oncology, Mayo Clinic Rochester, MN, USA
| | | | | | | | - Geoffrey B Johnson
- Department of Radiology, Mayo Clinic Rochester, MN, USA
- Department of Immunology, Mayo Clinic Rochester, MN, USA
| | | | - Oliver Sartor
- Department of Radiology, Mayo Clinic Rochester, MN, USA
- Department of Oncology, Mayo Clinic Rochester, MN, USA
- Department of Urology, Mayo Clinic Rochester, MN, USA
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Brosch-Lenz JF, Delker A, Schmidt F, Tran-Gia J. On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies. Nuklearmedizin 2023; 62:379-388. [PMID: 37827503 DOI: 10.1055/a-2179-6872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.
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Affiliation(s)
| | - Astrid Delker
- Department of Nuclear Medicine, LMU University Hospital, Munich, Germany
| | - Fabian Schmidt
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, Tuebingen, Germany
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Tuebingen, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany
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