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Hami R, Apeke S, Redou P, Gaubert L, Dubois LJ, Lambin P, Visvikis D, Boussion N. Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images. J Imaging 2023; 9:124. [PMID: 37367472 DOI: 10.3390/jimaging9060124] [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: 05/23/2023] [Revised: 06/10/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023] Open
Abstract
Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the "5 Rs" have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.
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Affiliation(s)
- Rihab Hami
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
| | - Sena Apeke
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CERV, European Center for Virtual Reality, ENIB, CEDEX 3, 29238 Brest, France
| | - Pascal Redou
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CERV, European Center for Virtual Reality, ENIB, CEDEX 3, 29238 Brest, France
| | - Laurent Gaubert
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CERV, European Center for Virtual Reality, ENIB, CEDEX 3, 29238 Brest, France
| | - Ludwig J Dubois
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Dimitris Visvikis
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CHRU BREST, 29200 Brest, France
| | - Nicolas Boussion
- INSERM UMR 1101 "LaTIM", CEDEX 3, 29238 Brest, France
- CHRU BREST, 29200 Brest, France
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Zhou T, Noeuveglise A, Modzelewski R, Ghazouani F, Thureau S, Fontanilles M, Ruan S. Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning. Comput Med Imaging Graph 2023; 106:102218. [PMID: 36947921 DOI: 10.1016/j.compmedimag.2023.102218] [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: 01/27/2023] [Revised: 02/13/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023]
Abstract
Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time. There is still little work to deal with this issue. In this paper, we present a deep learning-based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Following that, a multi-scale multi-channel feature fusion model and a nonlinear correlation learning module are developed to learn the effective features. The correlation between multi-channel features is modeled by a nonlinear equation. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence location from the limited dataset.
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Affiliation(s)
- Tongxue Zhou
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
| | | | - Romain Modzelewski
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, 76038, France
| | - Fethi Ghazouani
- Université de Rouen Normandie, LITIS - QuantIF, Rouen 76183, France
| | - Sébastien Thureau
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, 76038, France
| | - Maxime Fontanilles
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, 76038, France
| | - Su Ruan
- Université de Rouen Normandie, LITIS - QuantIF, Rouen 76183, France.
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Ji H, Lafata K, Mowery Y, Brizel D, Bertozzi AL, Yin FF, Wang C. Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application. Front Oncol 2022; 12:895544. [PMID: 35646643 PMCID: PMC9135979 DOI: 10.3389/fonc.2022.895544] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/11/2022] [Indexed: 01/03/2023] Open
Abstract
PurposeTo develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.MethodsBased on the classic reaction–diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder–decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation 18FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyngeal patients with paired 18FDG-PET studies before and after 20-Gy delivery (2 Gy/day fraction) by intensity-modulated radiotherapy (IMRT). In a two-branch deep learning execution, the proposed CNN learns specific terms in the biological model from paired 18FDG-PET images and spatial dose distribution in one branch, and the biological model generates post-20-Gy 18FDG-PET image prediction in the other branch. As in 2D execution, 718/233/230 axial slices from 38/13/13 patients were used for training/validation/independent test. The prediction image results in test cases were compared with the ground-truth results quantitatively.ResultsThe proposed method successfully generated post-20-Gy 18FDG-PET image outcome prediction with breakdown illustrations of biological model components. Standardized uptake value (SUV) mean values in 18FDG high-uptake regions of predicted images (2.45 ± 0.25) were similar to ground-truth results (2.51 ± 0.33). In 2D-based Gamma analysis, the median/mean Gamma Index (<1) passing rate of test images was 96.5%/92.8% using the 5%/5 mm criterion; such result was improved to 99.9%/99.6% when 10%/10 mm was adopted.ConclusionThe developed biologically guided deep learning method achieved post-20-Gy 18FDG-PET image outcome predictions in good agreement with ground-truth results. With the breakdown biological modeling components, the outcome image predictions could be used in adaptive radiotherapy decision-making to optimize personalized plans for the best outcome in the future.
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Affiliation(s)
- Hangjie Ji
- Department of Mathematics, North Carolina State University, Raleigh, NC, United States
| | - Kyle Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
- Department of Radiology, Duke University Medical Center, Durham, NC, United States
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
| | - Yvonne Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - David Brizel
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Andrea L. Bertozzi
- Mechanical and Aerospace Engineering Department, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Mathematics, University of California, Los Angeles, Los Angeles, CA, United States
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States
- *Correspondence: Chunhao Wang,
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Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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Caputo JG, Cruz-Pacheco G, Sarels B. Stopping a reaction-diffusion front. Phys Rev E 2021; 103:032210. [PMID: 33862694 DOI: 10.1103/physreve.103.032210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 02/25/2021] [Indexed: 06/12/2023]
Abstract
We revisit the problem of pinning a reaction-diffusion front by a defect, in particular by a reaction-free region. Using collective variables for the front and numerical simulations, we compare the behaviors of a bistable and monostable front. A bistable front can be pinned as confirmed by a pinning criterion, the analysis of the time independent problem, and simulations. Conversely, a monostable front can never be pinned, it gives rise to a secondary pulse past the defect and we calculate the time this pulse takes to appear. These radically different behaviors of bistable and monostable fronts raise issues for modelers in particular areas of biology, as for example, the study of tumor growth in the presence of different tissues.
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Affiliation(s)
- Jean-Guy Caputo
- Laboratoire de Mathématiques, INSA Rouen Normandie - B.P. 8, Avenue de l'Université, 76801 Saint-Etienne du Rouvray, France
| | - Gustavo Cruz-Pacheco
- Departmento Matemáticas y Mecánica, I.I.M.A.S.-U.N.A.M., Apdo. Postal 20-726, 01000 México D.F., México
| | - Benoît Sarels
- Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France
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6
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Jarrett AM, Hormuth DA, Wu C, Kazerouni AS, Ekrut DA, Virostko J, Sorace AG, DiCarlo JC, Kowalski J, Patt D, Goodgame B, Avery S, Yankeelov TE. Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia 2020; 22:820-830. [PMID: 33197744 PMCID: PMC7677708 DOI: 10.1016/j.neo.2020.10.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 12/12/2022]
Abstract
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA; Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA; Livestrong Cancer Institutes, Austin, TX, USA
| | - Chengyue Wu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Anum S Kazerouni
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - Jeanne Kowalski
- Livestrong Cancer Institutes, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA
| | | | - Boone Goodgame
- Department of Oncology, The University of Texas at Austin, Austin, TX, USA; Department of Internal Medicine, The University of Texas at Austin, Austin, TX, USA; Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA; Livestrong Cancer Institutes, Austin, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA; Department of Oncology, The University of Texas at Austin, Austin, TX, USA; Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA.
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7
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Elazab A, Wang C, Gardezi SJS, Bai H, Hu Q, Wang T, Chang C, Lei B. GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images. Neural Netw 2020; 132:321-332. [DOI: 10.1016/j.neunet.2020.09.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 08/27/2020] [Accepted: 09/06/2020] [Indexed: 01/28/2023]
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8
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Jarrett AM, Hormuth DA, Adhikarla V, Sahoo P, Abler D, Tumyan L, Schmolze D, Mortimer J, Rockne RC, Yankeelov TE. Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer. Sci Rep 2020; 10:20518. [PMID: 33239688 PMCID: PMC7688955 DOI: 10.1038/s41598-020-77397-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/29/2020] [Indexed: 12/20/2022] Open
Abstract
While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA
| | - Vikram Adhikarla
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
| | - Prativa Sahoo
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
| | - Daniel Abler
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Lusine Tumyan
- Department of Radiology, City of Hope National Medical Center, Duarte, CA, USA
| | - Daniel Schmolze
- Department of Pathology, City of Hope National Medical Center, Duarte, CA, USA
| | - Joanne Mortimer
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, 1500 E Duarte Rd, Bldg. 74, Duarte, CA, 91010, USA.
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas At Austin, Austin, TX, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, USA.
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, The University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W Dean Keeton Street Stop C0800, Austin, TX, 78712, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Hormuth DA, Jarrett AM, Lima EABF, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data. JCO Clin Cancer Inform 2020; 3:1-10. [PMID: 30807209 DOI: 10.1200/cci.18.00055] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Multiparametric imaging is a critical tool in the noninvasive study and assessment of cancer. Imaging methods have evolved over the past several decades to provide quantitative measures of tumor and healthy tissue characteristics related to, for example, cell number, blood volume fraction, blood flow, hypoxia, and metabolism. Mechanistic models of tumor growth also have matured to a point where the incorporation of patient-specific measures could provide clinically relevant predictions of tumor growth and response. In this review, we identify and discuss approaches that use multiparametric imaging data, including diffusion-weighted magnetic resonance imaging, dynamic contrast-enhanced magnetic resonance imaging, diffusion tensor imaging, contrast-enhanced computed tomography, [18F]fluorodeoxyglucose positron emission tomography, and [18F]fluoromisonidazole positron emission tomography to initialize and calibrate mechanistic models of tumor growth and response. We focus the discussion on brain and breast cancers; however, we also identify three emerging areas of application in kidney, pancreatic, and lung cancers. We conclude with a discussion of the future directions for incorporating multiparametric imaging data and mechanistic modeling into clinical decision making for patients with cancer.
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Affiliation(s)
| | | | | | | | - David T Fuentes
- The University of Texas MD Anderson Cancer Center, Houston, TX
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10
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Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020; 22:309-341. [PMID: 32501772 PMCID: PMC7520881 DOI: 10.1146/annurev-bioeng-062117-121105] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
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Affiliation(s)
- Andreas Mang
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Spyridon Bakas
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Shashank Subramanian
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA); Department of Radiology; and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ,
| | - George Biros
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
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11
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Scheufele K, Subramanian S, Mang A, Biros G, Mehl M. IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION. SIAM JOURNAL ON SCIENTIFIC COMPUTING : A PUBLICATION OF THE SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS 2020; 42:B549-B580. [PMID: 33071533 PMCID: PMC7561052 DOI: 10.1137/19m1275280] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We present a novel formulation for the calibration of a biophysical tumor growth model from a single-time snapshot, multiparametric magnetic resonance imaging (MRI) scan of a glioblastoma patient. Tumor growth models are typically nonlinear parabolic partial differential equations (PDEs). Thus, we have to generate a second snapshot to be able to extract significant information from a single patient snapshot. We create this two-snapshot scenario as follows. We use an atlas (an average of several scans of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of the patient. Then, using the patient scan and the atlas, we combine image-registration algorithms and parameter estimation algorithms to achieve a better estimate of the healthy patient scan and the tumor growth parameters that are consistent with the data. Our scheme is based on our recent work (Scheufele et al., Comput. Methods Appl. Mech. Engrg., to appear), but we apply a different and novel scheme where the tumor growth simulation in contrast to the previous work is executed in the patient brain domain and not in the atlas domain yielding more meaningful patient-specific results. As a basis, we use a PDE-constrained optimization framework. We derive a modified Picard-iteration-type solution strategy in which we alternate between registration and tumor parameter estimation in a new way. In addition, we consider an ℓ 1 sparsity constraint on the initial condition for the tumor and integrate it with the new joint inversion scheme. We solve the sub-problems with a reduced space, inexact Gauss-Newton-Krylov/quasi-Newton method. We present results using real brain data with synthetic tumor data that show that the new scheme reconstructs the tumor parameters in a more accurate and reliable way compared to our earlier scheme.
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Affiliation(s)
- Klaudius Scheufele
- Institut for Parallel and Distributed Systems, Universität Stuttgart, Universitätsstraße 38, 70569, Stuttgart, Germany
| | - Shashank Subramanian
- Oden Institute for Computational Engineering and Sciences, University of Austin, 201 E. 24th Street, Austin, TX 78712-1229
| | - Andreas Mang
- Department of Mathematics, University of Houston, 3551 Cullen Blvd., Houston, TX 77204-3008
| | - George Biros
- Oden Institute for Computational Engineering and Sciences, University of Austin, 201 E. 24th Street, Austin, TX 78712-1229
| | - Miriam Mehl
- Institut for Parallel and Distributed Systems, Universität Stuttgart, Universitätsstraße 38, 70569, Stuttgart, Germany
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Subramanian S, Scheufele K, Mehl M, Biros G. WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS. INVERSE PROBLEMS 2020; 36:045006. [PMID: 33746330 PMCID: PMC7971430 DOI: 10.1088/1361-6420/ab649c] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
We present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction-diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of Magnetic Resonance Imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver.
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Affiliation(s)
- Shashank Subramanian
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th Street, Austin, Texas, USA
| | - Klaudius Scheufele
- Institute for Parallel and Distributed Systems, Universität Stuttgart, Universitatsstraßë38, Stuttgart, Germany
| | - Miriam Mehl
- Institute for Parallel and Distributed Systems, Universität Stuttgart, Universitatsstraßë38, Stuttgart, Germany
| | - George Biros
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th Street, Austin, Texas, USA
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Angeli S, Emblem KE, Due-Tonnessen P, Stylianopoulos T. Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI. NEUROIMAGE-CLINICAL 2018; 20:664-673. [PMID: 30211003 PMCID: PMC6134360 DOI: 10.1016/j.nicl.2018.08.032] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 07/25/2018] [Accepted: 08/31/2018] [Indexed: 01/09/2023]
Abstract
Previous studies to simulate brain tumor progression, often investigate either temporal changes in cancer cell density or the overall tissue-level growth of the tumor mass. Here, we developed a computational model to bridge these two approaches. The model incorporates the tumor biomechanical response at the tissue level and accounts for cellular events by modeling cancer cell proliferation, infiltration to surrounding tissues, and invasion to distant locations. Moreover, acquisition of high resolution human data from anatomical magnetic resonance imaging, diffusion tensor imaging and perfusion imaging was employed within the simulations towards a realistic and patient specific model. The model predicted the intratumoral mechanical stresses to range from 20 to 34 kPa, which caused an up to 4.5 mm displacement to the adjacent healthy tissue. Furthermore, the model predicted plausible cancer cell invasion patterns within the brain along the white matter fiber tracts. Finally, by varying the tumor vascular density and its invasive outer ring thickness, our model showed the potential of these parameters for guiding the timing (83–90 days) of cancer cell distant invasion as well as the number (0–2 sites) and location (temportal and/or parietal lobe) of the invasion sites.
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Affiliation(s)
- Stelios Angeli
- Cancer Biophysics laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Kyrre E Emblem
- Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Paulina Due-Tonnessen
- Department of Radiology, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.
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Apeke S, Gaubert L, Boussion N, Lambin P, Visvikis D, Rodin V, Redou P. Multi-Scale Modeling and Oxygen Impact on Tumor Temporal Evolution: Application on Rectal Cancer During Radiotherapy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:871-880. [PMID: 29610067 DOI: 10.1109/tmi.2017.2771379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We present a multi-scale approach of tumor modeling in order to predict its evolution during radiotherapy. Within this context we focus on three different scales of tumor modeling: microscopic (individual cells in a voxel), mesoscopic (population of cells in a voxel) and macroscopic (whole tumor), with transition interfaces between these three scales. At the cellular level, the description is based on phase transfer probabilities in the cellular cycle. At the mesoscopic scale we represent populations of cells according to different stages in a cell cycle. Finally, at the macroscopic scale, the tumor description is based on the use of FDG PET image voxels. These three scales exist naturally: biological data are collected at the macroscopic scale, but the pathological behavior of the tumor is based on an abnormal cell-cycle at the microscopic scale. On the other hand, the introduction of a mesoscopic scale is essential in order to reduce the gap between the two extreme, in terms of resolution, description levels. It also reduces the computational burden of simulating a large number of individual cells. As an application of the proposed multi-scale model, we simulate the effect of oxygen on tumor evolution during radiotherapy. Two consecutive FDG PET images of 17 rectal cancer patients undergoing radiotherapy are used to simulate the tumor evolution during treatment. The simulated results are compared with those obtained on a third FDG PET image acquired two weeks after the beginning of the treatment.
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Hatt M, Lee JA, Schmidtlein CR, Naqa IE, Caldwell C, De Bernardi E, Lu W, Das S, Geets X, Gregoire V, Jeraj R, MacManus MP, Mawlawi OR, Nestle U, Pugachev AB, Schöder H, Shepherd T, Spezi E, Visvikis D, Zaidi H, Kirov AS. Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211. Med Phys 2017; 44:e1-e42. [PMID: 28120467 DOI: 10.1002/mp.12124] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 12/09/2016] [Accepted: 01/04/2017] [Indexed: 12/14/2022] Open
Abstract
PURPOSE The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. APPROACH A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. FINDINGS A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. CONCLUSIONS Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.
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Affiliation(s)
- Mathieu Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, IBSAM, Brest, France
| | - John A Lee
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | | | | | - Curtis Caldwell
- Sunnybrook Health Sciences Center, Toronto, ON, M4N 3M5, Canada
| | | | - Wei Lu
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shiva Das
- University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Xavier Geets
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | - Vincent Gregoire
- Université catholique de Louvain (IREC/MIRO) & FNRS, Brussels, 1200, Belgium
| | - Robert Jeraj
- University of Wisconsin, Madison, WI, 53705, USA
| | | | | | - Ursula Nestle
- Universitätsklinikum Freiburg, Freiburg, 79106, Germany
| | - Andrei B Pugachev
- University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Heiko Schöder
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | | | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, United Kingdom
| | | | - Habib Zaidi
- Geneva University Hospital, Geneva, CH-1211, Switzerland
| | - Assen S Kirov
- Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Kim M, Phillips MH. A feasibility study of dynamic adaptive radiotherapy for nonsmall cell lung cancer. Med Phys 2017; 43:2153. [PMID: 27147327 DOI: 10.1118/1.4945023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The final state of the tumor at the end of a radiotherapy course is dependent on the doses given in each fraction during the treatment course. This study investigates the feasibility of using dynamic adaptive radiotherapy (DART) in treating lung cancers assuming CBCT is available to observe midtreatment tumor states. DART adapts treatment plans using a dynamic programming technique to consider the expected changes of the tumor in the optimization process. METHODS DART is constructed using a stochastic control formalism framework. It minimizes the total expected number of tumor cells at the end of a treatment course, which is equivalent to maximizing tumor control probability, subject to the uncertainty inherent in the tumor response. This formulation allows for nonstationary dose distributions as well as nonstationary fractional doses as needed to achieve a series of optimal plans that are conformal to the tumor over time, i.e., spatiotemporally optimal plans. Sixteen phantom cases with various sizes and locations of tumors and organs-at-risk (OAR) were generated using in-house software. Each case was planned with DART and conventional IMRT prescribing 60 Gy in 30 fractions. The observations of the change in the tumor volume over a treatment course were simulated using a two-level cell population model. Monte Carlo simulations of the treatment course for each case were run to account for uncertainty in the tumor response. The same OAR dose constraints were applied for both methods. The frequency of replanning was varied between 1, 2, 5 (weekly), and 29 times (daily). The final average tumor dose and OAR doses have been compared to quantify the potential dosimetric benefits of DART. RESULTS The average tumor max, min, mean, and D95 doses using DART relative to these using conventional IMRT were 124.0%-125.2%, 102.1%-114.7%, 113.7%-123.4%, and 102.0%-115.9% (range dependent on the frequency of replanning). The average relative maximum doses for the cord and esophagus, mean doses for the heart and lungs, and D05 for the unspecified tissue resulting 84%-102.4%, 99.8%-106.9%, 66.9%-85.6%, 58.2%-78.8%, and 85.2%-94.0%, respectively. CONCLUSIONS It is feasible to apply DART to the treatment of NSCLC using CBCT to observe the midtreatment tumor state. Potential increases in the tumor dose and reductions in the OAR dose, particularly for parallel OARs with mean or dose-volume constraints, could be achieved using DART compared to nonadaptive IMRT.
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Affiliation(s)
- Minsun Kim
- Department of Radiation Oncology, University of Washington, Seattle, Washington 98195-6043
| | - Mark H Phillips
- Departments of Radiation Oncology and Neurological Surgery, University of Washington, Seattle, Washington 98195-6043
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Meghdadi N, Soltani M, Niroomand-Oscuii H, Ghalichi F. Image based modeling of tumor growth. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2016; 39:601-13. [PMID: 27596102 DOI: 10.1007/s13246-016-0475-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Accepted: 08/16/2016] [Indexed: 01/11/2023]
Abstract
Tumors are a main cause of morbidity and mortality worldwide. Despite the efforts of the clinical and research communities, little has been achieved in the past decades in terms of improving the treatment of aggressive tumors. Understanding the underlying mechanism of tumor growth and evaluating the effects of different therapies are valuable steps in predicting the survival time and improving the patients' quality of life. Several studies have been devoted to tumor growth modeling at different levels to improve the clinical outcome by predicting the results of specific treatments. Recent studies have proposed patient-specific models using clinical data usually obtained from clinical images and evaluating the effects of various therapies. The aim of this review is to highlight the imaging role in tumor growth modeling and provide a worthwhile reference for biomedical and mathematical researchers with respect to tumor modeling using the clinical data to develop personalized models of tumor growth and evaluating the effect of different therapies.
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Affiliation(s)
- N Meghdadi
- Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran.,Computational Medicine Institute, Tehran, Iran
| | - M Soltani
- Division of Nuclear Medicine, Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287-0807, USA. .,Department of Mechanical Engineering, K. N. T. University of Technology, Tehran, Iran. .,Cancer Biology Research Center, Tehran University of Medical Sciences, Tehran, Iran. .,Computational Medicine Institute, Tehran, Iran.
| | - H Niroomand-Oscuii
- Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran.
| | - F Ghalichi
- Division of Biomechanics, Department of Mechanical Engineering, Sahand University of Technology, East Azerbaijan, Tabriz, Iran
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Desbordes P, Petitjean C, Ruan S. Segmentation of lymphoma tumor in PET images using cellular automata: A preliminary study. Ing Rech Biomed 2016. [DOI: 10.1016/j.irbm.2015.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Mi H, Petitjean C, Dubray B, Vera P, Ruan S. Robust feature selection to predict tumor treatment outcome. Artif Intell Med 2015; 64:195-204. [PMID: 26303106 DOI: 10.1016/j.artmed.2015.07.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 07/01/2015] [Accepted: 07/01/2015] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Recurrence of cancer after treatment increases the risk of death. The ability to predict the treatment outcome can help to design the treatment planning and can thus be beneficial to the patient. We aim to select predictive features from clinical and PET (positron emission tomography) based features, in order to provide doctors with informative factors so as to anticipate the outcome of the patient treatment. METHODS In order to overcome the small sample size problem of datasets usually met in the medical domain, we propose a novel wrapper feature selection algorithm, named HFS (hierarchical forward selection), which searches forward in a hierarchical feature subset space. Feature subsets are iteratively evaluated with the prediction performance using SVM (support vector machine). All feature subsets performing better than those at the preceding iteration are retained. Moreover, as SUV (standardized uptake value) based features have been recognized as significant predictive factors for a patient outcome, we propose to incorporate this prior knowledge into the selection procedure to improve its robustness and reduce its computational cost. RESULTS Two real-world datasets from cancer patients are included in the evaluation. We extract dozens of clinical and PET-based features to characterize the patient's state, including SUV parameters and texture features. We use leave-one-out cross-validation to evaluate the prediction performance, in terms of prediction accuracy and robustness. Using SVM as the classifier, our HFS method produces accuracy values of 100% and 94% on the two datasets, respectively, and robustness values of 89% and 96%. Without accuracy loss, the prior-based version (pHFS) improves the robustness up to 100% and 98% on the two datasets, respectively. CONCLUSIONS Compared with other feature selection methods, the proposed HFS and pHFS provide the most promising results. For our HFS method, we have empirically shown that the addition of prior knowledge improves the robustness and accelerates the convergence.
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Affiliation(s)
- Hongmei Mi
- QUANTification en Imagerie Fonctionnelle - Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (EA4108 - FR CNRS 3638), University of Rouen, 22, Boulevard GAMBETTA, 76183 Rouen, France.
| | - Caroline Petitjean
- QUANTification en Imagerie Fonctionnelle - Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (EA4108 - FR CNRS 3638), University of Rouen, 22, Boulevard GAMBETTA, 76183 Rouen, France
| | - Bernard Dubray
- QUANTification en Imagerie Fonctionnelle - Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (EA4108 - FR CNRS 3638), University of Rouen, 22, Boulevard GAMBETTA, 76183 Rouen, France; Centre Henri Becquerel, Rue d'Amiens, 76038 Rouen, France
| | - Pierre Vera
- QUANTification en Imagerie Fonctionnelle - Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (EA4108 - FR CNRS 3638), University of Rouen, 22, Boulevard GAMBETTA, 76183 Rouen, France; Centre Henri Becquerel, Rue d'Amiens, 76038 Rouen, France
| | - Su Ruan
- QUANTification en Imagerie Fonctionnelle - Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (EA4108 - FR CNRS 3638), University of Rouen, 22, Boulevard GAMBETTA, 76183 Rouen, France
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Voros S, Moreau-Gaudry A. How Sensor, Signal, and Imaging Informatics May Impact Patient Centered Care and Care Coordination. Yearb Med Inform 2015; 10:102-5. [PMID: 26293856 DOI: 10.15265/iy-2015-025] [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: 11/24/2022] Open
Abstract
OBJECTIVE This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2015 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2014, with a focus on patient centered care coordination. METHODS The two section editors performed a systematic initial selection and a double blind peer review process to select a list of candidate best papers in the domain published in 2014, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used. This selection was peer-reviewed by external reviewers. RESULTS The review process highlighted articles illustrating two current trends related to care coordination and patient centered care: the enhanced capacity to predict the evolution of a disease based on patient-specific information can impact care coordination; similarly, better perception of the patient and his treatment could lead to enhanced personalized care with a potential impact on care coordination. CONCLUSIONS This review shows the multiplicity of angles from which the question of patient-centered care can be addressed, with consequences on care coordination that will need to be confirmed and demonstrated in the future.
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Affiliation(s)
- S Voros
- Sandrine Voros, Laboratoire TIMC-IMAG, équipe GMCAO, IN3S, pavillon Taillefer, Faculté de Médecine, 38706 La Tronche Cedex, France, Tel: +33 4 56 52 00 09, Fax +33 4 56 52 00 55, E-mail:
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Mi H, Petitjean C, Vera P, Ruan S. Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images. Med Image Anal 2015; 23:84-91. [PMID: 25988489 DOI: 10.1016/j.media.2015.04.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Revised: 02/26/2015] [Accepted: 04/24/2015] [Indexed: 11/30/2022]
Abstract
Tumor response to treatment varies among patients. Patient-specific prediction of tumor evolution based on medical images during the treatment can help to build and adapt patient's treatment planning in a non-invasive way. Personalized tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual's images. The model parameters are often estimated by optimizing a cost function constructed based on the tumor delineations. In this paper, we propose a joint framework for tumor growth prediction and tumor segmentation in the context of patient's therapeutic follow ups. Throughout the treatment, a series of sequential positron emission tomography (PET) images are acquired for tumor response monitoring. We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic tumor segmentation method on PET images. Moreover, we propose an iterative scheme of RW, making the segmentation more performant. Furthermore, the obtained segmentation is applied to the process of model parameter estimation so as to get the model based prediction of tumor evolution. We evaluate our methods on 7 lung tumor patients, totaling 29 PET exams, under radiotherapy by comparing the obtained tumor prediction and tumor segmentation with manual tumor delineation by expert. Our system produces promising results when compared to the state-of-the-art methods.
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Affiliation(s)
- Hongmei Mi
- QuantIF - LITIS (EA4108 - FR CNRS 3638), University of Rouen, Rouen 76000, France.
| | - Caroline Petitjean
- QuantIF - LITIS (EA4108 - FR CNRS 3638), University of Rouen, Rouen 76000, France
| | - Pierre Vera
- QuantIF - LITIS (EA4108 - FR CNRS 3638), University of Rouen, Rouen 76000, France; Centre Henri-Becquerel, Rouen 76038, France
| | - Su Ruan
- QuantIF - LITIS (EA4108 - FR CNRS 3638), University of Rouen, Rouen 76000, France
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