1
|
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.
Collapse
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
| |
Collapse
|
2
|
Hamming-Vrieze O, Depauw N, Craft DL, Chan AW, Rasch CRN, Verheij M, Sonke JJ, Kooy HM. Impact of setup and range uncertainties on TCP and NTCP following VMAT or IMPT of oropharyngeal cancer patients. Phys Med Biol 2019; 64:095001. [PMID: 30921775 DOI: 10.1088/1361-6560/ab1459] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
3
|
Development of 3D biological effective dose distribution software program. JOURNAL OF RADIOTHERAPY IN PRACTICE 2017. [DOI: 10.1017/s1460396916000339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
AbstractPurposeTo develop a software program to convert physical dose distribution into biological effective dose (BED).MethodsThe MATLAB-based BED distribution software program was designed to import the radiotherapy treatment plan from the computer treatment planning system and to convert the physical dose distribution into the BED distribution. The BED calculation was based on the linear-quadratic-linear model (LQ-L model). Besides radiobiological parameters, other specific data could be fed in through the panel. The accuracy of the program was verified by comparing the BED distribution with manual calculation.ResultsThis software program was able to import the radiotherapy treatment plans and pull out pixel-wised physical dose for BED calculation, and display the isoBED lines on the computed tomographic (CT) image. The verification of BED dose distribution was performed in both phantom and clinical cases. It revealed that there were no differences between the program and manual BED calculations.ConclusionIt is feasible and practical to use this in-house BED distribution software program in clinical practices and research work. However, it should be used with caution as the validity of the program depends on the accuracy of the published biological parameters.
Collapse
|
4
|
Geng C, Paganetti H, Grassberger C. Prediction of Treatment Response for Combined Chemo- and Radiation Therapy for Non-Small Cell Lung Cancer Patients Using a Bio-Mathematical Model. Sci Rep 2017; 7:13542. [PMID: 29051600 PMCID: PMC5648928 DOI: 10.1038/s41598-017-13646-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 09/19/2017] [Indexed: 12/13/2022] Open
Abstract
The goal of this work was to develop a mathematical model to predict Kaplan-Meier survival curves for chemotherapy combined with radiation in Non-Small Cell Lung Cancer patients for use in clinical trial design. The Gompertz model was used to describe tumor growth, radiation effect was simulated by the linear-quadratic model with an α/β-ratio of 10, and chemotherapy effect was based on the log-cell kill model. To account for repopulation during treatment, we considered two independent methods: 1) kickoff-repopulation using exponential growth with a decreased volume doubling time, or 2) Gompertz-repopulation using the gradually accelerating growth rate with tumor shrinkage. The input parameters were independently estimated by fitting to the SEER database for untreated tumors, RTOG-8808 for radiation only, and RTOG-9410 for sequential chemo-radiation. Applying the model, the benefit from concurrent chemo-radiation comparing to sequential for stage III patients was predicted to be a 6.6% and 6.2% improvement in overall survival for 3 and 5-years respectively, comparing well to the 5.3% and 4.5% observed in RTOG-9410. In summary, a mathematical model was developed to model tumor growth over extended periods of time, and can be used for the optimization of combined chemo-radiation scheduling and sequencing.
Collapse
Affiliation(s)
- Changran Geng
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, 30 Fruit Street, Boston, MA, 02114, USA.
- Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China.
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, 30 Fruit Street, Boston, MA, 02114, USA
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, 30 Fruit Street, Boston, MA, 02114, USA.
| |
Collapse
|
5
|
Towards multidimensional radiotherapy: key challenges for treatment individualisation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:934380. [PMID: 25834635 PMCID: PMC4365339 DOI: 10.1155/2015/934380] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Accepted: 09/03/2014] [Indexed: 12/03/2022]
Abstract
Functional and molecular imaging of tumours have offered the possibility of redefining the target in cancer therapy and individualising the treatment with a multidimensional approach that aims to target the adverse processes known to impact negatively upon treatment result. Following the first theoretical attempts to include imaging information into treatment planning, it became clear that the biological features of interest for targeting exhibit considerable heterogeneity with respect to magnitude, spatial, and temporal distribution, both within one patient and between patients, which require more advanced solutions for the way the treatment is planned and adapted. Combining multiparameter information from imaging with predictive information from biopsies and molecular analyses as well as in treatment monitoring of tumour responsiveness appears to be the key approach to maximise the individualisation of treatment. This review paper aims to discuss some of the key challenges for incorporating into treatment planning and optimisation the radiobiological features of the tumour derived from pretreatment PET imaging of tumour metabolism, proliferation, and hypoxia and combining them with intreatment monitoring of responsiveness and other predictive factors with the ultimate aim of individualising the treatment towards the maximisation of response.
Collapse
|