Journal of Fudan University(Natural Science), Volume. 64, Issue 3, 243(2025)
Personalized Tumor Model Reconstruction Based on Brain Imaging and Parameters Optimization of Tumor Treating Fields
[1] [1] KORSHOEJ A R, HANSEN F L, THIELSCHER A, et al. Impact of tumor position, conductivity distribution and tissue homogeneity on the distribution of tumor treating fields in a human brain: A computer modeling study[J].PLOS One, 2017,12(6): e0179214.
[2] [2] RANJBARZADEH R, CAPUTO A, TIRKOLAEE E B, et al. Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools[J].Computers in Biology and Medicine, 2023,152: 106405.
[3] [3] PALPAN F A, VIVANCOS S C, RODA J M, et al. Assessment of pre-operative measurements of tumor size by MRI methods as survival predictors in wild type IDH glioblastoma[J].Frontiers in Oncology, 2020,10: 1662.
[4] [4] BYRD B K, KRISHNASWAMY V, GUI J, et al. The shape of breast cancer[J].Breast Cancer Research and Treatment, 2020,183(2): 403-410.
[5] [5] CONNELLY J, HORMIGO A, MOHILIE N, et al. Planning TTFields treatment using the Novo TAL system-clinical case series beyond the use of MRI contrast enhancement[J].Bio Med Central Cancer, 2016,16(1): 842.
[6] [6] STUPP R, TAILLIBERT S, KANNER A, et al. Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: A randomized clinical trial[J].The Joural of the American Medical Association, 2017,318(23): 2306-2316.
[7] [7] CHAUDHRY A, BENSON L, VARSHAVER M, et al. Novo TTFTM-100A system (tumor treating fields) transducer array layout planning for glioblastoma: A Novo TALTM system user study[J].World Journal of Surgical Oncology, 2015,13(1): 316.
[8] [8] SWANSON K D, LOK E, WONG E T. An overview of alternating electric fields therapy (NovoTTF therapy) for the treatment of malignant glioma[J].Current Neurology and Neuroscience Reports, 2016,16(1): 8.
[9] [9] NEUDORFER C, BUTENKO K, OXENFORD S, et al. Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks[J].NeuroImage, 2023,268: 119862.
[10] [10] BERGER B, LAVAF A, DEROSE P M, et al. Patient-specific segmentation-based treatment planning vs. Novo TAL for TTFields therapy in glioblastoma[J].International Journal of Radiation Oncology*Biology*Physics, 2023,117(Suppl2): e87.
[11] [11] BAID U, GHODASARA S, MOHAN S, et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification[A]. arXiv, 2021: 2107.02314.
[12] [12] MENZE B H, JAKAB A, BAUER S, et al. The multimodal brain tumor image segmentation benchmark (BRATS)[J]. IEEETransactions on Medical Imaging, 2015,34(10): 1993-2024.
[13] [13] BAKAS S, AKBARI H, SOTIRAS A, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features[J].Scientific Data, 2017,4(1): 170117.
[14] [14] RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015: 234-241.
[15] [15] ISENSEE F, JAEGER P F, KOHL S A A, et al. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation[J].Nature Methods, 2021,18(2): 203-211.
[16] [16] LUU H M, PARK S H. Extending n n U-Net for brain tumor segmentation[C]//International MICCAI Brainlesion Workshop. Singapore: Springer, 2022: 173-186.
[17] [17] WANG W, CHEN C, DING M, et al. TransBTS: Multimodal brain tumor segmentation using transformer[A]. arXiv: 2103.04430, 2021.
[18] [18] XING Z, YU L, WAN L, et al. Nested Former: Nested modality-aware transformer for brain tumor segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Singapore: Springer, 2022: 140-150.
[19] [19] FEDOROV A, BEICHEL R, KALPATHY-CRAMER J, et al. 3D slicer as an image computing platform for the quantitative imaging network[J].Magnetic Resonance Imaging, 2012,30(9): 1323-1341.
[20] [20] DEWEYERT A, IREDALE E, XU H, et al. Diffuse intrinsic pontine glioma cells are vulnerable to low intensity electric fields delivered by intratumoral modulation therapy[J].Journal of Neuro-Oncology, 2019,143(1): 49-56.
[21] [21] STEIGERWALD F, MATTHIES C, VOLKMANN J. Directional deep brain stimulation[J].Neurotherapeutics, 2019,16(1): 100-104.
[22] [22] GENTILAL N, MIRANDA P C. Heat transfer during TTFields treatment: Influence of the uncertainty of the electric and thermal parameters on the predicted temperature distribution[J].Computer Methods and Programs in Biomedicine, 2020,196: 105706.
[23] [23] IREDALE E, DEWEYERT A, HOOVER D A, et al. Optimization of multi-electrode implant configurations and programming for the delivery of non-ablative electric fields in intratumoral modulation therapy[J].Medical Physics, 2020,47(11): 5441-5454.
[24] [24] WIESER H P, CISTERNAS E, WAHL N, et al. Development of the open-source dose calculation and optimization toolkit mat Rad[J].Medical Physics, 2017,44(6): 2556-2568.
[25] [25] LIN D, WANG M, CHEN Y, et al. Trends in intracranial glioma incidence and mortality in the united states, 1975-2018[J].Frontiers in Oncology, 2021,11: 748061.
[26] [26] WENGER C, SALVADOR R, BASSER P J, et al. Improving tumor treating fields treatment efficacy in patients with glioblastoma using personalized array layouts[J].International Journal of Radiation Oncology*Biology*Physics, 2016,94(5): 1137-1143.
[27] [27] ANDERSON D N, OSTING B, VORWERK J, et al. Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes[J].Journal of Neural Engineering, 2018,15(2): 026005.
[28] [28] KORSHOEJ A R, SRENSEN J CHR H, VON OETTINGEN G, et al. Optimization of tumor treating fields using singular value decomposition and minimization of field anisotropy[J].Physics in Medicine & Biology, 2019,64(4): 04NT03.
[29] [29] IREDALE E, VOIGT B, RANKIN A, et al. Planning system for the optimization of electric field delivery using implanted electrodes for brain tumor control[J].Medical Physics, 2022,49(9): 6055-6067.
[30] [30] WU J, FU R, FANG H, et al. MedSegDiff: Medical image segmentation with diffusion probabilistic model[A]. arXiv, 2023: 2211.00611.
[31] [31] CHEN T, WANG C, SHAN H. BerDiff: Conditional Bernoulli diffusion model for medical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Vancouver, Canada: Springer, 2023: 491-501.
[32] [32] CHEN T, WANG C, CHEN Z, et al. HiDiff: Hybrid diffusion framework for medical image segmentation[J].IEEE Transactions on Medical Imaging, 2024: 3570-3583.
[33] [33] MA J, HE Y, LI F, et al. Segment anything in medical images[J].Nature Communications, 2024,15(1): 654.
Get Citation
Copy Citation Text
ZHOU Yuxing, MA Yingtong, JIA Fumin. Personalized Tumor Model Reconstruction Based on Brain Imaging and Parameters Optimization of Tumor Treating Fields[J]. Journal of Fudan University(Natural Science), 2025, 64(3): 243