État des lieux de la radiothérapie adaptative en 2019 : de la mise en place à l’utilisation clinique

Badey, A.; Barateau, A.; Delaby, N.; Fau, Pierre; García, R.; Crevoisier, R. de; Lisbona, A.

doi:10.1016/j.canrad.2019.07.142

Cited by 12 publications

(3 citation statements)

References 112 publications

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“…Thus, an innovative approach called adaptive RT (ART) has been proposed to address this challenge. ART involves adjusting the treatment plan to accommodate anatomical variations throughout the treatment course [4,5]. This is accomplished by assessing the patient's anatomical changes and re-calculating the dose distribution based on the updated images.…”

Section: Introductionmentioning

confidence: 99%

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

Khamfongkhruea,

Prakarnpilas,

Thongsawad

et al. 2024

Radiat Oncol J

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Purpose: To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam CT (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC). Materials and Methods:This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning CT (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference. Results: MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53. 15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images. Conclusion: These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

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Section: Introductionmentioning

confidence: 99%

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

Khamfongkhruea,

Prakarnpilas,

Thongsawad

et al. 2024

Radiat Oncol J

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“…[ 1,2 ] To date, treatment of cancer is limited to chemotherapy, radiotherapy and surgery. [ 3 ] However, chemotherapy [ 4,5,6 ] and radiotherapy [ 7,8 ] have serious side effects on normal cells. Although surgery can completely remove the primary tumor and visible metastasis, [ 9 ] the curative effect is often limited by micro‐metastases that invade adjacent tissues or spread to distant tissues.…”

Section: Introductionmentioning

confidence: 99%

“…[1,2] To date, treatment of cancer is limited to chemotherapy, radiotherapy and surgery. [3] However, chemotherapy [4,5,6] and radiotherapy [7,8] have serious side synthetic HAP crystallites have now been used extensively and successfully as implants or coatings on implant prostheses. [23] Nanostructured materials generally offer improved performance over their larger particle-sized counterparts because of their unique physicochemical properties.…”

Section: Introductionmentioning

confidence: 99%

The Janus Nature of Nanohydroxyapatite in Tumor Progression

Shen

Wan

et al. 2021

Adv Funct Materials

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Nanohydroxyapatite (nHAP) has broad applications because of its nanoscopical dimension, large specific surface area, biocompatibility, and low cytotoxicity. Researchers recently discovered that nHAP synthesized in vitro inhibits the growth of different types of tumor cells. Nanohydroxyapatite with potent drug adsorption and loading capacity has potential applications in tumor diagnosis and treatment. Because local tumors and areas of tumor metastasis also produce pathological nHAP in vivo to promote progression and invasion, the role of nHAP in tumorigenesis and development is perceived literature by many as Janus, the double‐faced deity in ancient Roman mythology. In the present review, two types of nHAP (those synthesized in vitro and those produced in vivo) that are affiliated with tumors, their mechanisms in tumor progression as well as their applications in tumor treatment are elucidated to create a backdrop for future research in this exciting, yet controversial arena.

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