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1.
Phys Med ; 119: 103305, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38320358

RESUMO

PURPOSE: To propose an artificial intelligence (AI)-based method for personalized and real-time dosimetry for chest CBCT acquisitions. METHODS: CT images from 113 patients who underwent radiotherapy treatment were collected for simulating thorax examinations using cone-beam computed tomography (CBCT) with the Monte Carlo technique. These simulations yielded organ dose data, used to train and validate specific AI algorithms. The efficacy of these AI algorithms was evaluated by comparing dose predictions with the actual doses derived from Monte Carlo simulations, which are the ground truth, utilizing Bland-Altman plots for this comparative analysis. RESULTS: The absolute mean discrepancies between the predicted doses and the ground truth are (0.9 ± 1.3)% for bones, (1.2 ± 1.2)% for the esophagus, (0.5 ± 1.3)% for the breast, (2.5 ± 1.4)% for the heart, (2.4 ± 2.1)% for lungs, (0.8 ± 0.6)% for the skin, and (1.7 ± 0.7)% for integral. Meanwhile, the maximum discrepancies between the predicted doses and the ground truth are (14.4 ± 1.3)% for bones, (12.9 ± 1.2)% for the esophagus, (9.4 ± 1.3)% for the breast, (14.6 ± 1.4)% for the heart, (21.2 ± 2.1)% for lungs, (10.0 ± 0.6)% for the skin, and (10.5 ± 0.7)% for integral. CONCLUSIONS: AI models that can make real-time predictions of the organ doses for patients undergoing CBCT thorax examinations as part of radiotherapy pre-treatment positioning were developed. The results of this study clearly show that the doses predicted by analyzed AI models are in close agreement with those calculated using Monte Carlo simulations.


Assuntos
Inteligência Artificial , Tomografia Computadorizada de Feixe Cônico Espiral , Humanos , Dosagem Radioterapêutica , Radiometria/métodos , Tomografia Computadorizada de Feixe Cônico/métodos , Método de Monte Carlo , Imagens de Fantasmas , Doses de Radiação
2.
Int J Mol Sci ; 24(9)2023 Apr 27.
Artigo em Inglês | MEDLINE | ID: mdl-37175668

RESUMO

ETS2 repressor factor (ERF) insufficiency causes craniosynostosis (CRS4) in humans and mice. ERF is an ETS domain transcriptional repressor regulated by Erk1/2 phosphorylation via nucleo-cytoplasmic shuttling. Here, we analyze the onset and development of the craniosynostosis phenotype in an Erf-insufficient mouse model and evaluate the potential of the residual Erf activity augmented by pharmacological compounds to ameliorate the disease. Erf insufficiency appears to cause an initially compromised frontal bone formation and subsequent multisuture synostosis, reflecting distinct roles of Erf on the cells that give rise to skull and facial bones. We treated animals with Mek1/2 and nuclear export inhibitors, U0126 and KPT-330, respectively, to increase Erf activity by two independent pathways. We implemented both a low dosage locally over the calvaria and a systemic drug administration scheme to evaluate the possible indirect effects from other systems and minimize toxicity. The treatment of mice with either the inhibitors or the administration scheme alleviated the synostosis phenotype with minimal adverse effects. Our data suggest that the ERF level is an important regulator of cranial bone development and that pharmacological modulation of its activity may represent a valid intervention approach both in CRS4 and in other syndromic forms of craniosynostosis mediated by the FGFR-RAS-ERK-ERF pathway.


Assuntos
Craniossinostoses , Fatores de Transcrição , Animais , Camundongos , Craniossinostoses/tratamento farmacológico , Craniossinostoses/genética , Sistema de Sinalização das MAP Quinases , Fosforilação , Proteínas Repressoras , Crânio
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