Staging of Prostate Cancer

Dotan, Zohar; Ramon, Jacob

doi:10.1007/978-3-540-40901-4_8

Cited by 4 publications

(5 citation statements)

References 137 publications

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“…In Matlab and other similar systems, the constraints can be set as a matrix equation. Main simulation dataset comes from [20][21][22][23][24][25]68,74,75,80,81,[85][86][87][88][89][90][91][92][93][94]99,101] . The GA simulations results that were done [98,99,101] with numerical-experimental interval-data for GA implemented arrays were used for imaging process.…”

Section: Mathematical and Programming Methodsmentioning

confidence: 99%

Radiotherapy Hyperfractionated 3DIsodosezones Planning Optimization Method forLung Tumors withBED Pareto-Multiobjective Model

Casesnoves

2024

IJMCR

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3D Isodoselines and Isodosezones were presented in previous publication. Isodosezones were put out and applied on prostate tumors. In this further improvement, 3D Isodosezones are got with programming innovation with new software-engineering programming for lung cancer. BED model for radiotherapy hypofractionated treatment planning optimization is used. Interior Optimization (IO) for lung tumor BED model hyperfractionated Treatment Planning Optimization (TPO) application is further demonstrated. The implemented data was got with additional-dual constrained evolutionary algorithm for BED-LQ model (Biological Effective Dose) in this cancer type. Results for TPO with 3D IO-Graphical Optimization show a number of surfactal IO 3D Isodoselines/zones with proven accuracy-feasibility of the novelty of the technique. Programming software for surfactal-isodoselines/zones methods solutions show a series of 3D IO graphs for TPO. Applications for lung tumors radiotherapy and stereotactic radiosurgery treatments are briefed.

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Section: Mathematical and Programming Methodsmentioning

confidence: 99%

Radiotherapy Hyperfractionated 3DIsodosezones Planning Optimization Method forLung Tumors withBED Pareto-Multiobjective Model

Casesnoves

2024

IJMCR

View full text Add to dashboard Cite

show abstract

“…In Matlab and other similar systems, the constraints can be set as a matrix equation. Simulation dataset from comes from [20][21][22][23][24][25]68,74,75,80,81,[85][86][87][88][89][90][91][92][93][94]100] . The GA simulations results that were done [98,100] with numerical-experimental interval-data for GA implemented arrays were used for imaging process.…”

Section: Mathematical and Programming Methodsmentioning

confidence: 99%

Radiotherapy Treatment Planning Optimization Improved 3d-Surfactal Isodoselines Series for Prostate Cancer With Bed Pareto-Multiobjective Model 3d-Dataset

Casesnoves

2023

ijmcr

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3D Isodoselines innovation with Interior Optimization (IO) for prostate tumor BED model hyperfractionated Treatment Planning Optimization (TPO) application is further demonstrated. The implemented data was got with additional-dual constrained evolutionary algorithm for BED-LQ model (Biological Effective Dose) in this cancer type. Results for TPO with 3D IO-Graphical Optimization show a number of surfactal IO 3D Isodoselines with proven accuracy-feasibility of the novelty of the technique. Programming software for surfactal-isodoselines methods solutions show a series of 3D IO graphs for TPO. Applications for prostate tumors radiotherapy and stereotactic radiosurgery treatments are briefed.

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“…Prostate cancer (PCa) is generally diagnosed at late age, with 75% of all cases found in men over 65 years old (1,2), while the formation of precursor neoplastic lesions is initiated years earlier (3). While localized PCa can be cured, metastatic disease cannot, and its treatment is a clinical challenge (4,5).…”

Section: Introductionmentioning

confidence: 99%

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

Passier

Genderen

Zaalberg

et al. 2023

Preprint

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Over ten percent of men will be diagnosed with prostate cancer (PCa) during their lifetime. Arising from luminal cells of the prostatic acinus, PCa is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of PCa. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of PCa development. The model was based on prior reports and in-house data of tumor cells co-cultured with Cancer Associated Fibroblasts (CAFs) and pro-tumor and/or anti-tumor macrophages. Growth patterns depicted by the model were pathologically validated on H&E slide images of human PCa specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. Additionally, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 cancer patients with matching characteristics, showing strong association between predicted tumor load and patients' clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient.

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Staging of Prostate Cancer

Cited by 4 publications

References 137 publications

Radiotherapy Hyperfractionated 3DIsodosezones Planning Optimization Method forLung Tumors withBED Pareto-Multiobjective Model

Radiotherapy Hyperfractionated 3DIsodosezones Planning Optimization Method forLung Tumors withBED Pareto-Multiobjective Model

Radiotherapy Treatment Planning Optimization Improved 3d-Surfactal Isodoselines Series for Prostate Cancer With Bed Pareto-Multiobjective Model 3d-Dataset

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

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