Environmental factors in breast cancer invasion: a mathematical modelling review

Simmons, Alex; Burrage, Pamela; Nicolau, Dan V.; Lakhani, Sunil R.; Burrage, Kevin

doi:10.1016/j.pathol.2016.11.004

Cited by 28 publications

(23 citation statements)

References 94 publications

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“…In addition to this, results of using multiscale modelling of growing tumors had long term negative effects on phenotypes [27]. Conclusions about negative effects of experimentally verified models of breast cancer on phenotypes can be found in the paper [28]. Experiences and mistakes that have been approved and verified through the given works, limit the research in this paper in the conclusions.…”

Section: Results Anddiscussionmentioning

confidence: 92%

Segmentation and Classification of Bi-Rads Medical Images with the Imaging Biomarkers According To Level of Detail

Ivković¹,

Petrović²,

Dakovic

et al. 2020

Teh. vjesn

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Segmentation is one of the latest directions of digital imaging development, presented by partial segments which are parts of the same image. The currently used algorithms are rare and far from ideal. Depending on the situation, algorithms behave differently, so it is almost impossible to single out a unique conclusion. This paper gives a proposal for new segmentation techniques with the help of Imaging Biomarkers for the digital image processing with auxiliary Hybrid edge detection algorithm and especially a defined threshold of detection. Matlab software is used for giving a particular conceptual design of a partial display of images and for correlating adequate numerical methods of digital processing. The study results confirm the quality of the proposed algorithm and position it for practical use in classification of BI-RADS breasts classified by density.

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Section: Results Anddiscussionmentioning

confidence: 92%

Segmentation and Classification of Bi-Rads Medical Images with the Imaging Biomarkers According To Level of Detail

Ivković¹,

Petrović²,

Dakovic

et al. 2020

Teh. vjesn

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“…257 This was corroborated by another multiscale study, which found that environmental pressure encouraged predictable phenotypes, first with models and then experimentally verified with breast cancer models. Readers interested in more comprehensive knowledge of this type of modeling might refer to a recent review by Simmons et al 258 The extant examples of computational modeling only scratch the surface a field that is becoming increasingly accessible, and QMIB will be essential to validating such promising multiscale models in the future.…”

Section: Computational Cancer Modelingmentioning

confidence: 99%

Review of quantitative multiscale imaging of breast cancer

et al. 2018

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Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.

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“…Breast cancer, the most common female cancer, is highly heterogeneous and is probably caused by numerous changes in the genome of specific cells over extended time periods . Changes of normal‐phenotype breast cells to cancerous‐phenotype cells are affected by a series of factors, such as the local and non‐native environment, lifestyle, dietary habits, and genetic inheritance, which can disrupt cells' physical characteristics, behavior, and communication pathways .…”

Section: Introductionmentioning

confidence: 99%

“…Changes of normal‐phenotype breast cells to cancerous‐phenotype cells are affected by a series of factors, such as the local and non‐native environment, lifestyle, dietary habits, and genetic inheritance, which can disrupt cells' physical characteristics, behavior, and communication pathways . Ductal carcinoma in situ (DCIS) of the breast, a noninvasive and nonobligate precursor lesion, represents a transition from normal tissue to an invasive ductal breast carcinoma (IDC) through a multifactorial process …”

Section: Introductionmentioning

confidence: 99%

Combined application of pharamcokinetic DCE‐MRI and IVIM‐DWI could improve detection efficiency in early diagnosis of ductal carcinoma in situ

Tao

Zhang

et al. 2019

J Applied Clin Med Phys

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Purpose Ductal carcinoma in situ (DCIS) is a precursor of invasive ductal breast carcinoma (IDC). This study aimed to use pharamcokinetic dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) and intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) for the early diagnosis of DCIS. Methods Forty‐seven patients, including 25 with DCIS (age: 28–70 yr, mean age: 48.7 yr) and 22 with benign disease (age: 25–67 yr, mean age: 43.1 yr) confirmed by pathology, underwent pharamcokinetic DCE‐MRI and IVIM‐DWI in this study. The quantitative parameters Ktrans, Kep, Ve, Vp, and D, f, D* were obtained by processing of DCE‐MRI and IVIM‐DWI images with Omni‐Kinetics and MITK‐Diffusion softwares, respectively. Parameters were analyzed statistically using GraphPad Prism and MedCalc softwares. Results All low‐grade DCIS lesions demonstrated mass enhancement with clear boundaries, while most middle‐grade and high‐grade DCIS lesions showed non‐mass‐like enhancement (NMLE). DCIS lesions were significantly different from benign lesions in terms of Ktrans, Kep, and D (t = 5.959, P < 0.0001; t = 5.679, P < 0.0001; and t = 5.629, P < 0.0001, respectively). The AUC of Ktrans, Kep, D and the combined indicator of Ktrans, Kep, and D were 0.936, 0.902, 0.860, and 0.976, respectively. There was a significant difference in diagnostic efficacy only between D and the combined indicator (Z = 2.408, P = 0.016). Conclusion DCE‐MRI and IVIM‐DWI could make for the early diagnosis of DCIS, and reduce the misdiagnosis of DCIS and over‐treatment of benign lesions.

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Environmental factors in breast cancer invasion: a mathematical modelling review

Cited by 28 publications

References 94 publications

Segmentation and Classification of Bi-Rads Medical Images with the Imaging Biomarkers According To Level of Detail

Segmentation and Classification of Bi-Rads Medical Images with the Imaging Biomarkers According To Level of Detail

Review of quantitative multiscale imaging of breast cancer

Combined application of pharamcokinetic DCE‐MRI and IVIM‐DWI could improve detection efficiency in early diagnosis of ductal carcinoma in situ

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