Estimation of disease progression parameters from case-control data: application to mammographic patterns and breast cancer natural history

Couto, Elisabeth; D, Harrison; Duffy, SW; Myles, Jamie D.; Sala, Evis; Warren, Ruth; Day, N. E.; Luben, Robert; Chen, Chien-Jen

doi:10.1080/135952201753173015

Cited by 8 publications

(1 citation statement)

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“…The studies conducted to date have not pointed out any specific regions on screening mammograms in which there was an increased probability of containing changes leading to breast cancer. However, the region of interest (ROI) has been investigated in terms of the position and size using a specific high risk population (BRCA 1, BRCA 2) by Li et al (2004) and Couto et al (2001). The geographical occurrence of tumours in mammograms has been studied by Brown et al (2001) on an interval database.…”

Section: Introductionmentioning

confidence: 99%

A method to determine the mammographic regions that show early changes due to the development of breast cancer

Karemore¹,

Nielsen²,

Karssemeijer³

et al. 2014

Phys. Med. Biol.

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It is well understood nowadays that changes in the mammographic parenchymal pattern are an indicator of a risk of breast cancer and we have developed a statistical method that estimates the mammogram regions where the parenchymal changes, due to breast cancer, occur. This region of interest is computed from a score map by utilising the anatomical breast coordinate system developed in our previous work. The method also makes an automatic scale selection to avoid overfitting while the region estimates are computed by a nested cross-validation scheme. In this way, it is possible to recover those mammogram regions that show a significant difference in classification scores between the cancer and the control group. Our experiments suggested that the most significant mammogram region is the region behind the nipple and that can be justified by previous findings from other research groups. This result was conducted on the basis of the cross-validation experiments on independent training, validation and testing sets from the case-control study of 490 women, of which 245 women were diagnosed with breast cancer within a period of 2-4 years after the baseline mammograms. We additionally generalised the estimated region to another, mini-MIAS study and showed that the transferred region estimate gives at least a similar classification result when compared to the case where the whole breast region is used. In all, by following our method, one most likely improves both preclinical and follow-up breast cancer screening, but a larger study population will be required to test this hypothesis.

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Section: Introductionmentioning

confidence: 99%