Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample

Pertuz, Said; Sassi, Antti; Holli-Helenius, Kirsi; Kamarainen, Joni; Rinta-Kiikka, Irina; Laaperi, Anna-Leena; Arponen, Otso

doi:10.1016/j.ejrad.2019.108710

Cited by 13 publications

(23 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…For example, Perutz and colleagues created OpenBreast [30], by applying logistic regression analyses to multiple features as we did in creating Cirrus, this time using digital mammograms but with a much smaller dataset. They recently reported that OpenBreast had an OPERA of 2.5, equivalent to an extraordinarily high IQRR of almost 10 but with a wide 95% CI (given the small dataset) for which the lower bound was 4 [30]. From the Supplementary Material [30], the most prominent feature contributing to Open Breast was 'maximum gray-level value', which could be picking up the aspects captured by Cirrocumulus (the amount of brightest areas).…”

Section: Discussionmentioning

confidence: 99%

“…They recently reported that OpenBreast had an OPERA of 2.5, equivalent to an extraordinarily high IQRR of almost 10 but with a wide 95% CI (given the small dataset) for which the lower bound was 4 [30]. From the Supplementary Material [30], the most prominent feature contributing to Open Breast was 'maximum gray-level value', which could be picking up the aspects captured by Cirrocumulus (the amount of brightest areas). The next most prominent feature was 'high gray-level run' followed by 'run-percentage' and then both 'short-run emphasis' and 'long-run emphasis', separated by 'gray-level range'.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Hopper

Nguyen

Schmidt

et al. 2020

JCM

View full text Add to dashboard Cite

This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Hopper

Nguyen

Schmidt

et al. 2020

JCM

View full text Add to dashboard Cite

show abstract

“…Addition of parenchymal features often increased the AUC by 0.05 though Pertuz noted an increase in AUC from 0.609 to 0.786 when using texture features in the contralateral breast at the time of cancer diagnosis. 48 Furthermore as noted in Table 2, there was substantial variation in the number of texture features included and the method of their identification for inclusion (human defined or machine identified).…”

Section: Resultsmentioning

confidence: 99%

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Anandarajah¹,

Chen

Colditz³

et al. 2021

Preprint

View full text Add to dashboard Cite

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to prediction of future breast cancer including the time from mammogram to diagnosis of breast cancer, and methods for the identification of texture features and selection of features for inclusion in analysis. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. Of these, 7 assessed texture features from film mammograms images, 3 did not report details of the image used, and the others used full field mammograms from Hologic, GE and other manufacturers. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. Reduction in number of features chosen for analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

show abstract

“…The inclusion criteria for cases were: (1) no known history of previously detected breast malignancies or previous invasive breast operations, (2) no reported breast cancer-related symptoms, and 3) availability of mammograms from the previous screening round, because these are the images used for the analysis in order to make our results clinically more relevant. We searched for controls (i.e., women with normal mammograms at the time of screening with no suspicious findings requiring further biopsies, and no known history of breast malignancies or invasive operations 9 ) that were matched by screening and birth years and the mammographic system. No other inclusion or exclusion criteria were applied.…”

Section: Experimental Design and Imaging Datamentioning

confidence: 99%

Image retrieval‐based parenchymal analysis for breast cancer risk assessment

et al. 2021

Self Cite

View full text Add to dashboard Cite

This research on breast cancer risk assessment aims to develop models that predict the likelihood of breast cancer. In recent years, the computerized analysis of visual texture patterns in mammograms,namely parenchymal analysis, has shown great potential for risk assessment. However, the visual complexity and heterogeneity of visual patterns limit the performance of parenchymal analysis in large populations. In this work, we propose a method to create individualized risk assessment models based on the radiological visual appearance (radiomic phenotypes) of the mammograms. Methods: We developed a content-based image retrieval system to stratify mammographic analysis according to the similarities of their radiomic phenotypes. We collected 1144 mammograms from 286 women following a casecontrol study design. We compared the classical parenchymal analysis with the proposed approach using the area under the ROC curve (AUC) with 95% confidence intervals (CI). Statistical significance was assessed using DeLong's test (p <0.05). Results: At a patient level,AUC values of 0.504 (95% CI:0.398-0.611) with classical parenchymal analysis increased to 0.813 (95% CI: 0.734-0.892) when the radiomic phenotypes are incorporated with the proposed method. In risk estimation from individual, standard mammographic views, the highest performance was obtained with the mediolateral oblique view of the right breast (RMLO), with an AUC value of 0.727 (95% CI: 0.634-0.820). Differences in performance among views were statistically significant (p < 0.05) Conclusions: These results indicate that the utilization of radiomic phenotypes increases the performance of computerized risk assessment based on parenchymal analysis of mammographic images. Significance: The creation of individualized risk assessment models may be leveraged to target personalized screening and prevention recommendations according to the person's risk.

show abstract

Clinical evaluation of a fully-automated parenchymal analysis software for breast cancer risk assessment: A pilot study in a Finnish sample

Cited by 13 publications

References 26 publications

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Image retrieval‐based parenchymal analysis for breast cancer risk assessment

Contact Info

Product

Resources

About