Image retrieval‐based parenchymal analysis for breast cancer risk assessment. Issue 2 (15th December 2021)

• Record Type: Journal Article
• Authors: Padilla, Astrid; Arponen, Otso; Rinta‐Kiikka, Irina; Pertuz, Said
• Is Part Of: Medical physics. Volume 49:Issue 2(2022)
• Page Start: 1055
• Page End: 1064
• Publication Date: 2021-12-15
• Subjects: breast cancer -- breast parenchyma -- computer vision -- image retrieval -- mammography -- radiomics -- risk assessment; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15378
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: 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 case‐control 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 < $p<$ 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 $p<0.05$ ) 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.