Image processing can cause some malignant soft-tissue lesions to be missed in digital mammography images

Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach

Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a patch size of 224 px and an overlap of 0.25. It resulted in AUC-ROC = 0.896 ± 0.017, F1-score 81.8 ± 3.2, accuracy 81.6 ± 3.2, precision 82.4 ± 3.3, and recall 81.6 ± 3.2. Evaluation with the Chinese Mammography Database, with 5-fold cross-validation, patient-wise breakdowns, and transfer learning, resulted in AUC-ROC 0.848 ± 0.015, F1-score 78.6 ± 2.0, accuracy 78.4 ± 1.9, precision 78.8 ± 2.0, and recall 78.4 ± 1.9. The CLAM algorithm's attentional maps indicate the features most relevant to the algorithm in the images. Our approach was more effective than in many other studies, allowing for some explainability and identifying erroneous predictions based on the wrong premises.

Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study

BACKGROUND

This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers.

METHODS

This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls.

RESULTS

FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001).

CONCLUSION

FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.

An analysis of screen-detected invasive cancers by grade in the English breast cancer screening programme: are we failing to detect sufficient small grade 3 cancers?

OBJECTIVE

Randomised controlled trials have shown a reduction in breast cancer mortality from mammography screening and it is the detection of high-grade invasive cancers that is responsible for much of this effect. We determined the detection rates of invasive cancers by grade, size and type of screen and estimated relative sensitivities with emphasis on grade 3 detection.

METHODS

This observational study analysed data from over 11 million screening episodes (67,681 invasive cancers) from the English NHS breast screening programme over seven screening years 2009/2010 to 2015/2016 for women aged 45-70.

RESULTS

At prevalent (first) screens (which are unaffected by screening interval), the detection rate of small (< 15 mm) invasive cancers was 0.95 per 1000 for grade 1, but for grade 3 only 0.30 per 1000. The ratio of small (< 15 mm) to large (≥ 15 mm) cancers was 1.8:1 for grade 1 but reversed to 0.5:1 for grade 3. We estimated that the relative sensitivity for grade 3 invasive cancers was 52% of that for grade 1 and the relative sensitivity for small (< 15 mm) grade 3 only 26% of that for small (< 15 mm) grade 1 invasive cancers.

CONCLUSIONS

Sensitivity for small grade 3 invasive cancers is poor compared with that for grade 1 and 2 invasive cancers and larger grade 3 malignancies. This observation is likely a limitation of the current technology related to the absence of identifiable mammographic features for small high-grade cancers. Future work should focus on technologies and strategies to improve detection of these clinically most significant cancers.

KEY POINTS

• The detection of small high-grade invasive cancers is vital to reduce breast cancer mortality. • We estimate the sensitivity for small grade 3 invasive cancers may be only 26% of that of small grade 1 invasive cancers. This is likely to be associated with the non-specific mammographic features for these cancers. • New technologies and appropriate strategies using current technology are required to maximise the detection of small grade 3 invasive cancers.

OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data

Supplemental material is available for this article.

Mammography cancer detection: comparison of single 8MP and pair of 5MP reporting monitors

OBJECTIVE:

To compare breast cancer detection using a single 8MP display with using a standard pair of 5MP monitors.

METHODS:

An observer study was carried out in which mammograms were read using full field views only, and again with the additional use of magnified quadrant views. Seven observers read 300 cases, one view per breast, using each display type. Cases comprised 100 normal cases and 200 cases with cancers of subtle or very subtle appearance: 100 with malignant calcification clusters and 100 with non-calcified lesions. JAFROC software was used to analyse the results.

RESULTS:

When mammograms were viewed full field only, observers performed better (p = 0.050) in detecting malignant calcification clusters when using the pair of 5MP monitors compared with a single 8MP monitor. This result became non-significant when results were generalised to a population of readers. Performance in detecting calcification clusters was improved by using quadrant view in addition to full field view when using either the pair of 5MP monitors or the 8MP monitor. There was no significant difference in detection of all types of cancer between the pair of 5MP monitors and the 8MP monitor when quadrant zoom was used.

CONCLUSION:

Providing quadrant view is used in addition to full field view, there is no significant difference in cancer detection between the 8MP monitor and the pair of 5MP monitors.

ADVANCES IN KNOWLEDGE:

Effect of magnification on the detectability of subtle malignant calcification clusters in breast screening.