Image features of true positive and false negative cancers in screening mammograms

Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images

Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the 'starting point set' of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer (only the healthy side) and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as 'wavelet-H_glcm_Correlation' and 'wavelet-H_firstorder_Maximum', showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was ≥0.95. Radiomics features from mammography images can help predict breast cancer risk.

Screening Sensitivity According to Breast Cancer Location

PURPOSE

To examine the relation between breast cancer location and screening mammogram sensitivity, and assess whether this association is modified by body mass index (BMI) or breast density.

METHODS

This study is based on all interval cancers (n = 481) and a random sample of screen-detected cancers (n = 481) diagnosed in Quebec Breast Cancer Screening Program participants in 2007. Film-screening mammograms, diagnostic mammograms, and ultrasound reports (when available) were requested for these cases. The breast cancer was then localised in mediolateral oblique (MLO) and craniocaudal (CC) projections of the breast by 1 experienced radiologist. The association between cancer location and screening sensitivity was assessed by logistic regression. Adjusted sensitivity and sensitivity ratios were obtained by marginal standardisation.

RESULTS

A total of 369 screen-detected and 268 interval cancers could be localised in MLO and/or CC projections. The 2-year sensitivity reached 68%. Overall, sensitivity was not statistically associated with location of the cancer. However, sensitivity seems lower in MLO posterior inferior area for women with BMI ≥ 25 kg/m² compared to sensitivity in central area for women with lower BMI (adjusted sensitivity ratio: 0.58, 95% confidence interval [CI]: 0.17-0.98). Lower sensitivity was also observed in subareolar areas for women with breast density ≥ 50% compared to the central areas for women with lower breast density (for MLO and CC projections, adjusted sensitivity ratio and 95% CI of, respectively, 0.54 [0.13-0.96] and 0.46 [0.01-0.93]).

CONCLUSIONS

Screening sensitivity seems lower in MLO posterior inferior area in women with higher BMI and in subareolar areas in women with higher breast density. When interpreting screening mammograms, radiologists need to pay special attention to these areas.

Incorporating Breast Anatomy in Computational Phenotyping of Mammographic Parenchymal Patterns for Breast Cancer Risk Estimation

We retrospectively analyzed negative screening digital mammograms from 115 women who developed unilateral breast cancer at least one year later and 460 matched controls. Texture features were estimated in multiple breast regions defined by an anatomically-oriented polar grid, and were weighted by their position and underlying dense versus fatty tissue composition. Elastic net regression with cross-validation was performed and area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate ability to predict breast cancer. We also compared our anatomy-augmented features to current state-of-the-art in which parenchymal texture was assessed without considering breast anatomy and evaluated the added value of the extracted features to breast density, body-mass-index (BMI) and age as baseline predictors. Our anatomy-augmented texture features resulted in higher discriminatory capacity (AUC = 0.63 vs. AUC = 0.59) when breast anatomy was not considered (p = 0.021), with dense tissue regions and the central breast quadrant being more heavily weighted. Texture also improved baseline models (from AUC = 0.62 to AUC = 0.67, p = 0.029). Our findings suggest that incorporating breast anatomy information could augment imaging markers of breast cancer risk with the potential to improve personalized breast cancer risk assessment.

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

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.

Stereoscopic digital mammography: improved specificity and reduced rate of recall in a prospective clinical trial

PURPOSE

To compare stereoscopic digital mammography (DM) with standard DM for the rate of patient recall and the detection of cancer in a screening population at elevated risk for breast cancer.

MATERIALS AND METHODS

Starting in September 2004 and ending in December 2007, this prospective HIPAA-compliant, institutional review board-approved screening trial, with written informed consent, recruited female patients at elevated risk for breast cancer (eg, personal history of breast cancer or breast cancer in a close relative). A total of 1298 examinations from 779 patients (mean age, 58.6 years; range, 32-91 years) comprised the analyzable data set. A paired study design was used, with each enrolled patient serving as her own control. Patients underwent both DM and stereoscopic DM examinations in a single visit, findings of which were interpreted independently by two experienced radiologists, each using a Breast Imaging Reporting and Data System (BI-RADS) assessment (BI-RADS category 0, 1, or 2). All patients determined to have one or more findings with either or both modalities were recalled for standard diagnostic evaluation. The results of 1-year follow-up or biopsy were used to determine case truth.

RESULTS

Compared with DM, stereoscopic DM showed significantly higher specificity (91.2% [1167 of 1279] vs 87.8% [1123 of 1279]; P = .0024) and accuracy (90.9% [1180 of 1298] vs 87.4% [1135 of 1298]; P = .0023) for detection of cancer. Sensitivity for detection of cancer was not significantly different for stereoscopic DM (68.4% [13 of 19]) compared with DM (63.2% [12 of 19], P .99). The recall rate for stereoscopic DM was 9.6% (125 of 1298) and that for DM was 12.9% (168 of 1298) (P = .0018).

CONCLUSION

Compared with DM, stereoscopic DM significantly improved specificity for detection of cancer, while maintaining comparable sensitivity. The recall rate was significantly reduced with stereoscopic DM compared with DM.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120382/-/DC1.

Radiological and pathological findings of interval cancers in a multi-centre, randomized, controlled trial of mammographic screening in women from age 40-41 years

AIM

The aim of this study was to analyse the radiographic findings of the screening mammograms of women with interval cancer who participated in a multi-centre, randomized, controlled trial of mammographic screening in women from age 40-48 years.

MATERIALS AND METHODS

The screening and diagnostic mammograms of 208 women with interval cancers were reviewed. Abnormalities were classified as malignant, subtle and non-specific.

RESULTS

Eighty-seven (42%) of women had true, 66 (32%) occult and 55 (26%) false-negative interval cancers. The features most frequently missed or misinterpreted were granular microcalcification (38%), asymmetric density (27%) and distortion (22%). Thirty-seven percent of abnormal previous screens were classified as malignant, 39% subtle change and 21% as non-specific. Granular calcifications were significantly more common on the diagnostic mammograms of false-negative interval cancers than those of true interval cancers (28 versus 14%, p=0.04). Occult interval cancers were more likely to be <10 mm and <15 mm in invasive pathological size than other interval cancers (p=0.03 and 0.005, respectively). True interval cancers were more likely to be histologically grade 3 than other interval cancers (p=0.04). Women who developed true and false-negative interval cancers had similar background patterns, but women with occult cancers had a higher proportion of dense patterns (p<0.05).

CONCLUSION

Interval cancers in a young screening population have a high proportion of occult lesions that are small and occur in dense background patterns. The proportion of interval cancers that are false negative is similar that seen in older populations and granular microcalcification is the commonest missed mammographic feature.

A preliminary report on the role of spatial frequency analysis in the perception of breast cancers missed at mammography screening

RATIONALE AND OBJECTIVES

Because several factors are involved in cancer detection, a malignant lesion that is visible on a mammogram will not necessarily be reported by the radiologist reading the case. Indeed, a significant fraction of screening-detected cancers are visible in retrospect, and were perceived by the radiologist when the case was read, but were either reported as benign findings or dismissed as variations of normal breast tissue. In this preliminary report the spatial frequency characteristics of clinically missed lesions are investigated by analyzing the mammogram acquired when the lesion was sent for biopsy and the most recent prior mammogram. For control purposes, the contralateral breast is also analyzed, when this breast is lesion free.

MATERIALS AND METHODS

A database of 70 mammogram cases was assembled. Each case contained eight films: craniocaudal (CC) and mediolateral oblique (MLO) of the breast where a biopsy-proven lesion was found, CC and MLO of the contralateral breast, and CC and MLO of both breasts in the most recent prior mammogram. The dictated reports for all of these cases were obtained. Both benign and malignant lesions were used. The films were digitized and an region of interest surrounding each lesion was segmented from the image for processing using wavelet packets to extract spatial frequency information. The corresponding area was also segmented from the prior mammogram and from the contralateral breast, when this breast was lesion-free. Analysis of variance was used to determine if statistically significant differences existed between the derived features of cancer in the current and prior mammograms.

RESULTS

The data suggests that malignant lesions reported in the prior mammogram as being benign differed from correctly reported malignant lesions and from correctly reported benign lesions. They also differed from nonreported malignant lesions. In addition, the spatial frequency representation of cancer significantly differed in the current and prior cases from the representation of normal breast tissue.

CONCLUSION

Spatial frequency analysis may be useful to differentiate malignant lesions that are reported as benign and correctly reported benign lesions.