Breast cancer detection by dedicated breast positron emission tomography according to the World Health Organization classification of breast tumors

Clinical Applications of Dedicated Breast Positron Emission Tomography

Breast-specific positron imaging systems provide higher sensitivity than whole-body PET for breast cancer detection. The clinical applications for breast-specific positron imaging are similar to breast MRI including preoperative local staging and neoadjuvant therapy response assessment. Breast-specific positron imaging may be an alternative for patients who cannot undergo breast MRI. Further research is needed in expanding the field-of-view for posterior breast lesions, increasing biopsy capability, and reducing radiation dose. Efforts are also necessary for developing appropriate use criteria, increasing availability, and advancing insurance coverage.

Molecular Breast Imaging and Positron Emission Mammography

There is growing interest in application of functional imaging modalities for adjunct breast imaging due to their unique ability to evaluate molecular/pathophysiologic changes, not visible by standard anatomic breast imaging. This has led to increased use of nuclear medicine dedicated breast-specific single photon and coincidence imaging systems for multiple indications, such as supplemental screening, staging of newly diagnosed breast cancer, evaluation of response to neoadjuvant treatment, diagnosis of local disease recurrence in the breast, and problem solving. Studies show that these systems maybe especially useful for specific subsets of patients, not well served by available anatomic breast imaging modalities.

Performance of dedicated breast PET in breast cancer screening: comparison with digital mammography plus digital breast tomosynthesis and ultrasound

OBJECTIVE

To compare the diagnostic performance of dedicated breast positron emission tomography (dbPET) in breast cancer screening with digital mammography plus digital breast tomosynthesis (DM-DBT) and breast ultrasound (US).

METHODS

Women who participated in opportunistic whole-body PET/computed tomography cancer screening programs with breast examinations using dbPET, DM-DBT, and US between 2016-2020, whose results were determined pathologically or by follow-up for at least 1 year, were included. DbPET, DM-DBT, and US assessments were classified into four diagnostic categories: A (no abnormality), B (mild abnormality), C (need for follow-up), and D (recommend further examination). Category D was defined as screening positive. Each modality's recall rate, sensitivity, specificity, and positive predictive value (PPV) were calculated per examination to evaluate their diagnostic performance for breast cancer.

RESULTS

Out of 2156 screenings, 18 breast cancer cases were diagnosed during the follow-up period (10 invasive cancers and eight ductal carcinomas in situ [DCIS]). The recall rates for dbPET, DM-DBT, and US were 17.8%, 19.2%, and 9.4%, respectively. The recall rate of dbPET was highest in the first year and subsequently decreased to 11.4%. dbPET, DM-DBT, and US had sensitivities of 72.2%, 88.9%, and 83.3%; specificities of 82.6%, 81.4%, and 91.2%; and PPVs of 3.4%, 3.9%, and 7.4%, respectively. The sensitivities of dbPET, DM-DBT, and US for invasive cancers were 90%, 100%, and 90%, respectively. There were no significant differences between the modalities. One case of dbPET-false-negative invasive cancer was identified in retrospect. DbPET had 50% sensitivity for DCIS, while that of both DM-DBT and US was 75%. Furthermore, the specificity of dbPET in the first year was the lowest among all periods, and modalities increased over the years to 88.7%. The specificity of dbPET was significantly higher than that of DM-DBT (p < 0.01) in the last 3 years.

CONCLUSIONS

DbPET had a compatible sensitivity to DM-DBT and breast US for invasive breast cancer. The specificity of dbPET was improved and became higher than that of DM-DBT. DbPET may be a feasible screening modality.

Deep learning model with collage images for the segmentation of dedicated breast positron emission tomography images

BACKGROUND

Dedicated breast positron emission tomography (dbPET) has high contrast and resolution optimized for detecting small breast cancers, leading to its noisy characteristics. This study evaluated the application of deep learning to the automatic segmentation of abnormal uptakes on dbPET to facilitate the assessment of lesions. To address data scarcity in model training, we used collage images composed of cropped abnormal uptakes and normal breasts for data augmentation.

METHODS

This retrospective study included 1598 examinations between April 2015 and August 2020. A U-Net-based model with an uptake shape classification head was trained using either the original or augmented dataset comprising collage images. The Dice score, which measures the pixel-wise agreement between a prediction and its ground truth, of the models was compared using the Wilcoxon signed-rank test. Moreover, the classification accuracies were evaluated.

RESULTS

After applying the exclusion criteria, 662 breasts were included; among these, 217 breasts had abnormal uptakes (mean age: 58 ± 14 years). Abnormal uptakes on the cranio-caudal and mediolateral maximum intensity projection images of 217 breasts were annotated and labeled as focus, mass, or non-mass. The inclusion of collage images into the original dataset yielded a Dice score of 0.884 and classification accuracy of 91.5%. Improvement in the Dice score was observed across all subgroups, and the score of images without breast cancer improved significantly from 0.750 to 0.834 (effect size: 0.76, P = 0.02).

CONCLUSIONS

Deep learning can be applied for the automatic segmentation of dbPET, and collage images can improve model performance.

Four Patterns of Abnormal Ring-Like Uptakes on Dedicated Breast PET

ABSTRACT

The high resolution of dedicated breast PET (dbPET) enables the visualization of small breast cancers and a heterogeneity of breast tumors. Some tumors present with a ring-like appearance, the central uptake defect possibly reflecting intratumoral fibrosis and necrosis, associated with high-grade malignancy, and a triple-negative subtype. However, a ring-like finding is not only found in high-grade breast cancers. We describe 4 representative patterns of ring-like uptakes on dbPET: high-grade invasive cancer, intracystic tumor, extended noninvasive carcinoma, and change after vacuum-assisted breast biopsy. Ring-like uptakes on dbPET should be evaluated in association with clinical information.

Malignant prediction of incidental findings using ring-type dedicated breast positron emission tomography

The classification according to uptake patterns and metabolic parameters on ring-type dedicated breast positron emission tomography (dbPET) is useful for detecting breast cancer. This study investigated the performance of dbPET for incidental findings that were not detected by mammography and ultrasonography. In 1,076 patients with breast cancer who underwent dbPET, 276 findings were incidentally diagnosed before treatment. Each finding was categorized as focus (uptake size ≤ 5 mm), mass (> 5 mm), or non-mass (multiple uptake) according to uptake patterns. Non-mass uptakes were additionally classified based on their distributions as-linear, focal, segmental, regional, or diffuse. Thirty-two findings (11.6%) were malignant and 244 (88.4%) were benign. Visually, 227 (82.3%) findings were foci, 7 (2.5%) were masses, and 42 (15.2%) were non-masses. Malignant rates of focus, mass, and non-mass were 9.7%, 28.6%, and 19.0%, respectively. In the non-mass findings, 23 were regional and diffuse distributions, and presented as benign lesions. Focus uptake with low lesion-to-background ratio (LBR) and no hereditary risk were relatively low (2.7%) in breast cancer. In multivariate analysis, LBR and hereditary risk were significantly associated with breast cancer (p = 0.006 and p = 0.013, respectively). Uptake patterns, LBR, and hereditary risk are useful for predicting breast cancer risk in incidental dbPET findings.