Performance of dedicated breast positron emission tomography in the detection of small and low-grade breast cancer.

PURPOSE: This study compares the sensitivity of dedicated breast positron emission tomography (DbPET) and whole body positron emission tomography (WBPET) in detecting invasive breast cancer based on tumor size and biology. Further, we explored the relationship between maximum standardized uptake value (SUVmax) of DbPET and biological features of the tumor. METHODS: A total of 639 invasive breast cancer lesions subjected to both DbPET and WBPET before surgery, between January 2016 and May 2019, were included in the study. The sensitivity of DbPET and WBPET in detection and the biology of the tumor according to the clinicopathological features were retrospectively evaluated. RESULTS: The overall sensitivity of DbPET was higher than that of WBPET (91.4% vs. 80.3%, p < 0.001). Subcentimetric tumors were significant (80.9% vs. 54.3%, p < 0.001). Regardless of the nuclear grade, DbPET could detect more lesions than WBPET. The SUVmax was positively correlated with tumor size (R = 0.395, p < 0.001) and the nuclear grade (p < 0.001). Luminal A-like breast cancer had significantly lower SUVmax values than the other subtypes (p < 0.001). CONCLUSIONS: DbPET is superior to WBPET in the detection of subcentimetric, low-grade breast cancers. Further, by using SUVmax, DbPET can distinguish luminal A-like breast cancer from the other subtypes.

The relationship between ring-type dedicated breast PET and immune microenvironment in early breast cancer.

PURPOSE: 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET) is related to the biological parameters and prognosis of breast cancer. However, whether whole-body PET (WBPET) and dedicated breast PET (DbPET) can reflect the tumor microenvironment is unclear. This study investigated the relationship between stromal tumor-infiltrating lymphocytes (TILs) and maximum standardized uptake value (SUVmax) in WBPET and DbPET. METHODS: A total of 125 invasive breast cancers underwent WBPET and ring-type DbPET and resected specimens were pathologically assessed. The impact of SUVmax on the tumor biological parameters and TILs was retrospectively evaluated. SUVmax was classified as high and low relative to the median values (WBPET-SUVmax: 2.2 and DbPET-SUVmax: 6.0). RESULTS: SUVmax correlated with tumor size, nuclear grade, Ki-67 labeling index, and TILs in both WBPET and DbPET (all p < 0.001). In multiple linear regression analysis, tumor size, Ki-67 labeling index, and TILs predicted SUVmax in WBPET and DbPET. The cutoff values of tumor size, Ki-67 labeling index, and TILs predicting high SUVmax were 20 mm, 20%, and 20%, respectively. In multivariate analysis, the predictive factors for high SUVmax were tumor size and Ki-67 labeling index for WBPET and tumor size and TILs for DbPET. High SUVmax in DbPET was related to high numbers of TILs after propensity score matching analysis; however, WBPET was not (p = 0.007 and p = 0.624, respectively). CONCLUSIONS: Both SUVmax values in WBPET and DbPET predicted TIL concentration of the primary breast cancer. In DbPET, SUVmax represented the immune microenvironment after adjusting for tumor biological factors.

Unexpected Abnormal Uptake in the Breasts at Dedicated Breast PET: Incidentally Detected Small Cancers or Nonmalignant Features?

OBJECTIVE: The purposes of this study were to evaluate the diagnostic performance of dedicated breast PET (dbPET) in cases of unexpected uptake and to define parameters associated with malignancy. MATERIALS AND METHODS: There are two types of high-resolution dbPET systems. One has two platelike detectors that compress the breast. This study was conducted with the other type, on which the patient lies prone and the breast hangs through a ring without compression. In total, 627 consecutively registered women underwent 18F-FDG PET/CT and dbPET for pretherapeutic or posttherapeutic evaluation of breast cancer, prior suspicion of breast cancer, or cancer screening. Areas of abnormal FDG uptake, excluding known breast cancers, were analyzed. Uptake was morphologically categorized as focus, mass, or non-mass. Quantitative values were obtained, including the maximum standardized uptake value (SUVmax), peak SUV (SUVpeak), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and lesion-to-background ratio (LBR). Clinical parameters were also assessed. Parameters were compared between breast cancer and noncancer groups; multivariate logistic regression analysis was performed. RESULTS: Of 40 instances of abnormal uptake, 13 were breast cancer. Morphologic features differed between cancer and noncancer groups (p = 0.0122). Among the cancers, 76.9% (10/13) had mass, 15.4% (2/13) had nonmass, and 7.7% (1/13) had focus uptake. Of noncancerous findings, 3.7% (1/27) were mass, 40.7% (11/27) nonmass, and 55.6% (15/27) focus uptake. SUVpeak (p = 0.0234), TLG (p = 0.0017), MTV (p = 0.004), and LBR (p = 0.0432) also differed between groups. Results of multivariate analysis indicated that morphologic category at dbPET was independently associated with malignancy. CONCLUSION: Morphologic features of abnormal uptake at dbPET are associated with breast cancer and may be useful for diagnosing lesions of unknown histologic composition.

Intratumoral heterogeneity on dedicated breast positron emission tomography predicts malignancy grade of breast cancer.

PURPOSE: Dedicated breast positron emission tomography (DbPET) provides detailed high-resolution images and can detect intratumoral heterogeneity using 18F-fluorodeoxyglucose (FDG). We aimed to evaluate the correlation between FDG uptake on DbPET and the clinicopathological features of breast cancer, particularly those with an intratumoral heterogeneous distribution of FDG on DbPET. METHODS: We evaluated 195 consecutive patients with invasive breast cancer who underwent preoperative whole-body PET (WBPET) and DbPET concurrently between January 2016 and March 2017. The relationships between clinicopathological factors and the maximum standard uptake values (SUVmax) of DbPET and WBPET, including clinical stage, nuclear grade, Ki67 proliferation index, estrogen receptor (ER) and human epidermal growth factor receptor type 2 (HER2) statuses, and the intratumoral heterogeneous distribution of FDG on DbPET, were evaluated. RESULTS: The SUVmax of DbPET was significantly correlated with clinical T stage, N stage, nuclear grade, and Ki67 proliferation index (all p < 0.001) as well as the ER (p = 0.006) and HER2 (p = 0.040) statuses. Intratumoral heterogeneous distribution of FDG on DbPET was significantly related with high nuclear grade (p = 0.016) and high Ki67 proliferation index (p = 0.015) but not with clinical T stage, N stage, and ER and HER2 statuses. CONCLUSIONS: The SUVmax of DbPET correlates with clinicopathological factors and also WBPET does. In addition, intratumoral heterogeneity on DbPET provides predictive value for malignancy grade and could inform therapeutic decisions.

Local and whole-body staging in patients with primary breast cancer: a comparison of one-step to two-step staging utilizing 18F-FDG-PET/MRI.

OBJECTIVES: The purpose of this study was to compare the diagnostic value of a one-step to a two-step staging algorithm utilizing 18F-FDG PET/MRI in breast cancer patients. METHODS: A total of 38 patients (37 females and one male, mean age 57 ± 10 years; range 31-78 years) with newly diagnosed, histopathologically proven breast cancer were prospectively enrolled in this trial. All PET/MRI examinations were assessed for local tumor burden and metastatic spread in two separate reading sessions: (1) One-step algorithm comprising supine whole-body 18F-FDG PET/MRI, and (2) Two-step algorithm comprising a dedicated prone 18F-FDG breast PET/MRI and supine whole-body 18F-FDG PET/MRI. RESULTS: On a patient based analysis the two-step algorithm correctly identified 37 out of 38 patients with breast carcinoma (97%), while five patients were missed by the one-step 18F-FDG PET/MRI algorithm (33/38; 87% correct identification). On a lesion-based analysis 56 breast cancer lesions were detected in the two-step algorithm and 44 breast cancer lesions could be correctly identified in the one-step 18F-FDG PET/MRI (79%), resulting in statistically significant differences between the two algorithms (p = 0.0015). For axillary lymph node evaluation sensitivity, specificity and accuracy was 93%, 95 and 94%, respectively. Furthermore, distant metastases could be detected in seven patients in both algorithms. CONCLUSION: The results demonstrate the necessity and superiority of a two-step 18F-FDG PET/MRI algorithm, comprising dedicated prone breast imaging and supine whole-body imaging, when compared to the one-step algorithm for local and whole-body staging in breast cancer patients.

Multimodality Imaging for Evaluating Response to Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: Neoadjuvant chemotherapy is becoming the standard of care for patients with locally advanced breast cancer. Conventional imaging modalities used for the assessment of tumor response to neoadjuvant chemotherapy rely on changes in size or morphologic characteristics and, therefore, are inherently limited. CONCLUSION: Functional imaging technologies evaluate vascular, metabolic, biochemical, and molecular changes in cancer cells and have a unique ability to detect specific biologic tumor markers, assess therapeutic targets, predict early response to neoadjuvant chemotherapy, and guide individualized cancer therapy.

Advances in Breast PET Instrumentation.

Dedicated breast PET scanners currently have a spatial resolution in the 1.5 to 2 mm range, and the ability to provide tomographic images and quantitative data. They are also commercially available from a few vendors. A review of past and recent advances in the development and performance of dedicated breast PET scanners is summarized.

A position-adaptive noise-reduction method using a deep denoising filter bank for dedicated breast positron emission tomography images.

Dedicated breast positron emission tomography (db-PET) is more sensitive than whole-body positron emission tomography and is thus expected to detect early stage breast cancer and determine treatment efficacy. However, it is challenging to decrease the sensitivity of the chest wall side at the edge of the detector, resulting in a relative increase in noise and a decrease in detectability. Longer acquisition times and injection of larger amounts of tracer improve image quality but increase the burden on the patient. Therefore, this study aimed to improve image quality via reconstruction with shorter acquisition time data using deep learning, which has recently been widely used as a noise reduction technique. In our proposed method, a multi-adaptive denoising filter bank structure was introduced by training the training data separately for each detector area because the noise characteristics of db-PET images vary at different locations. Input and ideal images were reconstructed based on 1- and 7-min collection data, respectively, using list mode data. The deep learning model used residual learning with an encoder-decoder structure. The image quality of the proposed method was superior to that of existing noise reduction filters such as Gaussian filters and nonlocal mean filters. Furthermore, there was no significant difference between the maximum standardized uptake values before and after filtering using the proposed method. Taken together, the proposed method is useful as a noise reduction filter for db-PET images, as it can reduce the patient burden, scan time, and radiotracer amount in db-PET examinations.

The quest for multifunctional and dedicated PET instrumentation with irregular geometries.

We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

Can the Newer Model of Breast-Specific Positron Emission Tomography Reduce the "Blind Area"?

Objectives: Breast-specific positron emission tomography (PET) provides higher sensitivity and spatial resolution than whole-body PET/CT, but it has a blind area. Mammary glands near the chest wall sometimes present outside the field of view (FOV). A newer, dedicated breast PET (dbPET) model has a cylindrical detector with a larger diameter than previous models, so it is expected to eliminate or reduce blind areas. This study aimed to compare breast images acquired on the new dbPET model with images acquired on an older dbPET model to evaluate blind area reduction. Methods: The nipple-to-chest wall distance (mm), maximum breast cross-sectional area at the FOV edge (cm2) on the dbPET transverse images of the scanners, and the effects of patient age and body mass index (BMI) were compared. Results: There was no significant difference in the nipple-to-chest wall distance between the models (p = 0.223). The maximum breast cross-sectional area at the FOV edge was significantly larger on the newer model's images (p < 0.001). There was no significant correlation between breast size and the rate of change in both parameters. Conclusions: The new ring-type dbPET scanners with larger diameter detectors did not reduce the blind area observed on older dbPET scanners.

Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning.

Dual panel PET systems, such as Breast-PET (B-PET) scanner, exhibit strong asymmetric and anisotropic spatially-variant deformations in the reconstructed images due to the limited-angle data and strong depth of interaction effects for the oblique LORs inherent in such systems. In our previous work, we studied time-of-flight (TOF) effects and image-based spatially-variant PSF resolution models within dual-panel PET reconstruction to reduce these deformations. The application of PSF based models led to better and more uniform quantification of small lesions across the field of view (FOV). However, the ability of such a model to correct for PSF deformation is limited to small objects. On the other hand, large object deformations caused by the limited-angle reconstruction cannot be corrected with the PSF modeling alone. In this work, we investigate the ability of deep-learning (DL) networks to recover such strong spatially-variant image deformations using first simulated PSF deformations in image space of a generic dual panel PET system and then using simulated and acquired phantom reconstructions from dual panel B-PET system developed in our lab at University of Pennsylvania. For the studies using real B-PET data, the network was trained on the simulated synthetic data sets providing ground truth for objects resembling experimentally acquired phantoms on which the network deformation corrections were then tested. The synthetic and acquired limited-angle B-PET data were reconstructed using DIRECT-RAMLA reconstructions, which were then used as the network inputs. Our results demonstrate that DL approaches can significantly eliminate deformations of limited angle systems and improve their quantitative performance.

Effects of Detector Thickness on Geometric Sensitivity and Event Positioning Errors in the Rectangular PET/X Scanner.

We used simulations to investigate the relationship between sensitivity and spatial resolution as a function of crystal thickness in a rectangular PET scanner intended for quantitative assessment of breast cancers. The system had two 20 × 15-cm2 and two 10 × 15-cm2 flat detectors forming a box, with the larger detectors separated by 4 or 8 cm. Depth-of-interaction (DOI) resolution was modeled as a function of crystal thickness based on prior measurements. Spatial resolution was evaluated independent of image reconstruction by deriving and validating a surrogate metric from list-mode data (dFWHM). When increasing crystal thickness from 5 to 40 mm, and without using DOI information, the dFWHM for a centered point source increased from 0.72 to 1.6 mm. Including DOI information improved dFWHM by 12% and 27% for 5- and 40-mm-thick crystals, respectively. For a point source in the corner of the FOV, use of DOI information improved dFWHM by 20% (5-mm crystal) and 44% (40-mm crystal). Sensitivity was 7.7% for 10-mm-thick crystals (8-cm object). Increasing crystal thickness on the smaller side detectors from 10 to 20 mm (keeping 10-mm crystals on the larger detectors) boosted sensitivity by 24% (relative) and degraded dFWHM by only ~3%/8% with/without DOI information. The benefits of measuring DOI must be evaluated in terms of the intended clinical task of assessing tracer uptake in small lesions. Increasing crystal thickness on the smaller side detectors provides substantial sensitivity increase with minimal accompanying loss in resolution.

Reproducibility assessment of uptake on dedicated breast PET for noise discrimination.

OBJECTIVES: Dedicated breast PET (dbPET) systems have improved the detection of small breast cancers but have increased false-positive diagnoses due to an increased chance of noise detection. This study examined whether reproducibility assessment using paired images helped to improve noise discrimination and diagnostic performance in dbPET. METHODS: This study included 21 patients with newly diagnosed breast cancer who underwent [18F]FDG-dbPET and contrast-enhanced breast MRI. A 10-min dbPET data scan was acquired per breast, and two sets of reconstructed images were generated (named dbPET-1 and dbPET-2, respectively), each of which consisted of randomly allocated 5-min data from the 10-min data. Uptake spots higher than the background were indexed for the study with visual assessment. All indexed uptakes on dbPET-1 were evaluated using dbPET-2 for reproducibility. MRI findings based on the Breast Imaging-Reporting and Data System (BI-RADS) 2013 were used as the gold standard. Uptake spots that corresponded to BI-RADS 1 on MRI were considered noise, while those with BI-RADS 4b-6 were considered malignancies. The diagnostic performance of dbPET for malignancy was evaluated using four different criteria: any uptake on dbPET-1 regarded as positive (criterion A), a subjective visual assessment of dbPET-1 (criterion B), reproducibility assessment between dbPET-1 and dbPET-2 (criterion C), and a combination of B and C (criterion D). RESULTS: A total of 213 indexed uptake spots were identified on dbPET-1, including 152, 15, 6, 6, and 34 lesions classified as BI-RADS MRI categories 1, 2, 4b, 4c, and 5, respectively. Overall, 31.9% of the index uptake values were reproducible. All malignant lesions were reproducible, whereas 93.4% of noise was not reproducible. The sensitivities for malignancy for criteria A, B, C, and D were 100%, 91.3%, 100%, and 91.3%, respectively, with positive predictive values (PPVs) of 21.4%, 68.9%, 67.6%, and 82.4%, respectively. CONCLUSIONS: Our results demonstrated that reproducibility assessment helped reduce false-positive findings caused by noise on dbPET without lowering the sensitivity for malignancy. While subjective visual assessment was also efficient in increasing PPV, it occasionally missed malignant uptake.

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.

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.

Use of 64Cu-DOTA-Trastuzumab PET to Predict Response and Outcome of Patients Receiving Trastuzumab Emtansine for Metastatic Breast Cancer: A Pilot Study.

We hypothesized that functional imaging with 64Cu-DOTA-trastuzumab PET/CT would predict the response to the antibody-drug conjugate trastuzumab-emtansine (T-DM1). Methods: Ten women with metastatic human epidermal growth factor receptor 2-positive breast cancer underwent 18F-FDG PET/CT and 64Cu-DOTA-trastuzumab PET/CT on days 1 and 2 before treatment with T-DM1. Results: T-DM1-responsive patients had higher uptake than nonresponsive patients. Day 1 minimum SUVmax (5.6 vs. 2.8, P < 0.02), day 2 minimum SUVmax (8.1 vs. 3.2, P < 0.01), and day 2 average SUVmax (8.5 vs. 5.4, P < 0.05) for 64Cu-DOTA-trastuzumab all favored responding patients. Tumor-level response suggested threshold dependence on SUVmax Patients with a day 2 minimum SUVmax above versus below the threshold had a median time to treatment failure of 28 mo versus 2 mo (P < 0.02). Conclusion: Measurement of trastuzumab uptake in tumors via PET/CT is promising for identifying patients with metastatic breast cancer who will benefit from T-DM1.

Evaluation of the performance of a high-resolution time-of-flight PET system dedicated to the head and breast according to NEMA NU 2-2012 standard.

BACKGROUND: This study evaluated the physical performance of a positron emission tomography (PET) system dedicated to the head and breast according to the National Electrical Manufacturers Association (NEMA) NU2-2012 standard. METHODS: The spatial resolution, sensitivity, scatter fraction, count rate characteristics, corrections for count losses and randoms, and image quality of the system were determined. All measurements were performed according to the NEMA NU2-2012 acquisition protocols, but image quality was assessed using a brain-sized phantom. Furthermore, scans of the three-dimensional (3D) Hoffmann brain phantom and mini-Derenzo phantom were acquired to allow visual evaluation of the imaging performance for small structures. RESULTS: The tangential, radial, and axial full width at half maximum (FWHM) at a 10-mm offset in half the axial field of view were measured as 2.3, 2.5, and 2.9 mm, respectively. The average system sensitivity at the center of the field of view and at a 10-cm radial offset was 7.18 and 8.65 cps/kBq, respectively. The peak noise-equivalent counting rate was 35.2 kcps at 4.8 kBq/ml. The corresponding scatter fraction at the peak noise-equivalent counting rate was 46.8%. The peak true rate and scatter fraction at 8.6 kBq/ml were 127.8 kcps and 54.3%, respectively. The percent contrast value for a 10-mm sphere was approximately 50%. On the 3D Hoffman brain phantom image, the structures of the thin layers composing the phantom were visualized on the sagittal and coronal images. On the mini-Derenzo phantom, each of the 1.6-mm rods was clearly visualized. CONCLUSION: Taken together, these results indicate that the head- and breast-dedicated PET system has high resolution and is well suited for clinical PET imaging.

A Proposed Dedicated Breast PET Lexicon: Standardization of Description and Reporting of Radiotracer Uptake in the Breast.

Dedicated breast positron emission tomography (dbPET) is a new diagnostic imaging modality recently used in clinical practice for the detection of breast cancer and the assessment of tumor biology. dbPET has higher spatial resolution than that of conventional whole body PET systems, allowing recognition of detailed morphological attributes of radiotracer accumulation within the breast. 18F-fluorodeoxyglucose (18F-FDG) accumulation in the breast may be due to benign or malignant entities, and recent studies suggest that morphology characterization of 18F-FDG uptake could aid in estimating the probability of malignancy. However, across the world, there are many descriptors of breast 18F-FDG uptake, limiting comparisons between studies. In this article, we propose a lexicon for breast radiotracer uptake to standardize description and reporting of image findings on dbPET, consisting of terms for image quality, radiotracer fibroglandular uptake, breast lesion uptake.

Clinical practice guidelines for high-resolution breast PET, 2019 edition.

Breast positron emission tomography (PET) has had insurance coverage when performed with conventional whole-body PET in Japan since 2013. Together with whole-body PET, accurate examination of breast cancer and diagnosis of metastatic disease are possible, and are expected to contribute significantly to its treatment planning. To facilitate a safer, smoother, and more appropriate examination, the Japanese Society of Nuclear Medicine published the first edition of practice guidelines for high-resolution breast PET in 2013. Subsequently, new types of breast PET have been developed and their clinical usefulness clarified. Therefore, the guidelines for breast PET were revised in 2019. This article updates readers as to what is new in the second edition. This edition supports two different types of breast PET depending on the placement of the detector: the opposite-type (positron emission mammography; PEM) and the ring-shaped type (dedicated breast PET; dbPET), providing an overview of these scanners and appropriate imaging methods, their clinical applications, and future prospects. The name "dedicated breast PET" from the first edition is widely used to refer to ring-shaped type breast PET. In this edition, "breast PET" has been defined as a term that refers to both opposite- and ring-shaped devices. Up-to-date breast PET practice guidelines would help provide useful information for evidence-based breast imaging.