Distribution pattern of FDG uptake using ring-type dedicated breast PET in comparison to whole-body PET/CT scanning in invasive breast cancer

Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer

BACKGROUND

Since it has been found that the maximum metabolic activity of a cancer lesion shifts toward the lesion edge during cancer progression, normalized distances from the hot spot of radiotracer uptake to tumor centroid (NHOC) and tumor perimeter (NHOP) have been suggested as novel F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters that can reflect cancer aggressiveness. This study aimed to investigate whether NHOC and NHOP parameters could predict pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients.

METHODS

This study retrospectively enrolled 135 female patients with breast cancer who underwent pretreatment FDG PET/CT and received NAC and subsequent surgical resection. From PET/CT images, normalized distances of maximum SUV and peak SUV-to-tumor centroid (NHOCmax and NHOCpeak) and -to-tumor perimeter (NHOPmax and NHOPpeak) were measured, in addition to conventional PET/CT parameters.

RESULTS

Of 135 patients, 32 (23.7%) achieved pathological complete response (pCR), and 34 (25.2%) had events during follow-up. In the receiver operating characteristic (ROC) curve analysis, NHOCmax showed the highest area under the ROC curve value (0.710) for predicting pCR, followed by NHOCpeak (0.694). In the multivariate logistic regression analysis, NHOCmax, NHOCpeak, and NHOPmax were independent predictors for pCR (p < 0.05). In the multivariate survival analysis, NHOCpeak (p = 0.026) was an independent predictor for PFS along with metabolic tumor volume, with patients having higher NHOCpeak showing worse PFS.

CONCLUSION

NHOCpeak on pretreatment FDG PET/CT could be a potential imaging parameter for predicting NAC response and survival in patients with breast cancer.

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.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

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.

The Clinical Value of Breast Specific Gamma Imaging and Positron Imaging: An Update

In the management of patients with breast cancer (BC), a mammography contributed to screen an early-stage patient, to plan a therapy strategy, to evaluate a therapy outcome, to detect a recurrence, and to reduce a mortality. Currently, various imaging modalities, such as CT, MR, Ultrasound (US), SPECT/CT, PET/CT, PET/MR have been utilized for the management of BC patients. In order to overcome a limited spatial resolution and sensitivity of whole-body systems in nuclear medicine imaging, dedicated breast imaging modalities were developed. One is a gamma imaging system with single/dual head scintillation detectors or semiconductor detectors associated with light compression device for breast parenchyma. Radiopharmaceutical for the gamma imaging is ^99mTc-sestamibi. Another is a positron imaging system with opposite-type panel detectors and ring-shaped type detectors. Radiopharmaceutical for positron imaging is ¹⁸F-fluorodeoxyglucose. The breast-specific gamma and positron imaging systems were utilized mainly to detect small lesions less than 1 cm in diameter especially in patients with dense breast, to evaluate an effect of preoperative neo-adjuvant therapy, to plan surgical procedures (conservative-surgery vs mastectomy), and to detect a recurrence. By combining higher sensitivity and spatial resolution scanners with new radiopharmaceuticals, an information on molecular-level pathology of BC is increasingly available in an individual patient. This article reviewed clinical impact and future perspective of this field.

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.

Deep learning for image classification in dedicated breast positron emission tomography (dbPET)

OBJECTIVE

This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images.

METHODS

Of the 1598 women who underwent dbPET examination between April 2015 and August 2020, a total of 618 breasts on 309 examinations for 284 women who were diagnosed with BC or non-BC were analyzed in this retrospective study. The Xception-based DL model was trained to predict BC or non-BC using dbPET images from 458 breasts of 109 BCs and 349 non-BCs, which consisted of mediallateral and craniocaudal maximum intensity projection images, respectively. It was tested using dbPET images from 160 breasts of 43 BC and 117 non-BC. Two expert radiologists and two radiology residents also interpreted them. Sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were calculated.

RESULTS

Our DL model had a sensitivity and specificity of 93% and 93%, respectively, while radiologists had a sensitivity and specificity of 77-89% and 79-100%, respectively. Diagnostic performance of our model (AUC = 0.937) tended to be superior to that of residents (AUC = 0.876 and 0.868, p = 0.073 and 0.073), although not significantly different. Moreover, no significant differences were found between the model and experts (AUC = 0.983 and 0.941, p = 0.095 and 0.907).

CONCLUSIONS

Our DL model could be applied to dbPET and achieve the same diagnostic ability as that of experts.

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.