Evaluation of primary breast cancers using dedicated breast PET and whole-body PET

Hathi, Deep; Li, Wen; Seo, Youngho; Flavell, Robert R.; Kornak, John; Franc, Benjamin L.; Joe, Bonnie N.; Esserman, Laura J.; Hylton, Nola M.; Jones, Ella F.

doi:10.1038/s41598-020-78865-3

Cited by 13 publications

(11 citation statements)

References 45 publications

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“…The observed qualitative differences in spatial and signal intensity heterogeneity in D-PET may be largely driven by the higher voxel resolution and tumor tissue fraction. In a comparative study, spatial heterogeneity features showed statistically significant differences between D-PET and WB-PET [ 6 ]. The precise quantification of tumor heterogeneity may allow an accurate prediction of ALN metastasis.…”

Section: Discussionmentioning

confidence: 99%

“…Since WB-PET/CT is performed with patients in the supine position, there is a collapse of breast volume and blurring owing to respiratory motion [ 4 , 5 ]. In contrast, Mammi-PET comprises a single ring detector that translates axially over the length of the breast; the prone position enables full-breast volume imaging by avoiding breast compression [ 6 ]. The Lymph-PET device contains movable double-planar confronted detectors with an axilla capability view for precisely detecting hot lesions.…”

Section: Introductionmentioning

confidence: 99%

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Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Cheng

Ren²,

et al. 2022

Cancers

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Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Cheng

Ren²,

et al. 2022

Cancers

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“…The development of new precision radiotracers binds imaging activity to specific clinical targets, advancing personalized (or precision) medicine [1][2][3][4]. In addition to scanning of the body with sequentially performed whole-body (WB) PET/CT (computed tomography) scanners and emerging simultaneous PET/MRI (magnetic resonance imaging), the applications for PET imaging increasingly involve the visualization of specific organs with dedicated systems [5][6][7][8]. Compared to WB PET scanners, an organ-targeted PET system is capable of higher efficiency, higher spatial resolution, and higher signal-to-noise ratio, resulting in better image contrast and enabling more precise PET examinations.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to WB PET scanners, an organ-targeted PET system is capable of higher efficiency, higher spatial resolution, and higher signal-to-noise ratio, resulting in better image contrast and enabling more precise PET examinations. Indeed, an organ-targeted PET camera with optimized geometry can position detectors in close proximity to the organ of interest to facilitate (1) more efficient gamma-ray detection; (2) higher spatial resolution; and (3) reduced unwanted signal from elsewhere in the body, improving the noise-equivalent count rate (NECR) within the field of view (FOV) due to a reduction of false coincidences [7][8][9][10]. Potentially, this may significantly lower a radiotracer dose, thereby reducing radiation exposure associated with PET molecular imaging.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a High-Sensitivity Organ-Targeted PET Camera

Stiles

Baldassi

Bubon

et al. 2022

Sensors

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The aim of this study is to evaluate the performance of the Radialis organ-targeted positron emission tomography (PET) Camera with standardized tests and through assessment of clinical-imaging results. Sensitivity, count-rate performance, and spatial resolution were evaluated according to the National Electrical Manufacturers Association (NEMA) NU-4 standards, with necessary modifications to accommodate the planar detector design. The detectability of small objects was shown with micro hotspot phantom images. The clinical performance of the camera was also demonstrated through breast cancer images acquired with varying injected doses of 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG) and qualitatively compared with sample digital full-field mammography, magnetic resonance imaging (MRI), and whole-body (WB) PET images. Micro hotspot phantom sources were visualized down to 1.35 mm-diameter rods. Spatial resolution was calculated to be 2.3 ± 0.1 mm for the in-plane resolution and 6.8 ± 0.1 mm for the cross-plane resolution using maximum likelihood expectation maximization (MLEM) reconstruction. The system peak noise equivalent count rate was 17.8 kcps at a 18F-FDG concentration of 10.5 kBq/mL. System scatter fraction was 24%. The overall efficiency at the peak noise equivalent count rate was 5400 cps/MBq. The maximum axial sensitivity achieved was 3.5%, with an average system sensitivity of 2.4%. Selected results from clinical trials demonstrate capability of imaging lesions at the chest wall and identifying false-negative X-ray findings and false-positive MRI findings, even at up to a 10-fold dose reduction in comparison with standard 18F-FDG doses (i.e., at 37 MBq or 1 mCi). The evaluation of the organ-targeted Radialis PET Camera indicates that it is a promising technology for high-image-quality, low-dose PET imaging. High-efficiency radiotracer detection also opens an opportunity to reduce administered doses of radiopharmaceuticals and, therefore, patient exposure to radiation.

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“…Because of the limited spatial resolution of whole-body PET/CT to detect small breast cancers, high-resolution dedicated breast PET (dbPET) was developed for the detection of early-stage breast cancers. Previous reports have shown that ring-shaped dbPET can visualize smaller breast cancers better than whole-body PET/CT [ 6 , 7 ]. In addition, since dbPET has a highly sensitive detector and does not use X-ray CT to perform attenuation correction, it is expected to greatly reduce the radiation dose to patients when it is used alone compared to whole-body PET/CT for local assessment and screening of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Image quality evaluation of real low-dose breast PET

Satoh

Imai²,

Ikegawa³

et al. 2022

Jpn J Radiol

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Purpose To evaluate the clinical feasibility of high-resolution dedicated breast positron emission tomography (dbPET) with real low-dose 18F-2-fluorodeoxy-d-glucose (18F-FDG) by comparing images acquired with full-dose FDG. Materials and methods Nine women with no history of breast cancer and previously scanned by dbPET injected with a clinical 18F-FDG dose (3 MBq/kg) were enrolled. They were injected with 50% of the clinical 18F-FDG dose and scanned with dbPET for 10 min for each breast 60 and 90 min after injection. To investigate the effect of the scan start time and acquisition time on image quality, list-mode data were divided into 1, 3, 5, and 7 min (and 10 min with 50% FDG injected) from the start of acquisition and reconstructed. The reconstructed images were visually and quantitatively compared for contrast between mammary gland and fat (contrast) and for coefficient of variation (CV) in the mammary gland. Results In visual evaluation, the contrast between the mammary gland and fat acquired at a 50% dose for 7 min was comparable and even better in smoothness than that in the images acquired at a 100% dose. No visual difference between the images with a 50% dose was found with scan start times 60 and 90 min after injection. Quantitative evaluation showed a slightly lower contrast in the image at 60 min after 50% dosing, with no difference between acquisition times. There was no difference in CV between conditions; however, smoothness decreased with shorter acquisition time in all conditions. Conclusions The quality of dbPET images with a 50% FDG dose was high enough for clinical application. Although the optimal scan start time for improved lesion-to-background mammary gland contrast remained unknown in this study, it will be clarified in future studies of breast cancer patients.

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Evaluation of primary breast cancers using dedicated breast PET and whole-body PET

Cited by 13 publications

References 45 publications

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

Evaluation of a High-Sensitivity Organ-Targeted PET Camera

Image quality evaluation of real low-dose breast PET

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