Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT. METHODS: Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. RESULTS: The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively. CONCLUSIONS: We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies.

Semi-automated histogram analysis of normal bone marrow using 18F-FDG PET/CT: correlation with clinical indicators.

BACKGROUND: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is increasingly applied to the diagnosis of bone marrow failure such as myeloproliferative neoplasm, aplastic anemia, and myelodysplastic syndrome, as well as malignant lymphoma and multiple myeloma. However, few studies have shown a normal FDG uptake pattern. This study aimed to establish a standard of bone marrow FDG uptake by a reproducible quantitative method with fewer steps using deep learning-based organ segmentation. METHODS: Bone marrow PET images were obtained using segmented whole-spine and pelvic bone marrow cavity CT as mask images using a commercially available imaging workstation that implemented an automatic organ segmentation algorithm based on deep learning. The correlation between clinical indicators and quantitative PET parameters, including histogram features, was evaluated. RESULTS: A total of 98 healthy adults were analyzed. The volume of bone marrow PET extracted in men was significantly higher than that in women (p < 0.0001). Univariate and multivariate regression analyses showed that mean of standardized uptake value corrected by lean body mass (SULmean) and entropy in both men and women were inversely correlated with age (all p < 0.0001), and SULmax in women were also inversely correlated with age (p = 0.011). CONCLUSION: A normal FDG uptake pattern was demonstrated by simplified FDG PET/CT bone marrow quantification.

Diagnostic Performance of the Support Vector Machine Model for Breast Cancer on Ring-Shaped Dedicated Breast Positron Emission Tomography Images.

OBJECTIVE: The aim of this study was to evaluate the diagnostic ability of support vector machine (SVM) for early breast cancer (BC) using dedicated breast positron emission tomography (dbPET). METHODS: We evaluated 116 abnormal fluorodeoxyglucose (FDG) uptakes less than 2 cm on dbPET images in 105 women. Fluorodeoxyglucose uptake patterns and quantitative PET parameters were compared between BC and noncancer groups. Diagnostic accuracy of the SVM model including quantitative parameters was compared with that of visual assessment based on FDG-uptake pattern. RESULTS: Age, maximum standardized uptake value, peak standardized uptake value, total lesion glycolysis, metabolic tumor volume, and lesion-to-contralateral background ratio were significantly different between BC and noncancer groups. Area under the curve, sensitivity, specificity, and accuracy for FDG-uptake pattern of visual assessment were 0.77, 0.57, 0.77, and 0.71, respectively; those of an SVM model including age, maximum standardized uptake value, total lesion glycolysis, and lesion-to-contralateral background ratio were 0.89, 0.94, 0.77, and 0.85, respectively. CONCLUSIONS: Support vector machine showed high diagnostic performance for BC using dbPET.

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.

18F-FDG-PET/CT is effective in distinguishing myelofibrosis due to bone marrow infiltration of diffuse large B-cell lymphoma from triple-negative primary myelofibrosis.

Although myelofibrosis is mainly associated with myeloproliferative neoplasms (MPN), especially primary myelofibrosis (PMF), a variety of hematological malignancies, including acute myeloid leukemia, multiple myeloma and malignant lymphoma, also cause myelofibrosis with markedly varying degrees of severity. Thus, it is extremely important to accurately diagnose the underlying diseases that cause fibrosis in bone marrow. Analyses of JAK2, MPL and calreticulin gene mutations are useful for distinguishing MPN from other diseases, since 90% of MPN patients have a mutation in one of these genes. However, 10% of PMF patients do not have mutations in any of these genes, and these patients have a disease known as triple negative PMF. It is sometimes difficult to accurately distinguish triple negative PMF from secondary myelofibrosis caused by other diseases. Herein, we present a case of diffuse large B cell lymphoma (DLBCL) with bone marrow involvement, mimicking triple negative primary myelofibrosis. 18F-FDG-PET was useful for correctly diagnosing DLBCL.

Prognostic Value of Semiautomatic CT Volumetry in Patients With Stage I Non-Small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy.

OBJECTIVE: The aim of this study was to examine the prognostic value of semiautomated 3-dimensional image analysis-based parameters from pretreatment computed tomography (CT) in patients with non-small cell lung cancer treated with stereotactic body radiation therapy. METHODS: We evaluated 91 patients. We defined 2 parameters: (1) VOL, extracted tumor volume with a threshold ≥ -400 HU; and (2) ATN, the mean CT attenuation value. The maximum standardized uptake value (SUVmax) was recorded as F-fluorodeoxyglucose positron emission tomography/CT parameter. The prognostic values were assessed using the Kaplan-Meier and a Cox proportional hazards analysis. RESULTS: Overall survival and progression-free survival were significantly worse in patients with larger VOL, higher ATN, and higher SUVmax. VOL was an independent indicator of overall survival, whereas ATN was an indicator of progression-free survival, lymph node recurrence, and distant metastasis. However, SUVmax was not an independent prognostic factor for either outcome. CONCLUSIONS: VOL and ATN from 3-dimensional image analysis of CT scans have prognostic values for non-small cell lung cancer patients treated with stereotactic body radiation therapy.

Volume-based parameters measured by using FDG PET/CT in patients with stage I NSCLC treated with stereotactic body radiation therapy: prognostic value.

PURPOSE: To evaluate the prognostic importance and predictive performance of volume-based parameters of fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in patients with stage I non-small cell lung cancer (NSCLC) after stereotactic body radiation therapy (SBRT). MATERIALS AND METHODS: This study had institutional review board approval. All patients gave written informed consent for SBRT as well as for future anonymous use of clinical data. Data in 88 patients with stage I NSCLC (68 patients with T1N0M0 disease and 20 with T2aN0M0 disease) who had undergone FDG PET/CT and then SBRT were retrospectively evaluated. Seventy-seven tumors were histopathologically proved (48 adenocarcinomas, 24 squamous cell carcinomas, and five unspecified non-small cell carcinomas), and the remaining 11 tumors were diagnosed clinically without histopathologic diagnosis. Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were analyzed. The MTV of the primary tumor was calculated as all voxels with an SUV of 2.5 or greater within the isocontour line, while TLG was calculated as MTV multiplied by the average SUV, by using fixed thresholds of either 50% (TLG50) or 60% (TLG60) of the maximum intratumoral FDG activity. The prognostic importance of PET parameters and other clinicopathologic variables (age, sex, tumor size, tumor location [peripheral or central], and biologically effective dose) was assessed by using Cox proportional hazards regression analysis of overall survival (OS) and disease-free survival (DFS) for both univariate and multiple-variable analyses. RESULTS: The median follow-up period was 33 months. At 3 years, OS and DFS were 70.0% and 49.7%, respectively. In the univariate analyses, SUVmax (P = .001), MTV (P = .002), TLG50 (P = .001), and TLG60 (P < .001) were found to be significantly associated with DFS. In multiple variable analysis, these parameters were also significantly associated with DFS (P = .011 for SUVmax, P = .010 for MTV, P = .004 for TLG50, and P = .005 for TLG60). Only volumetric parameters (MTV, TLG50, and TLG60) were significant indicators of DFS in patients with tumors larger than 3 cm. CONCLUSION: SUVmax, MTV, and TLG at FDG PET/CT have a prognostic role for patients with NSCLC treated with SBRT. When tumors are larger than 3 cm, only MTV and TLG are predictive of DFS.

Whole-body total lesion glycolysis measured on fluorodeoxyglucose positron emission tomography/computed tomography as a prognostic variable in metastatic breast cancer.

BACKGROUND: In this retrospective study, the authors evaluated the prognostic value of whole-body total lesion glycolysis (WTLG) on FDG/PET images in metastatic breast cancer (MBC) patients. METHODS: We retrospectively evaluated 54 MBC patients who were diagnosed as having one or more metastatic lesions between June 2005 and March 2013. Twenty-four patients were diagnosed at the initial presentation (group A) and 30 patients were diagnosed for the first time at some point after a surgery (group B). Patients were excluded if they had received chemotherapy within 30 days before PET/CT. SUVmax and total TLG were calculated for all lesions in each patient and the highest SUVmax and the whole-body TLG (WTLG) values were used as independent variables for the analyses. Mean ages and the proportions of histopathological subtypes were compared between two groups using Mann-Whitney U test and Fisher's exact test, respectively. The prognostic significance of PET parameters was assessed using Cox proportional hazards regression analysis. RESULTS: For groups A and B, the median follow-up period was 26 months (range, 3-58 months) and 40.5 months (range, 3-69 months), and the median age was 61 years (range, 42-81 years) and 59 years (range, 24-74 years), respectively. There were no significant differences between two groups in age (p = 0.294) or histopathological subtype (p = 0.384). In the univariate analyses, WTLG was found to be significantly associated with overall survival (OS) for patients of group A (p = 0.012). In the multivariate analysis, WTLG was also significantly associated with OS (p = 0.015). Only hormonal receptor level was a significant indicator of longer OS in patients with recurrent MBC (group B). CONCLUSIONS: This study demonstrated that WTLG on PET/CT is an independent prognostic factor for survival in breast cancer patients with metastases at the initial presentation.

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.

Organ-Specific Positron Emission Tomography Scanners for Breast Imaging: Comparison between the Performances of Prior and Novel Models.

The performances of photomultiplier tube (PMT)-based dedicated breast positron emission tomography (PET) and silicon photomultiplier tube (SiPM)-based time-of-flight (TOF) PET, which is applicable not only to breast imaging but also to head imaging, were compared using a phantom study. A cylindrical phantom containing four spheres (3-10 mm in diameter) filled with 18F-FDG at two signal-to-background ratios (SBRs), 4:1 and 8:1, was scanned. The phantom images, which were reconstructed using three-dimensional list-mode dynamic row-action maximum likelihood algorithm with various ß-values and post-smoothing filters, were visually and quantitatively compared. Visual evaluation showed that the 3 mm sphere was more clearly visualized with higher ß and smaller post-filters, while the background was noisier; SiPM-based TOF-PET was superior to PMT-based dbPET in sharpness, smoothness, and detectability, although the background was noisier at the SBR of 8:1. Quantitative evaluation revealed that the detection index (DI) and recovery coefficient (CRC) of SiPM-based TOF-PET images were higher than those of PMT-based PET images, despite a higher background coefficient of variation (CVBG). The two organ-specific PET systems showed that a 3 mm lesion in the breast could be visualized at the center of the detector, and there was less noise in the SiPM-based TOF-PET image.

Dedicated phantom tools using traceable 68Ge/68Ga point-like sources for dedicated-breast PET and positron emission mammography scanners.

Since the early 2000s, many types of positron emission tomography (PET) scanners dedicated to breast imaging for the diagnosis of breast cancer have been introduced. However, conventional performance evaluation methods developed for whole-body PET scanners cannot be used for such devices. In this study, we developed phantom tools for evaluating the quantitative accuracy of positron emission mammography (PEM) and dedicated-breast PET (dbPET) scanners using novel traceable point-like 68Ge/68 Ga sources. The PEM phantom consisted of an acrylic cube (100 × 100 × 40 mm) and three point-like sources. The dbPET phantom comprised an acrylic cylinder (ø100 × 100 mm) and five point-like sources. These phantoms were used for evaluating the fundamental responses of clinical PEM and dbPET scanners to point-like inputs in a medium. The results showed that reasonable recovery values were obtained based on region-of-interest analyses of the reconstructed images. The developed phantoms using traceable 68Ge/68 Ga point-like sources were useful for evaluating the physical characteristics of PEM and dbPET scanners. Thus, they offer a practical, reliable, and universal measurement scheme for evaluating various types of PET scanners using common sets of sealed sources.

Impairment of heme biosynthesis induces short circadian period in body temperature rhythms in mice.

It has been demonstrated that the function of mammalian clock gene transcripts is controlled by the binding of heme in vitro. To examine the effects of heme on biological rhythms in vivo, we measured locomotor activity (LA) and core body temperature (T(b)) in a mouse model of porphyria with impaired heme biosynthesis by feeding mice a griseofulvin (GF)-containing diet. Mice fed with a 2.0% GF-containing diet (GF2.0) transiently exhibited phase advance or phase advance-like phenomenon by 1-3 h in terms of the biological rhythms of T(b) or LA, respectively (both, P < 0.05) while mice were kept under conditions of a light/dark cycle (12 h:12 h). We also observed a transient, ~0.3 h shortening of the period of circadian T(b) rhythms in mice kept under conditions of constant darkness (P < 0.01). Interestingly, the observed duration of abnormal circadian rhythms in GF2.0 mice lasted between 1 and 3 wk after the onset of GF ingestion; this finding correlated well with the extent of impairment of heme biosynthesis. When we examined the effects of therapeutic agents for acute porphyria, heme, and hypertonic glucose on the pathological status of GF2.0 mice, it was found that the intraperitoneal administration of heme (10 mg·kg(-1)·day(-1)) or glucose (9 g·kg(-1)·day(-1)) for 7 days partially reversed (50%) increases in urinary δ-aminolevulinic acids levels associated with acute porphyria. Treatment with heme, but not with glucose, suppressed the phase advance (-like phenomenon) in the diurnal rhythms (P < 0.05) and restored the decrease of heme (P < 0.01) in GF2.0 mice. These results suggest that impairments of heme biosynthesis, in particular a decrease in heme, may affect phase and period of circadian rhythms in animals.

Image quality evaluation of real low-dose breast PET.

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.

Effect of radioactivity outside the field of view on image quality of dedicated breast positron emission tomography: preliminary phantom and clinical studies.

OBJECTIVE: Semi-quantitative positron emission tomography (PET) values, such as the maximum standardized uptake value (SUVmax), are widely used to identify malignant lesions and evaluate the response to treatment. The image quality of ring-shaped dedicated breast positron emission tomography (dbPET) has been known to decrease the closer it is to the detector's edge. This study aimed to investigate the effect of radioactivity (RI) outside the detector field of view (FOV) on the image quality of the ring-shaped dbPET. METHODS: A breast phantom containing the left myocardium, which was prepared using a 3D printer, filled with 18F-fluorodeoxyglucose (FDG) solution with various RI concentration ratios (RCRs) of myocardium to background and scanned with the edge of an apex positioned exactly in line with the edge of the FOV of the dbPET scanner. The phantom image quality was visually and quantitatively evaluated. Following the phantom study, left-right breast differences (the left breast uptake ratio to the right breast (LUR)) on clinical dbPET images of 74 women were quantitatively evaluated. The relationships between these parameters, clinical indices, and FDG uptake in the left myocardium on PET/computed tomography (CT) images were analyzed. RESULTS: The phantom study showed that the higher the RCR of the myocardium and the closer it is to the top edge of the phantom, the higher is the pixel value of the dbPET images. In a clinical study, LUR was significantly correlated with myocardial SUVmax (r = 0.96, p < 0.0001) and metabolic myocardial volume (r = 0.63, p = 0.001) for whole-body PET/CT imaging. Although no significant correlations were found between LUR and age (r = 0.05, p = 0.6865), body mass index (r = 0.03, p = 0.8178), or distance between the left myocardial apex and chest wall (r = 0.16, p = 0.1667). CONCLUSIONS: FDG uptake in the myocardium affected dbPET images of the left breast, especially near the chest wall. Further, the effect of RI outside the FOV, such as in the myocardium, must be considered in the quantitative evaluation of breast cancer using dbPET.

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.

Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.

This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1−5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.

Diagnosis of peritoneal dissemination: comparison of 18F-FDG PET/CT, diffusion-weighted MRI, and contrast-enhanced MDCT.

OBJECTIVE: The purpose of this study was to compare the diagnostic performance of (18)F-FDG PET/CT, MRI with and without diffusion-weighted imaging (DWI), and contrast-enhanced MDCT in the detection of peritoneal dissemination of malignant tumors. MATERIALS AND METHODS: We retrospectively evaluated the cases of 107 patients who underwent PET/CT and 130 patients who underwent MRI and contrast-enhanced MDCT. Twenty-six patients who underwent PET/CT and 23 who underwent MRI and contrast-enhanced MDCT were found to have peritoneal dissemination. All images were independently evaluated by two radiologists using a 5-point grading system. The results of PET/CT, T1- and T2-weighted MRI without DWI, MRI with DWI (b = 1,000 s/mm(2)), and contrast-enhanced MDCT were compared patient by patient and lesion by lesion by use of receiver operating characteristics analysis. Sensitivity, specificity, and positive predictive value were calculated and compared by use of the chi-square test. RESULTS: Patient by patient, the area under the receiver operating characteristics curve of MRI without DWI (0.88) was significantly less than that of the other modalities (contrast-enhanced MDCT, 0.91; MRI with DWI, 0.93; PET/CT, 0.97). The sensitivity of PET/CT (94%) was significantly higher than that of MRI without DWI (70%). The specificities of the modalities were not significantly different. In lesion-by-lesion analysis, MRI without DWI had significantly lower sensitivity (56%) than the other modalities (contrast-enhanced MDCT, 76%; MRI with DWI, 84%; PET/CT, 89%). The positive predictive value of PET/CT (93%) was significantly higher than that of the other three modalities (contrast-enhanced MDCT, 73%; MRI without DWI, 70%; MRI with DWI, 72%). CONCLUSION: PET/CT is the most useful technique for pathologic staging in the care of patients with malignant disease. If PET/CT is not available, DWI can be used as a screening tool.

Evaluation of image quality at the detector's edge of dedicated breast positron emission tomography.

BACKGROUND: Using phantoms and clinical studies in prone hanging breast imaging, we assessed the image quality of a commercially available dedicated breast PET (dbPET) at the detector's edge, where mammary glands near the chest wall are located. These are compared to supine PET/CT breast images of the same clinical subjects. METHODS: A breast phantom with four spheres (16-, 10-, 7.5-, and 5-mm diameter) was filled with 18F-fluorodeoxyglucose solution (sphere-to-background activity concentration ratio, 8:1). The spheres occupied five different positions from the top edge to the centre of the detector and were scanned for 5 min in each position. Reconstructed images were visually evaluated, and the contrast-to-noise ratio (CNR), contrast recovery coefficient (CRC) for all spheres, and coefficient of variation of the background (CVB) were calculated. Subsequently, clinical images obtained with standard supine PET/CT and prone dbPET were retrospectively analysed. Tumour-to-background ratios (TBRs) between breast cancer near the chest wall (close to the detector's edge; peripheral group) and at other locations (non-peripheral group) were compared. The TBR of each lesion was compared between dbPET and PET/CT. RESULTS: Closer to the detector's edge, the CNR and CRC of all spheres decreased while the CVB increased in the phantom study. The disadvantages of this placement were visually confirmed. Regarding clinical images, TBR of dbPET was significantly higher than that of PET/CT in both the peripheral (12.38 ± 6.41 vs 6.73 ± 3.5, p = 0.0006) and non-peripheral (12.44 ± 5.94 vs 7.71 ± 7.1, p = 0.0183) groups. There was no significant difference in TBR of dbPET between the peripheral and non-peripheral groups. CONCLUSION: The phantom study revealed poorer image quality at < 2-cm distance from the detector's edge than at other more central parts. In clinical studies, however, the visibility of breast lesions with dbPET was the same regardless of the lesion position, and it was higher than that in PET/CT. dbPET has a great potential for detecting breast lesions near the chest wall if they are at least 2 cm from the edge of the FOV, even in young women with small breasts.

Optimal relaxation parameters of dynamic row-action maximum likelihood algorithm and post-smoothing filter for image reconstruction of dedicated breast PET.

OBJECTIVE: This study aimed to determine the optimal ß value of the relaxation control parameter and the post-smoothing filter in the list-mode dynamic row-action maximum likelihood algorithm (LM-DRAMA) to detect early stage breast cancer with high-resolution dedicated breast positron emission tomography (dbPET) in phantom and clinical studies. METHODS: A breast phantom containing four spheres (5, 7.5, 10, and 16 mm in diameter) was filled with 18F-fluorodeoxyglucose solution (sphere-to-background ratio, 8:1) and scanned on a dbPET scanner. The images were reconstructed using LM-DRAMA with different ß values (5, 20, or 100) and Gaussian post-filters (0, 0.78, 1.17, 1.56, 1.95, or 2.34 mm). Other conditions were according to those routinely used (1 iteration and 128 subsets including attenuation and scatter correction). Image quality was evaluated visually and by computing the coefficient of variation of the background (CVBG), detectability index (DI), and contrast recovery coefficient. Parameters optimized in these phantom studies were applied to 25 clinical data sets. Variabilities for different reconstruction methods in visual scores, the maximum standardized uptake value of breast cancer, and the tumor-to-background uptake ratio were estimated. RESULTS: The reconstruction images of the phantom with higher ß values and smaller post-filters yielded higher visual scores for detectability and DI and lower smoothness and CVBG scores. Based on the phantom study, the ß values and post-filter were optimized for clinical dbPET images except for ß5 and 2.34 mm post-filter. Applying the other reconstructions to clinical studies showed that ß100 provided higher quantitative parameter values. The detectability of lesions was similar for ß100 and ß20 and decreased with larger post-filters. The lesion detection rate was similar for ß100 and ß20 and decreased with larger post-filter. CONCLUSION: The relaxation coefficient factor ß20 and a 0.78- or 1.17-mm post-filter were optimal for dbPET image reconstruction with balanced spatial resolution and noise. However, they should be selected according to the impact on the dbPET image and the purpose of the examination.