Phase I/IIa trial of 18F-prostate specific membrane antigen (PSMA) 1007 PET/CT in healthy volunteers and prostate cancer patients.

OBJECTIVE: 18F-PSMA 1007 is a promising PET tracer for prostate cancer. We aimed to examine the safety, biodistribution, radiation dosimetry, and clinical effectiveness in Japanese healthy volunteers and patients with prostate cancer. METHODS: Part A evaluated the pharmacokinetics and exposure doses in three healthy volunteers. Part B evaluated the diagnostic accuracy in patients with untreated preoperative prostate cancer (Cohort 1, n = 7) and patients with biochemical recurrence (Cohort 2, n = 3). All subjects received a single dose of 3.7 MBq/kg 18F-PSMA 1007. Results: 18F-PSMA 1007 was found to be safe and well tolerated in all subjects. No serous AEs or drug-related AEs were identified during the present study. The average blood radioactivity concentration reached a maximum of 47.87 ± 1.05 (percentage of injected dose [%ID]/ml) at 5 min and then decreased to 1.60 ± 0.78 in 6 h. The systemic radioactivity reached a maximum of 211.05 ± 6.77 (%ID$\times$103) at 5 min and decreased to 7.18 ± 3.91 in 6 h. The sensitivity and positive predictive value were 100% and 100% based on both pathologic and imaging confirmation as gold standard. In Cohort 1, 15 primary foci (11.9%) were >5 mm in the largest diameter and identified in 39 of 126 segments (30.1%). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for 60 min uptake time acquisition were 80.0, 96.5, 91.4, 91.2 and 91.3%, respectively. CONCLUSIONS: Our study revealed that 18F-PSMA 1007 was safe, well tolerated and showed high accuracy in the diagnosis of prostate cancer.

Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer.

We investigated whether 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography images restored via deep learning (DL) improved image quality and affected axillary lymph node (ALN) metastasis diagnosis in patients with breast cancer. Using a five-point scale, two readers compared the image quality of DL-PET and conventional PET (cPET) in 53 consecutive patients from September 2020 to October 2021. Visually analyzed ipsilateral ALNs were rated on a three-point scale. The standard uptake values SUVmax and SUVpeak were calculated for breast cancer regions of interest. For "depiction of primary lesion", reader 2 scored DL-PET significantly higher than cPET. For "noise", "clarity of mammary gland", and "overall image quality", both readers scored DL-PET significantly higher than cPET. The SUVmax and SUVpeak for primary lesions and normal breasts were significantly higher in DL-PET than in cPET (p < 0.001). Considering the ALN metastasis scores 1 and 2 as negative and 3 as positive, the McNemar test revealed no significant difference between cPET and DL-PET scores for either reader (p = 0.250, 0.625). DL-PET improved visual image quality for breast cancer compared with cPET. SUVmax and SUVpeak were significantly higher in DL-PET than in cPET. DL-PET and cPET exhibited comparable diagnostic abilities for ALN metastasis.

Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners.

Deep learning (DL) image quality improvement has been studied for application to 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). It is unclear, however, whether DL can increase the quality of images obtained with semiconductor-based PET/CT scanners. This study aimed to compare the quality of semiconductor-based PET/CT scanner images obtained by DL-based technology and conventional OSEM image with Gaussian postfilter. For DL-based data processing implementation, we used Advanced Intelligent Clear-IQ Engine (AiCE, Canon Medical Systems, Tochigi, Japan) and for OSEM images, Gaussian postfilter of 3 mm FWHM is used. Thirty patients who underwent semiconductor-based PET/CT scanner imaging between May 6, 2021, and May 19, 2021, were enrolled. We compared AiCE images and OSEM images and scored them for delineation, image noise, and overall image quality. We also measured standardized uptake values (SUVs) in tumors and healthy tissues and compared them between AiCE and OSEM. AiCE images scored significantly higher than OSEM images for delineation, image noise, and overall image quality. The Fleiss kappa value for the interobserver agreement was 0.57. Among the 21 SUV measurements in healthy organs, 11 (52.4%) measurements were significantly different between AiCE and OSEM images. More pathological lesions were detected in AiCE images as compared with OSEM images, with AiCE images showing higher SUVs for pathological lesions than OSEM images. AiCE can improve the quality of images acquired with semiconductor-based PET/CT scanners, including the noise level, contrast, and tumor detection capability.

Deep learning-based image quality improvement of 18F-fluorodeoxyglucose positron emission tomography: a retrospective observational study.

BACKGROUND: Deep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images obtained with the DL method with those obtained using a Gaussian filter. METHODS: Fifty patients with a mean age of 64.4 (range, 19-88) years who underwent 18F-FDG PET/CT between April 2019 and May 2019 were included in the study. PET images were obtained with the DL method in addition to conventional images reconstructed with three-dimensional time of flight-ordered subset expectation maximization and filtered with a Gaussian filter as a baseline for comparison. The reconstructed images were reviewed by two nuclear medicine physicians and scored from 1 (poor) to 5 (excellent) for tumor delineation, overall image quality, and image noise. For the semi-quantitative analysis, standardized uptake values in tumors and healthy tissues were compared between images obtained using the DL method and those obtained with a Gaussian filter. RESULTS: Images acquired using the DL method scored significantly higher for tumor delineation, overall image quality, and image noise compared to baseline (P < 0.001). The Fleiss' kappa value for overall inter-reader agreement was 0.78. The standardized uptake values in tumor obtained by DL were significantly higher than those acquired using a Gaussian filter (P < 0.001). CONCLUSIONS: Deep learning method improves the quality of PET images.

Optimal phase analysis of electrocardiogram-gated computed tomography angiography in patients with Stanford type A acute aortic dissection.

OBJECTIVE: To determine the phase that facilitates flap observation of the ascending aorta in Stanford type A acute aortic dissection with perfused false lumen. METHODS: We reconstructed retrospective Electrocardiogram-gated Computed Tomography Angiography images of the ascending aorta of all 20 patients to 20 phases of curved-multiplanar reconstruction in 5% increment. One radiologist created and randomized 10 cross-sectional images of each phase for every patient and two radiologists scored these images on a 5-point scale depending on the degree of flap stoppage. We calculated the average score for each phase of each case and compared them among the three groups. RESULTS: Image scores were significantly better in the 65 %-100 % R-R interval group than those in the 5%-30 % (p < 2e-16) and 35 %-60 % R-R interval groups(p = 7.2e-10). Similar scores were observed in the Heart Rate > 70 group (p = 0.00039, 2.2e-14). Moreover a similar tendency was observed in the arrhythmia group (p = 0.0035, 0.294). No difference was found in the degree of flap stoppage in the 65 %-100 % R-R interval group between the Heart Rate > 70 and Heart Rate ≤ 70 groups (p = 0.466) and between the arrhythmia and non-arrhythmia groups (p = 0.1240). CONCLUSION: In observing the ascending aorta, We obtained a good image at 65 %-100 % R-R interval and similar tendency was observed in the patients with arrhythmia.

[Molecular analysis of sporadic colon cancer]. / Análisis molecular del cáncer de colon esporádico.

BACKGROUND: In Chile, colorectal cancer (CRC) is often diagnosed in late stages. Thus, surgical treatment must be complemented with chemotherapy. KRAS mutations and microsatellite instability have been detected in these tumors. However, the response to treatment in patients without KRAS mutations varies and requires a better understanding. AIM: To determine the frequency and distribution of somatic point mutations in KRAS, BRAF and PIK3CA genes and microsatellite instability status (MSI) in patients with colon cancer (CC). MATERIAL AND METHODS: A prospective observational study of patients undergoing surgery for colon cancer. Tumor-derived DNA was analyzed by polymerase chain reaction (PCR) for the most frequent mutations of KRAS, BRAF and PIK3CA. PCR was also used to analyze MSI. RESULTS: Fifty-eight patients with sporadic CC were analyzed, 16 showed KRAS mutations (G12R, G12D, G12V, G13D) and out of the 42 patients that did not show any mutation, 10 had mutations in BRAF (V600E) and PIK3CA (E542K, E545D, E545K, Q546E, H1047R). BRAF mutations alone or in combination with PIK3CA mutations were observed in 27% of high MSI tumors and in 2% of tumors without instability (p < 0.049). A higher percentage of high MSI tumors were located in the right colon (p < 0.001), and showed BRAF mutation (p < 0.020). CONCLUSIONS: The highest percentage of high MSI and BRAF mutations was observed in the right colon. Therefore, this study suggests the presence of different molecular features between right and left colon tumors that should be considered when defining the therapeutic management.

Análisis molecular del cáncer de colon esporádico / Molecular analysis of sporadic colon cancer

Background: In Chile, colorectal cancer (CRC) is often diagnosed in late stages. Thus, surgical treatment must be complemented with chemotherapy. KRAS mutations and microsatellite instability have been detected in these tumors. However, the response to treatment in patients without KRAS mutations varies and requires a better understanding. Aim: To determine the frequency and distribution of somatic point mutations in KRAS, BRAF and PIK3CA genes and microsatellite instability status (MSI) in patients with colon cancer (CC). Material and Methods: A prospective observational study of patients undergoing surgery for colon cancer. Tumor-derived DNA was analyzed by polymerase chain reaction (PCR) for the most frequent mutations of KRAS, BRAF and PIK3CA. PCR was also used to analyze MSI. Results: Fifty-eight patients with sporadic CC were analyzed, 16 showed KRAS mutations (G12R, G12D, G12V, G13D) and out of the 42 patients that did not show any mutation, 10 had mutations in BRAF (V600E) and PIK3CA (E542K, E545D, E545K, Q546E, H1047R). BRAF mutations alone or in combination with PIK3CA mutations were observed in 27% of high MSI tumors and in 2% of tumors without instability (p < 0.049). A higher percentage of high MSI tumors were located in the right colon (p < 0.001), and showed BRAF mutation (p < 0.020). Conclusions: The highest percentage of high MSI and BRAF mutations was observed in the right colon. Therefore, this study suggests the presence of different molecular features between right and left colon tumors that should be considered when defining the therapeutic management.