Image quality evaluation of real low-dose breast PET

Generative AI and large language models in nuclear medicine: current status and future prospects

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development.

Comparison of state-of-the-art biopsy systems for ultrasound-guided breast biopsy using a chicken breast phantom

PURPOSE

To compare different biopsy systems with different-sized needles by determining the weight of the tissue cores, which is one of the important factors for precise pathological diagnoses, and to provide a rationale for choosing the appropriate breast biopsy system with the appropriate needle for breast cancer biopsy.

METHODS

Six different vacuum-assisted biopsy (VAB) systems and one core needle biopsy (CNB) system with different-sized needles in different modes were compared, representing 15 total combinations. Tissue cores were obtained from a chicken breast phantom, which is a common substitute for human breast tissue. Five cores were taken for each combination and weighed.

RESULTS

The CNB combination provided significantly lighter tissue cores compared with the VAB combinations with the same-size (14-G) needle (P < 0.01). The combinations using the thickest needle obtained the heaviest among all systems (P < 0.02). The untethered battery-free VAB system yielded the lightest specimen among the VAB systems with the same-sized (12-G) needle (P < 0.04). The percent coefficient of variation (%CV) of the core weights obtained using VAB without a basket was significantly smaller compared with the core weights obtained using VAB with a basket (P < 0.01).

CONCLUSION

VAB systems can yield larger tissue cores compared with CNB systems. The size of the tissue cores varies even with the same-sized needle among different VAB systems. When performing a breast tissue biopsy, it is important to consider not only CNB versus VAB but also what specific device to use with which needle size.

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions

PURPOSE

This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS).

MATERIALS AND METHODS

In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions.

RESULTS

Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements.

CONCLUSION

This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

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.

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

BACKGROUND AND OBJECTIVES

This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images.

MATERIALS AND METHODS

We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient.

RESULTS

The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar.

CONCLUSION

The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

From FDG and beyond: the evolving potential of nuclear medicine

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

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.

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.

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.