Positron Emission Tomography-computed Tomography Imaging Research Articles

Prognostic value of PSMA PET in predicting long-term biochemical control following curative intent treatment for prostate cancer.

The aim of this study is to investigate the prognostic value of 68Ga-labelled prostate-specific membrane antigen (PSMA) positron emission tomography (PET) metrics in predicting long-term biochemical failure-free survival (BFFS) following curative intent treatment for prostate cancer. We completed a prospective study that followed men who had PSMA PET for staging of newly diagnosed prostate cancer between 2015 and 2017 who went on to have curative intent treatment with radiotherapy (RT) or radical prostatectomy (RP). PSMA PET CT imaging was reported and the intraprostatic maximum standardised uptake value (SUVmax) was recorded. The primary outcome was BFFS. Statistical analysis included descriptive statistics, Cox proportional hazards (PH) models, Kaplan-Meier survival analysis and a regression tree structured method. A total of 183 men were included in the analysis with a median age of 66 years and the majority of patients (55.2%) had ISUP grade 1-3 disease. All patients had PSMA PET staging prior to curative intent treatment with RP (66.1%) or external beam radiotherapy (33.9%). PSMA-avid pelvic nodes were present in 26 patients but were not associated with worse biochemical control. A PSMA SUVmax of the prostate primary greater than the median (>5.6) was associated with a lower BFFS (HR: 4.4, 95% CI 1.42-3.72, P = 0.01). A multivariate Cox model incorporating initial biopsy grade, age and PSMA SUVmax showed that PSMA SUVmax was an independent predictor of BFFS. The RT-structured method identified an optimal threshold of 6.8 for PSMA SUVmax, above which patients with ISUP 1-3 disease had a significantly worse BFFS. PSMA SUVmax is a strong predictor of BFFS in patients with non-metastatic prostate cancer who underwent curative intent treatment. Patients with low-risk disease on biopsy (ISUP 1-3) but high PSMA SUVmax may have biochemical failure risk analogous to higher-risk disease (ISUP 4-5). These findings allow for further risk stratification and prognosis of patients with newly diagnosed prostate cancer planned for definitive treatment.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Predicting non-small cell lung cancer lymph node metastasis: integrating ctDNA mutation/methylation profiling with positron emission tomography-computed tomography (PET-CT) scan: protocol for a prospective clinical trial (LUNon-invasive Study).

Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer cases and remains a leading cause of cancer-related death. Lymph node metastasis (LNM) significantly affects recurrence, survival rates, and treatment options. While lymph node sampling is standard for surgically removing operable NSCLC, it can lead to complications. Positron emission tomography-computed tomography (PET-CT) helps assess preoperative LNM despite false positive or negative rates. Additionally, circulating tumor DNA (ctDNA) detects minimal residual disease with high sensitivity and specificity. Whether ctDNA can predict LNM in operable NSCLC remains uncertain. Our goal is to develop a precise model for predicting NSCLC LNM using non-invasive ctDNA/methylation profiling combined with PET-CT imaging. This is a prospective study conducted in three stages. We will enroll patients with clinical stage I-IIIB [8th tumor, node, metastasis (TNM) staging] NSCLC requiring lobectomy plus lymph node sampling/dissection. The distribution of clinical stages in the enrolled population is as follows: clinical stage cN0 (n=100) and cN1/cN2 (n=100). During Stage 1, we will establish LNMs-specific ctDNA methylation signatures and compare negative predictive value (NPV) rates of LNMs using preoperative blood ctDNA somatic mutation/methylation alone or combined with PET-CT across different groups. For Stage 2, we will compare detection rates between ctDNA somatic mutation/methylation profiles alone or combined with PET-CT and traditional mediastinoscopy/endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). As for Stage 3, ctDNA-free interval (CFI) and disease-free survival between systematic lymph node presence and absence in patients will be compared with preoperative negative ctDNA profiling and/or PET-CT. In Stage 3, patients will be followed up for 5 years to collect recurrence and survival data. Post-surgery follow-up ctDNA tests will be conducted every 3 months for the first 2 years, every 6 months for years 3-4, and annually in year five. Demographics and baseline data will be summarized with mean, standard deviation, median, max, and min values. Tests will include t-tests, Welch/Behren-Fisher test, and Wilcoxon rank-sum test for continuous variables. Categorical data will be presented as counts/percentages and compared using χ2 test or Fisher's exact test. By utilizing preoperative ctDNA/methylation profiling in conjunction with PET-CT, this study is expected to yield substantial evidence for accurately predicting LNM before surgery. This will help inform surgeons in selecting the appropriate intraoperative lymph node dissection strategy for operable NSCLC patients. This study is registered on www.clinicaltrials.gov (NCT06358222).

Fully automated classification of pulmonary nodules in positron emission tomography-computed tomography imaging using a two-stage multimodal learning approach.

Lung cancer is a malignant tumor, for which pulmonary nodules are considered to be significant indicators. Early recognition and timely treatment of pulmonary nodules can contribute to improving the survival rate of patients with cancer. Positron emission tomography-computed tomography (PET/CT) is a noninvasive, fusion imaging technique that can obtain both functional and structural information of lung regions. However, studies of pulmonary nodules based on computer-aided diagnosis have primarily focused on the nodule level due to a reliance on the annotation of nodules, which is superficial and unable to contribute to the actual clinical diagnosis. The aim of this study was thus to develop a fully automated classification framework for a more comprehensive assessment of pulmonary nodules in PET/CT imaging data. We developed a two-stage multimodal learning framework for the diagnosis of pulmonary nodules in PET/CT imaging. In this framework, Stage I focuses on pulmonary parenchyma segmentation using a pretrained U-Net and PET/CT registration. Stage II aims to extract, integrate, and recognize image-level and feature-level features by employing the three-dimensional (3D) Inception-residual net (ResNet) convolutional block attention module architecture and a dense-voting fusion mechanism. In the experiments, the proposed model's performance was comprehensively validated using a set of real clinical data, achieving mean scores of 89.98%, 89.21%, 84.75%, 93.38%, 86.83%, and 0.9227 for accuracy, precision, recall, specificity, F1 score, and area under curve values, respectively. This paper presents a two-stage multimodal learning approach for the automatic diagnosis of pulmonary nodules. The findings reveal that the main reason for limiting model performance is the nonsolitary property of nodules in pulmonary nodule diagnosis, providing direction for future research.

Heterogeneity of fibroblast activation protein expression in the microenvironment of an intracranial tumor cohort: head-to-head comparison of gallium-68 FAP inhibitor-04 (68Ga-FAPi-04) and fluoride-18 fluoroethyl-L-tyrosine (18F-FET) in positron emission tomography-computed tomography imaging.

Cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME) can interact with tumor parenchymal cells to promote tumor growth and migration. Fibroblast activation protein (FAP) expressed by CAFs can be targeted with positron emission tomography (PET) tracers, but studies on FAP expression patterns in intracranial tumors remain scarce. We aimed to evaluate FAP expression patterns in intracranial tumors with gallium-68 FAP inhibitor-04 (68Ga-FAPi-04) and immunohistochemical staining and to observe the interactions between CAFs and tumor cells with a head-to-head comparison of 68Ga-FAPi-04 and fluoride-18 fluoroethyl-L-tyrosine (18F-FET) for PET quantification analysis. We prospectively enrolled 22 adult patients with intracranial mass lesions. 68Ga-FAPi-04 and 18F-FET PET-computed tomography (PET/CT) brain imaging were applied before surgery. Maximal tumor-to-brain ratio (TBRmax), metabolic tumor volume (MTV), and total lesion tracer uptake (TLU) was obtained, and different thresholds were used for 68Ga-FAPi-04-positive lesion delineation owing to the lack of relevant guidelines. The MTV and TLU ratios of both tracers were calculated. Linear regression was applied to observe the differential efficacy of semiquantitative PET parameters. A total of 22 patients with a mean age of 50±13 years (range, 27-69 years) were enrolled. Heterogeneous patterns of 68Ga-FAPi-04 uptake [median of maximal standardized uptake value (SUVmax) =3.8; range, 0.1-19.1] were found. More malignant tumors, including brain metastasis, glioblastoma, and medulloblastoma, generally exhibited more significant 68Ga-FAPi-04 uptake than did the less malignant tumors, while the SUVmax and TBRmax exhibited nonsignificant differences across three intracranial lesion groups of primary brain tumor, brain metastasis, and noncancerous disease (SUVmax: P=0.092; TBRmax: P=0.189). Immunohistochemistry staining showed different stromal FAP expression status in various intracranial lesions. In 15 patients with positive 68Ga-FAPi-04 intracranial tumor uptake, the MTVFAPi:MTVFET ratio had differential efficacy in various types of intracranial tumors [95% confidence interval (CI): 0.572-7.712; P=0.027], and further quantification analyses confirmed the differential ability of the MTVFAPi:MTVFET ratio (95% CI: -0.045 to 11.013, P=0.052; 95% CI: 0.044-17.903, P=0.049; 95% CI: -1.131 to 30.596, P=0.065) with different isocontour volumetric thresholds. This head-to-head study demonstrated heterogeneous FAP expression in intracranial tumors. The FAP expression volume percentage in tumor parenchyma may therefore offer benefit with respect to differentiating between intracranial tumor types.

Design, Synthesis, and Biological Evaluation of Novel Nonsteroidal 18F-Labeled PET Probes Specifically Targeting Estrogen Receptor α.

The estrogen receptor α positive (ERα+) subtype represents nearly 70% of all breast cancers (BCs), which seriously threaten women's health. Positron emission computed tomography (PET) characterizes its superiority in detecting the recurrence and metastasis of BC. In this article, an array of novel PET probes ([18F]R-1, [18F]R-2, [18F]R-3, and [18F]R-4) targeting ERα based on the tetrahydropyridinyl indole scaffold were developed. Among them, [18F]R-3 and [18F]R-4 showed good target specificity toward ERα and could distinguish MCF-7 (ERα+) and MDA-MB-231 (ERα-) tumors efficiently. Especially, [18F]R-3 could differentiate the ERα positive/negative tumors successfully with a higher tumor-to-muscle uptake ratio (T/M) than that of [18F]R-4. The radioactivity of [18F]R-3 in the MCF-7 tumor was 5.24 ± 0.84%ID/mL and its T/M ratio was 2.49 ± 0.62 at 25 min postinjection, which might be the optimal imaging time point in PET scanning. On the contrary, [18F]R-3 did not accumulate in the MDA-MB-231 tumor at all. The autoradiography analysis of [18F]R-3 on the MCF-7 tumor-bearing mice model was consistent with the PET imaging results. [18F]R-3 exhibited the pharmacokinetic property of rapid distribution and slow clearance, making it suitable for use as a diagnostic PET probe. Overall, [18F]R-3 was capable of serving as a PET radiotracer to delineate the ERα+ tumor and was worthy of further exploitation.

Surgery versus no surgery in platinum-sensitive relapsed ovarian cancer: final overall survival analysis of the SOC-1 randomized phase 3 trial.

Surgery for platinum-sensitive, relapsed ovarian cancer (PSROC) is widely practiced but had contradictory survival outcomes in previous studies. In this multicenter, open-label, phase 3 trial, women with PSROC, and having had one previous therapy and no platinum-based chemotherapy (platinum-free interval) of 6 months or more, were randomly assigned to either the surgery group (182 patients) or the no-surgery group (control) (175 patients). Patients with resectable diseases were eligible according to the international model (iMODEL), combined with a positron emission tomography-computed tomography imaging. Overall survival (OS) and progression-free survival were coprimary endpoints in hierarchical testing, and a significantly longer progression-free survival with surgery was previously reported. Final analysis of OS was planned at data maturity of 59%. Between 19 July 2012 and 3 June 2019, 357 patients were enrolled. Median follow-up was 82.5 months. Median OS was 58.1 months with surgery and 52.1 months for control (hazard ratio (HR) 0.80, 95% confidence interval (CI) 0.61-1.05, P = 0.11). The predefined threshold for statistical significance was not met, but prespecified sensitivity analysis was performed. Overall, 61 of 175 (35%) patients in control had crossed over to surgery following subsequent relapse, and adjusted HR for death in the surgery group compared with control was 0.76, 95% CI 0.58-0.99. In subgroup analysis of relapse sites by imaging, median survival was not estimable in the surgery group and was 69.5 months in control in patients with <20 sites (HR 0.69, 95% CI 0.46-1.03). Patients with a complete resection had the most favorable outcome, with a median OS of 73.0 months. Twenty-four of 182 (13.2%) patients remained relapse free and alive >60 months in the surgery group as compared with five of 175 (2.9%) patients in the control group. In patients with PSROC, surgery did not increase OS in the intention-to-treat population but resulted in a prolongation of survival following adjustment of crossover.ClinicalTrials.gov registration: NCT01611766 .

MSCLK: Multi-scale fully separable convolution neural network with large kernels for early diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is identified as a central nervous system disease that exhibits irreversible degeneration, while mild cognitive impairment (MCI) is viewed as the preliminary stage of AD, and its pathogenesis is notably intricate. MCI contains two stages: early MCI (EMCI), and late MCI (LMCI). EMCI diagnosis can prevent EMCI from progressing to LMCI, and then to AD. Therefore, accurate diagnosis of EMCI/LMCI is crucial for developing the early intervention and treatment strategies of AD. Currently, most existing EMCI/LMCI diagnostic methods use single modality images, while different modality images carry different complementary information that helps for accurate diagnosis of EMCI/LMCI, and the lesion area is usually not limited to a single brain area, which involves multiple regions. In this case, conventional convolution operations cannot be able to accurately extract the pathological features of AD. In this work, we propose a novel Multi-scale fully Separable Convolution neural network with Large Kernels (MSCLK) method to diagnose early Alzheimer’s disease with structural Magnetic Resonance Imaging (sMRI) images. MSCLK mainly consists of the multi-scale 3D fully separable convolution modules and the deep metric learning module. The multi-scale convolution that contains both small and large kernels is used to effectively capture the discrimination features of different scale acceptance domains. 3D fully separable convolution is used to reduce parameters and overfitting. The deep metric learning is used to learn hard samples that are similar but belong to different classes. We also propose a variant method of MSCLK (called MSCLK-Fusion MRI and PET, MSCLK-FMP) by adding the pixel-level fusion module and feature-level fusion module into the MSCLK framework to integrate the sMRI image and the Positron Emission Computed Tomography (PET) image for further improving the accuracy of EMCI vs. LMCI classification task. The pixel-level fusion is used to achieve early pixel-level fusion of sMRI and PET images, and the feature-level fusion is used to achieve high-dimensional feature-level fusion of sMRI and PET images. Experimental results on the ADNI database show that the performance of our MSCLK and MSCLK-FMP are superior to other state-of-the-art methods. The accuracy of MSCLK achieves 98.89%, 95.97%, 96.39% and 98.76% for AD vs. EMCI, AD vs. LMCI, EMCI vs. NC and LMCI vs. NC classification tasks, respectively, and MSCLK-FMP achieves 93.93% for EMCI vs. LMCI classification task, indicating that MSCLK/MSCLK-FMP can be effectively used for diagnosing MCI patients. Moreover, our MSCLK-FMP is capable of pinpointing key brain areas involved in the pathological progression of MCI, such as the Temporal_Inf, the Hippocampus, the Precuneus, the Precentral, and the Thalamus. These findings contribute to uncovering the early onset of AD pathogenesis.

18F-Fluorodeoxyglucose-Avid Benign Anthracotic Mediastinal Lymphadenitis Mimicking Metastatic Lymphadenopathy.

The staging of malignancy is critical for its effective management. 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) imaging is a common modality for malignancy staging, which identifies areas of FDG avidity. However, multiple benign etiologies can cause false-positive 18F FDG-avid nodes. Among these, extrapulmonary involvement of anthracosis in the form of lymphadenopathy is a rare entity. In patients with concomitant malignancies, the presence of 18F FDG-avid anthracotic lymph nodal enlargement may mimic nodal metastasis. Endosonography-guided tissue acquisition may help differentiate between the two. Herein, we describe six cases of FDG-avid benign anthracotic lymphadenitis detected during staging workups for patients with malignancies who later underwent curative resection.

AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma

ObjectivesDistinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules. Materials and methodsOur retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments. ResultsSolid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies. ConclusionIn small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.

The paraneurium and the tumefactive appearance of peripheral nerve neurolymphomatosis: illustrative case.

Peripheral neurolymphomatosis (NL) is an often-misdiagnosed condition characterized by lymphomatous infiltration within the peripheral nerves. Its rarity and complexity frequently result in delayed diagnosis and suboptimal patient outcomes. This study aims to elucidate the role of the paraneurium (circumneurium) in NL, emphasizing its diagnostic and therapeutic significance. A 72-year-old man presented with lesions on his right lower eyelid. Initial diagnostics were inconclusive until an excisional biopsy confirmed extranodal marginal zone lymphoma. Following a complete metabolic response to rituximab treatment, the patient relapsed 14 months later with progressive lymphoma and bilateral sciatic nerve involvement, as confirmed by positron emission tomography-computed tomography and magnetic resonance imaging. This paper underscores the critical role of the paraneurium in NL, enhancing understanding of its pathophysiology. Integrating advanced imaging techniques have proved essential in accurately identifying neurolymphomatous involvement within the paraneurium. This study paves the way for more effective management strategies in NL and similar conditions, focusing on improving patient care and outcomes.

Positron emission tomography-computed tomography vs. brain magnetic resonance imaging for the detection ofcerebral metastases of melanoma: a 5-year retrospective study.

Patients with melanoma present a high risk of developing extracutaneous metastases. Positron emission tomography--computed tomography (PET-CT) is one of the preferred examinations for the staging of oncological patients. It is not the method of choice to detect brain metastases, but this technique has shown significant improvement and allows the detection of some of them. However, it is unclear how it performs compared with magnetic resonance imaging (MRI), the current gold standard for diagnosing brain metastases. To compare the accuracy of PET-CT and cerebral MRI to detect brain metastases in patients with melanoma. We retrospectively included all patients diagnosed with melanoma stage IIC-IV (American Joint Committee on Cancer 8th Edition, 2017) who presented at the skin tumour board of the University Hospital of Bern between January 2018 and December 2022. All radiological reports extracted from the patient management system were analysed to assess discrepancy between the visibility of brain metastases on PET-CT and brain MRI. In this study including 393 patients, brain MRI demonstrated significantly better performance than PET-CT in detecting brain metastases. In 47 patients, cerebral metastases were detected completely, detected partially, or not detected by PET-CT in 2 (4%), 15 (32%) and 30 (64%), respectively. Despite the increasing performance of PET-CT, this study highlights the crucial role of brain MRI, which remains the gold standard to detect cerebral metastases. Brain MRI should be performed in patients with high-risk melanoma from stage IIC to exclude brain metastases.

Surgical treatment of orbital tumors in a single center: Analysis and results.

Orbital tumors, arising within the bony orbit and its contents, present diverse challenges due to their varied origins and complex anatomical context. These tumors, classified as primary, secondary, or metastatic, are further subdivided into intraconal and extraconal based on their relationship with the muscle cone. This classification significantly influences surgical approach and management. This study highlights surgical experiences with orbital tumors, underscoring the importance of tailored surgical approaches based on the lesion's site and its proximity to the optic nerve. This retrospective study at the National Institute of Cancer's Head and Neck Department (2005-2014) analyzed 29 patients with orbital tumors treated with surgery, radiotherapy, chemotherapy, or combinations of them. Patient demographics, tumor characteristics, and treatment responses were evaluated using computed tomography (CT), magnetic resonance imaging, and positron emission tomography-CT imaging. Malignant tumors often required orbital exenteration and reconstruction, highlighting the study's commitment to advancing orbital tumor treatment. 29 patients (18 females and 11 males, age 18-88 years, mean 53.5 years) with orbital tumors exhibited symptoms such as decreased vision and exophthalmos. Tumors included primary lesions like choroidal melanoma and secondary types like epidermoid carcinoma. Treatments varied, involving a multidisciplinary team for surgical approaches like exenteration, with follow-up from 1 to 9 years. Radiotherapy and chemotherapy were used for specific cases. Our study underscores the need for a multidisciplinary approach in treating orbital tumors, involving various surgical specialists and advanced technologies like neuronavigation for tailored treatment. The integration of surgery with radiotherapy and chemotherapy highlights the effectiveness of multidimensional treatment strategies.

Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images.

We developed a fully automated artificial intelligence(AI)AI-based-based method for detecting suspected lymph node metastases in prostate-specific membrane antigen (PSMA)(PSMA) positron emission tomography-computed tomography (PET-CT)(PET-CT) images of prostate cancer patients by using data augmentation that adds synthetic lymph node metastases to the images to expand the training set. Synthetic data were derived from original training images to which new synthetic lymph node metastases were added. Thus, the original training set from a previous study (n = 420) was expanded by one synthetic image for every original image (n = 840), which was used to train an AI model. The performance of the AI model was compared to that of nuclear medicine physicians and a previously developed AI model. The human readers were alternately used as a reference and compared to either another reading or AI model. The new AI model had an average sensitivity of 84% for detecting lymph node metastases compared with 78% for human readings. Our previously developed AI method without synthetic data had an average sensitivity of 79%. The number of false positive lesions were slightly higher for the new AI model (average 3.3 instances per patient) compared to human readings and the previous AI model (average 2.8 instances per patient), while the number of false negative lesions was lower. Creating synthetic lymph node metastases, as a form of data augmentation, on [18F]PSMA-1007F]PSMA-1007 PETPET-CT-CT images improved the sensitivity of an AI model for detecting suspected lymph node metastases. However, the number of false positive lesions increased somewhat.

Internal mammary lymphadenopathy in breast cancer: a narrative review and update.

While the axillary nodal basin is the most common lymphatic drainage pathway of the breast, the internal mammary (IM) lymph node chain plays a significant role in breast cancer staging and treatment. It has been identified as sentinel nodal drainage in approximately 13-37% of patients. Despite this, the data is still limited with regard to diagnosis and management when there is suspicion or confirmation of IM lymph node (IMLN) involvement by metastatic breast cancer. The objective of this publication is to provide a comprehensive assessment of the current body of literature surrounding the diagnosis, management and prognostic value of IMLNs in breast cancer treatment. Review of the literature published regarding IMLN diagnosis, significance, and management was completed in PubMed. Additional focus was placed on reviewing articles published within the past 10 years as foundation for an update regarding the current practice and future directions in this space. Improved imaging techniques, with positron emission tomography-computed tomography and magnetic resonance imaging, have led to increase in the identification of IM lymphadenopathy, yielding surgical staging of the IM nodes nearly obsolete. While IM nodal metastases may play a role in overall survival (OS), it has not been demonstrated to be an independent risk factor for increased locoregional recurrence. IM nodal irradiation (IMNI) therapy has been a mainstay in the treatment of IM disease in the context of breast cancer. IMNI has demonstrated improvement in OS and risk of distant recurrence. Wide variations in radiation practices for patients with IM lymphadenopathy exist internationally, highlighting the lack of clear data driven consensus guidelines. Herein, we provide an updated assessment of the current diagnosis, clinical significance, and management of IM lymphadenopathy for breast cancer patients.

Antibiotic treatment modestly reduces protection against Mycobacterium tuberculosis reinfection in macaques.

Concomitant immunity is generally defined as an ongoing infection providing protection against reinfection . Its role in prevention of tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is supported by epidemiological evidence in humans as well as experimental evidence in mice and non-human primates (NHPs). Whether the presence of live Mtb, rather than simply persistent antigen, is necessary for concomitant immunity in TB is still unclear. Here, we investigated whether live Mtb plays a measurable role in control of secondary Mtb infection. Using cynomolgus macaques, molecularly barcoded Mtb libraries, positron emission tomography-computed tomography (PET CT) imaging, flow cytometry, and cytokine profiling, we evaluated the effect of antibiotic treatment after primary infection on immunological response and bacterial establishment, dissemination, and burden post-secondary infection. Our data provide evidence that, in this experimental model, treatment with antibiotics after primary infection reduced inflammation in the lung but was not associated with a significant change in bacterial establishment, dissemination, or burden in the lung or lymph nodes. Nonetheless, treatment of the prior infection with antibiotics did result in a modest reduction in protection against reinfection: none of the seven antibiotic-treated animals demonstrated sterilizing immunity against reinfection, while four of the seven non-treated macaques were completely protected against reinfection. These findings support that antibiotic-treated animals were still able to restrict bacterial establishment and dissemination after rechallenge compared to naïve macaques, but not to the full extent of non-antibiotic-treated macaques.

Probable Novel APP Met671Leu Mutation in a Chinese Han Family with Early-Onset Alzheimer's Disease.

Familial Alzheimer's disease (AD) is a rare disease caused by autosomal-dominant mutations. APP (encoding amyloid precursor protein), PSEN1 (encoding presenilin 1), and PSEN2 (encoding presenilin 2) are the most common genes cause dominant inherited AD. This study aimed to demonstrate a Chinese early-onset AD pedigree presenting as progressive memory impairment, apraxia, visual-spatial disorders, psychobehavioral disorders, and personality changes with a novel APP gene mutation. The family contains four patients, three carries and three normal family members. The proband underwent brain magnetic resonance imaging (MRI), 18F-fludeoxyglucose positron emission tomography (18F-FDG-PET), cerebrospinal fluid amyloid detection, 18F-florbetapir (AV-45) Positron Emission Computed Tomography (PET) imaging, whole-exome sequencing and Sanger sequencing. Brain MRI images showed brain atrophy, especially in the entorhinal cortex, temporal hippocampus, and lateral ventricle dilation. The FDG-PET showed hypometabolism in the frontotemporal, parietal, and hippocampal regions. 18F-florbetapir (AV-45) PET imaging showed cerebral cortex Aβ protein deposition. The cerebrospinal fluid amyloid protein test showed Aβ42/Aβ40 ratio decreases, pathological phosphor-tau level increases. Whole-exome sequencing detected a new missense mutation of codon 671 (M671L), which was a heterozygous A to T point mutation at position 2011 (c.2011A > T) in exon 16 of the amyloid precursor protein, resulting in the replacement of methionine to Leucine. The co-separation analysis was validated in this family. The mutation was found in 3 patients, 3 clinical normal members in the family, but not in the other 3 unaffected family members, 100 unrelated normal subjects, or 100 sporadic patients with AD. This mutation was probably pathogenic and novel in a Chinese Han family with early-onset AD.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.

Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images. We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias. Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches. We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

A comparison study of 68gallium-prostate-specific membrane antigen positron emission tomography-computed tomography and multiparametric magnetic resonance imaging for locoregional staging of prostate cancer.

Prostate cancer (PCa) is the most common malignancy in men aged 50 years and older and the second cause of cancer death among men. Accurate staging of PCa preoperatively is of high importance for treatment decisions and patient management. Conventional imaging modalities (ultrasound, computed tomography [CT], and magnetic resonance imaging) are inaccurate for the staging of PCa. Newer modality multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scan show promising results for the staging of PCa. Only fewer studies are available for comparison of these modalities with histopathology as reference. The objective of our study is to evaluate the diagnostic accuracy of independent 68gallium PSMA (68Ga-PSMA) PET-CT compared with mpMRI for preoperative staging of PCa, using histopathology as the reference standard. From August 2021 to December 2022, 30 patients of biopsy-proven PCa were prospectively enrolled as per eligibility criteria. Preoperatively, 68Ga-PSMA PET scan and mpMRI were done in all the patients. Extracapsular extension (ECE), seminal vesicle invasion (SVI), and lymph node metastasis (LNM) were investigated separately. Subsequently, the patients underwent robotic-assisted radical prostatectomy with bilateral pelvic lymph node dissection. mpMRI prostate was more sensitive (66.66%) but less specific than PSMA PET-CT (55.55%) for ECE. mpMRI and PSMA PET-CT both had similar sensitivity (83.3%) and specificity (87.5%) for SVI. PSMA PET-CT was more sensitive (85.71%) and specific (95.6%) than mpMRI prostate (62.5% and 91.30%, respectively) for LNM. PSMA PET-CT is more specific for the detection of ECE and more sensitive and specific for the detection of LNM than mpMRI, and similar for the detection of SVI. mpMRI provides only local staging, while PSMA PET-CT provides information about local, regional, and distal staging. Overall, PSMA PET-CT is superior to mpMRI for locoregional staging of PCa.

Applications Used to Increase the Accuracy Rate of Transthoracic Lung Biopsies Performed in Centers for Molecular Biological Diagnosis and Targeted Therapy for Oncology.

Purpose This study aims to assess the significance of imaging techniques and needle thickness employed in transthoracic core needle biopsy for determining the cancer type and subtypes, ultimately guiding the treatment of lung cancer. Material and methods Between 2018 and 2023, a cohort of 350 patients (69.7% male, 30.3% female) underwent CT-guided lung biopsy, predominantly utilizing core biopsies. Fine needle aspiration biopsies employed 18 or 20 G Chiba needles, while core needle biopsies utilized 16 or 18-gauge coaxial system semi-automatic needles. The preferred needle and biopsy sample size were 16 G in thickness and 2 cm in length. Pre-procedure positron emission tomography-computed tomography (PET-CT) images aided in identifying the most homogenous lesion with the highest SUV max value, guiding biopsy sample extraction. Post-procedure control CT evaluated complications according to the Society of Interventional Radiology (SIR) reporting standard. Results The average age of biopsied patients was 65.48 +/- 12.32 SD (range: 18-90). Tru-cut biopsy was predominant (69.7%), utilizing a larger number of 16G needles. Pathological diagnoses were mostly malignant (76.6%), with lesion sizes averaging 35.98 +/- 17.90 SD (range: 5-105 mm) and distances to pleura averaging 13.48 +/- 13.54 SD (range: 0-86 mm). Malignancy prevalence was higher in males (56.8%), tru-cut biopsies (72.7%), 16G needles used for tru-cut (47.7%), and PET-CT evaluation (59.1%). Complications were identified in 22% of cases, with distance to pleura significantly associated (p < 0.001). No significant differences in complication risk were observed between FNAB and tru-cut and between needle gauges (20 G-18 G and 16 G) (p: 0.734, p: 0.638, respectively). Conclusion The study underscores the paramount importance of biopsy sample size in diagnosing lung cancers and determining targeted therapy. Optimal biopsy localization, informed by pre-procedure imaging techniques, is crucial. Hence, the recommendation is to utilize the thickest needles and largest samples for lung biopsies.