Positron Emission Tomography-computed Tomography Imaging Research Articles

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.

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.

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.

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.

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.

Plasma-Based Scaffold Containing Bone-Marrow Mononuclear Cells Promotes Wound Healing in a Mouse Model of Pressure Injury.

Pressure injuries, or pressure ulcers, are a common problem that may lead to infections and major complications, besides being a social and economic burden due to the costs of treatment and hospitalization. While surgery is sometimes necessary, this also has complications such as recurrence or wound dehiscence. Among the newer methods of pressure injury treatment, advanced therapies are an interesting option. This study examines the healing properties of bone marrow mononuclear cells (BM-MNCs) embedded in a plasma-based scaffold in a mouse model. Pressure ulcers were created on the backs of mice (2 per mouse) using magnets and assigned to a group of ulcers that were left untreated (Control, n = 15), treated with plasma scaffold (Plasma, n = 15), or treated with plasma scaffold containing BM-MNC (Plasma + BM-MNC, n = 15). Each group was examined at three time points (3, 7, and 14 days) after the onset of treatment. At each time point, animals were subjected to biometric assessment, bioluminescence imaging, and tomography. Once treatment had finished, skin biopsies were processed for histological and wound healing reverse transcription polymerase chain reaction (RT-PCR) array studies. While wound closure percentages were higher in the Plasma and Plasma + BM-MNC groups, differences were not significant, and thus descriptive data are provided. In all individuals, the presence of donor cells was revealed by immunohistochemistry on posttreatment onset Days 3, 7, and 14. In the Plasma + BM-MNC group, less inflammation was observed by positron emission tomography-computed tomography (PET/CT) imaging of the mice at 7 days, and a complete morphometabolic response was produced at 14 days, in accordance with histological results. A much more pronounced inflammatory process was observed in controls than in the other two groups, and this persisted until Day 14 after treatment onset. RT-PCR array gene expression patterns were also found to vary significantly, with the greatest difference noted between both treatments at 14 days when 11 genes were differentially expressed.

Prostate cancer management in Southeast Asian countries: a survey of clinical practice patterns.

Prostate cancer (PC) has a serious public health impact, and its incidence is rising due to the aging population. There is limited evidence and consensus to guide the management of PC in Southeast Asia (SEA). We present real-world data on clinical practice patterns in SEA for advanced PC care. A paper-based survey was used to identify clinical practice patterns and obtain consensus among the panelists. The survey included the demographics of the panelists, the use of clinical guidelines, and clinical practice patterns in the management of advanced PC in SEA. Most panelists (81%) voted prostate-specific antigen (PSA) as the most effective test for early PC diagnosis and risk stratification. Nearly 44% of panelists agreed that prostate-specific membrane antigen positron emission tomography-computed tomography imaging for PC diagnostic and staging information aids local and systemic therapy decisions. The majority of the panel preferred abiraterone acetate (67%) or docetaxel (44%) as first-line therapy for symptomatic mCRPC patients. Abiraterone acetate (50%) is preferred over docetaxel as a first-line treatment in metastatic castration-sensitive prostate cancer patients with high-volume disease. However, the panel did not support the use of abiraterone acetate in non-metastatic castration-resistant prostate cancer (nmCRPC) patients. Apalutamide (75%) is the preferred treatment option for patients with nmCRPC. The cost and availability of modern treatments and technologies are important factors influencing therapeutic decisions. All panelists supported the use of generic versions of approved therapies. The survey results reflect real-world management of advanced PC in a SEA country. These findings could be used to guide local clinical practices and highlight the financial challenges of modern healthcare.

Comparison between 18F-FDG PET/CT and diffusion-weighted imaging in detection of invasive ductal breast carcinoma.

Breast carcinoma is the most common type of cancer in females. This study aims to compare fluorine-18-fluorodeoxyglucose (18F-FDG) uptake pattern and apparent diffusion coefficient (ADC) value for the detection of the primary tumour and axillary metastases of invasive ductal breast carcinoma. This study included 40 breast carcinoma lesions taken from 39 patients. After staging by positron emission tomography-computed tomography (PET/CT) and diffusion-weighted magnetic resonance imaging (MRI), breast surgery with axillary lymph node dissection or sentinel lymph node biopsy was performed. Primary lesion detection rate for PET/CT and diffusion-weighted MRI was high with 39 of 40 lesions (97.5%). The sensitivity and specificity for the detection of metastatic lymph nodes in axilla were 40.9%, 88.9%, with 18F-FDG PET/CT scans and 40.9%, 83.3%, for dw-MRI, respectively. No significant correlation was detected between ADC and SUVmax or SUVmax ratios. Estrogen receptor (p=0.007) and progesterone receptor (p=0.036) positive patients had lower ADC values. Tumour SUVmax was lower in T1 than T2 tumour size (p=0.027) and progesterone receptor-positive patients (p=0.029). Tumour/background SUVmax was lower in progesterone receptor-positive patients (p=0.004). Tumour/liver SUVmax was higher in grade III patients (p=0.035) and progesterone receptor negative status (p=0.043). This study confirmed the high detection rate of breast carcinoma in both modalities. They have same sensitivity for the detection of axillary lymph node metastases, whereas the PET/CT scan had higher specificity. Furthermore, ADC, SUVmax and SUVmax ratios showed some statistical significance among the patient groups according to different pathological parameters.

18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study.

This study aims to develop and validate a radiomics model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) images to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC). One hundred eighty-five patients receiving neoadjuvant chemoimmunotherapy for NSCLC at 5 centers from January 2019 to December 2022 were included and divided into a training cohort and a validation cohort. Radiomics models were constructed via the least absolute shrinkage and selection operator (LASSO) method. The performances of models were evaluated by the area under the receiver operating characteristic curve (AUC). In addition, genetic analyses were conducted to reveal the underlying biological basis of the radiomics score. After the LASSO process, 9 PET-CT radiomics features were selected for pCR prediction. In the validation cohort, the ability of PET-CT radiomics model to predict pCR was shown to have an AUC of 0.818 (95% confidence interval [CI], 0.711, 0.925), which was better than the PET radiomics model (0.728 [95% CI, 0.610, 0.846]), CT radiomics model (0.732 [95% CI, 0.607, 0.857]), and maximum standard uptake value (0.603 [95% CI, 0.473, 0.733]) (p < 0.05). Moreover, a high radiomics score was related to the upregulation of pathways suppressing tumor proliferation and the infiltration of antitumor immune cell. The proposed PET-CT radiomics model was capable of predicting pCR to neoadjuvant chemoimmunotherapy in NSCLC patients. This study indicated that the generated 18F-fluorodeoxyglucose positron emission tomography-computed tomography radiomics model could predict pathological complete response to neoadjuvant chemoimmunotherapy, implying the potential of our radiomics model to personalize the neoadjuvant chemoimmunotherapy in lung cancer patients. • Recognizing patients potentially benefiting neoadjuvant chemoimmunotherapy is critical for individualized therapy of lung cancer. • [18F]FDG PET-CT radiomics could predict pathological complete response to neoadjuvant immunotherapy in non-small cell lung cancer. • [18F]FDG PET-CT radiomics model could personalize neoadjuvant chemoimmunotherapy in lung cancer patients.

Abstract A147: A HER2 targeted polylysine dendrimer nanoparticle radiotheranostic demonstrates excellent tumor accumulation, rapid clearance from circulation, and promising performance in PET-CT imaging

Abstract Background: Development of targeted radiotheranostics for human epidermal growth factor receptor type 2 positive (HER2+) tumors has lagged advances made in other receptor systems such as prostate-specific membrane antigen (PSMA) in prostate cancer. Myelosuppression is a dose limiting toxicity in clinical studies of trastuzumab radiotheranostics due to slow clearance of this large radiotherapeutic from circulation. Conjugation of chemotherapeutics such as taxanes and irinotecan/SN38 to highly optimized dendrimer nanoparticles improves pharmacokinetics (PK), tumor uptake and antitumor efficacy in vivo. Dendrimers can be used as targeted radiotheranostics with potential to achieve superior PK, enhanced tumor killing, and reduced toxicity (improved therapeutic index) compared to traditional antibody-based radiotheranostics. A radio-imaging and biodistribution study in mice implanted with HER2+ tumors directly compared the imaging performance, in positron emission tomography-computed tomography (PET-CT), and kinetics of tumor and normal tissue uptake of a HER2-targeted dendrimer radiotheranostic to radiolabeled trastuzumab. Methods: A HER2 targeted VHH (single domain antibody [sdAb], or antigen binding fragment of heavy chain only camelid antibody) was covalently linked to a generation 4 dendrimer with 16 deferoxamine (DFO) chelation groups on its surface (DEP-HER2-DFO). Trastuzumab was covalently linked to DFO (trastuzumab-DFO). DEP-HER2-DFO and trastuzumab-DFO were successfully radiolabeled with 89Zr to high specific activity. Female BALB/c nude mice were inoculated subcutaneously with BT474 HER2+ breast cancer cells. When tumors reached 150mm3, a single dose of DEP-HER2-DFO-89Zr and trastuzumab-DFO-89Zr (3MBq) was administered intravenously. Uptake was evaluated in tumor and normal tissues by ex vivo biodistribution (n=3/group). In vivo biodistribution (n=3/group) was determined using quantified PET-CT data (4h-5d). PET-CT images were obtained on day 2 and 4. Results: Both DEP-HER2-DFO-89Zr and trastuzumab-DFO-89Zr showed sustained tumor accumulation of &amp;gt;20% injected dose per gram (ID/g) over several days with low levels of normal tissue uptake (&amp;lt;5% ID/g), as determined by ex vivo biodistribution. However, DEP-HER2-DFO-89Zr was cleared from the vascular circulation more quickly than trastuzumab-DFO-89Zr resulting in superior tumor/blood, tumor/heart, and tumor/lung ratios. These findings were corroborated by quantified PET-CT data. Excellent signal to noise (tumor to normal tissue) was found for DEP-HER2-DFO-89Zr on whole body PET-CT images. DEP-HER2-DFO-89Zr was stable in serum and not susceptible to radiolysis over 7 days. Conclusions: DEP-HER2-DFO-89Zr achieved high levels of accumulation in tumor, low uptake in normal tissues and relatively fast clearance from circulation, yielding favorable tumor/blood ratios compared with trastuzumab-DFO-89Zr. The dendrimer-based DEP-HER2 radiotheranostic offers advantages over antibody-based radiotheranostics and is under development ahead of a clinical biodistribution study. Citation Format: Cameron N Johnstone, Graham P Heery, Brian D Kelly, James Humphries, Dewan T Akhter, Kristofer J Thurecht, Daniel Yuen, Michael Giannis, Ken Yong, Gareth Chadwick, Helena Barker, Anne Cargill, Anthony Eglezos, Alex Castellarnau, Jeremy R A Paull, Richard Hufton. A HER2 targeted polylysine dendrimer nanoparticle radiotheranostic demonstrates excellent tumor accumulation, rapid clearance from circulation, and promising performance in PET-CT imaging [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A147.

Correlation analysis of positron emission tomography/computed tomography-magnetic resonance imaging of cannabinoid type 1 receptor in the lumbar spine and brain of aged osteoporosis female cynomolgus monkeys.

Although cannabinoid receptor 1 (CB1R) antagonists can inhibit bone loss in osteoporosis mouse models, different strains of mice show different bone mass phenotypes after knock out the CB1R gene. The relationship between CB1R and bone metabolism is complex, and its regulatory role in bone metabolism and as a therapeutic target for osteoporosis requires further investigation. Based on lumbar spine volumetric bone mineral density (vBMD) data of healthy female cynomolgus monkeys aged 1-25 years, naturally aged postmenopausal female osteoporotic monkeys and normal young monkeys were screened by detecting lumbar vertebrae vBMD and estradiol levels in this study. Positron emission tomography-computed tomography (PET/CT) and magnetic resonance imaging (MRI) scans were performed on the lumbar spine and brain of the two groups of monkeys using the probe [11C]OMAR, which specifically targets CB1R, and the difference in the CB1R expression of osteoporotic monkeys was evaluated. The vBMD values of two standard deviations (SDs) below the peak bone value (428.1±53.8 g/cm3) were set as the reference standard for osteoporosis vBMD. Of the 49 healthy female cynomolgus monkeys, 4 postmenopausal older osteoporotic monkeys (18-26 years) and 5 young control monkeys (6-7 years) were selected, and the mean vBMD of the lumbar spine of the two groups was 295.07±19.11 and 419.72±16.14 g/cm3, respectively (P<0.0001). Radioactive uptake in the lumbar spine was linearly and negatively correlated with vBMD (r=-0.7977; P=0.01). Dynamic PET/MR imaging of the brains showed that CB1R was upregulated in the osteoporosis group, and there was a negative linear correlation between the vBMD and area under the time-radioactivity curve (AUC) of the thalamus (r=-0.8506; P=0.0153) and prefrontal cortex (r=-0.8306; P=0.0207). In this study, PET/CT-MRI molecular imaging technology revealed that CB1R was upregulated in the lumbar spine and brain of the osteoporosis monkeys and that CB1R may be regulated by the brain-bone axis. CB1R antagonist may be a potential drug for the treatment of osteoporosis.

Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features.

Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g., bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels. Typically, only one set of parameters is employed, resulting in a single value or "flavour" for each radiomics feature. In contrast, we propose "tensor radiomics" (TR) where tensors of features calculated using multiple parameter combinations (i.e., flavours) are utilized to optimize the creation of radiomics signatures. We provide illustrative instances of TR implementation in positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI), and CT by leveraging machine learning (ML) and deep learning (DL) methodologies, as well as reproducibility analyses: (I) to predict overall survival (OS) in lung cancer (CT) and head and neck cancer (PET-CT), TR was employed by varying bin sizes. This approach involved use of a hybrid deep neural network called 'TR-Net' and two ML-based techniques for combining different flavours. (II) TR was constructed by incorporating different segmentation perturbations and various bin sizes to classify the response of late-stage lung cancer to first-line immunotherapy using CT images. (III) In MRI of glioblastoma (GBM), TR was implemented to generate multi-flavour radiomics features, enabling enhanced analysis and interpretation. (IV) TR was employed via multiple PET-CT fusions in head and neck cancer. Flavours based on different fusions were created using Laplacian pyramids and wavelet transforms. Our findings demonstrated that TR outperformed conventional radiomics features in lung cancer CT and head and neck cancer PET-CT images, significantly enhancing OS prediction accuracy. TR also improved classification of lung cancer response to therapy and exhibited notable advantages in reproducibility compared to single-flavour features in MR imaging of GBM. Moreover, in head and neck cancer, TR through multiple PET-CT fusions exhibited improved performance in predicting OS. We conclude that the proposed TR paradigm has significant potential to improve performance in different medical imaging tasks. By incorporating multiple flavours of radiomics features, TR overcomes limitations associated with individual features and shows promise in enhancing prognostic capabilities in clinical settings.

Prognostic Value of Semi-Quantitative 18F-FDG PET/CT Parameters in Hodgkin’s Lymphoma

Objective: To assess the prognostic value of maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) determined by 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) imaging in Hodgkin’s lymphoma patients. Methods: A total of 148 Hodgkin’s lymphoma patients diagnosed with lymph node biopsy from October 2014 to October 2015 were retrospectively analyzed followed by categorizing into good (125 cases) and poor (23 cases) prognosis groups. The chi-squared test was used to analyze the clinicopathological characteristics of Hodgkin’s lymphoma patients with the semi-quantitative 18F-FDG PET/CT parameters; the Spearman method was used to analyze the correlation between the semi-quantitative parameters and clinicopathological features of Hodgkin’s lymphoma; receiver operating characteristic curve was used to analyze the predictive value of the semi-quantitative parameters for poor prognosis of Hodgkin’s lymphoma patients. Results: Mean SUVmax, MTV, and TLG of the 148 cases of Hodgkin’s lymphoma were 7.26 ± 2.38, 12.46 ± 3.14 cm3, and 76.83 ± 18.56 g, respectively. Significant variations in the Ann Arbor stage and clinical classification were observed with different levels of semi-quantitative parameters (P &lt; 0.05). The semi-quantitative parameters were not correlated with age and gender (P &gt; 0.05) but positively correlated with Ann Arbor stage and clinical classification (P &lt; 0.05). These parameters in the poor prognosis group were higher than those in the good prognosis group (P &lt; 0.05). The area under the curve (AUC) of SUVmax, MTV, and TLG in predicting the poor prognosis group was 0.881, 0.875, and 0.838, with cut-off values of 7.264, 12.898 cm3, and 74.580g, as well as specificity of 88.8%, 84.0%, and 78.4%, and sensitivity of 87.0%, 87.0%, and 78.3%, respectively; the AUC of the combined prediction was 0.986, with a specificity of 97.6% and sensitivity of 86.3%. Conclusion: The semi-quantitative 18F-FDG PET/CT parameters provide valuable insights for Hodgkin’s lymphoma prognosis assessment.

LSAM: L2-norm self-attention and latent space feature interaction for automatic 3D multi-modal head and neck tumor segmentation

Objective. Head and neck (H&N) cancers are prevalent globally, and early and accurate detection is absolutely crucial for timely and effective treatment. However, the segmentation of H&N tumors is challenging due to the similar density of the tumors and surrounding tissues in CT images. While positron emission computed tomography (PET) images provide information about the metabolic activity of the tissue and can distinguish between lesion regions and normal tissue. But they are limited by their low spatial resolution. To fully leverage the complementary information from PET and CT images, we propose a novel and innovative multi-modal tumor segmentation method specifically designed for H&N tumor segmentation. Approach. The proposed novel and innovative multi-modal tumor segmentation network (LSAM) consists of two key learning modules, namely L2-Norm self-attention and latent space feature interaction, which exploit the high sensitivity of PET images and the anatomical information of CT images. These two advanced modules contribute to a powerful 3D segmentation network based on a U-shaped structure. The well-designed segmentation method can integrate complementary features from different modalities at multiple scales, thereby improving the feature interaction between modalities. Main results. We evaluated the proposed method on the public HECKTOR PET-CT dataset, and the experimental results demonstrate that the proposed method convincingly outperforms existing H&N tumor segmentation methods in terms of key evaluation metrics, including DSC (0.8457), Jaccard (0.7756), RVD (0.0938), and HD95 (11.75). Significance. The innovative Self-Attention mechanism based on L2-Norm offers scalability and is effective in reducing the impact of outliers on the performance of the model. And the novel method for multi-scale feature interaction based on Latent Space utilizes the learning process in the encoder phase to achieve the best complementary effects among different modalities.