Search Results (46)

Ventricular tachycardia (VT) remains a major cause of morbidity and mortality in patients with structural heart disease. While catheter ablation has become a cornerstone in VT management, recurrence rates remain substantial due to limitations in electroanatomic mapping (EAM), particularly in cases of deep or heterogeneous arrhythmogenic substrates. Cardiac imaging, especially when multimodal and integrated with mapping systems, has emerged as a critical adjunct to enhance procedural efficacy, safety, and individualized strategy. This comprehensive review explores the evolving role of various imaging modalities, including echocardiography, cardiac magnetic resonance (CMR), computed tomography (CT), positron emission tomography (PET), and intracardiac echocardiography (ICE), in the preprocedural and intraprocedural phases of VT ablation. We highlight their respective strengths in substrate identification, anatomical delineation, and real-time guidance. While limitations persist, including costs, availability, artifacts in device carriers, and lack of standardization, future advances are likely to redefine procedural workflows. Full article

Tumor hypoxia involves limited oxygen supply within the tumor microenvironment and is closely associated with aggressiveness, metastasis, and resistance to common cancer treatment modalities such as chemotherapy and radiotherapy. Traditional methodologies for hypoxia assessment, such as the use of invasive probes and clinical biomarkers, are generally not very suitable for routine clinical applications. Radiomics provides a non-invasive approach to hypoxia assessment by extracting quantitative features from medical images. Thus, radiomics is important in diagnosis and the formulation of a treatment strategy for tumor hypoxia. This article discusses the various imaging techniques used for the assessment of tumor hypoxia including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). It introduces the use of radiomics with machine learning and deep learning for extracting quantitative features, along with its possible clinical use in hypoxic tumors. This article further summarizes the key challenges hindering the clinical translation of radiomics, including the lack of imaging standardization and the limited availability of hypoxia-labeled datasets. It also highlights the potential of integrating radiomics with multi-omics to enhance hypoxia visualization and guide personalized cancer treatment. Full article

Background/Objectives: The management of localized prostate cancer with regional lymph node involvement (N1M0) presents significant clinical challenges. While once considered indicative of systemic disease, improved imaging and evolving treatment paradigms have redefined node-positive disease as potentially curable. This systematic review aims to assess current evidence regarding treatment modalities and outcomes for patients with localized N1M0 prostate cancer. Methods: A systematic review was conducted to identify studies evaluating therapeutic strategies for N1M0 prostate cancer. Eligible studies included randomized controlled trials, retrospective analyses, and consensus guidelines. Treatment approaches reviewed included radical prostatectomy (RP) with pelvic lymph node dissection (PLND), whole pelvic radiotherapy (WPRT), prostate-only radiotherapy (PORT), androgen deprivation therapy (ADT), and metastasis-directed therapy (MDT), including stereotactic body radiotherapy (SBRT). Key outcomes included overall survival (OS), biochemical recurrence-free survival (bRFS), disease-free survival (DFS), and treatment-related toxicity. Results: Multimodal approaches—particularly the combination of ADT with WPRT or adjuvant radiotherapy following RP—were associated with improved survival outcomes. Patients with limited nodal burden and undetectable postoperative prostate-specific antigen (PSA) levels derived the most benefit. The use of prostate-specific antigen membrane positron-emission tomography/computed tomography (PSMA PET/CT) enhanced detection and guided MDT in oligorecurrent disease. SBRT, simultaneous integrated boost (SIB), and hypofractionated regimens demonstrated promising efficacy with acceptable toxicity profiles. Conclusions: Node-positive localized prostate cancer is optimally managed with individualized, multidisciplinary strategies. Combining systemic and locoregional treatments improves outcomes in selected patients. Ongoing prospective studies are warranted to refine patient selection, optimize treatment sequencing, and integrate novel imaging and systemic agents. Full article

While multi-parametric magnetic resonance imaging (mpMRI) is known to be a specific and reliable modality for the diagnosis of non-metastatic prostate cancer (PC), positron emission tomography (PET) using ⁶⁸Ga labeled ligands targeting the prostate-specific membrane antigen (PSMA) is known for its reliable detection of prostate cancer, being the most sensitive modality for the assessment of the extra-prostatic extension of the disease and the establishment of a diagnosis, even before biopsy. Background/Objectives: Here, we compared these modalities in regards to the localization of intraprostatic cancer lesions prior to local HDR brachytherapy. Methods: A cohort of 27 patients received both mpMRI and PSMA-PET/CT. Based on 24 intraprostatic segments, two readers each scored the risk of tumor-like alteration in each imaging modality. The detectability was evaluated using receiver operating characteristic (ROC) analysis. The histopathological findings from biopsy were used as the gold standard in each segment. In addition, we applied a patient-based “congruence” concept to quantify the interobserver and intermodality agreement. Results: For the ROC analysis, we included 447 segments (19 patients), with their respective histological references. The two readers of the MRI reached an AUC of 0.770 and 0.781, respectively, with no significant difference (p = 0.75). The PET/CT readers reached an AUC of 0.684 and 0.608, respectively, with a significant difference (p < 0.001). The segment-wise intermodality comparison showed a significant superiority of MRI (AUC = 0.815) compared to PET/CT (AUC = 0.690) (p = 0.006). Via a patient-based analysis, a superiority of MRI in terms of relative agreement with the biopsy result was observed (n = 19 patients). We found congruence scores of 83% (MRI) and 76% (PET/CT, p = 0.034), respectively. Using an adjusted “near total agreement” score (adjacent segments with positive scores of 4 or 5 counted as congruent), we found an increase in the agreement, with a score of 96.5% for MRI and 92.7% for PET/CT, with significant difference (p = 0.024). Conclusions: This study suggests that in a small collective of low-/intermediate risk prostate cancer, mpMRI is superior for the detection of intraprostatic lesions as compared to PSMA-PET/CT. We also found a higher relative agreement between MRI and biopsy as compared to that for PET/CT. However, further studies including a larger number of patients and readers are necessary to draw solid conclusions. Full article

Accurate tumor segmentation is crucial for clinical diagnosis, treatment planning, and efficacy evaluation in medical imaging. Although traditional image processing techniques have been widely applied in tumor segmentation, they often perform poorly when dealing with tumors that have low contrast, irregular shapes, or varying sizes. With the rise of deep learning, particularly the application of convolutional neural networks (CNNs) in medical image segmentation, significant progress has been made, especially in handling multimodal data such as positron emission tomography (PET) and computed tomography (CT). However, the “black-box” nature of CNNs presents challenges for interpretability, which is particularly important in clinical applications. To address this, we propose a tumor segmentation framework based on a multi-scale interpretability module (MSIM). Through ablation experiments and comparisons on three public datasets, we evaluate the performance of the proposed method. The ablation results show that the proposed method achieves an improvement of 1.6, 1.62, and 2.36 in the Dice Similarity Coefficient (DSC) on the Melanoma, Lymphoma, and Lung Cancer datasets, respectively, highlighting the benefits of the interpretability module. Furthermore, the method outperforms the best comparative methods on all three datasets, achieving DSC improvements of 1.46, 1.27, and 1.93, respectively. Finally, visualization and perturbation experiments further validate the effectiveness of our method in emphasizing critical features. Full article

Background: Hypoxia plays a critical role in lung cancer progression and treatment resistance by contributing to aggressive tumor behavior and poor therapeutic response. Molecular imaging, particularly positron emission tomography (PET), has become an essential tool for noninvasive hypoxia detection, providing valuable insights into tumor biology and aiding in personalized treatment strategies. Objective: This narrative review explores recent advancements in PET imaging for detecting hypoxia in lung cancer, with a focus on the development, characteristics, and clinical applications of various radiotracers. Findings: Numerous PET-based hypoxia radiotracers have been investigated, each with distinct pharmacokinetics and imaging capabilities. Established tracers such as ¹⁸F-Fluoromisonidazole (¹⁸F-FMISO) remain widely used, while newer alternatives like ¹⁸F-Fluoroazomycin Arabinoside (¹⁸F-FAZA) and ¹⁸F-Flortanidazole (¹⁸F-HX4) demonstrate improved clearance and image contrast. Additionally, ⁶⁴Cu-ATSM has gained attention for its rapid tumor uptake and hypoxia selectivity. The integration of PET with hybrid imaging modalities, such as PET/CT and PET/MRI, enhances the spatial resolution and functional interpretation, making hypoxia imaging a promising approach for guiding radiotherapy, chemotherapy, and targeted therapies. Conclusions: PET imaging of hypoxia offers significant potential in lung cancer diagnosis, treatment planning, and therapeutic response assessment. However, challenges remain, including tracer specificity, quantification variability, and standardization of imaging protocols. Future research should focus on developing next-generation radiotracers with enhanced specificity, optimizing imaging methodologies, and leveraging multimodal approaches to improve clinical utility and patient outcomes. Full article

Immune-mediated inflammatory diseases (IMIDs) are characterized by chronic systemic inflammation and multi-organ involvement. Fibrosis formation in IMIDs can cause tissue destruction and subsequently organ malfunction. Fibroblast activation protein inhibitor positron emission tomography/computed tomography (FAPi PET/CT) represents a novel imaging technique that holds great potential to visualize in vivo fibrosis. We here provide an overview of available evidence on FAPi PET/CT imaging to visualize fibrosis in various IMIDs, including interstitial lung diseases, immunoglobulin G4-related diseases, cardiovascular diseases, kidney diseases, and gastrointestinal diseases. FAPi PET/CT imaging demonstrates high sensitivity in detecting early fibrosis, correlating with disease severity, across different IMIDs, showing superiority compared to conventional imaging modalities. Although FAPi PET/CT might be a useful tool to assess fibrosis formation, thereby aiding in grading disease severity and staging, future studies should include larger sample sizes in a broad variety of IMIDs with emphasis on the optimization of imaging protocols to further validate its diagnostic value. Full article

Background: Accurate and timely diagnosis of endometrial cancer is crucial for guiding effective treatment and improving patient survival. Endometrial cancer diagnosis, staging, metastasis detection, and treatment planning utilize endometrial biopsy, magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT) scanning as crucial diagnostic modalities. Aggressive subtypes such as serous, mixed cell, clear cell, and grade 3 endometrioid carcinomas present considerable diagnostic and therapeutic obstacles given their unfavorable prognosis, underscoring the importance of accurate preoperative evaluation. Methods: A retrospective analysis was conducted using data from seventy patients diagnosed with serous, mixed cell, clear cell, or grade 3 endometrioid endometrial cancer, who received surgical treatment from 2020 to 2023. To assess the diagnostic capabilities of each modality in determining final pathology and disease staging, a comparison was performed using results from preoperative endometrial biopsy, MRI, PET/CT, and postoperative histopathology. Cohen’s kappa coefficient was employed to determine the level of agreement observed between pre- and postoperative results. Results: Endometrial biopsy demonstrated moderate yet statistically significant concordance with definitive histopathological diagnoses (κ = 0.537, p < 0.001); however, diagnostic errors were observed, especially in instances of mixed and clear cell carcinomas. MRI demonstrated efficacy in identifying local tumor invasion, yet its capacity to detect distant metastases was demonstrably limited. PET/CT was most effective in identifying distant metastases and omental involvement in advanced-stage disease. Conclusions: Definitive pathological diagnosis and staging of endometrial carcinoma are effectively established using endometrial biopsy and MRI. The utility of PET/CT is particularly pronounced in identifying distant metastases in patients with serous carcinoma and advanced-stage disease. Integrating biopsy, MRI, and PET/CT into a multimodal diagnostic strategy enhances diagnostic accuracy and enables personalized treatment planning, particularly for aggressive tumor subtypes. Full article

Background: Addressing the complex diagnostic challenges of Alzheimer’s disease (AD), this study introduces FusionNet, a groundbreaking framework designed to enhance AD classification through the integration of multi-modal and longitudinal imaging data. Methods: FusionNet synthesizes inputs from Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) scans, harnessing advanced machine learning strategies such as generative adversarial networks (GANs) for robust data augmentation, lightweight neural architectures for efficient computation, and deep metric learning for precise feature extraction. The model uniquely combines cross-sectional and temporal data, significantly enhancing diagnostic accuracy and enabling the early detection and ongoing monitoring of AD. The FusionNet architecture incorporates specialized feature extraction pathways for each imaging modality, a fusion layer to integrate diverse data sources effectively, and attention mechanisms to focus on salient diagnostic features. Results: Demonstrating superior performance, FusionNet achieves an accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. Conclusions: These results underscore its potential as a highly reliable diagnostic tool for AD, facilitating early intervention and tailored treatment strategies. FusionNet’s innovative approach not only improves diagnostic precision but also offers new insights into the progression of Alzheimer’s disease, supporting personalized patient care and advancing our understanding of this debilitating condition. Full article

This review presents a detailed examination of the most recent advancements in positron emission tomography–computed tomography (PET-CT) multimodal imaging over the past five years. The fusion of PET and CT technologies has revolutionized medical imaging, offering unprecedented insights into both anatomical structure and functional processes. The analysis delves into key technological innovations, including advancements in image reconstruction, data-driven gating, and time-of-flight capabilities, highlighting their impact on enhancing diagnostic accuracy and clinical outcomes. Illustrative case studies underscore the transformative role of PET-CT in lesion detection, disease characterization, and treatment response evaluation. Additionally, the review explores future prospects and challenges in PET-CT, advocating for the integration and evaluation of emerging technologies to improve patient care. This comprehensive synthesis aims to equip healthcare professionals, researchers, and industry stakeholders with the knowledge and tools necessary to navigate the evolving landscape of PET-CT multimodal imaging. Full article

Histiocytic sarcoma (HS) is an extremely rare but aggressive hematopoietic malignancy, and the prognosis has been reported to be rather unfavorable with a median overall survival of merely 6 months. We presented a 58-year-old female patient complaining of abdominal pain and fever, who was admitted to our institution in September 2021. Fluorine-18-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) scan showed enlargement of generalized multiple lymph nodes. Subsequently, laparoscopic retroperitoneal lesion biopsy and bone marrow aspiration were performed. The pathological findings indicated the diagnosis of HS concurrent with follicular lymphoma. The immunohistochemistry (IHC) staining of the tumor lesion revealed a high expression of CD38 and PD-L1 proteins. Furthermore, KRAS gene mutation was identified by means of next-generation sequencing. The patient exhibited poor treatment response to both first- and second-line cytotoxic chemotherapies. Therefore, she underwent six cycles of Daratumumab (anti-CD38 monoclonal antibody), Pazopanib (multi-target receptor tyrosine kinases inhibitor) combined with third-line chemotherapy, followed by involved-site radiotherapy and maintenance therapy with the PD-1 inhibitor Tislelizumab. Long-term partial remission was finally achieved after multi-modality treatment. Duration of remission and overall survival reached 22 and 32 months, respectively. Our case indicated that immuno-targeted treatment coupled with chemotherapy and radiotherapy might constitute a potential therapeutic option for HS. Full article

Background: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. Methods: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. Results: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [⁶⁸Ga]Ga-PSMA-11, [¹⁸F]DCFPyl, and [¹⁸F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. Conclusion: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings. Full article

Spondylodiscitis is defined by infectious conditions involving the vertebral column. The incidence of the disease has constantly increased over the last decades. Imaging plays a key role in each phase of the disease. Indeed, radiological tools are fundamental in (i) the initial diagnostic recognition of spondylodiscitis, (ii) the differentiation against inflammatory, degenerative, or calcific etiologies, (iii) the disease staging, as well as (iv) to provide clues to orient towards the microorganisms involved. This latter aim can be achieved with a mini-invasive procedure (e.g., CT-guided biopsy) or can be non-invasively supposed by the analysis of the CT, positron emission tomography (PET) CT, or MRI features displayed. Hence, this comprehensive review aims to summarize all the multimodality imaging features of spondylodiscitis. This, with the goal of serving as a reference for Physicians (infectious disease specialists, spine surgeons, radiologists) involved in the care of these patients. Nonetheless, this review article may offer starting points for future research articles. Full article

Oropharyngeal Squamous Cell Carcinoma (OPSCC) is one of the common forms of heterogeneity in head and neck cancer. Infection with human papillomavirus (HPV) has been identified as a major risk factor for OPSCC. Therefore, differentiating the HPV-positive and negative cases in OPSCC patients is an essential diagnostic factor influencing future treatment decisions. In this study, we investigated the accuracy of a deep learning-based method for image interpretation and automatically detected the HPV status of OPSCC in routinely acquired Computed Tomography (CT) and Positron Emission Tomography (PET) images. We introduce a 3D CNN-based multi-modal feature fusion architecture for HPV status prediction in primary tumor lesions. The architecture is composed of an ensemble of CNN networks and merges image features in a softmax classification layer. The pipeline separately learns the intensity, contrast variation, shape, texture heterogeneity, and metabolic assessment from CT and PET tumor volume regions and fuses those multi-modal features for final HPV status classification. The precision, recall, and AUC scores of the proposed method are computed, and the results are compared with other existing models. The experimental results demonstrate that the multi-modal ensemble model with soft voting outperformed single-modality PET/CT, with an AUC of 0.76 and $F_1$ score of 0.746 on publicly available TCGA and MAASTRO datasets. In the MAASTRO dataset, our model achieved an AUC score of 0.74 over primary tumor volumes of interest (VOIs). In the future, more extensive cohort validation may suffice for better diagnostic accuracy and provide preliminary assessment before the biopsy. Full article

Gliomas, a prevalent category of primary malignant brain tumors, pose formidable clinical challenges due to their invasive nature and limited treatment options. The current therapeutic landscape for gliomas is constrained by a “one-size-fits-all” paradigm, significantly restricting treatment efficacy. Despite the implementation of multimodal therapeutic strategies, survival rates remain disheartening. The conventional treatment approach, involving surgical resection, radiation, and chemotherapy, grapples with substantial limitations, particularly in addressing the invasive nature of gliomas. Conventional diagnostic tools, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), play pivotal roles in outlining tumor characteristics. However, they face limitations, such as poor biological specificity and challenges in distinguishing active tumor regions. The ongoing development of diagnostic tools and therapeutic approaches represents a multifaceted and promising frontier in the battle against this challenging brain tumor. The aim of this comprehensive review is to address recent advances in diagnostic tools and therapeutic approaches for gliomas. These innovations aim to minimize invasiveness while enabling the precise, multimodal targeting of localized gliomas. Researchers are actively developing new diagnostic tools, such as colorimetric techniques, electrochemical biosensors, optical coherence tomography, reflectometric interference spectroscopy, surface-enhanced Raman spectroscopy, and optical biosensors. These tools aim to regulate tumor progression and develop precise treatment methods for gliomas. Recent technological advancements, coupled with bioelectronic sensors, open avenues for new therapeutic modalities, minimizing invasiveness and enabling multimodal targeting with unprecedented precision. The next generation of multimodal therapeutic strategies holds potential for precision medicine, aiding the early detection and effective management of solid brain tumors. These innovations offer promise in adopting precision medicine methodologies, enabling early disease detection, and improving solid brain tumor management. This review comprehensively recognizes the critical role of pioneering therapeutic interventions, holding significant potential to revolutionize brain tumor therapeutics. Full article