68Ga-grazytracer PET for noninvasive assessment of response to immunotherapy in solid tumors and lymphomas: a phase 1/2 clinical trial.

To tackle the clinical challenge of noninvasively assessing immunotherapy efficacy in patients, here we used positron emission tomography (PET) with 68Ga-grazytracer, which targets granzyme B, a crucial effector molecule secreted by activated CD8+ T cells. In this phase 1/2 clinical trial (NCT05000372) involving a diverse cohort of 24 patients with solid tumors and lymphomas who received immunotherapies, including immune checkpoint inhibitors (either alone or with chemotherapies) and chimeric antigen receptor-T cell therapy, we examined the in vivo behaviors of 68Ga-grazytracer. Primary endpoints were safety, biodistribution, granzyme B specificity, and the predictive utility of 68Ga-grazytracer, while secondary endpoint was the relationship between 68Ga-grazytracer uptake and tumor immune phenotype. 68Ga-grazytracer exhibited a safe profile and specifically targeted granzyme B in patients. 68Ga-grazytracer PET showed superior predictive value for short-term prognosis and progression-free survival than those of conventional assessment criteria, including RECIST 1.1 and PERCIST. Moreover, the uptake of 68Ga-grazytracer in tumors was significantly higher in those with a "non-desert" immune phenotype than those with an immune "desert" phenotype, thereby meeting the primary and secondary endpoints of this trial. Collectively, we successfully visualized CD8+ T cell effector function in humans using 68Ga-grazytracer PET, offering insights for enhancing immunotherapy assessment, patient stratification and treatment planning.

Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors.

BACKGROUND: Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors. METHODS: A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (κ), and scale parameter (θ) were calculated for each b-value. In the staging step, the fractions (ƒ1, ƒ2, ƒ3) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 × 10-4 mm2/s (ƒ1), 1 × 10-4 mm2/s < Diffusion > 3 × 10-4 mm2/s (ƒ2), and Diffusion >3 × 10-4 mm2/s (ƒ3); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 × 10-4 mm2/s (ƒ11). Diffusion lower than 0.5 × 10-4 mm2/s (ƒ12). Diffusion lower than 0.8 × 10-4 mm2/s (ƒ13). RESULTS: The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (κ, ƒ1, ƒ2, ƒ3) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm2, ƒ1 had a better diagnostic performance than κ and apparent diffusion coefficient (sensitivity, 88%; specificity, 68%; P < .001). The best diagnostic performance was related to ƒ3 in b = 2000 s/mm2 (area under the curve = 0.891, sensitivity = 83%, specificity = 80%, P < .001). In the grading step, ƒ12 (area under the curve = 0.870, sensitivity = 92%, specificity = 72%, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm2. CONCLUSION: The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.

Comparison of a new MR rapid wash-out map with MR perfusion in brain tumors.

BACKGROUND: MR perfusion is a standard marker to distinguish progression and therapy-associated changes after surgery and radiochemotherapy for glioblastoma. TRAMs (Treatment Response Assessment Maps) were introduced, which are intended to facilitate the differentiation of vital tumor cells and radiation necrosis by means of late (20-90 min) contrast clearance and enhancement. The differences of MR perfusion and late-enhancement are not fully understood yet. METHODS: We have implemented and established a fully automated creation of rapid wash-out (15-20 min interval) maps in our clinic. We included patients with glioblastoma, CNS lymphoma or brain metastases who underwent our MR protocol with MR perfusion and rapid wash-out between 01/01/2024 and 30/06/2024. Since both wash-out and hyperperfusion are intended to depict the active tumor area, this study involves a quantitative and qualitative comparison of both methods. For this purpose, we volumetrically measured rCBV (relative cerebral blood volume) maps and rapid wash-out maps separately (two raters). Additionally, we rated the agreement between both maps on a Likert scale (0-10). RESULTS: Thirty-two patients were included in the study: 15 with glioblastoma, 7 with CNS lymphomas and 10 with brain metastasis. We calculated 36 rapid wash-out maps (9 initial diagnosis, 27 follow-up). Visual agreement of MR perfusion with rapid wash-out by rating were found in 44 ± 40% for initial diagnosis, and 75 ± 31% for follow-up. We found a strong correlation (Pearson coefficient 0.92, p < 0.001) between the measured volumes of MR perfusion and rapid wash-out. The measured volumes of MR perfusion and rapid wash-out did not differ significantly. Small lesions were often not detected by MR perfusion. Nevertheless, the measured volumes showed no significant differences in this small cohort. CONCLUSIONS: Rapid wash-out calculation is a simple tool that provides new information and, when used in conjunction with MR perfusion, may increase diagnostic accuracy. The method shows promising results, particularly in the evaluation of small lesions.

Radiomic prediction for durable response to high-dose methotrexate-based chemotherapy in primary central nervous system lymphoma.

BACKGROUND: The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic-based models to predict the durable response (DR) to high-dose methotrexate (HD-MTX)-based chemotherapy in PCNSL patients. METHODS: A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD-MTX-based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast-enhanced MR images. Multiple machine-learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic-clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. RESULTS: A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models. CONCLUSIONS: This radiomic signature and integrated models have the potential to accurately predict the DR to HD-MTX-based chemotherapy in PCNSL patients, providing valuable therapeutic insights.

The prognostic utility of 18F-FDG PET parameters in lymphoma patients under CAR-T-cell therapy: a systematic review and meta-analysis.

Background: Chimeric antigen receptor (CAR) T-cell therapy has attracted considerable attention since its recent endorsement by the Food and Drug Administration, as it has emerged as a promising immunotherapeutic modality within the landscape of oncology. This study explores the prognostic utility of [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG PET) in lymphoma patients undergoing CAR T-cell therapy. Through meta-analysis, pooled hazard ratio (HR) values were calculated for specific PET metrics in this context. Methods: PubMed, Scopus, and Ovid databases were explored to search for relevant topics. Dataset retrieval from inception until March 12, 2024, was carried out. The primary endpoints were impact of specific PET metrics on overall survival (OS) and progression-free survival (PFS) before and after treatment. Data from the studies were extracted for a meta-analysis using Stata 17.0. Results: Out of 27 studies identified for systematic review, 15 met the criteria for meta-analysis. Baseline OS analysis showed that total metabolic tumor volume (TMTV) had the highest HR of 2.66 (95% CI: 1.52-4.66), followed by Total-body total lesion glycolysis (TTLG) at 2.45 (95% CI: 0.98-6.08), and maximum standardized uptake values (SUVmax) at 1.30 (95% CI: 0.77-2.19). TMTV and TTLG were statistically significant (p < 0.0001), whereas SUVmax was not (p = 0.33). For PFS, TMTV again showed the highest HR at 2.65 (95% CI: 1.63-4.30), with TTLG at 2.35 (95% CI: 1.40-3.93), and SUVmax at 1.48 (95% CI: 1.08-2.04), all statistically significant (p ≤ 0.01). The ΔSUVmax was a significant predictor for PFS with an HR of 2.05 (95% CI: 1.13-3.69, p = 0.015). Conclusion: [18F]FDG PET parameters are valuable prognostic tools for predicting outcome of lymphoma patients undergoing CAR T-cell therapy.

Ultrasonographic and pathological features of primary adrenal lymphoma.

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International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation.

Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark dataset allowing TMTV to be tested and applied as a reproducible biomarker. Methods: Sixty baseline 18F-FDG PET/CT scans were identified with a range of disease distributions (20 follicular, 20 Hodgkin, and 20 diffuse large B-cell lymphoma). TMTV was measured by 12 nuclear medicine experts, each analyzing 20 cases split across subtypes, with each case processed by 3-4 readers. LIFEx or ACCURATE software was chosen according to reader preference. Analysis was performed stepwise: TMTV1 with automated preselection of lesions using an SUV of at least 4 and a volume of at least 3 cm3 with single-click removal of physiologic uptake; TMTV2 with additional removal of reactive bone marrow and spleen with single clicks; TMTV3 with manual editing to remove other physiologic uptake, if required; and TMTV4 with optional addition of lesions using mouse clicks with an SUV of at least 4 (no volume threshold). Results: The final TMTV (TMTV4) ranged from 8 to 2,288 cm3, showing excellent agreement among all readers in 87% of cases (52/60) with a difference of less than 10% or less than 10 cm3 In 70% of the cases, TMTV4 equaled TMTV1, requiring no additional reader interaction. Differences in the TMTV4 were exclusively related to reader interpretation of lesion inclusion or physiologic high-uptake region removal, not to the choice of software. For 5 cases, large TMTV differences (>25%) were due to disagreement about inclusion of diffuse splenic uptake. Conclusion: The proposed segmentation method enabled highly reproducible TMTV measurements, with minimal reader interaction in 70% of the patients. The inclusion or exclusion of diffuse splenic uptake requires definition of specific criteria according to lymphoma subtype. The publicly available proposed benchmark allows comparison of study results and could serve as a reference to test improvements using other segmentation approaches.

Spectrum of imaging findings of primary bone lymphoma in pediatric patients.

Primary bone lymphoma, a rare oncologic entity, may initially present with minimal symptoms. Presenting symptoms range from local pain and mild systemic symptoms to large palpable masses and pathologic fractures. The term "primary bone lymphoma" indicates the finding of bone involvement without other organ sites for at least 6 months. Although some radiological features may raise suspicion about this tumor form, there are no pathognomonic imaging findings, and the diagnosis will likely be delayed for a long time. The most critical radiological feature is soft tissue involvement associated with a preserved cortical layer, much more than expected for an infiltrating lesion. Anyway, very different radiological findings may be displayed in patients with primary bone lymphoma. Although these radiological features of primary bone lymphoma have been discussed in the literature by various authors, there is little data concerning imaging in pediatric patients. This paper aims to depict the possible spectrum of imaging features of primary bone lymphoma in the pediatric age, providing an exemplification pictorial essay extracted from a single institution experience in the year range period 2006-2022.

Enhancing diagnostic precision in EBV-related HLH: a multifaceted approach using 18F-FDG PET/CT and nomogram integration.

BACKGROUND: The hyperinflammatory condition and lymphoproliferation due to Epstein-Barr virus (EBV)-associated hemophagocytic lymphohistiocytosis (HLH) affect the detection of lymphomas by 18F-FDG PET/CT. We aimed to improve the diagnostic capabilities of 18F-FDG PET/CT by combining laboratory parameters. METHODS: This retrospective study involved 46 patients diagnosed with EBV-positive HLH, who underwent 18F-FDG PET/CT before beginning chemotherapy within a 4-year timeframe. These patients were categorized into two groups: EBV-associated HLH (EBV-HLH) (n = 31) and EBV-positive lymphoma-associated HLH (EBV + LA-HLH) (n = 15). We employed multivariable logistic regression and regression tree analysis to develop diagnostic models and assessed their efficacy in diagnosis and prognosis. RESULTS: A nomogram combining the SUVmax ratio, copies of plasma EBV-DNA, and IFN-γ reached 100% sensitivity and 81.8% specificity, with an AUC of 0.926 (95%CI, 0.779-0.988). Importantly, this nomogram also demonstrated predictive power for mortality in EBV-HLH patients, with a hazard ratio of 4.2 (95%CI, 1.1-16.5). The high-risk EBV-HLH patients identified by the nomogram had a similarly unfavorable prognosis as patients with lymphoma. CONCLUSIONS: The study found that while 18F-FDG PET/CT alone has limitations in differentiating between lymphoma and EBV-HLH in patients with active EBV infection, the integration of a nomogram significantly improves the diagnostic accuracy and also exhibits a strong association with prognostic outcomes.

New PET Tracers for Lymphoma.

While functional imaging with [18F]Fluoro-deoxy-glucose positron emission tomography (PET)/computed tomography is a well-established imaging modality in most lymphoma entities, novel tracers addressing cell surface receptors, tumor biology, and the microenvironment are being developed. Especially, with the emergence of immuno-PET targeting surface markers of lymphoma cells, a new imaging modality of immunotherapies is evolving, which might especially aid in relapsed and refractory disease stages. This review highlights different new PET tracers in indolent and aggressive lymphoma subtypes and summarizes the current state of immuno-PET imaging in lymphoma.

Fluorescence Confocal Microscopy Can Accelerate Diagnosis of Cervical Lymphadenopathy.

Fluorescence confocal microscopy (FCM) is an optical technique that uses laser light sources of different wavelengths to generate real-time images of fresh, unfixed tissue specimens. Unlike conventional histologic evaluation methods, FCM is able to assess fresh tissue samples without the associated cryo artifacts typically observed after frozen sectioning. The purpose of this study was to evaluate the utility of FCM imaging in the differential diagnosis of cervical lymphadenopathy. Twenty-two cervical lymph node specimens from patients with lymphadenopathy of unknown origin were imaged by FCM. Two pathologists independently evaluated the scans for suspicion of malignancy and preliminary diagnosis. Malignancy was reliably excluded or confirmed by both pathologists with a sensitivity of 90.9% for pathologist 1 and 100% for pathologist 2. The specificity was 100% for both pathologists. For the preliminary diagnosis, almost perfect agreement with the final diagnosis was observed for both pathologists (κ = 0.94 for pathologist 1 and κ = 1.00 for pathologist 2). This is the first study to investigate lymph node specimens with different diagnoses, including lymphoma, using FCM. Our results indicate that differential diagnosis of lymph node specimens is feasible in FCM images, thus encouraging further exploration of FCM imaging in lymph node specimens to accelerate diagnosis and open the possibility of digitizing diagnosis on fresh, unfixed tissue.

Diagnostic and prognostic value of independent and combined detection of computed tomography, ultrasound, and positron emission tomography - computed tomography in lymphoma.

Lymphoma is a malignant tumor originating from the lymphopoietic system, which can affect all tissues and organs of the body. Lymphoma is highly heterogeneous and the therapeutic effect varies greatly. Different pathological types and stages of lymphoma differ greatly in terms of treatment intensity and prognosis. Early diagnosis of lymphoma is very important to improve the prognosis of patients. Therefore, this work explored the diagnostic value of independent and combined detection of computed tomography (CT), ultrasound, and positron emission tomography - computed tomography (PET-CT) for lymphoma. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in the PET-CT and combination groups were greatly higher than those in the CT and ultrasound groups, showing obvious differences (P < 0.05). The area under curve (AUC) values in the CT group, ultrasound group, PET-CT group, and combination group were 0.632 (P = 0.032), 0.614 (P = 0.025), 0.793 (P = 0.002), and 0.859 (P = 0.001), respectively, exhibiting observable differences (P < 0.05). the sensitivity and specificity of PET/CT for lymphoma were higher than those of CT and ultrasound, which can clearly show the early mild results of lymphatic lymphoma. Therefore, the combined diagnosis of lymphatic lymphoma with PET/CT was of high clinical value.

Radioligand Therapy in Lymphoma: Past, Present, and Future.

In the1980s, radiolabeled cells helped understand the pathology of hemato-oncology. In the 1990s, preclinical trials evaluated radiolabeled immunotherapy with monoclonal antibodies (MoAbs) such as anti-CD20 agents labeled with Iodine-131 (Bexxar) or Yttrium-90 (Zevalin). Due to the safe and durable responses of radiolabeled MoAbs, the Food and Drug Administration approved these agents in the 2000s. Despite radioimmunotherapy's long journey, its application has recently decreased. This review will discuss the historical timeline of radioimmunotherapy, debate on advantages and difficulties, and explore trials. We will examine future directions of radioligand therapy in hemato-oncology, considering emerging molecules that may become the next theragnostic trend.

Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study.

PURPOSE: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI. METHODS: We collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin's concordance correlation coefficient (CCC). RESULTS: A total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80-0.82, PPV of 84.5-86.1%, and sensitivity of 77.6-81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland-Altman plots revealed minor tumor volume differences with 0.22-1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively. CONCLUSION: The nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images.

Long-Axial Field-of-View PET Imaging in Patients with Lymphoma: Challenges and Opportunities.

[18F]fluoro-2-deoxy-d-glucose PET/computed tomography has been implemented in the management of patients with lymphoma, offering real-time metabolic information on lymphoma with the promise of more accurate staging, treatment response assessment, prognostication, and early detection of disease recurrence. The clinical management of lymphoproliferative disease has recently, rapidly evolved from initial chemotherapeutic to the use of immunotherapy, targeted agents, and to the use of chimeric antigen receptor T-cell therapies. The implementation of these new systems and imaging protocols together with new tracer development creates, in the field of lymphoproliferative disease, both opportunities and challenges that will be detailed in this comprehensive literature review.

A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.

PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality. METHODS: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction. RESULTS: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889. CONCLUSION: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE .