Role of tumor-specific and whole-body imaging biomarkers for prediction of recurrence in patients with stage III colorectal cancer.

BACKGROUND: Imaging biomarkers are emerging as non-invasive predictors of cancer prognosis and clinical outcome. We assessed tumor-specific ("radiomics") and body composition imaging features ("morphomics") extracted from baseline pre-treatment CT for prediction of recurrence in patients with stage III colorectal cancer (CRC). METHODS: Patients with newly diagnosed stage III CRC were enrolled in this prospective observational study. Patients with available preoperative scans were included (N = 101). The tumor, if visible, was manually segmented and first-order radiomics features were extracted with a commercially available software. The morphomics features (reflecting muscle, fat, and bone characteristics) were extracted in a standardized fashion using a proprietary software and the values were adjusted and normalized based on a reference standard. Time to recurrence was the final outcome. Correlation between demographics, clinical features, radiomics, and morphomics features and outcome were assessed using univariate and multivariate tests as well as Kaplan-Meier and log-rank tests. RESULTS: Morphomic analysis was performed in all 101 patients. 60 patients had discrete tumors suitable for radiomics analysis. These patients had lower ECOG score (p < 0.05), more muscle mass (p > 0.05), and lower fat density (p > 0.05) compared to the patients in whom radiomics analysis could not be performed. Pathological stage (HR: 2.69; p = 0.03), CEA level after surgery (HR: 1.11 for 1 ng/mL; p < 0.005), bone mineral density (HR: 1.01 for 1 Hounsfield Unit; p < 0.01), and tumor skewness (HR: 0.33 for 1 unit; p < 0.05) had association with recurrence based on both univariate and multivariate analyses. A model using Cox's regression analyses was able to divide the patients into low-, medium-, and high-risk for recurrence. CONCLUSIONS: Both radiomics and morphomics features were independently associated with the risk of CRC recurrence and, when combined, each contributed valuable information to explain risk of recurrence. TRIAL REGISTRATION: Clinical trial.gov NCT02842203. Patient recruitment occurred between 22/07/2016 and 18/03/2020.

Chronic Traumatic Encephalopathy: A Comprehensive Narrative Review of Its Biomarkers.

Chronic traumatic encephalopathy (CTE) is a progressive and fatal neurological disorder linked to repeated traumatic brain injuries (TBIs), including concussions and blows to the head. This condition is characterized by the accumulation of abnormally structured hyperphosphorylated tau proteins (p-tau), forming neurofibrillary tangles, astrocytic tangles, and neurites in the brain. CTE is often diagnosed post-mortem, making it challenging to diagnose and predict its progression in living individuals. Despite recent advancements, no definitive pathological, radiological, or neurobiological marker consistently shows promise in diagnosing and predicting the disease. This review aims to summarize the available techniques and advancements in imaging-based, genetic, neuropsychological, and fluid biomarkers for CTE, evaluating their specificity and sensitivity. It will also highlight the limitations of each marker in diagnosing CTE and provide future research directions to enhance the accuracy of CTE diagnosis in living individuals.

Individualized multi-modal MRI biomarkers predict 1-year clinical outcome in first-episode drug-naïve schizophrenia patients.

Background: Antipsychotic medications offer limited long-term benefit to about 30% of patients with schizophrenia. We aimed to explore the individual-specific imaging markers to predict 1-year treatment response of schizophrenia. Methods: Structural morphology and functional topological features related to treatment response were identified using an individualized parcellation analysis in conjunction with machine learning (ML). We performed dimensionality reductions using the Pearson correlation coefficient and three feature selection analyses and classifications using 10 ML classifiers. The results were assessed through a 5-fold cross-validation (training and validation cohorts, n = 51) and validated using the external test cohort (n = 17). Results: ML algorithms based on individual-specific brain network proved more effective than those based on group-level brain network in predicting outcomes. The most predictive features based on individual-specific parcellation involved the GMV of the default network and the degree of the control, limbic, and default networks. The AUCs for the training, validation, and test cohorts were 0.947, 0.939, and 0.883, respectively. Additionally, the prediction performance of the models constructed by the different feature selection methods and classifiers showed no significant differences. Conclusion: Our study highlighted the potential of individual-specific network parcellation in treatment resistant schizophrenia prediction and underscored the crucial role of feature attributes in predictive model accuracy.

Hypoperfusion of periaqueductal gray as an imaging biomarker in chronic migraine beyond diagnosis: A 3D pseudocontinuous arterial spin labeling MR imaging.

BACKGROUND: The periaqueductal gray (PAG) is at the center of a powerful descending antinociceptive neuronal network, and is a key node in the descending pain regulatory system of pain. However, less is known about the altered perfusion of PAG in chronic migraine (CM). AIM: To measure the perfusion of PAG matter, an important structure in pain modulation, in CM with magnetic resonance (MR) perfusion without contrast administration. METHODS: Three-dimensional pseudocontinuous arterial spin labeling (3D-PCASL) and brain structure imaging were performed in 13 patients with CM and 15 normal subjects. The inverse deformation field generated by brain structure image segmentation was applied to the midbrain PAG template to generate individualized PAG. Then the perfusion value of the PAG area of the midbrain was extracted based on the individual PAG mask. RESULTS: Cerebral blood flow (CBF) value of PAG in CM patients (47.98 ± 8.38 mL/100 mg min) was significantly lower than that of the control group (59.87 ± 14.24 mL/100 mg min). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve was 0.77 (95% confidence interval [CI], 0.60, 0.94), and the cutoff value for the diagnosis of CM was 54.83 mL/100 mg min with a sensitivity 84.60% and a specificity 60%. CONCLUSION: Imaging evidence of the impaired pain conduction pathway in CM may be related with the decreased perfusion in the PAG, which could be considered as an imaging biomarker for the diagnosis and therapy evaluation.

Neurological prognosis prediction for cardiac arrest patients using quantitative imaging biomarkers from brain computed tomography.

PURPOSE: We aimed to predict the neurological prognosis of cardiac arrest (CA) patients using quantitative imaging biomarkers extracted from brain computed tomography images. METHODS: We retrospectively enrolled 86 CA patients (good prognosis, 32; poor prognosis, 54) who were treated at three hospitals between 2017 and 2019. We then extracted 1131 quantitative imaging biomarkers from whole-brain and local volumes of interest in the computed tomography images of the patients. The data were split into training and test sets containing 60 and 26 samples, respectively, and the training set was used to select representative quantitative imaging biomarkers for classification. In univariate analysis, the classification was evaluated using the p-value of the Brunner-Munzel test and area under the receiver operating characteristic curve (AUC) for the test set. In multivariate analysis, machine learning models reflecting nonlinear and complex relations were trained, and they were evaluated using the AUC on the test set. RESULTS: The best performance provided p = 0.009 (<0.01) and an AUC of 0.775 (95% confidence interval, 0.590-0.960) for the univariate analysis and an AUCof0.813 (95% confidence interval, 0.640-0.985) for the multivariate analysis. Overall, the gray level with the maximum gradient in the histogram of the three-dimensionally low-pass-filtered image was an important feature for prediction across the analyses. CONCLUSIONS: Quantitative imaging biomarkers can be used in neurological prognosis prediction for CA patients. Relevant biomarkers may contribute to protocolized computed tomography image acquisition to ensure proper decision support in acute care.

A Preliminary Study of Quantitative MRI Cartilage Loss Fraction and Its Association With Future Arthroplasty Using the Osteoarthritis Initiative Database.

Background and objective  Osteoarthritis (OA) is the most common arthritis in the world. Despite the high disease burden, there is no therapy to prevent, halt, or reverse OA, and many clinical trials relied on radiographic biomarkers for therapy response. It is important to identify patients with early OA who will eventually need arthroplasty, the end-stage treatment for osteoarthritis. This pilot study evaluates a novel MRI biomarker, cartilage loss fraction, for association with future arthroplasty and evaluates its feasibility of use and effect size estimates. Materials and methods Publicly available knee MRIs from the Osteoarthritis Initiative were used. A total of 38 participants with Kellgren-Lawrence (K-L) grade >1 and 38 participants with K-L grade ≤ 1 at enrollment were matched in age, sex, race, and BMI, and assessed for the degree of full-thickness cartilage loss, or cartilage loss fraction. Univariate conditional logistic regression analysis was performed for differences in cartilage loss fractions between groups. Receiver operating characteristic (ROC) curve analysis was performed to assess the association of MRI biomarkers and knee arthroplasty during the eight-year follow-up. Results The medial femoral condyle, medial tibial plateau, total, and two-year progression cartilage loss fractions were significantly higher in participants with K-L grade >1 (p < 0.01 for all) and showed high area under the curve (AUC) values on ROC analysis (812, 0.827, 0.917, and 0.933, respectively). These results were comparable or more strongly associated with other OA grading schemes. Conclusion MRI biomarker cartilage loss fractions are significantly higher in subjects with K-L grade >1 and show a strong association with arthroplasty. After further validation, cartilage loss fracture may be used to predict future arthroplasty.

Super T2-FLAIR mismatch sign: a prognostic imaging biomarker for non-enhancing astrocytoma, IDH-mutant.

PURPOSE: The T2-FLAIR mismatch sign is a highly specific diagnostic imaging biomarker for astrocytoma, IDH-mutant. However, a definitive prognostic imaging biomarker has yet to be identified. This study investigated imaging prognostic markers, specifically analyzing T2-weighted and FLAIR images of this tumor. METHODS: We retrospectively analyzed 31 cases of non-enhancing astrocytoma, IDH-mutant treated at our institution, and 30 cases from The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA). We defined "super T2-FLAIR mismatch sign" as having a significantly strong low signal comparable to cerebrospinal fluid at non-cystic lesions rather than just a pale FLAIR low-signal tumor lesion as in conventional T2-FLAIR mismatch sign. Cysts were defined as having a round or oval shape and were excluded from the criteria for the super T2-FLAIR mismatch sign. We evaluated the presence or absence of the T2-FLAIR mismatch sign and super T2-FLAIR mismatch sign using preoperative MRI and analyzed the progression-free survival (PFS) and overall survival (OS) by log-rank test. RESULTS: The T2-FLAIR mismatch sign was present in 17 cases (55%) in our institution and 9 cases (30%) within the TCGA-LGG dataset without any correlation with PFS or OS. However, the super T2-FLAIR mismatch sign was detected in 8 cases (26%) at our institution and 13 cases (43%) in the TCGA-LGG dataset. At our institution, patients displaying the super T2-FLAIR mismatch sign showed significantly extended PFS (122.7 vs. 35.9 months, p = 0.0491) and OS (not reached vs. 116.7 months, p = 0.0232). Similarly, in the TCGA-LGG dataset, those with the super T2-FLAIR mismatch sign exhibited notably longer OS (not reached vs. 44.0 months, p = 0.0177). CONCLUSION: The super T2-FLAIR mismatch is a promising prognostic imaging biomarker for non-enhancing astrocytoma, IDH-mutant.

Prospective evaluation of quantitative response parameter in patients with Gastrointestinal Stroma Tumor undergoing tyrosine kinase inhibitor therapy-Impact on clinical outcome.

The purpose of this study was to determine if dual-energy CT (DECT) vital iodine tumor burden (ViTB), a direct assessment of tumor vascularity, allows reliable response assessment in patients with GIST compared to established CT criteria such as RECIST1.1 and modified Choi (mChoi). From 03/2014 to 12/2019, 138 patients (64 years [32-94 years]) with biopsy proven GIST were entered in this prospective, multi-center trial. All patients were treated with tyrosine kinase inhibitors (TKI) and underwent pre-treatment and follow-up DECT examinations for a minimum of 24 months. Response assessment was performed according to RECIST1.1, mChoi, vascular tumor burden (VTB) and DECT ViTB. A change in therapy management could be because of imaging (RECIST1.1 or mChoi) and/or clinical progression. The DECT ViTB criteria had the highest discrimination ability for progression-free survival (PFS) of all criteria in both first line and second line and thereafter treatment, and was significantly superior to RECIST1.1 and mChoi (p < .034). Both, the mChoi and DECT ViTB criteria demonstrated a significantly early median time-to-progression (both delta 2.5 months; both p < .036). Multivariable analysis revealed 6 variables associated with shorter overall survival: secondary mutation (HR = 4.62), polymetastatic disease (HR = 3.02), metastatic second line and thereafter treatment (HR = 2.33), shorter PFS determined by the DECT ViTB criteria (HR = 1.72), multiple organ metastases (HR = 1.51) and lower age (HR = 1.04). DECT ViTB is a reliable response criteria and provides additional value for assessing TKI treatment in GIST patients. A significant superior response discrimination ability for median PFS was observed, including non-responders at first follow-up and patients developing resistance while on therapy.

Infarct density defined by ADC threshold is associated with long-term functional outcome after endovascular thrombectomy.

OBJECTIVES: Endovascular thrombectomy (EVT) dramatically improves clinical outcomes, but the reduction in final infarct volume only accounts for 10-15 % of the treatment benefit. We aimed to develop a novel MRI-ADC-based metric that quantify the degree of tissue injury to test the hypothesis that it outperforms infarct volume in predicting long-term outcome. MATERIALS AND METHODS: A single-center cohort consisted of consecutive acute stroke patients with anterior circulation large vessel occlusion, successful recanalization via EVT (mTICI ≥2b), and MRI of the brain between 12 h and 7 days post-EVT. Imaging was processed via RAPID software. Final infarct volume was based on the traditional ADC <620 threshold. Logistic regression quantified the association of lesion volumes and good outcome (90-day modified Rankin Scale ≤2) at a range of lower ADC thresholds (<570, <520, and <470). Infarct density was calculated as the percentage of the final infarct volume below the ADC threshold with the greatest effect size. Univariate and multivariate logistic regression quantified the association between imaging/clinical metrics and functional outcome. RESULTS: 120 patients underwent MRI after successful EVT. Lesion volume based on the ADC threshold <470 had the strongest association with good outcome (OR: 0.81 per 10 mL; 95 % CI: 0.66-0.99). In a multivariate model, infarct density (<470/<620 * 100) was independently associated with good outcome (aOR 0.68 per 10 %; 95 % CI: 0.49-0.95), but final infarct volume was not (aOR 0.98 per 10 mL; 95 % CI: 0.85-1.14). CONCLUSIONS: Infarct density after EVT is more strongly associated with long-term clinical outcome than infarct volume.

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.

BACKGROUND: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. METHODS: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test-retest reproducibility ADPKD patients. RESULTS: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test-retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. CONCLUSIONS: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted.

An rs-fMRI based neuroimaging marker for adult absence epilepsy.

OBJECTIVE: Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE. METHODS: This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing. RESULTS: Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %. SIGNIFICANCE: According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.

Serial Diffusion Tensor Imaging and Rate of Ventricular Blood Clearance in Patients with Intraventricular Hemorrhage.

BACKGROUND: We developed a noninvasive biomarker to quantify the rate of ventricular blood clearance in patients with intracerebral hemorrhage and extension to the ventricles-intraventricular hemorrhage. METHODS: We performed magnetic resonance imaging in 26 patients at 1, 14, 28, and 42 days of onset and measured their hematoma volume (HV), ventricular blood volume (VBV), and two diffusion metrics: fractional anisotropy (FA), and mean diffusivity (MD). The ipasilesional ventricular cerebral spinal fluid's FA and MD were associated with VBV and stroke severity scores (National Institute of Health Stroke Scale [NIHSS]). A subcohort of 14 patients were treated with external ventricular drain (EVD). A generalized linear mixed model was applied for statistical analysis. RESULTS: At day 1, the average HVs and NIHSS scores were 14.6 ± 16.7 cm3 and 16 ± 8, respectively. A daily rate of 2.1% and 1.3% blood clearance/resolution were recorded in HV and VBV, respectively. Ipsilesional ventricular FA (vFA) and ventricular MD (vMD) were simultaneously decreased (vFA = 1.3% per day, posterior probability [PP] > 99%) and increased (vMD = 1.5% per day, PP > 99%), respectively. Patients with EVD exhibited a faster decline in vFA (1.5% vs. 1.1% per day) and an increase in vMD (1.8% vs. 1.5% per day) as compared with patients without EVD. Temporal change in vMD was associated with VBV; a 1.00-cm3 increase in VBV resulted in a 5.2% decrease in vMD (PP < 99%). VBV was strongly associated with NIHSS score (PP = 97-99%). A larger cerebral spinal fluid drained volume was associated with a greater decrease (PP = 83.4%) in vFA, whereas a smaller volume exhibited a greater increase (PP = 94.8%) in vMD. CONCLUSIONS: In conclusion, vFA and vMD may serve as biomarkers for VBV status.

Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: A review.

Myelin water fraction (MWF) imaging has emerged as a promising magnetic resonance imaging (MRI) biomarker for investigating brain function and composition. This comprehensive review synthesizes the current state of knowledge on MWF as a biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination. The databases used include Web of Science, Scopus, Science Direct, and PubMed. We begin with a brief discussion of the theoretical foundations of MWF imaging, including its basis in MR physics and the mathematical modeling underlying its calculation, with an overview of the most adopted MRI methods of MWF imaging. Next, we delve into the clinical and research applications that have been explored to date, highlighting its advantages and limitations. Finally, we explore the potential of MWF to serve as a predictive biomarker for neurological disorders and identify future research directions for optimizing MWF imaging protocols and interpreting MWF in various contexts. By harnessing the power of MWF imaging, we may gain new insights into brain health and disease across the human lifespan, ultimately informing novel diagnostic and therapeutic strategies.

Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson's disease.

PURPOSE: While fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level. METHODS: In the current study, this technique was employed to explore Parkinson's disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI. RESULTS: Our findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni-Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni-Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036). CONCLUSION: Metabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration.

Imaging protocols for non-traumatic spinal cord injury: current state of the art and future directions.

INTRODUCTION: Non-traumatic spinal cord injury (NTSCI) is a term used to describe damage to the spinal cord from sources other than trauma. Neuroimaging techniques such as computerized tomography (CT) and magnetic resonance imaging (MRI) have improved our ability to diagnose and manage NTSCIs. Several practice guidelines utilize MRI in the diagnostic evaluation of traumatic and non-traumatic SCI to direct surgical intervention. AREAS COVERED: The authors review practices surrounding the imaging of various causes of NTSCI as well as recent advances and future directions for the use of novel imaging modalities in this realm. The authors also present discussions around the use of simple radiographs and advanced MRI modalities in clinical settings, and briefly highlight areas of active research that seek to advance our understanding and improve patient care. EXPERT OPINION: Although several obstacles must be overcome, it appears highly likely that novel quantitative imaging features and advancements in artificial intelligence (AI) as well as machine learning (ML) will revolutionize degenerative cervical myelopathy (DCM) care by providing earlier diagnosis, accurate localization, monitoring for deterioration and neurological recovery, outcome prediction, and standardized practice. Some intriguing findings in these areas have been published, including the identification of possible serum and cerebrospinal fluid biomarkers, which are currently in the early phases of translation.

Comparison of Vendor-Independent Software Tools for Liver Proton Density Fat Fraction Estimation at 1.5 T.

(1) Background: Open-source software tools are available to estimate proton density fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with graph cut (GC), magnitude-based (MAG), magnitude-only estimation with Rician noise modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization with graph cut (QPBO). The accuracy and reliability of the methods were evaluated in phantoms with known fat/water ratios and a patient cohort with various grades (S0-S3) of steatosis. Image acquisitions were performed at 1.5 Tesla (T). (3) Results: The PDFF estimates showed a nearly perfect correlation (Pearson r = 0.999, p < 0.001) and inter-rater agreement (ICC = from 0.995 to 0.999, p < 0.001) with true fat fractions. The absolute bias was low with all methods (0.001-1%), and an ANCOVA detected no significant difference between the algorithms in vitro. The agreement across the methods was very good in the patient cohort (ICC = 0.891, p < 0.001). However, MAG estimates (-2.30% ± 6.11%, p = 0.005) were lower than MAG-R. The field inhomogeneity artifacts were most frequent in MAG-R (70%) and GC (39%) and absent in QPBO images. (4) Conclusions: The tested algorithms all accurately estimate PDFF in vitro. Meanwhile, QPBO is the least affected by field inhomogeneity artifacts in vivo.

CD19-immunoPET for noninvasive visualization of CD19 expression in B-cell lymphoma patients.

Cell- and antibody-based CD19-directed therapies have demonstrated great potential for treating B-cell non-Hodgkin lymphoma (B-NHL). However, all these approaches suffer from limited response rates and considerable toxicity. Until now, therapy decisions have been routinely based on histopathological CD19 staining of a single lesion at initial diagnosis or relapse, disregarding heterogeneity and temporal alterations in antigen expression. To visualize in vivo CD19 expression noninvasively, we radiolabeled anti-human CD19 monoclonal antibodies with copper-64 (64Cu-αCD19) for positron emission tomography (CD19-immunoPET). 64Cu-αCD19 specifically bound to subcutaneous Daudi xenograft mouse models in vivo. Importantly, 64Cu-αCD19 did not affect the anti-lymphoma cytotoxicity of CD19 CAR-T cells in vitro. Following our preclinical validation, 64Cu-αCD19 was injected into four patients with follicular lymphoma, diffuse large B-cell lymphoma or mantle zone lymphoma. We observed varying 64Cu-αCD19 PET uptake patterns at different lymphoma sites, both within and among patients, correlating with ex vivo immunohistochemical CD19 expression. Moreover, one patient exhibited enhanced uptake in the spleen compared to that in patients with prior B-cell-depleting therapy, indicating that 64Cu-αCD19 is applicable for identifying B-cell-rich organs. In conclusion, we demonstrated the specific targeting and visualization of CD19+ B-NHL in mice and humans by CD19-immunoPET. The intra- and interindividual heterogeneous 64Cu-αCD19 uptake patterns of lymphoma lesions indicate variability in CD19 expression, suggesting the potential of CD19-immunoPET as a novel tool to guide CD19-directed therapies.

The development process of 'fit-for-purpose' imaging biomarkers to characterize the tumor microenvironment.

Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, reliable biomarkers for the assessment of immune features in patients with cancer are of significant importance. Biomarkers (BM) that enable a comprehensive and repeatable assessment of the tumor microenvironment (TME), the lymphoid system, and the dynamics induced by drug treatment can fill this gap. Medical imaging, notably positron emission tomography (PET) and magnetic resonance imaging (MRI), providing whole-body imaging BMs, might deliver such BMs. However, those imaging BMs must be well characterized as being 'fit for purpose' for the intended use. This review provides an overview of the key steps involved in the development of 'fit-for-purpose' imaging BMs applicable in drug development, with a specific focus on pharmacodynamic biomarkers for assessing the TME and its modulation by immunotherapy. The importance of the qualification of imaging BMs according to their context of use (COU) as defined by the Food and Drug Administration (FDA) and National Institutes of Health Biomarkers, EndpointS, and other Tools (BEST) glossary is highlighted. We elaborate on how an imaging BM qualification for a specific COU can be achieved.

Retinal Vascular Changes in Heart Failure with Preserved Ejection Fraction Using Optical Coherence Tomography Angiography.

Background: Systemic microvascular regression and dysfunction are considered important underlying mechanisms in heart failure with preserved ejection fraction (HFpEF), but retinal changes are unknown. Methods: This prospective study aimed to investigate whether retinal microvascular and structural parameters assessed using optical coherence tomography angiography (OCT-A) differ between patients with HFpEF and control individuals (i.e., capillary vessel density, thickness of retina layers). We also aimed to assess the associations of retinal parameters with clinical and echocardiographic parameters in HFpEF. HFpEF patients, but not controls, underwent echocardiography. Macula-centered 6 × 6 mm volume scans were computed of both eyes. Results: Twenty-two HFpEF patients and 24 controls without known HFpEF were evaluated, with an age of 74 [68-80] vs. 68 [58-77] years (p = 0.027), and 73% vs. 42% females (p = 0.034), respectively. HFpEF patients showed vascular degeneration compared to controls, depicted by lower macular vessel density (p < 0.001) and macular ganglion cell-inner plexiform layer thickness (p = 0.025), and a trend towards lower total retinal volume (p = 0.050) on OCT-A. In HFpEF, a lower total retinal volume was associated with markers of diastolic dysfunction (septal e', septal and average E/e': R2 = 0.38, 0.36, 0.25, respectively; all p < 0.05), even after adjustment for age, sex, diabetes mellitus, or atrial fibrillation. Conclusions: Patients with HFpEF showed clear levels of retinal vascular changes compared to control individuals, and retinal alterations appeared to be associated with markers of more severe diastolic dysfunction in HFpEF. OCT-A may therefore be a promising technique for monitoring systemic microvascular regression and cardiac diastolic dysfunction.