Small molecules offer some advantages for developing positron emission tomography (PET) tracers and are therefore a promising approach for imaging and therapy monitoring of programmed death ligand 1 (PD-L1) positive tumors. Here, we report six biphenyl PD-L1 radioligands using the NODA-GA-chelator for efficient copper-64 complexation. These radioligands contain varying numbers of sulfonic and/or phosphonic acid groups, serving as hydrophilizing units to lower the log D7.4 value down to -4.28. The binding affinities of compounds were evaluated using saturation binding and a real-time binding assay, with a highest binding affinity of 21 nM. Small-animal PET imaging revealed vastly different pharmacokinetic profiles depending on the quantity and type of hydrophilizing units. Of the investigated radioligands, [64Cu]Cu-3 showed the most favorable kinetics in vitro. This was also found in vivo, with a predominantly renal clearance and a specific uptake in the PD-L1-overexpressing tumor. With further modifications, this compound could be a promising candidate for the imaging of PD-L1 in the clinical setting.

Immune checkpoint inhibitor therapy targeting the PD-1/PD-L1 axis in cancer patients, is a promising oncological treatment. However, the number of non-responders remains high, causing a burden for the patient and the healthcare system. Consequently, a diagnostic tool to predict treatment outcomes would help with patient stratification. Molecular imaging provides said diagnostic tool by offering a whole-body quantitative assessment of PD-L1 expression, hence supporting therapy decisions. Four PD-L1 radioligand candidates containing a linker-chelator system for radiometalation, along with three hydrophilizing units-one sulfonic and two phosphonic acids-were synthesized. After labeling with 64Cu, log D7.4 values of less than -3.03 were determined and proteolytic stability confirmed over 94% intact compound after 48 h. Binding affinity was determined using two different assays, revealing high affinities up to 13 nM. µPET/CT imaging was performed in tumor-bearing mice to investigate PD-L1-specific tumor uptake and the pharmacokinetic profile of radioligands. These results yielded an unexpected in vivo distribution, such as low tumor uptake in PD-L1 positive tumors, high liver uptake, and accumulation in bone/bone marrow and potentially synovial spaces. These effects are likely caused by Ca2+-affinity and/or binding to macrophages. Despite phosphonic acids providing high water solubility, their incorporation must be carefully considered to avoid compromising the pharmacokinetic behavior of radioligands.

Noninvasive molecular imaging of the PD-1/PD-L1 immune checkpoint is of high clinical relevance for patient stratification and therapy monitoring in cancer patients. Here we report nine small-molecule PD-L1 radiotracers with solubilizing sulfonic acids and a linker-chelator system, designed by molecular docking experiments and synthesized according to a new, convergent synthetic strategy. Binding affinities were determined both in cellular saturation and real-time binding assay (LigandTracer), revealing dissociation constants in the single digit nanomolar range. Incubation in human serum and liver microsomes proved in vitro stability of these compounds. Small animal PET/CT imaging, in mice bearing PD-L1 overexpressing and PD-L1 negative tumors, showed moderate to low uptake. All compounds were cleared primarily through the hepatobiliary excretion route and showed a long circulation time. The latter was attributed to strong blood albumin binding effects, discovered during our binding experiments. Taken together, these compounds are a promising starting point for further development of a new class of PD-L1 targeting radiotracers.

Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low- and middle-income countries (LMICs).

Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop®, LumaGEM®, MarginProbe®) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary collaboration among radiologists, pathologists, and other involved specialties for improving cancer diagnostics.

Revolutionary technological advancements in cancer imaging and molecular biology techniques are indispensable for the accurate diagnosis and prognosis of cancer.

To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors.

Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs' correlation with volume and SUVmax was analyzed by calculating Pearson's correlation coefficients.

DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax.

RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

Our aim was to present a new data analysis technique for the early detection of tumorous lesions using single-photon emission computed tomography (SPECT) imaging. Beyond standardized uptake value (SUV) and standardized uptake concentration (SUC), the skewness and kurtosis parameters of whole liver activity distribution histograms were examined in SPECT images to reveal the presence of tumorous cells.

Four groups of mice were used in our experiment: a healthy control group, a group of obese mice with high body mass index, and two tumorous groups (primary liver cancer group with chemically induced hepatocellular carcinoma (HCC); metastatic liver tumor group-xenograft of human melanoma (HM)). For the SPECT measurements, ^99mTc-labeled aggregated albumin nanoparticles were administered intravenously 2 h before the liver SPECT scans (NanoSPECT/CT, Silver Upgrade, Mediso Ltd., Hungary) to image liver macrophages. Finally, SUV, SUC, skewness, and kurtosis of activity distributions were calculated from segmented whole liver volumes.

HCC animals showed moderate ^99mTc-albumin particle uptake with some visually identified cold spots indicating the presence of tumors. The visual detection of cold spots however was not a reliable marker of tumorous tissue in the metastatic group. The calculated SUV, SUC, and kurtosis parameters were not able to differentiate between the healthy and the tumorous groups. However, healthy and tumorous groups could be distinguished by comparing the skewness of the activity distribution.

Based on our results, ^99mTc-albumin nanoparticle injection followed by liver SPECT activity distribution skewness calculation is a suitable image analysis tool. This makes possible to effectively and quantitatively investigate liver macrophage inhomogeneity and identify invisible but present liver cold spot lesions. Skewness as a direct image-derived parameter is able to show altered tissue function even before the visual manifestation of liver tumor foci. The skewness of activity distribution might be related to an inhomogeneous distribution of macrophage cells as a consequence of microscopic tumor burden in the liver.

Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model's own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control ([Formula: see text]) calculated by the model. We find that the three simulated variables correlate with the calculated [Formula: see text]. In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.