The potential of x-ray virtual histology in the diagnosis of skin tumors.

BACKGROUND: Histopathological analysis represents the gold standard in clinical practice for diagnosing skin neoplasms. While the current diagnostic workflow has specialized in producing robust and accurate results, interpreting tissue architecture and malignant cellular morphology correctly remains one of the greatest challenges for pathologists. This paper aims to explore the prospect of applying x-ray virtual histology to human skin tumor excisions and correlating it with the histological validation. MATERIALS AND METHODS: Seven skin biopsies containing intriguing melanoma types and pigmented skin lesions were scanned using x-ray Computed micro-Tomography (µCT) and then sectioned for conventional histology assessment. RESULTS: The tissue microarchitecture reconstructed by µCT offers detailed insights into diagnosing the malignancy or benignity of the skin lesions. Three-dimensional reconstruction via x-ray virtual histology reveals infiltrative patterns in basal cell carcinoma and evaluated invasiveness in melanoma. The technology enables the identification of pagetoid distributions of neoplastic cells and the assessment of melanoma depth in three dimensions. CONCLUSION: Although the proposed approach is not intended to replace conventional histology, the non-destructive nature of the sample and the clarity provided by virtual inspection demonstrate the promising impact of µCT as a valid support method prior to conventional histological sectioning. Indeed, µCT images can suggest the optimal sectioning position before using a microtome, as is commonly performed in histological practice. Moreover, the three-dimensional nature of the proposed approach paves the way for a more accurate assessment of significant prognostic factors in melanoma, such as Breslow thickness, by considering the whole micro-volume rather than a two-dimensional observation.

Melanoma imaging and diagnosis: What does the future hold?

BACKGROUND: In Australia, artificial intelligence (AI) is increasingly being used in the field of melanoma diagnosis. Early diagnosis is arguably the most important prognostic factor for melanoma survival. The use of digital monitoring of naevi, especially dysplastic naevi, might reduce the number of biopsies needed in managing patients at risk of melanoma, especially in patients with high naevi counts. OBJECTIVE: This article discusses advances in imaging and early diagnosis including the use of AI in this process. DISCUSSION: The benefits of performing biopsies must be balanced with the potential to cause harm. Whole-body imaging can assist with more accurate detection of changing lesions and enable clinicians to focus on lesions where change is detected.

Fractional differentiation based image enhancement for automatic detection of malignant melanoma.

Recent improvements in artificial intelligence and computer vision make it possible to automatically detect abnormalities in medical images. Skin lesions are one broad class of them. There are types of lesions that cause skin cancer, again with several types. Melanoma is one of the deadliest types of skin cancer. Its early diagnosis is at utmost importance. The treatments are greatly aided with artificial intelligence by the quick and precise diagnosis of these conditions. The identification and delineation of boundaries inside skin lesions have shown promise when using the basic image processing approaches for edge detection. Further enhancements regarding edge detections are possible. In this paper, the use of fractional differentiation for improved edge detection is explored on the application of skin lesion detection. A framework based on fractional differential filters for edge detection in skin lesion images is proposed that can improve automatic detection rate of malignant melanoma. The derived images are used to enhance the input images. Obtained images then undergo a classification process based on deep learning. A well-studied dataset of HAM10000 is used in the experiments. The system achieves 81.04% accuracy with EfficientNet model using the proposed fractional derivative based enhancements whereas accuracies are around 77.94% when using original images. In almost all the experiments, the enhanced images improved the accuracy. The results show that the proposed method improves the recognition performance.

Thermal Signature of Pigmented Lesions as Observed by Digital Infrared Thermal Imaging.

This study examined the thermal signature of pigmented lesions observed by digital infrared thermal imaging as a possible adjunct to physician diagnosis. Thermal images of pigmented lesions were compared to clinical examination by a plastic surgeon interested in skin diseases, dermatoscopy, and histopathology. A total of 35 patients with 55 pigmented skin lesions were considered. We found that all lesions emitting a dark signal on thermal imaging, compared to the nearby skin, were benign, while only one of all benign lesions (1.9%) had a bright "warm" signal. Benign lesions with papule/nodular morphology were dark in 87.5% of patients. All lesions diagnosed as malignant melanoma, both dermatoscopically and histologically, had plaque morphology; yet, only half demonstrated some signals on thermal imaging. Based on these results, we concluded that thermal imaging could be used as an adjunct to diagnosis when examining skin lesions. This study provided an introduction to using thermal imaging for spotting skin lesions.

Dual-Modal Near-Infrared Organic Nanoparticles: Integrating Mild Hyperthermia Phototherapy with Fluorescence Imaging.

Purpose: Our study seeks to develop dual-modal organic-nanoagents for cancer therapy and real-time fluorescence imaging, followed by their pre-clinical evaluation on a murine model. Integrating NIR molecular imaging with nanotechnology, our aim is to improve outcomes for early-stage cutaneous melanoma by offering more effective and less invasive methods. This approach has the potential to enhance both photothermal therapy (PTT) and Sentinel Lymph Node Biopsy (SLNB) procedures for melanoma patients. Methods: NIR-797-isothiocyanate was encapsulated in poly(D,L-lactide-co-glycolide) acid (PLGA) nanoparticles (NPs) using a two-step protocol, followed by thorough characterization, including assessing loading efficiency, fluorescence stability, and photothermal conversion. Biocompatibility and cellular uptake were tested in vitro on melanoma cells, while PTT assay, with real-time thermal monitoring, was performed in vivo on tumor-bearing mice under irradiation with an 808 nm laser. Finally, ex vivo fluorescence microscopy, histopathological assay, and TEM imaging were performed. Results: Our PLGA NPs, with a diameter of 270 nm, negative charge, and 60% NIR-797 loading efficiency, demonstrated excellent stability and fluorescence properties, as well as efficient light-to-heat conversion. In vitro studies confirmed their biocompatibility and cellular internalization. In vivo experiments demonstrated their efficacy as photothermal agents, inducing mild hyperthermia with temperatures reaching up to 43.8 °C. Ex vivo microscopy of tumor tissue confirmed persistent NIR fluorescence and uniform distribution of the NPs. Histopathological and TEM assays revealed early apoptosis, immune cell response, ultrastructural damage, and intracellular material debris resulting from combined NP treatment and irradiation. Additionally, TEM analyses of irradiated zone margins showed attenuated cellular damage, highlighting the precision and effectiveness of our targeted treatment approach. Conclusion: Specifically tailored for dual-modal NIR functionality, our NPs offer a novel approach in cancer PTT and real-time fluorescence monitoring, signaling a promising avenue toward clinical translation.

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.

BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. METHODS: We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). RESULTS: The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. CONCLUSIONS: Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

Quantitative Perfusion-Weighted Magnetic Resonance Imaging in Uveal Melanoma.

Purpose: Perfusion-weighted imaging (PWI; magnetic resonance imaging [MRI]) has been shown to provide valuable biological tumor information in uveal melanoma (UM). Clinically used semiquantitative methods do not account for tumor pigmentation and eye movement. We hypothesize that a quantitative PWI method that incorporates these, provides a more accurate description of tumor perfusion than the current clinical method. The aim of this study was to test this in patients with UM before and after radiotherapy. Methods: Perfusion-weighted 3T MRIs were retrospectively analyzed in 47 patients with UM before and after radiotherapy. Tofts pharmacokinetic modeling was performed to determine vascular permeability (Ktrans), extracellular extravascular space (ve), and reflux rate (kep). These were compared with semiquantitative clinical parameters including peak intensity and outflow percentage. Results: The effect of tumor pigmentation on peak intensity and outflow percentage was statistically significant (P < 0.01) and relative peak intensity was significantly different between melanotic and amelanotic tumors (1.5 vs. 1.9, P < 0.01). Before radiotherapy, median tumor Ktrans was 0.63 min-1 (range = 0.06-1.42 min-1), median ve was 0.23 (range = 0.09-0.63), and median kep was 2.3 min-1 (range = 0.6-5.0 min-1). After radiotherapy, 85% showed a decrease in Ktrans and kep (P < 0.01). Changes in tumor pigmentation before and after radiotherapy were small and not significant (median increase in T1 of 33 ms, P = 0.55). Conclusions: Quantitative PWI parameters decreased significantly after radiotherapy and can therefore can serve as an early biomarker for treatment response assessment. However, due to the nonsignificant changes in tumor pigmentation before and after radiotherapy, the current semiquantitative method appears to be sufficiently sensitive for detection of changes in tumor perfusion.

Immune-related [18F]FDG PET findings in patients undergoing checkpoint inhibitors treatment: correlation with clinical adverse events and prognostic implications.

BACKGROUND: Direct comparisons between [18F]FDG PET/CT findings and clinical occurrence of immune-related adverse events (irAEs) based on independent assessments of clinical and imaging features in patients receiving immune checkpoint inhibitors (ICIs) are missing. Our aim was to estimate sites, frequency, and timing of immune-related PET findings during ICIs treatment in patients with melanoma and NSCLC, and to assess their correlation with clinical irAEs. Prognostic implications of immune-related events were also investigated. METHODS: Fifty-one patients with melanoma (47%) or NSCLC (53%) undergoing multiple PET examinations during anti-PD1/PDL1 treatment were retrospectively included. Clinical irAEs were graded according to CTCAE v.5.0. Abnormal PET findings suggestive of immune activation were described by two readers blinded to the clinical data. Progression-free survival (PFS) and overall survival (OS) were analyzed with the Kaplan-Meier method in patients stratified according to the presence of irAEs, immune-related PET findings or both. RESULTS: Twenty-one patients showed clinical irAEs only (n = 6), immune-related PET findings only (n = 6), or both (n = 9). In patients whose imaging findings corresponded to clinical irAEs (n = 7), a positive correlation between SUVmax and the severity of the clinical event was observed (rs=0.763, p = 0.046). Clinical irAEs occurred more frequently in patients without macroscopic disease than in metastatic patients (55% vs. 23%, p = 0.039). Patients who developed clinical irAEs had a significantly longer PFS than patients who remained clinically asymptomatic, both in the overall cohort (p = 0.011) and in the subgroup of (n = 35) patients with metastatic disease (p = 0.019). The occurrence of immune-related PET findings significantly stratified PFS in the overall cohort (p = 0.040), and slightly missed statistical significance in patients with metastatic disease (p = 0.08). The best stratification of PFS was achieved when all patients who developed immune-related events, either clinically relevant or detected by PET only, were grouped together both in the overall cohort (p = 0.002) and in patients with metastatic disease (p = 0.004). In the whole sample, OS was longer in patients who developed any immune-related events (p = 0.032). CONCLUSION: Patients with melanoma or NSCLC under ICI treatment can develop clinical irAEs, immune-related PET findings, or both. The occurrence of immune-related events has a prognostic impact. Combining clinical information with PET assessment improved outcome stratification.

Computerizing the first step of the two-step algorithm in dermoscopy: A convolutional neural network for differentiating melanocytic from non-melanocytic skin lesions.

IMPORTANCE: Convolutional neural networks (CNN) have shown performance equal to trained dermatologists in differentiating benign from malignant skin lesions. To improve clinicians' management decisions, additional classifications into diagnostic categories might be helpful. METHODS: A convenience sample of 100 pigmented/non-pigmented skin lesions was used for a cross-sectional two-level reader study including 96 dermatologists (level I: dermoscopy only; level II: clinical close-up images, dermoscopy, and textual information). Dermoscopic images were classified by a binary CNN trained to differentiate melanocytic from non-melanocytic lesions (FotoFinder Systems, Bad Birnbach, Germany). Primary endpoint was the accuracy of the CNN's classification in comparison with dermatologists reviewing level-II information. Secondary endpoints included dermatologists' accuracies according to their level of experience and the CNN's area under the curve (AUC) of receiver operating characteristics (ROC). RESULTS: The CNN revealed an accuracy and ROC AUC with corresponding 95 % confidence intervals (CI) of 91.0 % (83.8 % to 95.2 %) and 0.981 (0.962 to 1). In level I, dermatologists showed a mean accuracy of 83.7 % (82.5 % to 84.8 %). With level II information, the accuracy improved to 87.8 % (86.7 % to 88.9 %; p < 0.001). When comparing accuracies of CNN and dermatologists in level II, the CNN's accuracy was higher (91.0 % versus 87.8 %, p < 0.001). For experts with level II information results were on par with the CNN (91.0 % versus 90.4 %, p = 0.368). CONCLUSIONS: The tested CNN accurately differentiated melanocytic from non-melanocytic skin lesions and outperformed dermatologists. The CNN may support clinicians and could be used in an ensemble approach combined with other CNN models.

Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study.

BACKGROUND: To evaluate the role of the novel quantitative imaging biomarker (QIB) SUVX% of 18F-FDG uptake extracted from early 18F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patients with metastatic melanoma (mM) patients receiving immune-checkpoint inhibitors (ICI). PATIENTS AND METHODS: In this prospective non-interventional, one-centre clinical study, patients with mM, receiving ICI treatment, were regularly followed by 18F-FDG PET/CT. Patients were scanned at baseline, early point at week four (W4), week sixteen (W16) and week thirty-two (W32) after ICI initiation. A convolutional neural network (CNN) was used to segment three organs: lung, bowel, thyroid. QIB of irAE - SUVX% - was analyzed within the target organs and correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. RESULTS: A total of 242 18F-FDG PET/CT images of 71 mM patients were prospectively collected and analysed. The early W4 scan showed improved detection only for the thyroid gland compared to W32 scan (p=0.047). The AUROC for detection of irAE in the three target organs was highest when SUVX% was extracted from W16 scan and was 0.76 for lung, 0.53 for bowel and 0.81 for thyroid. SUVX% extracted from W4 scan did not improve detection of irAE compared to W16 scan (lung: p = 0.54, bowel: p = 0.75, thyroid: p = 0.3, DeLong test), as well as compared to W32 scan in lungs (p = 0.32) and bowel (p = 0.3). CONCLUSIONS: Early time point 18F-FDG PET/CT at W4 did not lead to statistically significant earlier detection of irAE. However, organ 18F-FDG uptake as quantified by SUVX% proved to be a consistent QIB of irAE. To better assess the role of 18F-FDG PET/CT in irAE detection, the time evolution of 18F-FDG PET/CT quantifiable inflammation would be of essence, only achievable in multi centric studies.

Shear-wave elastographic imaging in choroidal melanomas: clinical and hemodynamic correlations.

PURPOSE: This study evaluated the role of shear wave elastography imaging (SWEΙ) in uveal melanomas and the associations between SWEI and clinical and hemodynamic findings. STUDY DESIGN: Prospective, clinical study METHODS: Twelve patients with uveal melanomas, scheduled to undergo Ru-106 brachytherapy, were prospectively recruited from the Department of Ophthalmology of the University Hospital of Heraklion (September-December 2022). B-mode, hemodynamic and SWEI ultrasonography examinations were performed with the HiScan (OPTIKON 2000) and the LOGIQ E9 (GE Healthcare) sonographic systems, respectively. Differences in SWEI scores (kPa) between tumor (TS) and adjacent non-affected choroid (CS), as well as between TS and orbital fat (FS) were examined. Correlations between SWEI and intra-tumoral hemodynamic parameters, including peak systolic and end diastolic velocities and resistivity index (RI) were also examined. RESULTS: TS was significantly correlated with intra-tumoral RI (Pearson's bivariate correlation coefficient 0.681, p=0.015) and with maximal tumor height (Pearson's bivariate correlation coefficient 0.620, p=0.031). TS was significantly higher than both FS and CS scores (paired-samples t-test, p=0.003 and p=0.006, respectively). CONCLUSIONS: SWEI score is applicable as a quantitative biomechanical marker in the assessment of choroidal melanoma. Choroidal melanomas are stiffer than both adjacent choroid and orbital fat. Moreover, choroidal melanomas with higher RI as well as those with higher apical elevations display higher SWEI scores.

Synthesis and evaluation of anti-PD-L1-B11 antibody fragments for PET imaging of PD-L1 in breast cancer and melanoma tumor models.

There is a critical need to non-invasively assess the PD-L1 expression in tumors as a predictive biomarker for determining the efficacy of anti-PD-1/PD-L1 immunotherapies. Non-invasive imaging modality like positron emission tomography (PET) can be a powerful tool to assess the PD-L1 expression in the whole body including multiple metastases as a patient selection criterion for the anti-PD-1/PD-L1 immunotherapy. In this study, we synthesized B11-nanobody, B11-scFv and B11-diabody fragments from the full-length anti-PD-L1 B11 IgG. Out of the three antibody fragments, B11-diabody showed higher nM affinity towards PD-L1 antigen as compared to B11-scFv and B11-nanobody. All three antibody fragments were successfully radiolabeled with 64Cu, a PET radioisotope. For radiolabeling, the antibody fragments were first conjugated with p-SCN-Bn-NOTA followed by chelation with 64Cu. All three radiolabeled antibody fragments were found to be stable in mouse and human sera for up to 24 h. Additionally, all three [64Cu]Cu-NOTA-B11-antibody fragments were evaluated in PD-L1 negative and human PD-L1 expressing cancer cells and subcutaneous tumor models. Based on the results, [64Cu]Cu-NOTA-B11-diabody has potential to be used as a PET imaging probe for assessing PD-L1 expression in tumors as early as 4 h post-injection, allowing faster assessment compared to the full length IgG based PET imaging probe.

Classification of melanocytic lesions using direct illumination multispectral imaging.

With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

Intelligent skin lesion segmentation using deformable attention Transformer U-Net with bidirectional attention mechanism in skin cancer images.

BACKGROUND: In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment. METHODS: A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction. RESULTS: A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%. CONCLUSION: Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.

Assessing spatial distribution of bioindicator elements in various cutaneous tumors using correlative imaging with laser-ablation-based analytical methods.

Correlative imaging of cutaneous tumors provides additional information to the standard histopathologic examination. However, the joint progress in the establishment of analytical techniques, such as Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) in clinical practice is still limited. Their combination provides complementary information as it is also shown in our study in terms of major biotic (Ca, Mg, and P) and trace (Cu and Zn) elements. To elucidate changes in the elemental composition in tumors, we have compiled a set of malignant tumors (Squamous Cell Carcinoma, Basal Cell Carcinoma, Malignant Melanoma, and Epithelioid Angiosarcoma), one benign tumor (Pigmented Nevus) and one healthy-skin sample. The data processing was based on a methodological pipeline involving binary image registration and affine transformation. Thus, our paper brings a feasibility study of a practical methodological concept that enables us to compare LIBS and LA-ICP-MS results despite the mutual spatial distortion of original elemental images. Moreover, we also show that LIBS could be a sufficient pre-screening method even for a larger number of samples according to the speed and reproducibility of the analyses. Whereas LA-ICP-MS could serve as a ground truth and reference technique for preselected samples.

Investigating the In Vivo Biodistribution of Extracellular Vesicles Isolated from Various Human Cell Sources Using Positron Emission Tomography.

Positron emission tomography (PET) is a powerful tool for investigating the in vivo behavior of drug delivery systems. We aimed to assess the biodistribution of extracellular vesicles (EVs), nanosized vesicles secreted by cells isolated from various human cell sources using PET. EVs were isolated from mesenchymal stromal cells (MSCs) (MSC EVs), human macrophages (Mϕ EVs), and a melanoma cell line (A375 EVs) by centrifugation and were conjugated with deferoxamine for radiolabeling with Zr-89. PET using conjugated and radiolabeled EVs evaluated their in vivo biodistribution and tissue tropisms. Our study also investigated differences in mouse models, utilizing immunocompetent and immunocompromised mice and an A375 xenograft tumor model. Lastly, we investigated the impact of different labeling techniques on the observed EV biodistribution, including covalent surface modification and membrane incorporation. PET showed that all tested EVs exhibited extended in vivo circulation and generally low uptake in the liver, spleen, and lungs. However, Mϕ EVs showed high liver uptake, potentially attributable to the intrinsic tissue tropism of these EVs from the surface protein composition. MSC EV biodistribution differed between immunocompetent and immunodeficient mice, with increased spleen uptake observed in the latter. PET using A375 xenografts demonstrated efficient tumor uptake of EVs, but no preferential tissue-specific tropism of A375 EVs was found. Biodistribution differences between labeling techniques showed that surface-conjugated EVs had preferential blood circulation and low liver, spleen, and lung uptake compared to membrane integration. This study demonstrates the potential of EVs as effective drug carriers for various diseases, highlights the importance of selecting appropriate cell sources for EV-based drug delivery, and suggests that EV tropism can be harnessed to optimize therapeutic efficacy. Our findings indicate that the cellular source of EVs, labeling technique, and animal model can influence the observed biodistribution.

Thin Amelanotic and Hypomelanotic Melanoma: Clinicopathological and Dermoscopic Features.

Background and Objectives: Amelanotic/hypomelanotic melanomas (AHMs) account for 2-8% of all cutaneous melanomas. Due to their clinical appearance and the lack of specific dermoscopic indicators, AHMs are challenging to diagnose, particularly in thinner cutaneous lesions. The aim of our study was to evaluate the clinicopathological and dermoscopic features of thin AHMs. Identifying the baseline clinical-pathological features and dermoscopic aspects of thin AHMs is crucial to better understand this entity. Materials and Methods: We divided the AHM cohort into two groups based on Breslow thickness: thin (≤1.00 mm) and thick (>1.00 mm). This stratification helped identify any significant clinicopathological differences between the groups. For dermoscopic analysis, we employed the "pattern analysis" approach, which involves a simultaneous and subjective assessment of different criteria. Results: Out of the 2.800 melanomas analyzed for Breslow thickness, 153 were identified as AHMs. Among these, 65 patients presented with thin AHMs and 88 with thick AHMs. Red hair color and phototype II were more prevalent in patients with thin AHMs. The trunk was the most common anatomic site for thin AHMs. Patients with thin AHMs showed a higher number of multiple melanomas. Dermoscopic analysis revealed no significant difference between thin AHMs and thick AHMs, except for a more frequent occurrence of residual reticulum in thin AHMs. Conclusions: Thin AHMs typically affect individuals with lower phototypes and red hair color. These aspects can be related to the higher presence of pheomelanin, which provides limited protection against sun damage. This also correlates with the fact that the trunk, a site commonly exposed to intermittent sun exposure, is the primary anatomical location for thin AHMs. Multiple primary melanomas are more common in patients with thin AHMs, likely due to an intrinsic predisposition as well as greater periodic dermatologic follow-ups in this class of patients. Apart from the presence of residual reticulum, no other significant dermoscopic differences were observed, complicating the differential diagnosis between thin and thick AHMs based on dermoscopy alone.

A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors.

Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for "cancer of unknown primary", where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.