| 1. |
- Johansson, Junko, 1989, et al.
(författare)
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Presence of tumor-infiltrating CD8(+) T cells and macrophages correlates to longer overall survival in patients undergoing isolated hepatic perfusion for uveal melanoma liver metastasis
- 2020
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Ingår i: OncoImmunology. - : Informa UK Limited. - 2162-402X. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Uveal melanoma is a malignant tumor of the eye that often metastasizes to the liver conferring poor prognosis. When comparing immune profiles in peripheral blood of untreated patients with uveal melanoma liver metastasis and healthy blood donors, it was observed that immune cells of uveal melanoma patients carried immunosuppressive features. Patient blood contained an increased content of CD14(+)HLA-DR-/low M-MDSCs and inflammatory CD16(+) monocytes, while their dendritic cells expressed lower levels of activation markers. Melanoma patients also harbored an enhanced fraction of CD4(+)Foxp3(+) regulatory T cells, while their effector T cells expressed lower levels of the activation marker HLA-DR. Biopsies from liver metastases were obtained from patients with uveal melanoma that subsequently underwent hyperthermic isolated hepatic perfusion (IHP) with melphalan. There were trends indicating a positive correlation between a high infiltration of CD8(+) T cells in metastases and an activated immune cell profile in blood. High metastatic infiltration of CD8(+) T cells and CD68(+) macrophages, but not of immunosuppressive CD163(+) macrophages, correlated to a longer overall survival in patients treated with IHP. Hence, while the immune system of patients with uveal melanoma shows signs of immunosuppression, the presence of activated immune cells may correlate to a longer survival, at least following IHP treatment.
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| 2. |
- Kantere, Despoina, et al.
(författare)
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Label-free laser scanning microscopy targeting sentinel lymph node diagnostics: A feasibility study ex vivo
- 2020
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Ingår i: Translational Biophotonics. - : Wiley. - 2627-1850. ; 2:3
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Tidskriftsartikel (refereegranskat)abstract
- This proof-of-concept, observational study investigates the potential for label-free, laser scanning microscopy to identify melanoma metastatic features in lymph node tissues ex vivo - exploring the translational potential targeting sentinel lymph node diagnostics. Ten lymph node samples, of which seven are positive for melanoma metastases and three negative, were obtained from a tissue biobank. Imaging data acquired using multiphoton microscopy ex vivo were compared with histopathological findings. Morphologic features characteristic for histopathologic diagnosis of melanoma metastasis were confirmed in the positive samples. Four of the samples were complementary studied with reflectance mode confocal microscopy; however, visualization was found restricted to fibrous structures in this case. To conclude, the results imply that multiphoton microscopy based on autofluorescence is a promising label-free technique for visualization of cellular features in lymph node tissues.
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| 3. |
- Polesie, Sam, et al.
(författare)
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Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey.
- 2020
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Ingår i: Frontiers in medicine. - : Frontiers Media SA. - 2296-858X. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) has recently surfaced as a research topic in dermatology and dermatopathology. In a recent survey, dermatologists were overall positive toward a development with an increased use of AI, but little is known about the corresponding attitudes among pathologists working with dermatopathology. The objective of this investigation was to make an inventory of these attitudes. Participants and Methods: An anonymous and voluntary online survey was prepared and distributed to pathologists who regularly analyzed dermatopathology slides/images. The survey consisted of 39 question divided in five sections; (1) AI as a topic in pathology; (2) previous exposure to AI as a topic in general; (3) applications for AI in dermatopathology; (4) feelings and attitudes toward AI and (5) self-reported tech-savviness and demographics. The survey opened on March 13, 2020 and closed on May 5, 2020. Results: Overall, 718 responders (64.1% females) representing 91 countries were analyzed. While 81.5% of responders were aware of AI as an emerging topic in pathology, only 18.8% had either good or excellent knowledge about AI. In terms of diagnosis classification, 42.6% saw strong or very strong potential for automated suggestion of skin tumor diagnoses. The corresponding figure for inflammatory skin diseases was 23.0% (Padj < 0.0001). For specific applications, the highest potential was considered for automated detection of mitosis (79.2%), automated suggestion of tumor margins (62.1%) and immunostaining evaluation (62.7%). The potential for automated suggestion of immunostaining (37.6%) and genetic panels (48.3%) were lower. Age did not impact the overall attitudes toward AI. Only 6.0% of the responders agreed or strongly agreed that the human pathologist will be replaced by AI in the foreseeable future. For the entire group, 72.3% agreed or strongly agreed that AI will improve dermatopathology and 84.1% thought that AI should be a part of medical training. Conclusions: Pathologists are generally optimistic about the impact and potential benefit of AI in dermatopathology. The highest potential is expected for narrow specified tasks rather than a global automated suggestion of diagnoses. There is a strong need for education about AI and its use within dermatopathology.
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| 4. |
- Siarov, Jan, 1984, et al.
(författare)
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Digital Quantification of Melanocytic Density in Resection Margins of Lentigo Maligna Using SOX10 Versus Hematoxylin-Eosin Staining.
- 2021
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Ingår i: The American Journal of dermatopathology. - 1533-0311. ; 43:4, s. 273-277
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Tidskriftsartikel (refereegranskat)abstract
- Lentigo maligna (LM) represents an overgrowth of atypical melanocytes at the dermal-epidermal junction of chronically sun-damaged skin. The presence of LM on sun-damaged skin poses a diagnostic challenge because the solar-induced melanocytic hyperplasia makes it difficult to assess the LM margins. Melanocytic density can be used to discriminate sun-damaged skin from LM. The aim of this study was to quantify the melanocytic density at the surgical margins of scanned whole-slide images of LM comparing sections stained with H&E and SOX10. Twenty-six surgically excised LM diagnosed at the Department of Pathology at Sahlgrenska University Hospital were collected. The slides that contained the closest surgical margin or harbored the highest density of melanocytes at the margin were selected for serial sectioning using H&E and SOX10. Whole-slide imaging at ×40 magnification was used, and a circular field with a diameter of 0.5 mm at the surgical margin was superimposed on the image. Five blinded pathologists reviewed the slides in a randomized order. In the majority of the cases (24/26), the pathologists identified more melanocytes on the SOX10 slides than those on the H&E slides. On average, 2.5 times more melanocytes were counted using SOX10 compared with H&E (P < 0.05). Furthermore, the average group SD on the H&E slides was 4.12 compared with 2.83 on the SOX10 slides (P = 0.004). Thus, the use of SOX10 staining leads to higher melanocytic density counts compared with H&E staining when assessing the surgical margins of LM. The use of SOX10 staining also significantly decreased the interobserver variability between pathologists.
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| 5. |
- Yacob, F., et al.
(författare)
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Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images
- 2023
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Ingår i: Scientific Reports. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.
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