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- Mairinoja, L., et al.
(författare)
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Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models
- 2023
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Ingår i: American Journal of Pathology. - 0002-9440. ; 193:8, s. 1072-1080
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Tidskriftsartikel (refereegranskat)abstract
- The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalco-holic fatty liver disease and to analyze drug efficacy in preclinical models are needed. The present study developed a deep neural network-based model to quantify microvesicular and macrovesicular steatosis in the liver on hematoxylin-eosin-stained whole slide images, using the cloud-based platform, Aiforia Create. The training data included a total of 101 whole slide images from dietary interventions of wild-type mice and from two genetically modified mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of micro-vesicular and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists and correlated well with the liver fat content measured by EchoMRI ex vivo, and the correlation with total liver triglycerides was notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections and, thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts. (Am J Pathol 2023, 193: 1072-1080; https://doi.org/ 10.1016/j.ajpath.2023.04.014)
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- Herrera, M, et al.
(författare)
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Prognostic Interactions between FAP+ Fibroblasts and CD8a+ T Cells in Colon Cancer
- 2020
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 12:11
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Tidskriftsartikel (refereegranskat)abstract
- Inter-case variations in immune cell and fibroblast composition are associated with prognosis in solid tumors, including colon cancer. A series of experimental studies suggest immune-modulatory roles of marker-defined fibroblast populations, including FAP-positive fibroblasts. These studies imply that the fibroblast status of tumors might affect the prognostic significance of immune-related features. Analyses of a population-based colon cancer cohort demonstrated good prognosis associations of FAP intensity and CD8a density. Notably, a significant prognostic interaction was detected between these markers (p = 0.013 in nonadjusted analyses and p = 0.003 in analyses adjusted for cofounding factors) in a manner where the good prognosis association of CD8 density was restricted to the FAP intensity-high group. This prognostic interaction was also detected in an independent randomized trial-derived colon cancer cohort (p = 0.048 in nonadjusted analyses). In the CD8-high group, FAP intensity was significantly associated with a higher total tumor density of FoxP3-positive immune cells and a higher ratio of epithelial-to-stromal density of CD8a T cells. The study presents findings relevant for the ongoing efforts to improve the prognostic performance of CD8-related markers and should be followed by additional validation studies. Furthermore, findings support, in general, earlier model-derived studies implying fibroblast subsets as clinically relevant modulators of immune surveillance. Finally, the associations between FAP intensity and specific immune features suggest mechanisms of fibroblast-immune crosstalk with therapeutic potential.
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- Sjoblom, A, et al.
(författare)
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The Role of Human Chorionic Gonadotropin Beta (hCGβ) in HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma
- 2022
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- Background: This study was carried out to observe the upregulation of the free β-subunit of human chorionic gonadotropin (hCGβ) and its prognostic significance in human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma (OPSCC). Materials and methods: A total of 90 patients with OPSCC treated with curative intent at the Helsinki University Hospital (HUS), Helsinki, Finland, during 2012–2016 were included. Serum samples were collected prospectively, and their hCGβ concentrations (S-hCGβ) were determined by an immunofluorometric assay. The expression of hCGβ in tumor tissues was defined by immunohistochemistry (IHC). HPV determination was performed by combining p16-INK4 IHC and HPV DNA PCR genotyping. Overall survival (OS) and disease-specific survival (DSS) were used as survival endpoints. Results: S-hCGβ positivity correlated with poor OS in the whole patient cohort (p < 0.001) and in patients with HPV-negative OPSCC (p < 0.001). A significant correlation was seen between S-hCGβ and poor DSS in the whole cohort (p < 0.001) and in patients with HPV-negative OPSCC (p = 0.007). In a multivariable analysis, S-hCGβ was associated with poor DSS. Of the clinical characteristics, higher cancer stage and grade were associated with S-hCGβ positivity. No statistically significant correlation with tissue positivity of hCGβ was seen in these analyses. Conclusion: S-hCGβ may be a potential independent factor indicating poor prognosis, notably in HPV-negative OPSCC.
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| 24. |
- Sjoblom, A, et al.
(författare)
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Tumor-Associated Trypsin Inhibitor (TATI) as a Biomarker of Poor Prognosis in Oropharyngeal Squamous Cell Carcinoma Irrespective of HPV Status
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:11
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Tidskriftsartikel (refereegranskat)abstract
- Background: We studied the role of tumor-associated trypsin inhibitor (TATI) in serum and in tumor tissues among human papillomavirus (HPV)-positive and HPV-negative OPSCC patients. Materials and methods: The study cohort included 90 OPSCC patients treated at the Helsinki University Hospital (HUS), Helsinki, Finland, in 2012–2016. TATI serum concentrations (S-TATIs) were determined by an immunofluorometric assay. Immunostaining was used to assess tissue expression. HPV status was determined with a combination of p16 immunohistochemistry and HPV DNA PCR genotyping. The survival endpoints were overall survival (OS) and disease-specific survival (DSS). Results: A significant correlation was found between S-TATI positivity and poor OS (p < 0.001) and DSS (p = 0.04) in all patients. In HPV-negative cases, S-TATI positivity was linked to poor OS (p = 0.01) and DSS (p = 0.05). In HPV-positive disease, S-TATI positivity correlated with poor DSS (p = 0.01). S-TATI positivity was strongly associated with HPV negativity. TATI serum was negatively linked to a lower cancer stage. TATI expression in peritumoral lymphocytes was associated with favorable OS (p < 0.025) and HPV positivity. TATI expression in tumor and in peritumoral lymphocytes correlated with lower cancer stages. Conclusion: Our results suggest that S-TATI positivity may be a biomarker of poor prognosis in both HPV-positive and HPV-negative OPSCC.
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- 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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