Background: Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for diagnosis and prognosis prediction of STS. Patients and methods: Our retrospective, multicenter study included a total of 506 histopathological slides from 291 patients with STS. The Cancer Genome Atlas cohort (240 patients) served as training and validation set. A second, multicenter cohort (51 patients) served as an additional test set. The use of the DL model (DLM) as a clinical decision support system was evaluated by nine pathologists with different levels of expertise. For prognosis prediction, 139 slides from 85 patients with leiomyosarcoma (LMS) were used. Area under the receiver operating characteristic (AUROC) and accuracy served as main outcome measures. Results: The DLM achieved a mean AUROC of 0.97 (AE0.01) and an accuracy of 79.9% (AE6.1%) in diagnosing the five most common STS subtypes. The DLM significantly improved the accuracy of the pathologists from 46.3% (AE15.5%) to 87.1% (AE11.1%). Furthermore, they were significantly faster and more certain in their diagnosis. In LMS, the mean AUROC in predicting the disease-specific survival status was 0.91 (AE0.1) and the accuracy was 88.9% (AE9.9%). Cox regression showed the DLM's prediction to be a significant independent prognostic factor (P ¼ 0.008, hazard ratio 5.5, 95% confidence interval 1.56-19.7) in these patients, outperforming other risk factors. Conclusions: DL can be used to accurately diagnose frequent subtypes of STS from conventional histopathological slides. It might be used for prognosis prediction in LMS, the most prevalent STS subtype in our cohort. It can also help pathologists to make faster and more accurate diagnoses. This could substantially improve the clinical management of STS patients.
Chronic infection with hepatitis C (HCV) is a major risk factor in the development of cirrhosis and hepatocellular carcinoma. Lipid metabolism plays a major role in the replication and deposition of HCV at lipid droplets (LDs). We have demonstrated the importance of LD-associated proteins of the perilipin family in steatotic liver diseases. Using a large collection of 231 human liver biopsies with HCV, perilipins 1 and 2 have been localized to LDs of hepatocytes that correlate with the degree of steatosis and specific HCV genotypes, but not significantly with the HCV viral load. Perilipin 1- and 2-positive microvesicular steatotic foci were observed in 36% of HCV liver biopsies, and also in chronic hepatitis B, autoimmune hepatitis and mildly steatotic or normal livers, but less or none were observed in normal livers of younger patients. Microvesicular steatotic foci did not frequently overlap with glycogenotic/clear cell foci as determined by PAS stain in serial sections. Steatotic foci were detected in all liver zones with slight architectural disarrays, as demonstrated by immunohistochemical glutamine synthetase staining of zone three, but without elevated Ki67-proliferation rates. In conclusion, microvesicular steatotic foci are frequently found in chronic viral hepatitis, but the clinical significance of these foci is so far not clear.
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