No abstract

No abstract

Summary Background Histological evaluation of metabolic‐associated fatty liver disease (MAFLD) biopsies is subjective, descriptive and with interobserver variability. Aims To examine the relationship between different histological features (fibrosis, steatosis, inflammation and iron) measured with automated whole‐slide quantitative digital pathology and corresponding semiquantitative scoring systems, and the distribution of digital pathology measurements across Fatty Liver Inhibition of Progression (FLIP) algorithm and Steatosis, Activity and Fibrosis (SAF) scoring system Methods We prospectively included 136 consecutive patients who underwent liver biopsy for MAFLD at three Spanish centres (January 2017‐January 2020). Biopsies were scored by two blinded pathologists according to the Non‐alcoholic Steatohepatitis (NASH) Clinical Research Network system for fibrosis staging, the FLIP/SAF classification for steatosis and inflammation grading and Deugnier score for iron grading. Proportionate areas of collagen, fat, inflammatory cells and iron deposits were measured with computer‐assisted digital image analysis. A test‐retest experiment was performed for precision repeatability evaluation. Results Digital pathology showed strong correlation with fibrosis (r = 0.79; P < 0.001), steatosis (r = 0.85; P < 0.001) and iron (r = 0.70; P < 0.001). Performance was lower when assessing the degree of inflammation (r = 0.35; P < 0.001). NASH cases had a higher proportion of collagen and fat compared to non‐NASH cases (P < 0.005), whereas inflammation and iron quantification did not show significant differences between categories. Repeatability evaluation showed that all the coefficients of variation were ≤1.1% and all intraclass correlation coefficient values were ≥0.99, except those of collagen. Conclusion Digital pathology allows an automated, precise, objective and quantitative assessment of MAFLD histological features. Digital analysis measurements show good concordance with pathologists´ scores.

Isolated cases of basal cell carcinoma (BCC) with partial myoepithelial component have been described. However, myoepithelial differentiation has not been described in sarcomatoid basal cell carcinomas, which usually show features resembling osteosarcoma, chondrosarcoma, or leiomyosarcoma. We report a case of an 87‐year‐old man with a forehead lesion that histologically showed a minor component of conventional nodular BCC in transition with a major biphasic sarcomatoid growth composed of invasive spindle‐cell and epithelial‐like components, the latter with a reticular pattern and scattered ductal structures. Both components showed cytological atypia and high mitotic rate (26/10HPF), with atypical mitotic figures. BER‐EP4 immunostaining was exclusively found in the nodular BCC component whereas the sarcomatoid component revealed immunostaining for α‐smooth muscle actin (SMA), muscle‐specific actin (MSA), calponin, and p63 in both epithelial‐like and spindle‐cell populations. Focal immunoreactivity was observed in the epithelial component for S100 and glial fibrillary acidic protein (GFAP). Furthermore, EWSR1‐PBX1 gene fusion was also detected. This is to our knowledge, the first fully documented case of biphasic sarcomatoid BCC with myoepithelial carcinoma differentiation.

Traditional histological evaluation for grading liver disease severity is based on subjective and semi-quantitative scores. We examined the relationship between digital pathology analysis and corresponding scoring systems for the assessment of hepatic necroinflammatory activity. A prospective, multicenter study including 156 patients with chronic liver disease (74% nonalcoholic fatty liver disease-NAFLD, 26% chronic hepatitis-CH etiologies) was performed. Inflammation was graded according to the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network system and METAVIR score. Whole-slide digital image analysis based on quantitative (I-score: inflammation ratio) and morphometric (C-score: proportionate area of staining intensities clusters) measurements were independently performed. Our data show that I-scores and C-scores increase with inflammation grades (p < 0.001). High correlation was seen for CH (ρ = 0.85–0.88), but only moderate for NAFLD (ρ = 0.5–0.53). I-score (p = 0.008) and C-score (p = 0.002) were higher for CH than NAFLD. Our MATLAB algorithm performed better than QuPath software for the diagnosis of low-moderate inflammation (p < 0.05). C-score AUC for classifying NASH was 0.75 (95%CI, 0.65–0.84) and for moderate/severe CH was 0.99 (95%CI, 0.97–1.00). Digital pathology measurements increased with fibrosis stages (p < 0.001). In conclusion, quantitative and morphometric metrics of inflammatory burden obtained by digital pathology correlate well with pathologists’ scores, showing a higher accuracy for the evaluation of CH than NAFLD.