A fully automated image analysis method to quantify lung fibrosis in the bleomycin-induced rat model

<div><p>Intratracheal administration of bleomycin induces fibrosis in the lung, which is mainly assessed by histopathological grading that is subjective. Current literature highlights the need of reproducible and quantitative pulmonary fibrosis analysis. If some quantitative studies looked at fibrosis parameters separately, none of them quantitatively assessed both aspects: lung tissue remodeling and collagenization. To ensure reliable quantification, support vector machine learning was used on digitalized images to design a fully automated method that analyzes two important aspects of lung fibrosis: (i) areas having substantial tissue remodeling with appearance of dense fibrotic masses and (ii) collagen deposition. Fibrotic masses were identified on low magnification images and collagen detection was performed at high magnification. To insure a fully automated application the tissue classifier was trained on several independent studies that were performed over a period of four years. The detection method generates two different values that can be used to quantify lung fibrosis development: (i) percent area of fibrotic masses and (ii) percent of alveolar collagen. These two parameters were validated using independent studies from bleomycin- and saline-treated animals. A significant change of these lung fibrosis quantification parameters- increased amount of fibrotic masses and increased collagen deposition- were observed upon intratracheal administration of bleomycin and subsequent significant beneficial treatments effects were observed with BIBF-1120 and pirfenidone.</p></div

Fibrosis feature detection.

<p>Masson’s trichrome stained lung sections from study 3, Bleomycin (A, C, E, F) and Saline (B, D, F, H) animals, <b>A and B</b> at low magnification, scale bar 2.5mm <b>E and F</b> alveolar structure at high magnification, scale bar 50μm. Upper right square: zoom in one alveola. <b>C and D</b>: low scale detection model; false colour image indicates areas of dense fibrosis (green), alveolar tissue (blue), bronchi (red), background (yellow). <b>G and H:</b> high magnification detection; false colour image indicates collagen (blue), lung tissue (red), background (yellow). Upper right square: zoom in one alveola collagen detection.</p

Inter study reproducibility of the quantification method.

<p>Automated image analysis was applied to five independent studies, performed over 2 years (3 independent studies for each time point: D14 and D28). <b>A</b>: percentage of fibrotic masses per lung (excluding bronchi for normalization), <b>B</b>: percentage of alveolar collagen deposition. Mean with +/- SEM, Mann-Whitney statistical test. Each dot represents an individual animal and color represents independent studies. Study 2: red, study 3: purple, study 4: dark blue, study 5: green, and study 6: light blue.</p

Fibrosis features used for morphometric detection.

<p>Digitalized slides of Masson’s tri-chrome stained lung paraffin sections. Collagen is stained in blue and cell nuclei in red. <b>A</b>: alveolar thickening by collagen deposition. <b>B</b>: fibrotic masses composed of collagen, fibroblasts, and other components.</p

Quantitative analysis of anti-fibrotic compound effects.

<p>Lungs from Wistar rats treated with bleomycin in combination with pirfenidone (0,50%) and BIBF-1120 (50mg/kg) were collected at Day 14 and a Masson’s trichrome staining was performed for fibrosis parameter assessment using automated image analysis. <b>A</b>: percentage of fibrotic masses, <b>B</b>: percentage of alveolar collagen. Mean with +/- SEM, Mann-Whitney statistical test. Each dot represents an individual animal and color represents independent studies. Study 2 in red, and study 6 in blue.</p

sj-docx-1-spp-10.1177_01926233231178282 – Supplemental material for European Society of Toxicologic Pathology (Pathology 2.0 Molecular Pathology Special Interest Group): Review of In Situ Hybridization Techniques for Drug Research and Development

Supplemental material, sj-docx-1-spp-10.1177_01926233231178282 for European Society of Toxicologic Pathology (Pathology 2.0 Molecular Pathology Special Interest Group): Review of In Situ Hybridization Techniques for Drug Research and Development by Josep M. Monné Rodríguez, Anna-Lena Frisk, Robert Kreutzer, Thomas Lemarchand, Stephane Lezmi, Chandrassegar Saravanan, Birgit Stierstorfer, Céline Thuilliez, Enrico Vezzali, Grazyna Wieczorek, Seong-Wook Yun and Dirk Schaudien in Toxicologic Pathology</p

sj-tif-30-tpx-10.1177_01926233231182115 – Supplemental material for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

Supplemental material, sj-tif-30-tpx-10.1177_01926233231182115 for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology by Xavier Palazzi, Erio Barale-Thomas, Bhupinder Bawa, Jonathan Carter, Kyathanahalli Janardhan, Heike Marxfeld, Abraham Nyska, Chandrassegar Saravanan, Dirk Schaudien, Vanessa L. Schumacher, Robert H. Spaet, Simone Tangermann, Oliver C Turner and Enrico Vezzali in Toxicologic Pathology</p

sj-tif-5-tpx-10.1177_01926233231182115 – Supplemental material for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

Supplemental material, sj-tif-5-tpx-10.1177_01926233231182115 for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology by Xavier Palazzi, Erio Barale-Thomas, Bhupinder Bawa, Jonathan Carter, Kyathanahalli Janardhan, Heike Marxfeld, Abraham Nyska, Chandrassegar Saravanan, Dirk Schaudien, Vanessa L. Schumacher, Robert H. Spaet, Simone Tangermann, Oliver C Turner and Enrico Vezzali in Toxicologic Pathology</p

sj-tif-1-tpx-10.1177_01926233231182115 – Supplemental material for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

Supplemental material, sj-tif-1-tpx-10.1177_01926233231182115 for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology by Xavier Palazzi, Erio Barale-Thomas, Bhupinder Bawa, Jonathan Carter, Kyathanahalli Janardhan, Heike Marxfeld, Abraham Nyska, Chandrassegar Saravanan, Dirk Schaudien, Vanessa L. Schumacher, Robert H. Spaet, Simone Tangermann, Oliver C Turner and Enrico Vezzali in Toxicologic Pathology</p

sj-tif-16-tpx-10.1177_01926233231182115 – Supplemental material for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

Supplemental material, sj-tif-16-tpx-10.1177_01926233231182115 for Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology by Xavier Palazzi, Erio Barale-Thomas, Bhupinder Bawa, Jonathan Carter, Kyathanahalli Janardhan, Heike Marxfeld, Abraham Nyska, Chandrassegar Saravanan, Dirk Schaudien, Vanessa L. Schumacher, Robert H. Spaet, Simone Tangermann, Oliver C Turner and Enrico Vezzali in Toxicologic Pathology</p