{"id":8330,"date":"2024-10-09T21:38:01","date_gmt":"2024-10-10T01:38:01","guid":{"rendered":"https:\/\/labs.icahn.mssm.edu\/minervalab\/?page_id=8330"},"modified":"2026-10-29T13:23:25","modified_gmt":"2026-10-29T17:23:25","slug":"digital-pathology-slides","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/minervalab\/resources\/data-ark\/digital-pathology-slides\/","title":{"rendered":"Digital Pathology Slides"},"content":{"rendered":"

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Scientific Computing and Data<\/a> \/ Research Data Services<\/a> \/\u00a0Data Ark: Data Commons<\/a> \/ Digital Pathology Slides<\/p>\n

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AIR\u00b7MS: De-identified Digital Pathology Slides<\/b><\/h1>\n

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Overview<\/span><\/strong><\/h2>\n

The Department of Pathology, Molecular and Cell-Based Medicine, and Scientific Computing and Data are pleased to announce the incorporation of linked data into our research repository of over 2.5 million de-identified<\/b> digital pathology slides<\/b><\/span>. This new data linking, which connects de-identified patient information with digital images of pathology slides (stored on the Minerva supercomputer) will make it possible to perform image analysis and use machine-learning or artificial intelligence at scale across millions of slides, and open exciting opportunities for research and discovery. The linked slides are accessible to any user who agrees to a<\/span> <\/span>Data Use Agreement<\/span><\/b><\/a> and can be accessed through AI Ready Mount Sinai (AIR\u00b7MS)<\/a>.<\/b>\u00a0<\/span><\/p>\n

This extensive collection represents a wide range of organ systems, such as lung, heart, pancreas, kidney, liver, gastrointestinal, genitourinary, gynecological, hematological, and neuropathological systems. The repository encompasses a broad spectrum of disease processes, such as neoplastic, developmental, inflammatory, toxic, metabolic, genetic, degenerative, traumatic, and infectious pathologies.\u00a0\u00a0<\/span><\/p>\n

Slides have been prepared using diverse staining techniques, ranging from routine hematoxylin and eosin (H&E)<\/b> to specialized stains such as silver<\/b>, trichrome<\/b>, and immunohistochemistry<\/b>. Together, this rich and diverse data set provides a powerful resource for advancing the study of human disease through digital pathology.\u00a0<\/span><\/p>\n

To learn more about Digital Pathology Slides and how to use them in your research, be sure to join the upcoming training session on November 12, 2026, 10.00-11.00 a.m. EST<\/span>!<\/b><\/span><\/h3>\n

Register in advance for this webinar: <\/b><\/span>https:\/\/tinyurl.com\/yc5v3d57<\/u><\/b><\/a><\/p>\n

After registering, you will receive a confirmation email containing information about joining the webinar.<\/span><\/p>\n

\u00a0<\/div>\n
\u00a0<\/div>\n

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What Is Hosted Under AIR\u00b7MS?<\/h2>\n

Currently, AIR\u00b7MS hosts over 2.6 million de-identified digital pathology slides linked with de-identified patient information, with more becoming available on a continuing basis. The slides were scanned on the Philips Ultrafast at 40x magnification. iSyntax files were converted to TIF. Slides served through AIR\u00b7MS have been de-identified and digitized into a readable TIF format\u200b.<\/span><\/p>\n

[\/et_pb_text][et_pb_image admin_label=”Pathology Slide” src=”https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-content\/uploads\/sites\/342\/2024\/10\/DigitalPathologySlides.png” title_text=”DigitalPathologySlides” align=”center” _builder_version=”4.27.4″ _module_preset=”default” width=”80%” max_width=”80%” custom_margin=”10px||||false|false” global_colors_info=”{}”]<\/p>\n

[\/et_pb_image][et_pb_text admin_label=”Pathology Slide Caption” _builder_version=”4.27.4″ _module_preset=”default” text_line_height=”1.5em” width=”80%” max_width=”80%” module_alignment=”center” custom_margin=”50px||||false|false” global_colors_info=”{}”]<\/p>\n

Figure 1. Digitized pathology slides are gigapixel images that can span hundreds of thousands of pixels in each dimension.<\/p>\n

[\/et_pb_text][et_pb_text admin_label=”Demographics” _builder_version=”4.27.4″ _module_preset=”default” custom_margin=”50px||||false|false” global_colors_info=”{}” background_pattern_color=”rgba(0,0,0,0.2)” background_mask_color=”#ffffff” text_text_shadow_horizontal_length=”text_text_shadow_style,%91object Object%93″ text_text_shadow_horizontal_length_tablet=”0px” text_text_shadow_vertical_length=”text_text_shadow_style,%91object Object%93″ text_text_shadow_vertical_length_tablet=”0px” text_text_shadow_blur_strength=”text_text_shadow_style,%91object Object%93″ text_text_shadow_blur_strength_tablet=”1px” link_text_shadow_horizontal_length=”link_text_shadow_style,%91object Object%93″ link_text_shadow_horizontal_length_tablet=”0px” link_text_shadow_vertical_length=”link_text_shadow_style,%91object Object%93″ link_text_shadow_vertical_length_tablet=”0px” link_text_shadow_blur_strength=”link_text_shadow_style,%91object Object%93″ link_text_shadow_blur_strength_tablet=”1px” ul_text_shadow_horizontal_length=”ul_text_shadow_style,%91object Object%93″ ul_text_shadow_horizontal_length_tablet=”0px” ul_text_shadow_vertical_length=”ul_text_shadow_style,%91object Object%93″ ul_text_shadow_vertical_length_tablet=”0px” ul_text_shadow_blur_strength=”ul_text_shadow_style,%91object Object%93″ ul_text_shadow_blur_strength_tablet=”1px” ol_text_shadow_horizontal_length=”ol_text_shadow_style,%91object Object%93″ ol_text_shadow_horizontal_length_tablet=”0px” ol_text_shadow_vertical_length=”ol_text_shadow_style,%91object Object%93″ ol_text_shadow_vertical_length_tablet=”0px” ol_text_shadow_blur_strength=”ol_text_shadow_style,%91object Object%93″ ol_text_shadow_blur_strength_tablet=”1px” quote_text_shadow_horizontal_length=”quote_text_shadow_style,%91object Object%93″ quote_text_shadow_horizontal_length_tablet=”0px” quote_text_shadow_vertical_length=”quote_text_shadow_style,%91object Object%93″ quote_text_shadow_vertical_length_tablet=”0px” quote_text_shadow_blur_strength=”quote_text_shadow_style,%91object Object%93″ quote_text_shadow_blur_strength_tablet=”1px” header_text_shadow_horizontal_length=”header_text_shadow_style,%91object Object%93″ header_text_shadow_horizontal_length_tablet=”0px” header_text_shadow_vertical_length=”header_text_shadow_style,%91object Object%93″ header_text_shadow_vertical_length_tablet=”0px” header_text_shadow_blur_strength=”header_text_shadow_style,%91object Object%93″ header_text_shadow_blur_strength_tablet=”1px” header_2_text_shadow_horizontal_length=”header_2_text_shadow_style,%91object Object%93″ header_2_text_shadow_horizontal_length_tablet=”0px” header_2_text_shadow_vertical_length=”header_2_text_shadow_style,%91object Object%93″ header_2_text_shadow_vertical_length_tablet=”0px” header_2_text_shadow_blur_strength=”header_2_text_shadow_style,%91object Object%93″ header_2_text_shadow_blur_strength_tablet=”1px” header_3_text_shadow_horizontal_length=”header_3_text_shadow_style,%91object Object%93″ header_3_text_shadow_horizontal_length_tablet=”0px” header_3_text_shadow_vertical_length=”header_3_text_shadow_style,%91object Object%93″ header_3_text_shadow_vertical_length_tablet=”0px” header_3_text_shadow_blur_strength=”header_3_text_shadow_style,%91object Object%93″ header_3_text_shadow_blur_strength_tablet=”1px” header_4_text_shadow_horizontal_length=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_horizontal_length_tablet=”0px” header_4_text_shadow_vertical_length=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_vertical_length_tablet=”0px” header_4_text_shadow_blur_strength=”header_4_text_shadow_style,%91object Object%93″ header_4_text_shadow_blur_strength_tablet=”1px” header_5_text_shadow_horizontal_length=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_horizontal_length_tablet=”0px” header_5_text_shadow_vertical_length=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_vertical_length_tablet=”0px” header_5_text_shadow_blur_strength=”header_5_text_shadow_style,%91object Object%93″ header_5_text_shadow_blur_strength_tablet=”1px” header_6_text_shadow_horizontal_length=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_horizontal_length_tablet=”0px” header_6_text_shadow_vertical_length=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_vertical_length_tablet=”0px” header_6_text_shadow_blur_strength=”header_6_text_shadow_style,%91object Object%93″ header_6_text_shadow_blur_strength_tablet=”1px” box_shadow_horizontal_tablet=”0px” box_shadow_vertical_tablet=”0px” box_shadow_blur_tablet=”40px” box_shadow_spread_tablet=”0px” vertical_offset_tablet=”0″ horizontal_offset_tablet=”0″ z_index_tablet=”0″]<\/p>\n

Example: Digital pathology slides collected from 191,119 patients through October 1, 2024, with demographic information detailed below.<\/p>\n

[\/et_pb_text][et_pb_text admin_label=”Table Caption” _builder_version=”4.16″ _module_preset=”default” text_line_height=”1.5em” custom_margin=”30px||||false|false” global_colors_info=”{}”]<\/p>\n

Table 1. Cohort demographics: (a) gender, (b) race and ethnicity, and (c) age.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”Tables” column_structure=”1_3,1_3,1_3″ _builder_version=”4.16″ _module_preset=”default” custom_margin=”0px||0px||false|false” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_code admin_label=”Gender Table” _builder_version=”4.16″ _module_preset=”default” width=”40%” max_width=”40%” module_alignment=”left” custom_margin=”-100px||||false|false” global_colors_info=”{}”](a)<\/p>\n<\/p>\n\n\n <\/p>\n\n\n\n\n
Gender<\/th>\nCount<\/th>\n<\/tr>\n<\/thead>\n
Female<\/td>\n120,121<\/td>\n<\/tr>\n

<\/p>\n

Indeterminate<\/td>\n13<\/td>\n<\/tr>\n

<\/p>\n

Male<\/td>\n70,945<\/td>\n<\/tr>\n

<\/p>\n

Unknown<\/td>\n40<\/td>\n<\/tr>\n

<\/tbody>\n<\/table>\n

[\/et_pb_code][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_code admin_label=”Race Ethnicity Table” _builder_version=”4.16″ _module_preset=”default” width=”97%” max_width=”97%” module_alignment=”center” custom_margin=”-100px||||false|false” global_colors_info=”{}”](b)<\/p>\n<\/p>\n\n\n <\/p>\n\n\n\n\n\n\n\n\n
Race Ethnicity Combined<\/th>\nCount<\/th>\n<\/tr>\n<\/thead>\n
American Indian or Alaska Native<\/td>\n140<\/td>\n<\/tr>\n

<\/p>\n

Asian<\/td>\n14,071<\/td>\n<\/tr>\n

<\/p>\n

Black or African-American<\/td>\n28,323<\/td>\n<\/tr>\n

<\/p>\n

Hispanic<\/td>\n35,508<\/td>\n<\/tr>\n

<\/p>\n

Native Hawaiian or Pacific Islander<\/td>\n159<\/td>\n<\/tr>\n

<\/p>\n

Patient Declined<\/td>\n958<\/td>\n<\/tr>\n

<\/p>\n

White<\/td>\n66,515<\/td>\n<\/tr>\n

<\/p>\n

Unknown\/Other\/Not Reported<\/td>\n45,445<\/td>\n<\/tr>\n

<\/tbody>\n<\/table>\n

[\/et_pb_code][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_code admin_label=”Age Group” _builder_version=”4.16″ _module_preset=”default” width=”62%” max_width=”62%” module_alignment=”right” custom_margin=”-100px||||false|false” global_colors_info=”{}”](c)<\/p>\n<\/p>\n\n\n <\/p>\n\n\n\n\n\n\n\n\n\n\n
Age Group<\/th>\nCount<\/th>\n<\/tr>\n<\/thead>\n
0-10<\/td>\n1,377<\/td>\n<\/tr>\n

<\/p>\n

11-20<\/td>\n3,239<\/td>\n<\/tr>\n

<\/p>\n

21-30<\/td>\n14,950<\/td>\n<\/tr>\n

<\/p>\n

31-40<\/td>\n28,684<\/td>\n<\/tr>\n

<\/p>\n

41-50<\/td>\n31,704<\/td>\n<\/tr>\n

<\/p>\n

51-60<\/td>\n35,551<\/td>\n<\/tr>\n

<\/p>\n

61-70<\/td>\n38,128<\/td>\n<\/tr>\n

<\/p>\n

71-80<\/td>\n26,597<\/td>\n<\/tr>\n

<\/p>\n

81-90<\/td>\n9,424<\/td>\n<\/tr>\n

<\/p>\n

91+<\/td>\n1,465<\/td>\n<\/tr>\n

<\/tbody>\n<\/table>\n

[\/et_pb_code][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text admin_label=”Digital Slides Reduce Processing Time” _builder_version=”4.16″ _module_preset=”default” text_line_height=”1.5em” custom_margin=”50px||||false|false” global_colors_info=”{}”]<\/p>\n

The digital pathology technology effectively reduces process time<\/h4>\n

<\/span><\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/minervalab\/wp-content\/uploads\/sites\/342\/2024\/10\/DigitalPathology2.jpg” title_text=”Created with GIMP” align=”center” _builder_version=”4.16″ _module_preset=”default” width=”60%” max_width=”60%” module_alignment=”center” custom_margin=”30px||80px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”]<\/p>\n

[\/et_pb_image][et_pb_text admin_label=”Image Caption” _builder_version=”4.16″ _module_preset=”default” width=”60%” max_width=”60%” module_alignment=”center” custom_margin=”||20px||false|false” custom_padding=”0px||20px||false|false” global_colors_info=”{}”]<\/p>\n

Figure 1. Value stream mapping of simple (a) traditional glass slide vs. (b) digital slide consult. Image adapted from\u00a0Haghighi, Mehrvash,\u00a0et al. 2021. doi:\u00a010.4103\/jpi.jpi_74_21<\/a>.<\/p>\n

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\u00a0<\/h2>\n

Access<\/h2>\n