# IDC data release v23 - Nov 2025: new analysis results collections

**URL:** <https://discourse.canceridc.dev/t/idc-data-release-v23-nov-2025-new-analysis-results-collections/764>\
**Category:** Announcements\
**Tags:** release\
**Created:** [November 26, 2025, 8:44pm UTC](https://discourse.canceridc.dev/t/idc-data-release-v23-nov-2025-new-analysis-results-collections/764 "2025-11-26T20:44:37Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![fedorov](https://sea1.discourse-cdn.com/flex015/user_avatar/discourse.canceridc.dev/fedorov/32/3_2.png) [@fedorov](https://discourse.canceridc.dev/u/fedorov)\
**Post date:** [November 26, 2025, 8:44pm UTC](https://discourse.canceridc.dev/t/idc-data-release-v23-nov-2025-new-analysis-results-collections/764/1 "2025-11-26T20:44:37Z")

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The main highlight of this new IDC data release is in **new analysis results collections** , complementing the existing images with both expert- and AI-generated annotations. Although the total size in terabytes is modest, this release is a major milestone for IDC in both the variety of annotations and the volume of analysis results.

The annotations and analysis results we release have been generated by various groups, and presented in peer-reviewed publications. With this release, all of these become available in standard DICOM representation, viewable in IDC viewers, searchable, and linked to the original images using standard DICOM capabilities. We hope these new collections will support the development and comparison of new analysis tools, and will open new opportunities for secondary analyses of the annotated images.

To support these collections and new DICOM data types, we implemented **numerous new features and bug fixes in IDC viewers** - both the OHIF Viewer (support of planar annotations) and Slim (improved robustness of segmentation visualization, support of fractional segmentations and parametric maps, improved support of bulk annotations, new UI features). There are quite a few improvements we are still working on, but we believe the tools reached the point where they provide sufficient support for these collections.

We also **updated the IDC Portal** to improve usability, and added new features to support direct download from the browser at the collection, cohort and cart level (up to ~3TB in size – for larger subsets of data you will need to use command-line download client).

See full data release notes in [https://learn.canceridc.dev/data/data-release-notes#v23-nov-2025](https://learn.canceridc.dev/data/data-release-notes#v23-nov-2025).

Quick reference if you are new to IDC:

- [Getting started with IDC Portal](https://learn.canceridc.dev/portal/getting-started)
- [Quick instructions on using IDC viewers](https://learn.canceridc.dev/portal/visualization)
- [Getting started with programmatic access](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/part2_searching_basics.ipynb)

Brief summary of the new analysis results collections follows!

## `BoneMarrow-PediatricLeukemia`

This collection is updated and now includes expert annotations of selected cells with their location and type. Read more in [the preprint](http://arxiv.org/abs/2509.15895) linked from the collection details page.

- _collection details_: Höfener, H., Kock, F., Pontones, M. A., Ghete, T., Pfrang, D., Dickel, N., Kunz, M., Schacherer, D., Clunie, D. A., Fedorov, A., Westphal, M., & Metzler, M. (2025). BoneMarrowWSI-PediatricLeukemia: A Comprehensive Dataset of Bone Marrow Aspirate Smear Whole Slide Images with Expert Annotations and Clinical Data in Pediatric Leukemia [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.17486627](https://doi.org/10.5281/zenodo.17486627)
- _open in IDC Portal_: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection\_id=Community&collection\_id=bonemarrowwsi\_pediatricleukemia](https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=bonemarrowwsi_pediatricleukemia)
- _sample case_: [https://viewer.imaging.datacommons.cancer.gov/slim/studies/1.2.826.0.1.3680043.8.498.24144283661646178704245748389871574522/series/1.2.826.0.1.3680043.8.498.8159440367804505500314614006287096679](https://viewer.imaging.datacommons.cancer.gov/slim/studies/1.2.826.0.1.3680043.8.498.24144283661646178704245748389871574522/series/1.2.826.0.1.3680043.8.498.8159440367804505500314614006287096679)  
 ![2025-11-26_14-50-21](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/7e3fb473161a6cc64b685c0930191131e4707e2d.gif)

## `TCGA-SBU-TIL-Maps`

Tumor Infiltrating Lymphocyte annotations for a subset of TCGA slides generated using algorithms developed by Joel Saltz team at Stony Brook. Some of those [were shared earlier](https://doi.org/10.7937/K9/TCIA.2018.Y75F9W1) in CSV format by TCIA.

- collection details: Bridge, C., Abousamra, S., Saltz, J., Gupta, R., Kurc, T., Zhang, Y., Zhao, T., Batiste, R., Samaras, D., Bremer, E., Shroyer, K. R., Nguyen, V., Singh, P., Hou, L., Arnam, J. V., Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., … Fedorov, A. (2025). TCGA-SBU-TIL-Maps: AI-derived Tumor Infiltrating Lymphocyte maps for the TCGA collections [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.16966286](https://doi.org/10.5281/zenodo.16966286)
- open in IDC Portal: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis\_results\_id=TCGA-SBU-TIL-Maps](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TCGA-SBU-TIL-Maps)
- example visualization: [https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.49355557987400344027775686673704794205/series/1.3.6.1.4.1.5962.99.1.3140643155.174517037.1639523215699.2.0](https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.49355557987400344027775686673704794205/series/1.3.6.1.4.1.5962.99.1.3140643155.174517037.1639523215699.2.0)  
 ![2025-11-26_12-52-10](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/48a2f8ffa0d1a649823f5e129d34aface37bdeb3.gif)

## `TCGA-GBM360`

Aggressiveness maps described in [this Nature Communications paper](https://doi.org/10.1038/s41467-023-39933-0), generated using [`GBM360` software](https://github.com/gevaertlab/GBM360) from the Gevaert Lab at Stanford for a subset of TCGA pathology slides.

- _collection details_: Bridge, C., Zheng, Y., Gevaert, O., Clunie, D. A., & Fedorov, A. (2025). TCGA-GBM360: GBM360 aggressiveness maps for a subset of TCGA pathology slides [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.17470191](https://doi.org/10.5281/zenodo.17470191)
- _open in IDC Portal_: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis\_results\_id=TCGA-GBM360](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TCGA-GBM360)
- _example visualization_: [https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.103659964951665749659160840573802789777/series/1.3.6.1.4.1.5962.99.1.1131469279.179373488.1637514041823.2.0](https://viewer.imaging.datacommons.cancer.gov/slim/studies/2.25.103659964951665749659160840573802789777/series/1.3.6.1.4.1.5962.99.1.1131469279.179373488.1637514041823.2.0)  
 ![2025-11-26_13-02-46](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/3846ff764e0789da18f1bd40541bfe9158104d01.gif)

## `NLST-Sybil`

Expert annotations of the lung lesions in the NLST CT images converted from the JSON representation [released earlier](https://github.com/reginabarzilaygroup/Sybil) by the Regina Barzilay group at MIT, and described in [this Journal of Clinical Oncology paper](https://doi.org/10.1200/JCO.22.01345).

- _collection details_: Krishnaswamy, D., Clunie, D. A., & Fedorov, A. (2025). NLST-Sybil: Expert annotations of tumor regions in the NLST CT images [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.15643335](https://doi.org/10.5281/zenodo.15643335)
- _open in IDC Portal_: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis\_results\_id=NLST-Sybil](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=NLST-Sybil)
- _example visualization_: [https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.2.840.113654.2.55.179174366077312471225650070175400666493](https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.2.840.113654.2.55.179174366077312471225650070175400666493)  
 ![image](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/38646955d2eaf473b1005d3f7812c88b61d20e90.png)

## `NLSTSeg`

Volumetric segmentations of the lung lesions in the NLST CT images converted from NIfTI representation [published earlier by Chen et al.](https://doi.org/10.1038/s41597-025-05742-x).

- _collection details_: Krishnaswamy, D., Clunie, D. A., & Fedorov, A. (2025). NLSTSeg: Expert lesion segmentations and radiomics features for NLST CT images [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.17362625](https://doi.org/10.5281/zenodo.17362625)
- _open in IDC Portal_: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis\_results\_id=NLSTSeg](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=NLSTSeg)
- _example visualization_: [https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.2.840.113654.2.55.82681520469274836520987761108780143488](https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.2.840.113654.2.55.82681520469274836520987761108780143488)  
 ![image](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/4bdb8fee319f78d6619e013dc99b2a78efd0df11.jpeg)

## `Lung-PET-CT-Dx-Annotations`

Annotations of the lung lesions in the `Lung-PET-CT-Dx` collection, [originally shared in TCIA](https://doi.org/10.7937/TCIA.2020.NNC2-0461) in the XML format.

- _collection details_: Krishnaswamy, D., Clunie, D. A., & Fedorov, A. (2025). Lung-PET-CT-Dx-Annotations: Expert annotation of lung tumors for the Lung-PET-CT-Dx collection [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.16989820](https://doi.org/10.5281/zenodo.16989820)
- _open in IDC Portal_: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis\_results\_id=Lung-PET-CT-Dx-Annotations](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Lung-PET-CT-Dx-Annotations)
- _example visualization_: [https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.3.6.1.4.1.14519.5.2.1.6655.2359.333670311640916222763343578453](https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.3.6.1.4.1.14519.5.2.1.6655.2359.333670311640916222763343578453)  
 ![image](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/c8a60ecf1af000208018aa961d0e8df62459c503.jpeg)

## `ProstateX-Targets`

Annotations of the cancer-suspected biopsy targets in the `ProstateX` MR images, along with the indication of clinical significance and Gleason grade for the sampled regions, [originally shared in TCIA](https://doi.org/10.7937/K9TCIA.2017.MURS5CL) in the CSV format.

- _collection details_: Krishnaswamy, D., Clunie, D. A., & Fedorov, A. (2025). PROSTATEx-Targets: Point annotations of lesion targets for the PROSTATEx collection [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.15643313](https://doi.org/10.5281/zenodo.15643313)
- _open in IDC Portal_: [https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis\_results\_id=ProstateX-Targets](https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=ProstateX-Targets)
- _example visualization_: [https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.3.6.1.4.1.14519.5.2.1.7311.5101.316302757120840825688456720609](https://viewer.imaging.datacommons.cancer.gov/v3/viewer/?StudyInstanceUIDs=1.3.6.1.4.1.14519.5.2.1.7311.5101.316302757120840825688456720609)  
 ![image](https://us1.discourse-cdn.com/flex015/uploads/canceridc/original/1X/88817b1400d92432672b955e5d06f77e1f31d67c.jpeg)  
(yes, we do have remaining work to improve text annotations rendered by the viewer, but for now at least you can easily see the locations of the targets!)

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<div class="post-metadata">

**Author:** ![fedorov](https://sea1.discourse-cdn.com/flex015/user_avatar/discourse.canceridc.dev/fedorov/32/3_2.png) [@fedorov](https://discourse.canceridc.dev/u/fedorov)\
**Post date:** [November 26, 2025, 10:19pm UTC](https://discourse.canceridc.dev/t/idc-data-release-v23-nov-2025-new-analysis-results-collections/764/2 "2025-11-26T22:19:11Z")

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To see annotations as shown in the post above, you will need to load the specific annotations!

For radiology annotations, you will need to click on the individual SEG/SR series in the left panel.

For microscopy annotations, expand the Segmentations/Annotation Groups sections in the bottom right part of the interface and toggle visibility. You can also double-click individual annotation groups to zoom into the area where annotations are located (since, for example, `BoneMarrow-PediatricLeukemia` annotations cover just a few small rectangular regions of the image). These details are covered briefly in the visualization documentation page here: [Visualizing images | IDC User Guide](https://learn.canceridc.dev/portal/visualization).