LiangruiPan/PathGene-CSU_svs

Without the consent of Xiangya Hospital of Central South University or its Pathology Department, the use of the raw data is prohibited. Should any data be used for commercial purposes, we will hold the user legally accountable. We support the following 21 pre-trained foundation models to extract the feature representation of WSI. Please contact us by email before using. (Strongly recommended!!)

Patch Encoder	Embedding Dim	Args	Link
UNI	1024	--patch_encoder uni_v1 --patch_size 256 --mag 20	MahmoodLab/UNI
UNI2-h	1536	--patch_encoder uni_v2 --patch_size 256 --mag 20	MahmoodLab/UNI2-h
CONCH	512	--patch_encoder conch_v1 --patch_size 512 --mag 20	MahmoodLab/CONCH
CONCHv1.5	768	--patch_encoder conch_v15 --patch_size 512 --mag 20	MahmoodLab/conchv1_5
Virchow	2560	--patch_encoder virchow --patch_size 224 --mag 20	paige-ai/Virchow
Virchow2	2560	--patch_encoder virchow2 --patch_size 224 --mag 20	paige-ai/Virchow2
Phikon	768	--patch_encoder phikon --patch_size 224 --mag 20	owkin/phikon
Phikon-v2	1024	--patch_encoder phikon_v2 --patch_size 224 --mag 20	owkin/phikon-v2
Prov-Gigapath	1536	--patch_encoder gigapath --patch_size 256 --mag 20	prov-gigapath
H-Optimus-0	1536	--patch_encoder hoptimus0 --patch_size 224 --mag 20	bioptimus/H-optimus-0
H-Optimus-1	1536	--patch_encoder hoptimus1 --patch_size 224 --mag 20	bioptimus/H-optimus-1
MUSK	1024	--patch_encoder musk --patch_size 384 --mag 20	xiangjx/musk
Midnight-12k	3072	--patch_encoder midnight12k --patch_size 224 --mag 20	kaiko-ai/midnight
Kaiko	384/768/1024	--patch_encoder {kaiko-vits8, kaiko-vits16, kaiko-vitb8, kaiko-vitb16, kaiko-vitl14} --patch_size 256 --mag 20	1aurent/kaikoai-models-66636c99d8e1e34bc6dcf795
Lunit	384	--patch_encoder lunit-vits8 --patch_size 224 --mag 20	1aurent/vit_small_patch8_224.lunit_dino
Hibou	1024	--patch_encoder hibou_l --patch_size 224 --mag 20	histai/hibou-L
CTransPath-CHIEF	768	--patch_encoder ctranspath --patch_size 256 --mag 10	—
ResNet50	1024	--patch_encoder resnet50 --patch_size 256 --mag 20	—

Here, we provide the feature representations of WSIs extracted by CTransPath. Link: https://pan.baidu.com/s/1zpt7D_XNgqZpLnUyOmtkgA?pwd=8yn6 (password: 8yn6).

If you require features extracted by other pre-trained models, please contact panlr@hnu.edu.cn (strongly recommended).

PathGene Datasets

PathGene-CSU

Overview

PathGene-CSU comprises whole-slide images and matched genomic labels from 1,576 lung cancer patients, including adenocarcinoma and squamous cell carcinoma subtypes.

Genomic Annotation

All samples underwent NGS profiling. For each driver gene (TP53, EGFR, KRAS, ALK), the dataset provides:

1. Mutation status: presence/absence of any mutation.
2. Mutation subtype (TP53 only): wild-type, nonsense, missense.
3. Mutational exon:
   • TP53: EX5, EX6, EX7, EX8, other
   • EGFR: EX19, EX20, EX21
   • KRAS: EX2, other (EX3+merged)
   • ALK: EML4–ALK fusion, other (non-fusion)
4. Tumor Mutation Burden (TMB): Low vs. High (9 mut/Mb cutoff)

Prediction Tasks

1. Driver gene mutation status (early genetic screening)
2. Driver gene subtype & exon (precision genetic profiling)
3. TMB status (immune-therapy response prediction)
4. Future extensions: microenvironmental biomarkers

PathGene-TCGA-LUAD

Overview

PathGene-TCGA-LUAD contains 510 histopathology slides from 448 TCGA lung adenocarcinoma patients. Slides from the same tumor share identical genomic labels.

Genomic Annotation

Using cBioPortal data:

• Mutation status (TP53, EGFR, KRAS, ALK): 0 = wild-type, 1 = mutant
• TMB: Low vs. High (10 mut/Mb cutoff)
• TP53 subtype: wild-type, nonsense, missense
• Exon labels: Not provided (low per-exon counts)

Prediction Tasks

1. Driver gene mutation status
2. TMB status

License Licensed under the MIT License.