Derived per-slide features supporting the PathSeek-MAS study (npj Digital Medicine). The task is platinum response prediction in high-grade serous ovarian carcinoma (HGSOC) from H&E whole-slide images (WSIs), using a dual-agent model:

Virtual Protein Agent — virtual multiplex immunofluorescence (mIF): 21 protein markers predicted per patch by GigaTIME (gigatime_csv/).
Morphology Agent — UNI foundation-model patch embeddings (uni_pt/).

Cohorts (503 WSIs)

Cohort	Slides	Sensitive (label 1)	Resistant (label 0)	WSI source
TCGA-OV	156	117	39	GDC
PTRC-HGSOC	347	202	145	Chowdhury et al.

Label convention (both cohorts): label == 1 = Sensitive, label == 0 = Resistant. Ground truth: TCGA PlatinumStatus (Zhang et al., mmc2.xlsx); PTRC Tumor response (sensitive→1, refractory→0).

Files

TCGA-OV/   PTRC-HGSOC/
  gigatime_csv/   # one CSV per slide: virtual-mIF predicted protein values per patch
  uni_pt/         # one .pt per slide: UNI patch embeddings (dict: 'features' [N,1024], 'coords' [N,2])
  labels.csv      # slide_id, case_id, label

`gigatime_csv/*.csv` schema (27 columns)

patch_name, slide_id, row, col, then 23 channels (21 protein markers + 2 staining channels): DAPI, TRITC, Cy5, PD-1, CD14, CD4, T-bet, CD34, CD68, CD16, CD11c, CD138, CD20, CD3, CD8, PD-L1, CK, Ki67, Tryptase, Actin-D, Caspase3-D, PHH3-B, Transgelin. row, col are the patch grid coordinates (used to place values spatially on the WSI). Note: DAPI and Cy5 are staining/imaging channels, not protein markers; the remaining 21 are antibody-targeted proteins.

`uni_pt/*.pt`

torch.load(...) returns a dict with features ([n_patches, 1024] float) and coords ([n_patches, 2] int patch coordinates).

Preprocessing used by the model

Virtual-protein input: TCGA → log1p, PTRC → logit, then per-patch row-standardization.
Both agents pool patches via gated attention; fused by a cross-modal gating head.

Not included

Raw WSIs (~200 GB): TCGA-OV via the NCI GDC; PTRC-HGSOC via the original study. Not re-hosted.
Original third-party clinical supplementary tables — please cite the source publications.