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TCGA-GBM

Last updated 2026-02-04 16:27 · Cached 2026-02-08 16:58

Features Inference Manifest Tiling Training

Stage Summary Pipeline status at a glance Pipeline

Features
Encoders detected: gigapath_ft (858 files, 244 with labels), h-optimus-0 (858 files, 244 with labels), prov-gigapath (858 files, 244 with labels), uni (858 files, 244 with labels), virchow (858 files, 244 with labels), virchow2 (858 files, 244 with labels)
Inference
360 prediction(s) · 115 positive label(s) · dataset tcga
Manifest
244 rows, 13 columns
Tiling
Tiling manifest covers 860 case(s), 244 with labels
Training
90 training run(s)

Case Summary Track cohort coverage by encoder Cohort

Cases Inspect case-level tiles and encoders Cases

Manifest Overview Preview the manifest and target distribution Manifest

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244 rows · 9 columns

Target: PTEN_BINARY Target: PTEN

slide_id	PTEN_binary	DMP_ASSAY_ID	split_1	split_2	split_3	split_4	split_5	PTEN
TCGA-02-0003_TCGA-02-0003-01Z-00-DX1	0	TCGA-02-0003_TCGA-02-0003-01Z-00-DX1	test	train	test	train	train	Negative
TCGA-02-0033_TCGA-02-0033-01Z-00-DX1	0	TCGA-02-0033_TCGA-02-0033-01Z-00-DX1	test	train	test	test	train	Negative
TCGA-02-0047_TCGA-02-0047-01Z-00-DX1	0	TCGA-02-0047_TCGA-02-0047-01Z-00-DX1	test	train	test	test	train	Negative
TCGA-06-0122_TCGA-06-0122-01Z-00-DX2	0	TCGA-06-0122_TCGA-06-0122-01Z-00-DX2	train	test	train	train	test	Negative
TCGA-06-0124_TCGA-06-0124-01Z-00-DX6	1	TCGA-06-0124_TCGA-06-0124-01Z-00-DX6	train	test	train	train	test	Positive

Cross-Validation Performance Compare experiments across validation splits Validation

Training Runs Artifacts and metrics for each experiment Experiments

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Model	Target	Aggregator	Split	Epoch 200	Macro AUC	Macro BER	Macro FPR	Macro FNR	Artifact
prov-gigapath	PTEN	GMA PUB	split_2	120	0.5364010989010989	0.4697802197802198	0.2857142857142857	0.6538461538461539	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_3	120	0.567003367003367	0.4892255892255892	0.1636363636363636	0.8148148148148148	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_2	120	0.4395604395604395	0.5027472527472527	0.4285714285714285	0.5769230769230769	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_5	120	0.5024561403508773	0.5414035087719298	0.1228070175438596	0.96	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_3	120	0.5350168350168352	0.4245791245791246	0.1454545454545454	0.7037037037037037	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_2	120	0.5463598901098902	0.5054945054945055	0.3571428571428571	0.6538461538461539	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_4	120	0.5877192982456141	0.4466666666666666	0.3333333333333333	0.56	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_3	120	0.4895622895622896	0.4979797979797979	0.2181818181818181	0.7777777777777778	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_5	120	0.5571929824561404	0.527719298245614	0.175438596491228	0.88	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_1	120	0.5725371120107963	0.4932523616734143	0.1403508771929824	0.8461538461538461	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_5	120	0.4378947368421053	0.4901754385964912	0.1403508771929824	0.84	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_1	120	0.5573549257759784	0.49527665317139	0.2982456140350877	0.6923076923076923	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_5	120	0.4021052631578947	0.6266666666666667	0.3333333333333333	0.92	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_1	120	0.5006747638326585	0.5354251012145749	0.2631578947368421	0.8076923076923077	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_4	120	0.5301754385964913	0.5042105263157894	0.3684210526315789	0.64	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_1	120	0.5040485829959516	0.5074224021592443	0.2456140350877192	0.7692307692307693	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_4	120	0.5101754385964912	0.5817543859649122	0.4035087719298245	0.76	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_1	120	0.5573549257759784	0.49527665317139	0.2982456140350877	0.6923076923076923	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_3	120	0.4558922558922559	0.4515151515151515	0.2363636363636363	0.6666666666666666	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_2	120	0.5364010989010989	0.4697802197802198	0.2857142857142857	0.6538461538461539	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_5	120	0.4863157894736841	0.5252631578947369	0.2105263157894736	0.84	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_5	120	0.4863157894736842	0.5828070175438597	0.2456140350877192	0.92	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_2	120	0.4395604395604395	0.5027472527472527	0.4285714285714285	0.5769230769230769	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_5	120	0.4378947368421053	0.4901754385964912	0.1403508771929824	0.84	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_5	120	0.4021052631578947	0.6266666666666667	0.3333333333333333	0.92	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_2	120	0.5027472527472528	0.4711538461538461	0.25	0.6923076923076923	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_5	120	0.4863157894736841	0.5252631578947369	0.2105263157894736	0.84	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_3	120	0.5932659932659933	0.4528619528619528	0.0909090909090909	0.8148148148148148	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_4	120	0.5877192982456141	0.4466666666666666	0.3333333333333333	0.56	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_3	120	0.5350168350168352	0.4245791245791246	0.1454545454545454	0.7037037037037037	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_5	120	0.5571929824561404	0.527719298245614	0.175438596491228	0.88	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_4	120	0.5080701754385966	0.4603508771929824	0.2807017543859649	0.64	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_2	120	0.4800824175824175	0.5350274725274725	0.3392857142857143	0.7307692307692307	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_3	120	0.5932659932659933	0.4528619528619528	0.0909090909090909	0.8148148148148148	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_2	120	0.5027472527472528	0.4711538461538461	0.25	0.6923076923076923	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_4	120	0.5971929824561404	0.4778947368421052	0.3157894736842105	0.64	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_5	120	0.4863157894736842	0.5828070175438597	0.2456140350877192	0.92	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_1	120	0.4973009446693657	0.5354251012145749	0.2631578947368421	0.8076923076923077	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_4	120	0.5101754385964912	0.5817543859649122	0.4035087719298245	0.76	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_2	120	0.4196428571428571	0.5762362637362637	0.2678571428571428	0.8846153846153846	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_1	120	0.5199055330634278	0.5074224021592443	0.2456140350877192	0.7692307692307693	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_2	120	0.4196428571428571	0.5762362637362637	0.2678571428571428	0.8846153846153846	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_1	120	0.5199055330634278	0.5074224021592443	0.2456140350877192	0.7692307692307693	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_4	120	0.5971929824561404	0.4778947368421052	0.3157894736842105	0.64	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_3	120	0.4895622895622896	0.4979797979797979	0.2181818181818181	0.7777777777777778	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_3	120	0.594949494949495	0.4703703703703703	0.2	0.7407407407407407	`cumulative_results.csv`
uni	PTEN	GMA PUB	split_1	120	0.4973009446693657	0.5354251012145749	0.2631578947368421	0.8076923076923077	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_1	120	0.5040485829959516	0.5074224021592443	0.2456140350877192	0.7692307692307693	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_4	120	0.5301754385964913	0.5042105263157894	0.3684210526315789	0.64	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_1	120	0.5006747638326585	0.5354251012145749	0.2631578947368421	0.8076923076923077	`cumulative_results.csv`
gigapath_ft	PTEN	GMA PUB	split_2	120	0.5463598901098902	0.5054945054945055	0.3571428571428571	0.6538461538461539	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_4	120	0.5501754385964912	0.4792982456140351	0.4385964912280701	0.52	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_5	120	0.5024561403508773	0.5414035087719298	0.1228070175438596	0.96	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_1	120	0.5725371120107963	0.4932523616734143	0.1403508771929824	0.8461538461538461	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_3	120	0.594949494949495	0.4703703703703703	0.2	0.7407407407407407	`cumulative_results.csv`
prov-gigapath	PTEN	GMA PUB	split_3	120	0.567003367003367	0.4892255892255892	0.1636363636363636	0.8148148148148148	`cumulative_results.csv`
h-optimus-0	PTEN	GMA PUB	split_2	120	0.4800824175824175	0.5350274725274725	0.3392857142857143	0.7307692307692307	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_3	120	0.4558922558922559	0.4515151515151515	0.2363636363636363	0.6666666666666666	`cumulative_results.csv`
virchow	PTEN	GMA PUB	split_4	120	0.5080701754385966	0.4603508771929824	0.2807017543859649	0.64	`cumulative_results.csv`
virchow2	PTEN	GMA PUB	split_4	120	0.5501754385964912	0.4792982456140351	0.4385964912280701	0.52	`cumulative_results.csv`

External Inference Diagnostics Interactive ROC/PR curves for each external inference experiment Inference