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ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1

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Evaluation results

1 evaluation · 16 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.906 accuracy fraction · higher Uncertainty: 95% CI 0.901168608037274 to 0.911965346534653. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 18 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_acc'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.857 accuracy fraction · higher Uncertainty: 95% CI 0.840986286415509 to 0.874067454143898. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 19 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_acc_95spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.783 accuracy fraction · higher Uncertainty: 95% CI 0.761618480803836 to 0.802350492447195. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 20 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_acc_98spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.775 accuracy fraction · higher Uncertainty: 95% CI 0.745475789378343 to 0.801318966255286. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 21 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_acc_99spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.968 auprc unitless · higher Uncertainty: 95% CI 0.964339913255639 to 0.97206783946288. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 22 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_auprc'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.983 auroc unitless · higher Uncertainty: 95% CI 0.981274508337164 to 0.985414433343808. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 23 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_auroc'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.86 f1-score fraction · higher Uncertainty: 95% CI 0.851397137423632 to 0.869246999831996. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 24 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_f1'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.853 f1-score fraction · higher Uncertainty: 95% CI 0.833488935818593 to 0.872241115639961. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 25 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_f1_95spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.724 f1-score fraction · higher Uncertainty: 95% CI 0.669740331869782 to 0.763097868284926. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 26 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_f1_98spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.693 f1-score fraction · higher Uncertainty: 95% CI 0.626863941912179 to 0.745643308693344. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 27 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_f1_99spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.922 precision fraction · higher Uncertainty: 95% CI 0.911237722454427 to 0.932076508057875. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 28 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_ppv'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.796 sensitivity-at-95-percent-specificity fraction · higher Uncertainty: 95% CI 0.764480551727827 to 0.826633614574527. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 29 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_sen_95spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.588 sensitivity-at-98-percent-specificity fraction · higher Uncertainty: 95% CI 0.544390090427152 to 0.623529867685634. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 30 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_sen_98spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.561 sensitivity-at-99-percent-specificity fraction · higher Uncertainty: 95% CI 0.507309677595552 to 0.611341899274175. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 31 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_sen_99spe'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.808 recall fraction · higher Uncertainty: 95% CI 0.793956419471719 to 0.82135613845291. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 32 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_sensitivity'), column D 'mean'
Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)	Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026) Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples	0.961 specificity fraction · higher Uncertainty: 95% CI 0.956135649152419 to 0.966902025535582. Interval over the 50 outer test folds as printed in columns G and H Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ichorCNA-TF logistic regression, ichorCNA tumour fraction above 0.03 to 0.1 ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct Aggregation: Not reported A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing; Wang et al. 2026, Data file S2 (model scores and summary statistics) · Data file S2 sheet STATS_lr, row 33 (feat 'TF', ichorcna_strat '(0.03, 0.1]', .metric 'test_specificity'), column D 'mean'

Source checking is not independent reproduction. Release 2026-10-09-ba02f2f4a36e.

Evaluation procedure

ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct

Configuration: ichorCNA tumour fraction with logistic regression (ichorCNA-TF, Wang et al. 2026)
Protocol: UNITE cross-validation, cancers with ichorCNA tumour fraction above 0.03 to 0.1 vs healthy (Wang et al. 2026)
Dataset: UNITE cross-validation set: shallow WGS plasma cfDNA, 458 healthy and 1,232 cancer samples

origin: Independent external evaluation
configuration: Primary source as retrieved 2026-10-09
protocol id: ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct
dataset version: Wang et al. 2026 cross-validation set
split: Nested 5-fold cross-validation, 10 repeats
population: Healthy controls vs cancers with ichorCNA tumour fraction above 0.03 to 0.1
inputs: ichorCNA tumour fraction from 0.1x sWGS
adaptation: Model trained per outer fold with inner hyperparameter search
metric implementation: Not reported
aggregation: Mean, median, SD, 95% CI and SEM over 50 outer test folds
budget: Not reported

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split: Nested 5-fold cross-validation, 10 repeats
Adaptation: Model trained per outer fold with inner hyperparameter search
Scoring implementation: Not reported

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Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.

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Evidence table

Inspect claims, sources and review details

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38 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-09-ba02f2f4a36e
Property and statement	Original source and location	Review and provenance
attributes.comparison.adaptation Model trained per outer fold with inner hyperparameter search Context-only references	A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML Retrieved: 2026-10-09T20:27:02Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `61464a274501bb9529b2250eb4c112071b8768fa640e87cf13f14d7df9f6fb23` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
attributes.comparison.adaptation Model trained per outer fold with inner hyperparameter search Context-only references	Wang et al. 2026, Data file S2 (model scores and summary statistics) Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 Retrieved: 2026-10-09T20:23:13Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `d28e1dbf64df93fdefd7dd239ab773088b4f27000807c81dd84b0511c64efe12` Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx Inspected artifact
attributes.comparison.aggregation Mean, median, SD, 95% CI and SEM over 50 outer test folds Context-only references	A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML Retrieved: 2026-10-09T20:27:02Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `61464a274501bb9529b2250eb4c112071b8768fa640e87cf13f14d7df9f6fb23` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
attributes.comparison.aggregation Mean, median, SD, 95% CI and SEM over 50 outer test folds Context-only references	Wang et al. 2026, Data file S2 (model scores and summary statistics) Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 Retrieved: 2026-10-09T20:23:13Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `d28e1dbf64df93fdefd7dd239ab773088b4f27000807c81dd84b0511c64efe12` Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx Inspected artifact
attributes.comparison.budget Not reported Context-only references	A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML Retrieved: 2026-10-09T20:27:02Z	missing or unspecified No individual claim review recorded independent paper Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `61464a274501bb9529b2250eb4c112071b8768fa640e87cf13f14d7df9f6fb23` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
attributes.comparison.budget Not reported Context-only references	Wang et al. 2026, Data file S2 (model scores and summary statistics) Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 Retrieved: 2026-10-09T20:23:13Z	missing or unspecified No individual claim review recorded independent paper Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `d28e1dbf64df93fdefd7dd239ab773088b4f27000807c81dd84b0511c64efe12` Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx Inspected artifact
attributes.comparison.dataset_version Wang et al. 2026 cross-validation set Context-only references	A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML Retrieved: 2026-10-09T20:27:02Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `61464a274501bb9529b2250eb4c112071b8768fa640e87cf13f14d7df9f6fb23` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
attributes.comparison.dataset_version Wang et al. 2026 cross-validation set Context-only references	Wang et al. 2026, Data file S2 (model scores and summary statistics) Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 Retrieved: 2026-10-09T20:23:13Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `d28e1dbf64df93fdefd7dd239ab773088b4f27000807c81dd84b0511c64efe12` Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx Inspected artifact
attributes.comparison.inputs ichorCNA tumour fraction from 0.1x sWGS Context-only references	A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML Retrieved: 2026-10-09T20:27:02Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `61464a274501bb9529b2250eb4c112071b8768fa640e87cf13f14d7df9f6fb23` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
attributes.comparison.inputs ichorCNA tumour fraction from 0.1x sWGS Context-only references	Wang et al. 2026, Data file S2 (model scores and summary statistics) Original source ↗ Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]' Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1 Retrieved: 2026-10-09T20:23:13Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `d28e1dbf64df93fdefd7dd239ab773088b4f27000807c81dd84b0511c64efe12` Hash scope: artifact_sha256 is the zip as served; the member ady9432_data_file_s2.xlsx has SHA-256 32ae4ff3b7e1c85fa8662a57b45f77330e56e475f996f6489f25e36a81de65aa Archive member: ady9432_data_file_s2.xlsx Inspected artifact

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Sources and history

Release 2026-10-09-ba02f2f4a36e · Record review: source checked

2 source records and release history

A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing · Original source · Science Advances 12(28):eady9432, published 2026-07-10; PMC13353424 full-text XML
Wang et al. 2026, Data file S2 (model scores and summary statistics) · Original source · ady9432_data_file_s2.xlsx inside sciadv.ady9432_data_files_s1_and_s2.zip, PMC open-access copy PMC13353424.1

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Technical metadata and extraction receipts

Stable ID: ctdnafrag-20261009-eval-wang2026-ichorcna-tf-tf-3-10pct

areas: dna-genomes
contexts: clinical_research
origin: independent_paper
protocol: ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct
version: Primary source as retrieved 2026-10-09
comparison: protocol id: ctdnafrag-20261009-protocol-wang2026-unite-cv-tf-3-10pct; dataset version: Wang et al. 2026 cross-validation set; split: Nested 5-fold cross-validation, 10 repeats; population: Healthy controls vs cancers with ichorCNA tumour fraction above 0.03 to 0.1; inputs: ichorCNA tumour fraction from 0.1x sWGS; adaptation: Model trained per outer fold with inner hyperparameter search; metric implementation: Not reported; aggregation: Mean, median, SD, 95% CI and SEM over 50 outer test folds; budget: Not reported
source locator: Data file S2 sheet STATS_lr, rows with feat 'TF' and ichorcna_strat '(0.03, 0.1]'
limitations: Comparator built by the UNITE authors from ichorCNA output; ichorCNA itself was not developed by them.; At 99% specificity, 4 of 50 folds print the constant values (accuracy 0.495, F1 0, sensitivity 0) that mark failed folds elsewhere; the 98% rows have no per-fold values, and Methods P47 names only 95% and 99%.
missing metadata: comparison.metric implementation: reason: unreported; note: Metric code not described beyond the metric names

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