CoChat
Sign Up
Cited by 359Ovarian cancer diagnosis and treatmentRadiomics and Machine Learning in Medical ImagingAI in cancer detection

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

Open access
Boehm, Kevin M., Aherne, Emily A., Ellenson, Lora, Nikolovski, Ines, Alghamdi, Mohammed, Vázquez-García, Ignacio, Zamarin, Dmitriy, Long Roche, Kara, Liu, Ying, Patel, Druv, Aukerman, Andrew, Pasha, Arfath, Rose, Doori, Selenica, Pier, Causa Andrieu, Pamela I., Fong, Chris, Capanu, Marinela, Reis-Filho, Jorge S., Vanguri, Rami, Veeraraghavan, Harini, Gangai, Natalie, Sosa, Ramon, Leung, Samantha, McPherson, Andrew, Gao, JianJiong, MSK MIND Consortium, Lakhman, Yulia, Shah, Sohrab P.·2022·Nature Cancer
DOI 10.1038/s43018-022-00388-9PMC PMC9239907
Open this paper in CoChat — free accountOpen PDF

Abstract

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s43018-022-00388-9

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Schematic outline of the study. a–d, Multiple data modalities were acquired through routine diagnostics to inform clinical decision making (a): pre-treatment CE-CT scans of the abdomen and pelvis (b), pre-treatment H&E-stained diagnostic biopsies (c) and HRD status inferred from hybridization capture-based targeted sequencing or clinical HRD-DDR gene panels (d). e, Integrated multimodal analyses by late fusion to stratify patients by overall survival. Created with BioRender.com. GLSZM-SAE, gray level size zone matrix small area emphasis; GLRLM-GLV, gray level run length matrix gray level variance; Var, variance; Nuc, nuclear; NGS, next-generation sequencing; LSTs, large-scale state transitions; NtAI, number of subchromosomal regions with allelic imbalance extending to the telomere; LOH, loss of heterozygosity. Source data
Fig. 1 Schematic outline of the study. a–d, Multiple data modalities were acquired through routine diagnostics to inform clinical decision making (a): pre-treatment CE-CT scans of the abdomen and pelvis (b), pre-treatment H&E-stained diagnostic biopsies (c) and HRD status inferred from hybridization capture-based targeted sequencing or clinical HRD-DDR gene panels (d). e, Integrated multimodal analyses by late fusion to stratify patients by overall survival. Created with BioRender.com. GLSZM-SAE, gray level size zone matrix small area emphasis; GLRLM-GLV, gray level run length matrix gray level variance; Var, variance; Nuc, nuclear; NGS, next-generation sequencing; LSTs, large-scale state transitions; NtAI, number of subchromosomal regions with allelic imbalance extending to the telomere; LOH, loss of heterozygosity. Source dataCC BY · PubMed Central
Fig. 2 Overview of cohorts and data types acquired. a, Venn diagram of patients in the training cohort with available clinical imaging and inferred HRD status. b, Inferred subtypes, sequencing modality, dataset of origin, genes with five or more variants and signature 3 status of each patient. Gray represents sequenced genes without the aberrations shown and white represents an unsequenced gene. c, Kaplan–Meier analysis on OS stratified by HRD status (n = 377 patients). P values were calculated using the log-rank test. Sig., mutational signature; SNV, single-nucleotide variation; Amp., copy number amplification; WES, whole-exome sequencing. Source data
Fig. 2 Overview of cohorts and data types acquired. a, Venn diagram of patients in the training cohort with available clinical imaging and inferred HRD status. b, Inferred subtypes, sequencing modality, dataset of origin, genes with five or more variants and signature 3 status of each patient. Gray represents sequenced genes without the aberrations shown and white represents an unsequenced gene. c, Kaplan–Meier analysis on OS stratified by HRD status (n = 377 patients). P values were calculated using the log-rank test. Sig., mutational signature; SNV, single-nucleotide variation; Amp., copy number amplification; WES, whole-exome sequencing. Source dataCC BY · PubMed Central
Fig. 3 High-autocorrelation omental implants are associated with shorter OS. a, Segmented omental lesion (red) on CE-CT. b, The log HR is depicted for each radiomic feature derived from omental implants (n = 600 features). Features above the line were statistically significant by Cox regression after multiple testing correction of interquartile range-filtered features. c, Adnexal radiomic features (n = 600 features) were not significant by Cox regression after correction of interquartile range-filtered features. d, The hazard ratio with 95% CI as estimated by Cox regression is shown for the feature in the final model, the autocorrelation derived from the gray level co-occurrence matrix for the wavelet-filtered image. e, The value of this feature against OS is plotted for patients in the training set (n = 251 patients). f, Training and test concordance indices for the model are shown; the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. g,h, Two risk groups based on the model’s predicted risk score are shown for the training and test sets. P values were derived using the log-rank test. glcm, gray level co-occurrence matrix; gldm, gray level dependence matrix; glrlm, gray level run length matrix; glszm, gray level size zone matrix; ngtdm, neighboring gray tone difference matrix. Source data
Fig. 3 High-autocorrelation omental implants are associated with shorter OS. a, Segmented omental lesion (red) on CE-CT. b, The log HR is depicted for each radiomic feature derived from omental implants (n = 600 features). Features above the line were statistically significant by Cox regression after multiple testing correction of interquartile range-filtered features. c, Adnexal radiomic features (n = 600 features) were not significant by Cox regression after correction of interquartile range-filtered features. d, The hazard ratio with 95% CI as estimated by Cox regression is shown for the feature in the final model, the autocorrelation derived from the gray level co-occurrence matrix for the wavelet-filtered image. e, The value of this feature against OS is plotted for patients in the training set (n = 251 patients). f, Training and test concordance indices for the model are shown; the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. g,h, Two risk groups based on the model’s predicted risk score are shown for the training and test sets. P values were derived using the log-rank test. glcm, gray level co-occurrence matrix; gldm, gray level dependence matrix; glrlm, gray level run length matrix; glszm, gray level size zone matrix; ngtdm, neighboring gray tone difference matrix. Source dataCC BY · PubMed Central
Fig. 4 Weakly supervised deep learning accurately infers HGSOC tissue type on H&E. a, Annotated tiles normalized using Macenko’s method chosen at random. The number of tiles for each tissue type is shown. b, Workflow of ResNet-18 model trained using the annotated regions. c, Example of the model’s predictions for an annotated region. d, The confusion matrix aggregated across folds of cross-validation for each of the tissue classes. Source data
Fig. 4 Weakly supervised deep learning accurately infers HGSOC tissue type on H&E. a, Annotated tiles normalized using Macenko’s method chosen at random. The number of tiles for each tissue type is shown. b, Workflow of ResNet-18 model trained using the annotated regions. c, Example of the model’s predictions for an annotated region. d, The confusion matrix aggregated across folds of cross-validation for each of the tissue classes. Source dataCC BY · PubMed Central
Fig. 5 Interpretable histopathological features stratify HGSOC patients by OS. a, Tissue map from H&E slides with nuclear detections yielding tissue-type and cell-type features. b, Log HRs of the two chosen histological features (with 95% CI as estimated by Cox regression; fit on n = 243 patients). c, Training and test concordance indices are shown: the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. d,e, Kaplan–Meier survival analysis and log-rank test statistics for training (d) and test sets (e). f,g, H&E of extreme examples of the model’s inferred mean tumoral nuclear area (scale bar, 50 µm for each image). Source data
Fig. 5 Interpretable histopathological features stratify HGSOC patients by OS. a, Tissue map from H&E slides with nuclear detections yielding tissue-type and cell-type features. b, Log HRs of the two chosen histological features (with 95% CI as estimated by Cox regression; fit on n = 243 patients). c, Training and test concordance indices are shown: the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. d,e, Kaplan–Meier survival analysis and log-rank test statistics for training (d) and test sets (e). f,g, H&E of extreme examples of the model’s inferred mean tumoral nuclear area (scale bar, 50 µm for each image). Source dataCC BY · PubMed Central
Fig. 6 Multimodal integration improves stratification and identifies clinically significant subgroups. a, The test c-Indices for integration of combinations of multimodal features is shown: the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. Asterisks denote 95% confidence of significant ordering of the test set by 1000-fold permutation test. b, Log HRs of imaging without (top) and with (bottom) HRD integration. Two modalities are shown fitted on n = 122 patients (top) and three are shown fitted on n = 114 patients (bottom). c, Kaplan–Meier plot comparing high- and low- risk groups determined by the GRH model on the training set. P value calculated using the log-rank test. d, Kaplan–Meier plot comparing high- and low- risk groups test set. P value calculated using a log-rank test. e, Unique patients at risk of early death are identified by radiological, histopathological and genomic modalities. Only patients in the test set with uncensored outcomes (n = 23 patients) are shown. f, Kendall rank correlation coefficient of the risk quantile across pairs of the individual modalities, indicating low mutual ordering information between individual modalities in the training set. g, Kaplan–Meier plot of GRH model risk groups on PFS in the test set (one patient has unknown PFS.) P value calculated using the log-rank test. h, Distributions of GRH model score of low (blue) and high (green) CRS in the training set (n = 46 patients). Boxes denote interquartile range, with the center depicting the median and the whiskers denoting the entire distribution excluding any outliers. Significance was assessed by a one-sided Mann–Whitney U-test: P = 0.0044; **P < 0.01. perm.; permutation test; G, genomic model; H, histopathological model; R, radiological model; C, clinical model; NET, no evidence of tumor. Source data
Fig. 6 Multimodal integration improves stratification and identifies clinically significant subgroups. a, The test c-Indices for integration of combinations of multimodal features is shown: the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. Asterisks denote 95% confidence of significant ordering of the test set by 1000-fold permutation test. b, Log HRs of imaging without (top) and with (bottom) HRD integration. Two modalities are shown fitted on n = 122 patients (top) and three are shown fitted on n = 114 patients (bottom). c, Kaplan–Meier plot comparing high- and low- risk groups determined by the GRH model on the training set. P value calculated using the log-rank test. d, Kaplan–Meier plot comparing high- and low- risk groups test set. P value calculated using a log-rank test. e, Unique patients at risk of early death are identified by radiological, histopathological and genomic modalities. Only patients in the test set with uncensored outcomes (n = 23 patients) are shown. f, Kendall rank correlation coefficient of the risk quantile across pairs of the individual modalities, indicating low mutual ordering information between individual modalities in the training set. g, Kaplan–Meier plot of GRH model risk groups on PFS in the test set (one patient has unknown PFS.) P value calculated using the log-rank test. h, Distributions of GRH model score of low (blue) and high (green) CRS in the training set (n = 46 patients). Boxes denote interquartile range, with the center depicting the median and the whiskers denoting the entire distribution excluding any outliers. Significance was assessed by a one-sided Mann–Whitney U-test: P = 0.0044; **P < 0.01. perm.; permutation test; G, genomic model; H, histopathological model; R, radiological model; C, clinical model; NET, no evidence of tumor. Source dataCC BY · PubMed Central
Extended Data Fig. 1 Segmenting radiologist and CT vendor in training and test sets. a, Three fellowship-trained radiologists segmented the training (N = 298 patients) and test cases (N = 40 patients). b, Scanner vendors (same number as in preceding panel). Source data
Extended Data Fig. 1 Segmenting radiologist and CT vendor in training and test sets. a, Three fellowship-trained radiologists segmented the training (N = 298 patients) and test cases (N = 40 patients). b, Scanner vendors (same number as in preceding panel). Source dataCC BY · PubMed Central
Extended Data Fig. 2 Genomic features of the training and test sets. a, Distribution of large-scale state transitions and threshold. b, Signature 3 detections by SigMA with high confidence (HC; N = 48 patients) and low confidence (LC; N = 30 patients), Clock signature (N = 50 patients), Signature 18 (N = 1 patient), and MSI (N = 1 patient). (c) Signature 3 frequencies for all TCGA-OV cases with sequencing from43 are shown. 338 patients with low Sig. 3 and 47 with high Sig. 3. d,e Kaplan–Meier analyses of patients by genomic subtype in the training and test sets (p-value by log-rank test). f, Incorporating thresholded LST counts as indicators of HRD status did not increase the difference in OS of the HRD and HRP curves (p-value by log-rank test). g, Stratification by PFS using specific mutational subtypes: HRD-Deletion (HRD-DEL), HRD-Duplication (HRD-DUP), Foldback Inversion (FBI), and Tandem Duplications (TD) (p-value by multivariate log-rank test). h, Stratification by OS using the same mutational subtypes (p-value by log-rank test). i, Kaplan–Meier analysis by OS for only patients with explicit evidence of HRD or HRP, excluding presumed HRP (p-value by multivariate log-rank test). Source data
Extended Data Fig. 2 Genomic features of the training and test sets. a, Distribution of large-scale state transitions and threshold. b, Signature 3 detections by SigMA with high confidence (HC; N = 48 patients) and low confidence (LC; N = 30 patients), Clock signature (N = 50 patients), Signature 18 (N = 1 patient), and MSI (N = 1 patient). (c) Signature 3 frequencies for all TCGA-OV cases with sequencing from43 are shown. 338 patients with low Sig. 3 and 47 with high Sig. 3. d,e Kaplan–Meier analyses of patients by genomic subtype in the training and test sets (p-value by log-rank test). f, Incorporating thresholded LST counts as indicators of HRD status did not increase the difference in OS of the HRD and HRP curves (p-value by log-rank test). g, Stratification by PFS using specific mutational subtypes: HRD-Deletion (HRD-DEL), HRD-Duplication (HRD-DUP), Foldback Inversion (FBI), and Tandem Duplications (TD) (p-value by multivariate log-rank test). h, Stratification by OS using the same mutational subtypes (p-value by log-rank test). i, Kaplan–Meier analysis by OS for only patients with explicit evidence of HRD or HRP, excluding presumed HRP (p-value by multivariate log-rank test). Source dataCC BY · PubMed Central
Extended Data Fig. 3 Radiomic feature values by segmenting radiologist, CT scanner, and site. The radiomic feature chosen for the model by a segmenting radiologist, b CT vendor, and c whether the scan was acquired at our institution (MSKCC) or elsewhere. Source data
Extended Data Fig. 3 Radiomic feature values by segmenting radiologist, CT scanner, and site. The radiomic feature chosen for the model by a segmenting radiologist, b CT vendor, and c whether the scan was acquired at our institution (MSKCC) or elsewhere. Source dataCC BY · PubMed Central
Extended Data Fig. 4 Example cross-validation histopathologic tissue-type classifications. Three samples (a–c) chosen at random from all slides used for cross-validation.
Extended Data Fig. 4 Example cross-validation histopathologic tissue-type classifications. Three samples (a–c) chosen at random from all slides used for cross-validation.CC BY · PubMed Central
Extended Data Fig. 5 Histopathologic feature discovery. The logarithm of the univariate hazard ratio is depicted for each histopathologic feature (N = 281 features) before interquartile range-based filtering, with the cluster in the upper right quadrant comprising primarily features describing tumor nuclear diameter and size. Source data
Extended Data Fig. 5 Histopathologic feature discovery. The logarithm of the univariate hazard ratio is depicted for each histopathologic feature (N = 281 features) before interquartile range-based filtering, with the cluster in the upper right quadrant comprising primarily features describing tumor nuclear diameter and size. Source dataCC BY · PubMed Central
Extended Data Fig. 6 Histopathologic embeddings by specimen size. UMAP embeddings of the two-feature histopathologic signature (for N = 283 patients), with each slide’s point colorized by the relative specimen size (here, the quantile of the number of foreground tiles detected). Source data
Extended Data Fig. 6 Histopathologic embeddings by specimen size. UMAP embeddings of the two-feature histopathologic signature (for N = 283 patients), with each slide’s point colorized by the relative specimen size (here, the quantile of the number of foreground tiles detected). Source dataCC BY · PubMed Central
Extended Data Fig. 7 Test performance of histopathologic-radiomic model. a, Kaplan–Meier analysis of OS for the RH model’s risk scores (p-value by log-rank test). b, Kaplan–Meier analysis of PFS for the RH model’s risk scores (p-value by log-rank test). Source data
Extended Data Fig. 7 Test performance of histopathologic-radiomic model. a, Kaplan–Meier analysis of OS for the RH model’s risk scores (p-value by log-rank test). b, Kaplan–Meier analysis of PFS for the RH model’s risk scores (p-value by log-rank test). Source dataCC BY · PubMed Central
Extended Data Fig. 8 Learning only from cases with full information (N = 114) worsens performance. Log hazard ratios for radiomic features derived from a omental implants (N = 600 features) and b adnexal masses (N = 600 features; uncorrected p-values shown). c, Log hazard ratios for histopathologic features (N = 281 features; uncorrected p-values shown). d, Concordance indices for the test set by overall survival. The height of each bar shows the c-Index, and the lower and upper points of the respective error bars depict the 95% C.I. by leave-one-out bootstrapping. Asterisks denote 95% confidence of significant ordering of the test set by 1000-fold permutation test. e, KM analysis of OS for the GRH model in the test set. P-value calculated using the log-rank test. f, KM analysis of PFS for the GRH model in the test set. P-value calculated using the log-rank test. Source data
Extended Data Fig. 8 Learning only from cases with full information (N = 114) worsens performance. Log hazard ratios for radiomic features derived from a omental implants (N = 600 features) and b adnexal masses (N = 600 features; uncorrected p-values shown). c, Log hazard ratios for histopathologic features (N = 281 features; uncorrected p-values shown). d, Concordance indices for the test set by overall survival. The height of each bar shows the c-Index, and the lower and upper points of the respective error bars depict the 95% C.I. by leave-one-out bootstrapping. Asterisks denote 95% confidence of significant ordering of the test set by 1000-fold permutation test. e, KM analysis of OS for the GRH model in the test set. P-value calculated using the log-rank test. f, KM analysis of PFS for the GRH model in the test set. P-value calculated using the log-rank test. Source dataCC BY · PubMed Central
Extended Data Fig. 9 No robust association exists between individual modalities in the training set. a, The maximal magnitude of the Pearson correlation between individual modalities is 0.191. b, The maximal magnitude of the Spearman correlation between individual modalities is 0.192. Source data
Extended Data Fig. 9 No robust association exists between individual modalities in the training set. a, The maximal magnitude of the Pearson correlation between individual modalities is 0.191. b, The maximal magnitude of the Spearman correlation between individual modalities is 0.192. Source dataCC BY · PubMed Central
Extended Data Fig. 10 Chemotherapy response scores for all models on the test set. a–o, for C, G, GC, GH, GHC, GR, GRC, GRH, GRHC, H, HC, R, RC, RH, and RHC models, respectively. The box of each plot depicts the 25th, 50th, and 75th percentiles, and the whiskers depict the entire range except for outlier points beyond 1.5 times the interquartile range past from the median 50% of data. Significance was assessed by a one-sided Mann-Whitney U test without correction for multiple tests. * denotes p < 0.05, ** denotes p < 0.01, ns denotes p > 0.05. P-value in b is 0.012. Each plot depicts N = 9 patients with CRS 3/NET and N = 12 patients with CRS 1/2. Source data
Extended Data Fig. 10 Chemotherapy response scores for all models on the test set. a–o, for C, G, GC, GH, GHC, GR, GRC, GRH, GRHC, H, HC, R, RC, RH, and RHC models, respectively. The box of each plot depicts the 25th, 50th, and 75th percentiles, and the whiskers depict the entire range except for outlier points beyond 1.5 times the interquartile range past from the median 50% of data. Significance was assessed by a one-sided Mann-Whitney U test without correction for multiple tests. * denotes p < 0.05, ** denotes p < 0.01, ns denotes p > 0.05. P-value in b is 0.012. Each plot depicts N = 9 patients with CRS 3/NET and N = 12 patients with CRS 1/2. Source dataCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 54 references

Cited by

Sorted by citations

Items 201–300 of 359

Related

See all

Metadata

Authors
Boehm, K. M., Aherne, E. A., Ellenson, L., Nikolovski, I., Alghamdi, M., Vázquez-García, I., Zamarin, D., Long Roche, K., Liu, Y., Patel, D., Aukerman, A., Pasha, A., Rose, D., Selenica, P., Causa Andrieu, P. I., Fong, C., Capanu, M., Reis-Filho, J. S., Vanguri, R., Veeraraghavan, H., Gangai, N., Sosa, R., Leung, S., McPherson, A., Gao, J., MSK MIND Consortium, Lakhman, Y., & Shah, S. P.
Affiliations
Memorial Sloan Kettering Cancer Center (US)
Corresponding author
  • Lakhman, Yulia
  • Shah, Sohrab P.
ORCID
Venue
Nature Cancer
Publisher
Springer Science and Business Media LLC
Published
2022-06-28
Volume
3
Issue
6
Pages
723-733
ISSN
2662-1347 (ISSN-L)
DOI
10.1038/s43018-022-00388-9
PMC
PMC9239907
OpenAlex
W4283645969
Cited by
357
References
54
Citations by year
2026: 76 · 2025: 118 · 2024: 94 · 2023: 59 · 2022: 9
FWCI
59.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Cancer (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (T32GM007739)
  • U.S. Department of Health & Human Services | NIH | NCI | Division of Cancer Epidemiology and Genetics, National Cancer Institute (P30CA008748)
  • Grayer Fellowship (MSKCC) Cycle for Survival
  • Susan G. Komen Scholars Program Cycle for Survival
Topics
  • Health Sciences › Medicine › Oncology › Ovarian cancer diagnosis and treatment
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
high-grade serous ovarian cancer, multimodal data integration, risk stratification, prognostic factors, treatment response prediction, homologous recombination deficiency, H&E staining, contrast-enhanced CT, histopathology
MeSH terms
Machine Learning, Female, Humans, Ovarian Neoplasms, Cystadenocarcinoma, Serous, Risk Assessment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review