Hetairos is trained to predict 102 tumor subtypes
Hetairos was built using 6,115 slides from 4,961 patients with CNS tumors at the Department of Neuropathology, University Hospital Heidelberg (UKHD). All cases were annotated with molecular data to comply with or exceed the requirements of the 2021 WHO classification (CNS5+). This cohort includes CNS tumors from all age groups and is intended to largely mirror the incidence of tumor subtypes, with deliberate enrichment of some rare tumor types. Twenty percent of the UKHD dataset was used for internal validation. Hetairos was subsequently validated on ten external cohorts from four continents, comprising an additional 4,645 tumors and 5,498 slides (Fig. 1a). For each tumor, one or multiple slides containing H&E-stained tissue sections scanned at a magnification of at least 20× (approximately 0.5 µm per pixel) were available, together with the paired methylation-based molecular classification (Methods).
Fig. 1: Overview of study cohorts and Hetairos’s architecture.The alternative text for this image may have been generated using AI.
a, Summary of the internal dataset and the ten external datasets used in this study. Hetairos was trained and evaluated using the UKHD dataset (n = 6,115 slides). The trained model was additionally evaluated on a multicenter cohort to ensure generalizability (n = 5,498 slides). b, Tumor subtype distribution for the UKHD dataset (n = 6,115 slides). The outer ring displays the 102 tumor subtypes, while the inner ring denotes their corresponding supertypes. Full names and abbreviations of tumors are listed in Supplementary Table 1. c, Schematic of slide processing by Hetairos. Whole-slide images are split into nonoverlapping tiles, from which features are extracted using a pretrained vision transformer. Tile features, along with age and tumor location, are combined into a slide-level representation. Finally, an MLP predicts probabilities for each of the 102 tumor subtypes from the slide-level representation. dims, dimensions.
Source data
Methylation classifications were generated using version 12.8 of the Molecular Neuropathology methylation classifier. The predicted classes were further grouped—incorporating expert neuropathological input—into a simplified classification system of 102 diagnostically relevant tumor subtypes and 34 superfamilies, covering the full spectrum of CNS tumor subtypes (Methods, Fig. 1b and Supplementary Table 1). These 102 subtypes consolidate a range of provisional tumor subtypes from the full set of 184 methylation-based classes, which are either very rare or whose clinical relevance has yet to be established. The aggregated methylation-based subtypes were used as the ground truth for training Hetairos.
In line with epidemiology, the distribution of tumor classes in the UKHD dataset followed a long-tailed pattern, with around 30% of cases belonging to the superfamily of adult-type diffuse gliomas, including isocitrate dehydrogenase (IDH)-wild-type glioblastoma (Fig. 1b). Another 50% of cases originated from the superfamilies of ependymomas, meningiomas, low-grade glial/glioneuronal tumors, pediatric high-grade gliomas, and medulloblastomas.
The Hetairos model predicted each of the 102 CNS tumor subtypes from H&E whole-slide scans (Fig. 1c). Because the scanned slides were too large to be processed in a single step, they were first tiled into nonoverlapping areas of approximately 128 × 128 µm, followed by computational feature extraction using the Prov-GigaPath vision transformer model22. The resulting 1,536-dimensional features per tile were aggregated into slide-level embeddings to predict each of the 102 CNS tumor subtypes using a modified TransMIL model30 (Methods). This aggregation step enabled Hetairos to learn which image tiles are predictive and to focus on the most informative image areas. Additionally, patient age and anatomical tumor location can be incorporated to enhance model performance (see the ablation results in Supplementary Table 2). The model outputs the probability of each of the 102 tumor subtypes. By considering only local subsets of tiles, Hetairos can generate prediction maps that highlight the image areas most indicative of given tumor classes. More details are provided in the Methods.
Accurate classification of CNS tumors and identification of ambiguous cases
Over the past decade, the discovery of new tumor classes has been primarily based on molecular analyses, including methylation profiling. This approach consistently reveals clusters of tumors with shared underlying biological relationships that often appear morphologically unrelated. Intriguingly, a visualization of slide-level embeddings for the internal validation cohort using UMAP31 revealed that Hetairos learned internal representations that similarly cluster different groups of tumors (Fig. 2a). While the clustering is not yet as distinct as that found by methylation analysis, the emerging structures reflect the histopathological similarity of different tumor superfamilies, such as glial tumors, ependymal tumors, meningiomas and medulloblastomas.
Fig. 2: Performance of Hetairos in internal validation (n = 1,102 slides).The alternative text for this image may have been generated using AI.
a, Neighborhood embedding of slide representations. Slide-level embeddings from the internal validation set are visualized in two dimensions using UMAP (number of neighbors = 5, minimum distance = 0.5). Representative tiles for clusters are shown on either side. b, Accuracy of the most probable (top-1) and three most probable (top-3) subtypes predicted by Hetairos. c, Scatter plot illustrating the calibration of Hetairos’s predicted probabilities. d, Hetairos’s predictive performance across different confidence intervals. e, Scatter plot showing the correlation between training sample size and Hetairos’s predicted confidence.
Source data
The visible clustering of subtypes also reflects Hetairos’s ability to distinguish different tumor classes. Hetairos assigns a probability to each possible class, which usually centers around a single class or a few related classes. Of greatest interest is typically the class with the highest probability, which we term the top-1 prediction, and the corresponding estimated probability, which we refer to as Hetairos’s confidence. Top-1 predictions agreed with the methylation classification results (methylation score > 0.8) in 75% of all internal validation tumors (Fig. 2b) and remained robust in the presence of common histological artifacts (Supplementary Tables 3 and 4); additional performance metrics are provided in Supplementary Table 5. In 87% of cases, the true class label was among Hetairos’s three most likely classes (top-3 accuracy). Reassuringly, incorrect predictions were typically assigned lower confidence scores, showing a conservative tendency that is important to avoid confident mispredictions (Fig. 2c). In line with the methylation classifier, it is important for users of Hetairos to know the confidence levels at which the predictions are typically correct. We designated cases with confidence above 0.5 as ‘high confidence’ and those with confidence below 0.5 as ‘low confidence’. High- and low-confidence cases comprised 70% and 30% of tumors in the internal validation cohort, respectively (Fig. 2d). Hetairos’s top-1 accuracy among the high-confidence set was 0.88, demonstrating that the model can deliver an accurate and detailed initial diagnosis in the majority of cases, with accuracy further increasing as the confidence threshold rises (Supplementary Table 6).
In the low-confidence set, the accuracy dropped, as expected, to 0.46. When combining the three most likely predictions for the low-confidence set, the accuracy was still 0.71, which shows that Hetairos can often meaningfully reduce the set of possible diagnoses from 102 subtypes to just 3 even in low-confidence cases. Such narrowing of probable classes may help guide further diagnostic tests, potentially resolving the differential diagnoses with only a few or even a single immunohistochemistry test, single gene assay or chromosomal hybridization, rather than through high-throughput testing.
For half of the errors Hetairos made on the high-confidence cases, the correct class belonged to the same superfamily as the predicted class, resulting in an accuracy of 0.94 at the superfamily level. This pattern reflects the close morphological similarity of these entities and their consistent confusion, even within the hierarchical classification systems (Extended Data Fig. 1a–f). Among the low-confidence set, errors tended to occur across superfamilies and subtypes. In such errors, classes with higher incidence in the training data were predicted more often and with greater confidence. All 12 tumor subtypes with an average confidence below 0.25 had fewer than 20 occurrences in the training set (Figs. 2d,e and 3). Data augmentation strategies, including oversampling and color space transformation, appeared insufficient to further improve performance on these underrepresented classes (Extended Data Fig. 2a–h). These findings highlight the need for large datasets to confidently classify very rare tumor subtypes, such as liponeurocytomas, atypical teratoid/rhabdoid tumors and germinomas.
Fig. 3: Confusion matrix for internal validation by Hetairos (n = 1,102 slides).The alternative text for this image may have been generated using AI.
The matrix provides a comprehensive view of classification accuracy across 72 CNS tumor subtypes (classes with a sample size >1), with the vertical axis representing the true classes and the horizontal axis representing the predicted classes. Each cell displays the percentage of cases in each actual class that are classified into each predicted class. Rectangle boxes group classes that belong to the same superfamily. The sample size of each class is listed on the right.
Source data
Hetairos’s accuracy is preserved in external validation cohorts
To be widely applicable, digital pathology models must maintain predictive performance across centers, demographics and processing protocols32,33. To evaluate Hetairos’s performance in settings unobserved during training, we assembled ten validation cohorts from different institutions across four continents, comprising 4,645 cases and 5,498 slides covering the incident spectrum of CNS tumor diagnoses (Fig. 4a,b).
Fig. 4: Performance of Hetairos in external validation (n = 1,796 slides).The alternative text for this image may have been generated using AI.
a, External validation data consist of nine cohorts from seven countries across four continents. b, Bar plots showing the tumor subtype distribution in the external validation set. The observed frequencies closely mirror those of the internal validation set. c, Prediction accuracy on the external validation set. Hetairos achieved a top-1 accuracy of 0.68 and a top-3 accuracy of 0.84 in external validation. The green bar denotes the correct predictions, and the pink bar denotes the incorrect ones. d, Hetairos’s predictive performance across different confidence intervals in external validation. e, Scatter plot illustrating the correlation between differences in confidence and differences in accuracy between internal and external validation. Here, the differences were calculated as the values from the external validation set minus those from the internal validation set. f,g, Scatter plots illustrating the correlation between average predicted confidence and accuracy across different cohorts, grouped into high-confidence predictions (f) and low-confidence predictions (g). UCL, University College London; GHU Paris, Groupe Hospitalier Universitaire Paris Psychiatrie & Neurosciences; JLU Giessen, Justus Liebig University Giessen; UCT Frankfurt, University Cancer Center Frankfurt; ACCCC, AC Camargo Cancer Center; CQMU, Chongqing Medical University; Chile, Pontificia Universidad Católica de Chile.
Source data
Hetairos’s overall accuracy was lower in external validation cohorts than in the internal validation cohort (68% versus 75%) (Fig. 4c, Extended Data Fig. 3 and Supplementary Table 5). Reassuringly, however, this discrepancy was largely predicted, as the fraction of low-confidence predictions increased from 30% to 45% (Fig. 4d). This discrepancy may in part be attributed to modality differences between the external and internal validation sets (Extended Data Fig. 4a,b and Supplementary Table 7). Among the 55% high-confidence cases, the top-1 accuracy remained 0.87 (0.96 at the superfamily level), similar to the performance achieved in the internal validation cohort. Among low-confidence cases, the prediction accuracies were 0.45 for top-1 and 0.71 for combined top-3 predictions, consistent with those of low-confidence samples in the internal validation cohort. This provides evidence that Hetairos recognizes differences in slide characteristics and adjusts its confidence levels accordingly.
The change in average confidence per tumor class was −0.10 (range −0.53 to +0.30) and was usually accompanied by a corresponding change in observed accuracy, thereby maintaining calibration (Fig. 4e). While 59 of 79 tumor types exhibited a drop in confidence, there was also an increase in confidence and accuracy for 8 of the 79 tumor types, including medulloblastoma group 3 and schwannoma.
The external cohorts have distinct tumor class compositions due to the specialization of the individual centers. Nonetheless, the drop in confidence was of a similar magnitude across subcohorts. The accuracy on the respective high- and low-confidence cases matched or exceeded the model’s confidence, indicating that Hetairos’s predictions remain conservative (Fig. 4f,g).
Lastly, Hetairos was evaluated qualitatively on the EBRAINS Digital Brain Tumour Atlas (DBTA) cohort, which comprises 3,110 slides lacking methylation-based predictions. Instead, Hetairos predictions were compared to the detailed histopathological diagnoses provided (Extended Data Fig. 5a,b). Specific histology class names and their corresponding color codes are detailed in Supplementary Table 8. In this cohort, Hetairos’s confidence distribution was similar to that in other external validation cohorts, showing a 50–50% split between high- and low-confidence cases, with corresponding accuracies of 85.6% and 50.2%, respectively. The predicted tumor types showed good agreement with histopathological diagnoses, with discrepancies mostly occurring within tumor families. Additional results on external samples with low methylation scores (<0.8, with an average of 0.45) are provided in Extended Data Fig. 5c,d (note that methylation class annotations may differ from the final diagnoses for these cases).
Hetairos outperforms neuropathologists in H&E assessment
Identifying and narrowing differential diagnoses from H&E slides is an important first step in the diagnostic workflow and is pivotal for the efficient selection of subsequent diagnostic tests. While many institutes in high-income countries typically have a set of special stains (for example, PAS and reticulin) and immunohistochemistry tests (for example, GFAP, MAP2, NeuN and synaptophysin) readily available at initial inspection to assess tumor lineage, such diagnostic tools are often absent or restricted to a few cases in settings with more limited resources. Hence, we conducted a blinded side-by-side evaluation of 210 slides by five board-certified neuropathologists and Hetairos. The slides were selected to have a similar number of cases from each class (Supplementary Table 9). Neuropathologists were provided with a drop-down list containing the 102 methylation subtypes, which correspond directly to Hetairos’s output classes, and were asked to choose and rank their top-3 diagnoses from it. The neuropathologists participating in the evaluation had prior experience with methylation classification and were thus familiar with the provided classes. Based on H&E-stained sections only, Hetairos’s accuracy was consistently better than that of neuropathologists, who achieved an average top-1 accuracy of 0.30 (0.18–0.36) compared to Hetairos’s 0.68 (see the additional metrics provided in Supplementary Table 5). This gap narrowed when assessing top-3 accuracy, which was 0.50 (0.31–0.70) for humans and 0.84 for Hetairos (Fig. 5a). Human evaluators often appeared to struggle with identifying the single best choice among a large number of granular classes, but they were able to provide a plausible set of diagnoses. While Hetairos provided a calibrated range of confidence levels, it consistently outperformed human accuracy across all confidence intervals. The performance gap between human evaluators and Hetairos narrowed slightly within Hetairos’s lower confidence range (Fig. 5b).
Fig. 5: Hetairos outperforms neuropathologists in comparative testing (n = 210 slides).The alternative text for this image may have been generated using AI.
a, Bar plot comparing the top-1 and top-3 accuracies achieved by Hetairos and five neuropathologists (H1–H5) in the side-by-side comparison. Purple bars represent top-1 accuracy, while green bars represent top-3 accuracy. b, Scatter plot comparing the prediction accuracy of Hetairos and neuropathologists across different confidence intervals. Blue dots represent Hetairos’s accuracy, while purple dots represent the average accuracy of neuropathologists. The intervals were defined by Hetairos’s confidence. c, Scatter plot illustrating the correlation between training sample size and the accuracy difference between Hetairos and neuropathologists across tumor subtypes (represented by differently colored dots).
Source data
When considering individual tumor subtypes, Hetairos generally outperformed humans for classes represented by more than ten cases in the training cohort (Fig. 5c). For rare tumor subtypes with fewer than ten cases in the training cohort, human pathologists performed similarly to Hetairos. For some diagnoses, such as metastatic melanoma and teratoma—which Hetairos struggled to diagnose—human diagnoses were correct in two out of three cases and one out of one case, respectively. Together, these results indicate that Hetairos is currently better at diagnosing all but the rarest types of tumors.
AI-assisted diagnosis reaches methylation-level accuracy in 12 min
Named after the Greek term for ‘companion’, Hetairos is designed to assist neuropathologists in diagnostic work. As mentioned previously, the typical diagnostic workflow in neuropathology begins with a morphological assessment of an H&E section, followed by a series of immunohistochemical tests selected to narrow the differential diagnoses (Fig. 6a). Approximately 30% of cases cannot be resolved in terms of tumor classification and subtyping without advanced molecular testing. Most of these can be resolved by DNA methylation array analysis, whereas some require additional testing, such as DNA and RNA sequencing, to identify pathognomonic mutations or fusions. Some specimens are unsuitable for molecular analysis because of limited sample quantity or quality. Hetairos fits into this workflow as a tool to supplement the first-line method—that is, manual histopathological evaluation using H&E-stained FFPE slides. Owing to Hetairos’s WHO 2021-compatible granularity and well-calibrated predictions, it is intended to be used as a triaging tool to efficiently guide further molecular analyses.
Fig. 6: Hetairos assists in the pathological evaluation and resolution of molecularly inconclusive cases.The alternative text for this image may have been generated using AI.
a, Flowchart of diagnostic steps for CNS tumors in neuropathology. Following histological assessment of H&E slides, approximately 30% of the samples require further molecular testing. Among these, 2% are low-material samples and 10% are low-methylation-confidence samples for which diagnosis cannot be resolved using DNA methylation analysis. IHC, immunohistochemistry. b, Examples of H&E-stained sections and heatmaps corresponding to Hetairos’s highest probability prediction (shown below each image). Red-colored regions indicate areas that Hetairos identified as strongly indicative of the listed tumor class. c, Hetairos’s predictive performance across different confidence intervals on low-methylation-confidence samples (n = 50 slides). d, Hetairos’s predictive performance across different confidence intervals on low-material samples (n = 96 slides), with a stereotactic biopsy example shown on the left. Flowchart in a created in BioRender; Patel, A. https://biorender.com/crmoijd (2026).
Source data
The report provided by Hetairos highlights tissue areas of the H&E slide that are most informative for diagnostic prediction and those that contribute least, both in a prediction map and with exemplary magnifications (Fig. 6b; see also the example reports in Extended Data Fig. 6 and Supplementary Table 10). These illustrations enable neuropathologists to review Hetairos’s decision-making and may also guide the selection of optimal areas for extraction if further molecular testing is needed. For example, areas that are morphologically informative for the diagnosis of craniopharyngioma or meningioma are robustly separated from adjacent connective tissue.
Within the spectrum of glial morphology, pilocytic astrocytoma bulk tissue was clearly distinguished from the surrounding reactive gliosis (Fig. 6b). Similarly, Hetairos uniformly identified the prototypical histology of oligodendroglioma (Extended Data Fig. 6a). In astrocytoma, areas considered by Hetairos to be indicative of the second-best diagnosis, glioblastoma, coincided with microvascular proliferation—a characteristic feature of both diagnoses and a criterion for grade 4 tumors (Extended Data Figs. 6b and 7a,b).
Further examples of intratumoral heterogeneity were observed in meningiomas, where Hetairos frequently identified regions assigned to the benign category within intermediate-group cases and vice versa (Extended Data Fig. 8a–d). The biological underpinning of the distinct grading classes found within the same tumor was supported by Ki-67 staining patterns (that is, weaker staining in areas predicted to be benign). Taken together, these examples illustrate that Hetairos has the potential to capture subtle yet clinically relevant and biologically meaningful histologies that exhibit intratumoral heterogeneity and pose diagnostic challenges.
Even advanced technologies such as methylation analysis may sometimes fail to provide a clear prediction on their own, thus necessitating additional testing (for example, for fusions or mutations). Within a cohort of 50 samples diagnosed solely based on a combination of molecular assays, Hetairos correctly predicted 27 cases, demonstrating superiority over methylation analysis in some scenarios (Fig. 6c). For 96 specimens in which methylation analysis could not be performed because of limited tissue, particularly stereotactic biopsy samples, Hetairos correctly predicted 76 cases (Fig. 6d). This highlights the robustness and predictive capability of Hetairos in specimens with limited tumor content (Extended Data Fig. 9a,b).
To assess the potential clinical utility of Hetairos, the algorithm was prospectively evaluated alongside routine diagnostics from August 1, 2024, to June 1, 2025, at the Department of Neuropathology at UKHD. All cases that required molecular testing and met the inclusion criteria were included without any further selection. During this time, Hetairos was used to predict a total of 210 cases (Fig. 7a and Supplementary Table 11). Its results were compared to independent integrated diagnoses established by a combination of morphological assessment, immunohistochemistry, methylation classification, next-generation sequencing panel and RNA sequencing. Hetairos predictions were not made available to the neuropathologist and did not influence diagnostic or treatment decisions.
Fig. 7: Prospective clinical evaluation of Hetairos (n = 210 slides).The alternative text for this image may have been generated using AI.
a, Sankey plot showing the diagnostic path for the prospective validation cohort and the performance of Hetairos on those samples. Obtaining Hetairos’s predictions takes an average of 12 min once the scanned H&E slide is available. b, Comparison between the results from Hetairos and those from the methylation classifier on prospective samples with low methylation scores. c, Classification accuracy in low-methylation-score cases concordant or discordant with Hetairos’s high-confidence predictions.
On average, it takes 12 days from the receipt of the neurosurgical specimen to an integrated diagnosis. As illustrated in Fig. 7a, this corresponds to approximately 16 days for cases requiring molecular testing. Hetairos, running on consumer-grade hardware, took an average of 12 min to process a slide and generate the report. This indicates that, together with the time taken for staining and scanning, Hetairos substantially shortens the turnaround time, with results usually available within 24 h or up to 2 days after sample receipt, depending on fixation time. Among cases that could not be resolved by histology or immunohistochemistry alone, Hetairos yielded 63% high-confidence predictions and 37% low-confidence predictions. Hetairos’s top-1 predictions were found to agree with the eventual integrated diagnosis in 120 of 133 high-confidence cases (90.2%; Fig. 7a), highlighting Hetairos’s ability to deliver near-methylation accuracy within a substantially shorter timeframe. For cases with high methylation scores and high confidence, Hetairos achieved an accuracy of 94.3% (100 of 106), while for those with low Hetairos confidence, the accuracy was 45.5% (35 of 77). In this subgroup with low methylation scores, Hetairos and the methylation classifier showed comparable accuracy against the integrated diagnosis (Fig. 7b). Notably, among the low-methylation-score cases that were concordant with Hetairos’s high-confidence predictions, the accuracy reached 88.9% (16 of 18; Fig. 7c), further demonstrating how Hetairos can aid diagnostic decision-making when molecular results are inconclusive.
Granular tumor classification stratifies survival
Multiple studies have underscored the need for a granular classification of CNS tumors to reflect considerable differences in survival2,34,35,36,37. To demonstrate the prognostic utility of Hetairos’s detailed classification, we used data from 353 patients with CNS tumors in the MNP 2.0 trial2, for whom digital H&E images, methylation classification and survival data are available.
Among these data, Hetairos classified 165 cases into one of the four WHO-defined subtypes of medulloblastoma: WNT activated, sonic hedgehog (SHH) activated, group 3 and group 4 (Fig. 8a). Compared to the methylation class, the accuracies were 89% and 51% for high- and low-confidence predictions, respectively. Despite some misclassification among low-confidence predictions, Cox proportional hazards models confirmed that Hetairos’s subtypes exhibited notable differences in overall survival (P = 0.03). The 3-year overall survival rates were 58% for tumors classified as group 3 medulloblastoma, 81% for SHH-activated medulloblastoma, 88% for group 4 medulloblastoma and 100% for WNT-activated medulloblastoma. These results are in agreement with previous findings38.
Fig. 8: Risk stratification based on predictions by Hetairos.The alternative text for this image may have been generated using AI.
a, Analysis of medulloblastoma (n = 165 slides). Left, Kaplan–Meier analysis of the subgroups. MB_WNT, medulloblastoma, WNT activated; MB_SHH, medulloblastoma, SHH activated; MB_G3, medulloblastoma, group 3; MB_G4, medulloblastoma, group 4; non-MB, other CNS tumor types. Right, confusion matrix between Hetairos’s predictions and methylation classes. b, Ependymoma subtypes (n = 90 slides). Left, Kaplan–Meier analysis. EPN_PFA, posterior fossa ependymoma, group PFA; EPN_ST_ZFTA_FUS, supratentorial ependymoma, ZFTA fusion positive; EPN_MPE, myxopapillary ependymoma; EPN_SPINE, spinal ependymoma; EPN_PFB, posterior fossa ependymoma, group PFB; non-EPN, other CNS tumor types. Right, confusion matrix as in a. c, WHO-defined high-grade gliomas (HGGs) (n = 98 slides). Left, Kaplan–Meier curves. Right, confusion matrix for Hetairos’s predictions and methylation classes. In a–c, shaded areas in Kaplan–Meier plots represent 95% confidence intervals. For the confusion matrices, high- and low-confidence predictions are indicated as pairs (nhigh; nlow) in each tile. d, Harrell’s c-index for different predictor settings. Box plots show the median (center line), 25th–75th percentiles (box bounds) and 1.5× the interquartile range (whiskers). Each dot in the box plot represents the c-index for one of the ten outer cross-validation folds.
Source data
Similarly, Hetairos predicted 90 cases as five different WHO subtypes of ependymoma (Fig. 8b). The accuracy of high-confidence and low-confidence predictions was 100% and 48%, respectively. Overall survival among the predicted subtypes differed in the expected manner (P = 0.07). Group PFA posterior fossa ependymoma and ZFTA fusion-positive supratentorial ependymoma had poorer prognoses, with 3-year survival rates of 89% and 68%, respectively, compared to the other three ependymoma subtypes, all of which had 3-year survival rates of 100%.
Lastly, we applied Hetairos to classify samples histologically diagnosed as high-grade gliomas into more detailed subtypes. These subtypes were grouped into high- and low-risk categories based on independent prior knowledge2 (Methods). Notwithstanding the challenges of accurately classifying glioma subtypes, the survival curves exhibited the expected trend of worse overall survival in high-risk groups (3-year survival: 38% versus 55%; P = 0.2; Fig. 8c). Additionally, a multivariate survival analysis confirmed that Hetairos provides stronger prognostic value than the clinical baseline alone, offering a more effective alternative in the absence of molecular testing (Fig. 8d).
