Abstract B030: Multiple instance learning of large-scale DNA organization to characterize prostate cancer aggressiveness
Notice bibliographique
Résumé
Abstract Multiple instance learning (MIL) has become a popular approach to analyze histopathology datasets due to its weakly supervised nature. In particular, attention-based models can learn key instances of which labels are usually unknown or unavailable. For instance, large-scale DNA organization (LDO) analysis aims to make patient prognoses from quantitative features of nuclear morphometry and chromatin condensation calculated from images of the nucleus. Leveraging attention mechanisms allows a model to identify nuclei with alterations which likely contribute to patient outcome without individual cell labels. However, a crucial assumption of MIL that is often overlooked in histopathology applications, wherein a bag is positive if it has at least one positive instance. In a cancer context, this assumption is not robust, as a single malignantly transformed cell may be necessary but insufficient to cause carcinogenesis or malignant cancer progression. A reasonable adjustment is to learn a tolerable threshold of aberrant cells. The attention mechanism can be modified so that both key aggressive and indolent nuclei are identified in contrast to traditional MIL attention mechanisms which only give weight to positive instances. We demonstrate that this is an effective approach for prostate cancer (PCa) prognosis. A binary MIL classifier was trained to identify PCa patients (bags) of the indolent (negative) and aggressive (positive) outcomes. The cohort includes 38 Gleason score (GS) 6 patients who did not display signs of progression during active surveillance (AS) and 22 patients with GS 9 who died within 2 years of consultation. A linear attention layer identifies aggressive and indolent nuclei (instances) to generate a weighted mean representation of LDO features for the patient, reducing the influence of nuclei of ambiguous labels. To mitigate overfitting, weights are shared between the attention layer and patient classification layer and trained to optimize a combination of binary cross entropy loss on the nuclear and patient level. Patients in the training set were classified with a balanced accuracy of 0.851, and an F1-score of 0.815. This performance also translated to the patients in the holdout set, where the balanced accuracy and F1-scores of patient classification was 0.857 and 0.833 respectively. Tests on an independent cohort of 147 patients with GS 7+ also demonstrate that LDO score is correlated with GS, biochemical recurrence following brachytherapy, and progression in GS6 active surveillance patients. Future studies will analyze the performance of the trained classifiers on patients with GS7 and GS8 further, such as the classifier’s ability to rank severity of clinical outcomes with c-index. These results demonstrate the potential of the MIL-based LDO biomarker for prostate cancer patient prognosis and management. Further validation on the brachytherapy-treated cohort, and survival analysis will be done to assess the performance of the MIL classifier, and its potential benefit for prostate cancer management. Citation Format: Fumiya Inaba, Zhaoyang Chen, Anita Carraro, Paul Gallagher, Mira Keyes, Martial Guillaud, Calum MacAulay. Multiple instance learning of large-scale DNA organization to characterize prostate cancer aggressiveness [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B030.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».