Abstract B046: Enhancing the Diagnosis and Monitoring of Chronic Lymphocytic Leukemia with Artificial Intelligence-Driven Peripheral Blood Smear Image Analysis
Notice bibliographique
Résumé
Abstract Chronic lymphocytic leukemia (CLL), the most common leukemia in adults, presents with a broad morphological spectrum, particularly as the disease progresses. Peripheral blood (PB) smear review is essential for evaluating neoplastic cell morphology in CLL. Manual PB smear review, however, is labor-intensive, time-consuming, and subject to inter-observer variability. These limitations underscore the need for more objective and scalable diagnostic tools. Integrating artificial intelligence (AI) into CLL diagnostics may offer a promising solution for automating and standardizing PB smear evaluation. To develop the Convolutional Neural Network (CNN) models for CLL, we are expanding our current image database for MDA-LeukoLens system to include three more categories: (1) typical and atypical CLL cells, (2) prolymphocytes, and (3) large lymphoma cells. Given the variability in cell size across PB smears, we use the ratio of lymphocyte-to-RBC surface area, calculated as the square of the diameter ratio, for quantitative analysis. Approximately 10,000 CellaVision images per cell type are being collected for CNN model training. In addition to having one WBC, each CellaVision image also contains multiple RBCs. The image analysis model for this CLL project was used to create bounding boxes around all RBCs, and those with similar x- and y-axis diameters were selected to calculate the average RBC area per image. The area of each CNN-classified lymphocyte was then compared to the average RBC area in the same image by computing the ratio of the bounding box of the lymphocyte and the average bounding box area of the RBCs. RBCs in approximately 1,000 CellaVision images were labeled with bounding box to train the model for RBC cropping. An independent 1,001 CellaVision images containing atypical lymphoid cells from CLL patients were used to calculate the size ratio of lymphoid cells to RBCs. In the typical CLL cases (737 images), the size of atypical lymphoid cells was variable with the lymphocyte-to-RBC area ratio ranging from 0.5 to 4.49. Approximately 90% of lymphocytes were within the 1.0–2.5 range (1.0–1.49: 27.4%; 1.5–1.99: 42.9%; 2.0–2.49: 19.9%), with only 2.7% exceeding a ratio of 3.0. In the case of accelerated phase (67 images), the area ratio ranged from 2.0 to 7.99. 1.0–2.5: 6.0%; 2.5–4.0: 44.8%, 4.0–5.99: 40.3%, and 6.0–6.99: 7.5%. In the cases of Richter transformation (197 images), the area ratio ranged from 2.5 to 9.99, specifically, 1.0–2.5: 0%; 2.5–4.0: 10.2%; 4.0–5.99: 47.2%; and 6.0–9.99: 28.4%. In summary, our AI-assisted image analysis has revealed certain morphological variability among neoplastic B cells, even in classical CLL cases. A higher proportion of large lymphoid cells correlates with disease progression. This approach has demonstrated improved performance over existing automated imaging platforms and traditional reporting workflows. Overall, these findings support the utility of AI-assisted image analysis in reducing observer variability and providing a scalable framework for monitoring disease evolution and progression in CLL. Citation Format: J. Matthew. Liu, Yun Gong, Amaris Shi, Jeffrey Liu, Xiaoping Sun, Zhihong Hu. Enhancing the Diagnosis and Monitoring of Chronic Lymphocytic Leukemia with Artificial Intelligence-Driven Peripheral Blood Smear Image Analysis [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 B046.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».