Abstract B031: Artificial Intelligence-Enhanced Image Analysis of Peripheral Blood Smears Supports the Diagnosis and Monitoring of Acute Promyelocytic Leukemia
Notice bibliographique
Résumé
Abstract Acute promyelocytic leukemia (APL) is a high-risk leukemia requiring prompt diagnosis and treatment to reduce early mortality. Peripheral blood (PB) smears often provide excellent monolayer morphology, offering diagnostic information comparable to bone marrow aspirate smears. CellaVision, an automated imaging system widely used in hematology laboratories, enables automated classification of blood cells and generates high-resolution images to support accurate morphological assessment. To enhance early detection and precise monitoring of APL from PB smears, we developed a “MDA-LeukoLens” system, which combines two convolutional neural network (CNN) models (Model A and Model B) with a rule-based algorithm. Model A crops cells to minimize background noise, and Model B classifies the cropped CellaVision images. Both models were trained with the following configurations: 20 epochs, batch size of 8. After training, the models were evaluated using the following tools: Confusion Matrix, Precision-Recall Curve, Area Under the Curve (AUC), confidence interval (CI), and F1 score. For Model A, 11,000 CellaVision images were annotated, with bounding boxes drawn to identify cells for detection. Model A achieved an accuracy of over 99% in detecting and cropping white blood cells. For the development of model B, the database consists of over 113,236 CellaVision images representing 10 distinct cell types: abnormal promyelocytes, 10,196; basophils, 10,247; eosinophils, 10,201; erythroblasts/nucleated red blood cells (nRBCs), 10,288; left-shifted granulocytes, 11,000; lymphocytes, 10,104; monocytes, 10,012; myeloblasts including monoblasts, 10,656; neutrophils, 11,472; and plasma cells, 8,863. Smudge cells (n=10,197) were also included. This dataset was divided into training (83,679 images), validation (16,735 images), and testing (11,159 images) subsets. Model B achieved a validation classification accuracy of 96.2%. The rule-based algorithm incorporating four parameters (new diagnosis, characteristic Auer rods, strong myeloperoxidase expression, and elevated D-dimer level) further differentiate abnormal promyelocytes (APL cells) from their morphologic mimickers. The MDA-LeukoLens system was evaluated on 22,359 cells from 216 internal (MDACC) cases (148 APL and 68 non-APL) and 14,375 cells from 101 external cases (35 APL and 66 non-APL) from four other institutions. For APL cell classification, the sensitivity and accuracy were 0.965 and 0.991, respectively, for internal cases, and 0.965 and 0.981, respectively, for external cases. Notably, the system achieved 100% diagnostic accuracy for APL in both internal and external cases. Overall, CellaVision images provide diagnostic morphological evidence, which the MDA-LeukoLens system utilizes to identify APL cells with a classification performance comparable to the experienced hematopathologists. Through accurate classification, MDA-LeukoLens support the diagnosis and monitoring of APL by analyzing cell images from PB smears, thereby reducing diagnostic delays and facilitating early initiation of ATRA therapy. Citation Format: Xiaoping Sun, Amaris Shi, Jinchun Matthew. Liu, Jeffrey Liu, Yun Gong, Zhihong Hu. Artificial Intelligence-Enhanced Image Analysis of Peripheral Blood Smears Supports the Diagnosis and Monitoring of Acute Promyelocytic Leukemia [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 B031.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
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 ».