Dataset Information

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

ABSTRACT: This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.

SUBMITTER: Cheng FM

PROVIDER: S-EPMC11004172 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

Cheng Fu-Ming FM Lo Shih-Chang SC Lin Ching-Chan CC Lo Wen-Jyi WJ Chien Shang-Yu SY Sun Ting-Hsuan TH Hsu Kai-Cheng KC

Scientific reports 20240409 1

This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022 ...[more]

PMID: 38594383

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Project description:Early diagnosis of pediatric cancer is key for adequate patient management and improved outcome. Although multiparameter flow cytometry (MFC) has proven of great utility in the diagnosis and classification of hematologic malignancies, its application to non-hematopoietic pediatric tumors remains limited. Here we designed and prospectively validated a new single eight-color antibody combination-solid tumor orientation tube, STOT-for diagnostic screening of pediatric cancer by MFC. A total of 476 samples (139 tumor mass, 138 bone marrow, 86 lymph node, 58 peripheral blood, and 55 other body fluid samples) from 296 patients with diagnostic suspicion of pediatric cancer were analyzed by MFC vs. conventional diagnostic procedures. STOT was designed after several design-test-evaluate-redesign cycles based on a large panel of monoclonal antibody combinations tested on 301 samples. In its final version, STOT consists of a single 8-color/12-marker antibody combination (CD99-CD8/numyogenin/CD4-EpCAM/CD56/GD2/smCD3-CD19/cyCD3-CD271/CD45). Prospective validation of STOT in 149 samples showed concordant results with the patient WHO/ICCC-3 diagnosis in 138/149 cases (92.6%). These included: 63/63 (100%) reactive/disease-free samples, 43/44 (98%) malignant and 4/4 (100%) benign non-hematopoietic tumors together with 28/38 (74%) leukemia/lymphoma cases; the only exception was Hodgkin lymphoma that required additional markers to be stained. In addition, STOT allowed accurate discrimination among the four most common subtypes of malignant CD45- CD56++ non-hematopoietic solid tumors: 13/13 (GD2++ numyogenin- CD271-/+ nuMyoD1- CD99- EpCAM-) neuroblastoma samples, 5/5 (GD2- numyogenin++ CD271++ nuMyoD1++ CD99-/+ EpCAM-) rhabdomyosarcomas, 2/2 (GD2-/+ numyogenin- CD271+ nuMyoD1- CD99+ EpCAM-) Ewing sarcoma family of tumors, and 7/7 (GD2- numyogenin- CD271+ nuMyoD1- CD99- EpCAM+) Wilms tumors. In summary, here we designed and validated a new standardized antibody combination and MFC assay for diagnostic screening of pediatric solid tumors that might contribute to fast and accurate diagnostic orientation and classification of pediatric cancer in routine clinical practice.

Dataset Information

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

Publications

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

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