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Radiomics model and deep learning model based on T1WI image for acute lymphoblastic leukemia identification.
Cai, Q; Tang, H; Wei, W; Zhang, H; Jin, K; Yi, T.
Afiliación
  • Cai Q; Department of Radiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, China.
  • Tang H; College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.
  • Wei W; Department of Radiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, China.
  • Zhang H; MR Research Collaboration, Siemens Healthineers Ltd, Wuhan, Hubei, China.
  • Jin K; Department of Radiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, China. Electronic address: jinke001@sina.com.
  • Yi T; Department of Radiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, China. Electronic address: yiting127@126.com.
Clin Radiol ; 79(8): e1064-e1071, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38796378
ABSTRACT

AIM:

This study aimed to develop highly precise radiomics and deep learning models to accurately detect acute lymphoblastic leukemia (ALL) using a T1WI image. MATERIALS AND

METHODS:

A total of 604 brain magnetic resonance data of ALL group and normal children (NC) group. Two radiologists independently retrieved radiomics features after manually delineating the area of interest along the clivus at the median sagittal position of T1WI. According to the 91 ratio, all samples were randomly divided into the training cohort and the testing cohort. support vector machine was then used to classify the radiomics model using the features that had a correlation coefficient of greater than 0.99 in the training cohort. The Efficientnet-B3 network model received the training set images to create a deep learning model. The sensitivity, specificity, and area under the ROC curve were calculated in order to evaluate the diagnostic efficacy of the different models after the validation of two aforementioned models in the testing cohort.

RESULTS:

The deep learning model had a higher AUC value of 0.981 than the radiomics model's value of 0.962 in the testing cohort. Delong's test showed no statistical difference between the two models (P>0.05). The accuracy/sensitivity/specificity/negative predictive value/positive predictive value achieved 0.9180/0.9565/0.8947/0.9714/0.8462 for the radiomics model and 0.9344/0.8696/0.9737/0.9250/0.9524 for deep learning model.

CONCLUSIONS:

The deep learning and radiomics models showed high AUC values in the training and test cohorts. They also exhibited good diagnostic efficacy for predicting ALL.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Sensibilidad y Especificidad / Leucemia-Linfoma Linfoblástico de Células Precursoras / Aprendizaje Profundo Límite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Clin Radiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Sensibilidad y Especificidad / Leucemia-Linfoma Linfoblástico de Células Precursoras / Aprendizaje Profundo Límite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Clin Radiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido