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Contrast-Enhanced Mammography Radiomics Analysis for Preoperative Prediction of Breast Cancer Molecular Subtypes.
Zhu, Shuangshuang; Wang, Simin; Guo, Sailing; Wu, Ruoxi; Zhang, Jinggang; Kong, Mengyu; Pan, Liang; Gu, Yajia; Yu, Shengnan.
Afiliación
  • Zhu S; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.).
  • Wang S; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China (S.W., Y.G.).
  • Guo S; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.).
  • Wu R; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.).
  • Zhang J; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.).
  • Kong M; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.).
  • Pan L; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.).
  • Gu Y; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China (S.W., Y.G.).
  • Yu S; Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China (S.Z., S.G., R.W., J.Z., M.K., L.P., S.Y.). Electronic address: yushengnan2021@126.com.
Acad Radiol ; 31(6): 2228-2238, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38142176
ABSTRACT

BACKGROUND:

Predicting breast cancer molecular subtypes can help guide individualised clinical treatment of patients who need the rational preoperative treatment. This study aimed to investigate the efficacy of preoperative prediction of breast cancer molecular subtypes by contrast-enhanced mammography (CEM) radiomic features.

METHODS:

This retrospective two-centre study included women with breast cancer who underwent CEM preoperatively between August 2016 and May 2022. We included 356 patients with 386 lesions, which were grouped into training (n = 162), internal test (n = 160) and external test sets (n = 64). Radiomics features were extracted from low-energy (LE) images and recombined (RC) images and selected. Three dichotomous tasks were established according to postoperative immunohistochemical

results:

Luminal vs. non-Luminal, human epidermal growth factor receptor (HER2)-enriched vs. non-HER2-enriched, and triple-negative breast cancer (TNBC) vs. non-TNBC. For each dichotomous task, the LE, RC, and LE+RC radiomics models were built by the support vector machine classifier. The prediction performance of the models was assessed by the area under the receiver operating characteristic curve (AUC). Then, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the models. DeLong's test was utilised to compare the AUCs.

RESULTS:

Radiomics models based on CEM are valuable for predicting breast cancer molecular subtypes. The LE+RC model achieved the best performance in the test set. The LE+RC model predicted Luminal, HER2-enriched, and TNBC subtypes with AUCs of 0.93, 0.89, and 0.87 in the internal test set and 0.82, 0.83, and 0.69 in the external test set, respectively. In addition, the LE model performed more satisfactorily than the RC model.

CONCLUSION:

CEM radiomics features can effectively predict breast cancer molecular subtypes preoperatively, and the LE+RC model has the best predictive performance.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Mamografía / Medios de Contraste Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Acad Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Mamografía / Medios de Contraste Límite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Acad Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos