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A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer

  • Breast Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Objective

To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer.

Methods

From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 − ; (3) HR + vs. HR − ; and (4) non-luminal vs. luminal A or HR + /HER2−  and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered.

Results

The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set.

Conclusions

The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

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Data availability

Data are reported in the manuscript and at link https://zenodo.org/record/8392919

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Acknowledgements

The authors acknowledge the support from the Radiomics Group of “Alleanza Contro il Cancro” and the Italian Ministry of Health.

Funding

This research was funded by the Italian Ministry of Health through the Project RCR-2021–23671213 of the “Alleanza Contro il Cancro (ACC)” network.

Author information

Author notes

  1. Antonella Petrillo and Roberta Fusco have equally contributed to this work.

Authors and Affiliations

  1. Medical Oncology Division, Igea SpA, 80013, Naples, Italy

    Roberta Fusco

  2. Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy

    Antonella Petrillo, Teresa Petrosino, Paolo Vallone, Vincenza Granata, Maria Rosaria Rubulotta, Paolo Pariante & Nicola Raiano

  3. Pathology Division, Istituto Nazionale Tumori–IRCCS-Fondazione G. Pascale, 80131, Naples, Italy

    Giosuè Scognamiglio

  4. Direzione Scientifica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy

    Annarita Fanizzi

  5. SSD Fisica Sanitaria, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy

    Raffaella Massafra

  6. Struttura Semplice Dipartimentale Di Radiodiagnostica Senologica, IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124, Bari, Italy

    Miria Lafranceschina & Daniele La Forgia

  7. Radiology and Diagnostic Imaging, Istituto Di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy

    Laura Greco, Francesca Romana Ferranti, Valeria De Soccio & Antonello Vidiri

  8. Breast Imaging Division, IEO Istituto Europeo Di Oncologia, 20141, Milan, Italy

    Francesca Botta, Valeria Dominelli & Enrico Cassano

  9. Radiation Protection and Innovative Technology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy

    Eugenio Sorgente & Biagio Pecori

  10. Medical Physics, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy

    Vincenzo Cerciello

  11. Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy

    Luca Boldrini

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  1. Antonella Petrillo
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  2. Roberta Fusco
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  3. Teresa Petrosino
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Contributions

Each author contributed for methodology, enrollment of patient and investigation. Each author written, revised and approved the manuscript.

Corresponding author

Correspondence to Antonella Petrillo.

Ethics declarations

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Roberta Fusco and Vincenza Granata are editors in Radiologia Medica journal.

Ethical approval

This multicenter retrospective study is performed according to the rules distributed by the local institutional review board that approved the study.

Informed consent

All patients enrolled signed the informed consent.

Institutional review board

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee National Cancer Institute of Naples Pascale Foundation. The authorization number is: Executive Resolution No. 868 of 03/09/2020.

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Roberta Fusco and Vincenza Granata are editors in Radiologia Medica journal.

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Petrillo, A., Fusco, R., Petrosino, T. et al. A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer. Radiol med (2024). https://doi.org/10.1007/s11547-024-01817-8

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  • DOI: https://doi.org/10.1007/s11547-024-01817-8

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