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The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma

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Abstract

Background

Accurate prediction of the grade of invasive ductal carcinoma (IDC) before treatment is vital for individualized therapy and improving patient outcomes. This study aimed to develop and validate a mammography-based radiomics nomogram that would incorporate the radiomics signature and clinical risk factors in the preoperative prediction of the histological grade of IDC.

Methods

The data of 534 patients from our hospital with pathologically confirmed IDC (374 in the training cohort and 160 in the validation cohort) were retrospectively analyzed. A total of 792 radiomics features were extracted from the patients’ craniocaudal and mediolateral oblique view images. A radiomics signature was generated using the least absolute shrinkage and selection operator method. Multivariate logistic regression was adopted to establish a radiomics nomogram, the utility of which was evaluated using a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA).

Results

The radiomics signature was found to have a significant correlation with histological grade (P < 0.01), but the efficacy of the model is limited. The radiomics nomogram, which incorporated the radiomics signature and spicule sign into mammography, showed good consistency and discrimination in both the training cohort [area under the curve (AUC) = 0.75] and the validation cohort (AUC = 0.75). The calibration curves and DCA demonstrated the clinical usefulness of the proposed radiomics nomogram model.

Conclusions

A radiomics nomogram based on the radiomics signature and spicule sign can be used to predict the histological grade of IDC and assist in clinical decision-making for patients with IDC.

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

IDC:

Invasive ductal carcinoma

MRI:

Magnetic resonance imaging

CC:

Craniocaudal

MLO:

Mediolateral oblique

DICOM:

Digital imaging and communications in medicine

PACS:

Picture Archiving and Communication System

ROIs:

Regions of interest

RLM:

Run–length matrix

GLSZM:

Gray-level size zone matrix

GLCM:

Gray-level cooccurrence matrix

LASSO:

Least absolute shrinkage and selection operator

AUC:

Area under the curve

ROC:

Receiver-operating characteristic

DCA:

Decision curve analysis

CESM:

Contrast enhancement spectral mammography

References

  • Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  PubMed  Google Scholar 

  • Ahmeidat H, Purdie C, Jordan L, Fleming D, McCullough J, Evans A (2018) Non-histopathological parameters associated with upgrade of breast tumours yielding a core biopsy report of histological grade 2 ductal no special type to grade 3 on excision. Eur J Surg Oncol 44:1720–1724

    Article  PubMed  Google Scholar 

  • Blaichman J, Marcus JC, Alsaadi T, El-Khoury M, Meterissian S, Mesurolle B (2012) Sonographic appearance of invasive ductal carcinoma of the breast according to histologic grade. AJR Am J Roentgenol 199:402–408

    Article  Google Scholar 

  • Bray F, Ferlay J, Soerjomataram I et al (2018) Global Cancer Statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424

    Article  PubMed  Google Scholar 

  • Cui WJ, Wang C, Jia L et al (2019) Differentiation between G1 and G2/G3 phyllodes tumors of breast using mammography and mammographic texture analysis. Front Oncol 9:433

    Article  PubMed  PubMed Central  Google Scholar 

  • Ding J, Xing Z, Jiang Z et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56

    Article  Google Scholar 

  • Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer: I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19:403–410

    Article  CAS  PubMed  Google Scholar 

  • Elston CW, Ellis IO, Pinder SE (1999) Pathological prognostic factors in breast cancer. Crit Rev Oncol Hematol 31:209–223

    Article  CAS  PubMed  Google Scholar 

  • Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29:6880–6890

    Article  PubMed  Google Scholar 

  • Han L, Zhu Y, Liu Z et al (2019) Radiomic nomogram for prediction of axillarylymph node etastasis in breast cancer. Eur Radiol 29:3820–3829

    Article  PubMed  Google Scholar 

  • Harris GC, Denley HE, Pinder SE et al (2003) Correlation of histologic prognostic factors in core biopsies and therapeutic excisions of invasive breast carcinoma. Am J Surg Pathol 27:11–15

    Article  PubMed  Google Scholar 

  • Huang X, Cheng Z, Huang Y et al (2018) CT-based radiomics signature to discriminate high-grade from Low-grade colorectal adenocarcinoma. Acad Radiol 25:1285–1297

    Article  PubMed  Google Scholar 

  • Jung SC, Choi SH, Yeom JA et al (2013) Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods. PLoS ONE 8:e69323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim SM, Kim Y, Jeong K, Jeong H, Kim J (2018) Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography. Ultrasonography 37:36–42

    Article  PubMed  Google Scholar 

  • Lamb PM, Perry NM, Vinnicombe SJ, Wells CA (2000) Correlation between ultrasound characteristics, mammographic findings and histological grade in patients with invasive ductal carcinoma of the breast. Clin Radiol 55:40–44

    Article  CAS  PubMed  Google Scholar 

  • Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  PubMed  Google Scholar 

  • Li Z, Yu L, Wang X et al (2018) Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Clin Breast Cancer 18:e621–e627

    Article  PubMed  Google Scholar 

  • Liang W, Yang P, Huang R et al (2019) A combined nomogram model to preoperatively predict histologic grade in pancreatic neuroendocrine tumors. Clin Cancer Res 25:584–594

    Article  PubMed  Google Scholar 

  • Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol 28:1191–1206

    Article  CAS  PubMed  Google Scholar 

  • Liu Y, Zhang Y, Cheng R et al (2019) Radiomics analysis of apparent diffusion coefficient in cervical cancer: a preliminary study on histological grade evaluation. J Magn Reson Imaging 49:280–290

    Article  PubMed  Google Scholar 

  • Liu Z, Wang S, Dong D et al (2019) The Applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9:1303–1322

    Article  PubMed  PubMed Central  Google Scholar 

  • Ma W, Zhao Y, Ji Y et al (2019) Breast cancer molecular subtype prediction by mammographic radiomic features. Acad Radiol 26:196–201

    Article  PubMed  Google Scholar 

  • Mandó P, Rizzo M, de la Puente CP et al (2017) High histologic grade and high Ki-67 expression predict phenotypic alterations in node metastasis in primary breast cancers. J Breast Cancer 20:170–175

    Article  PubMed  PubMed Central  Google Scholar 

  • Mao N, Jiao Z, Duan S et al (2021) Preoperative prediction of histologic grade in invasive breast cancer by using contrast-enhanced spectral mammography-based radiomics. J Xray Sci Technol 29:763–772

    PubMed  Google Scholar 

  • Petrillo A, Fusco R, Di Bernardo E et al (2022) Prediction of breast cancer histological outcome by radiomics and artificial intelligence analysis in contrast-enhanced mammography. Cancers (basel) 14:2132

    Article  CAS  PubMed  Google Scholar 

  • Rakha EA, Reis-Filho JS, Baehner F et al (2010) Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res 12:207

    Article  PubMed  PubMed Central  Google Scholar 

  • Rakha EA, El-Sayed ME, Lee AH et al (2018) Prognostic significance of Nottingham histologic grade in invasive breast carcinoma. J Clin Oncol 26:3153–3158

    Article  Google Scholar 

  • Roknsharifi S, Fishman MDC, Agarwal MD, Brook A, Kharbanda V, Dialani V (2019) The role of diffusion-weighted imaging as supplement to dynamic contrast enhanced breast MRI: Can it help predict malignancy, histologic grade and recurrence? Acad Radiol 26:923–929

    Article  PubMed  Google Scholar 

  • Schrading S, Kuhl CK (2008) Mammographic, US, and MR imaging phenotypes of familial breast cancer. Radiology 246:58–70

    Article  PubMed  Google Scholar 

  • Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34

    Article  PubMed  Google Scholar 

  • Steyerberg EW, Vickers AJ (2008) Decision curve analysis: a discussion. Med Decis Making 28:146–149

    Article  PubMed  PubMed Central  Google Scholar 

  • Tian Q, Yan LF, Zhang X et al (2018) Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging 48:1518–1528

    Article  PubMed  Google Scholar 

  • Vasquez MM, Hu C, Roe DJ, Chen Z, Halonen M, Guerra S (2016) Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application. BMC Med Res Methodol 16:154

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang H, Hu D, Yao H et al (2019) Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Eur Radiol 29:6182–6190

    Article  PubMed  Google Scholar 

  • Wang Q, Li Q, Mi R et al (2019) Radiomics nomogram building from multiparametric MRI to predict grade in patients with glioma: a cohort study. J Magn Reson Imaging 49:825–833

    Article  CAS  PubMed  Google Scholar 

  • Yang J, Wang T, Yang L et al (2019) Preoperative prediction of axillary lymph node metastasis in breast cancer using mammography-based radiomics method. Sci Rep 9:4429

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang X, Xu X, Tian Q et al (2017) Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging 46:1281–1288

    Article  PubMed  PubMed Central  Google Scholar 

  • Zheng K, Tan JX, Li F et al (2018) Clinicopathologic factors related to the histological tumor grade of breast cancer in western china: an epidemiological multicenter study of 8619 female patients. Transl Oncol 11:1023–1033

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China

    Xiao-Cui Rong, Yi-He Kang, Gao-Feng Shi, Zhi-Gang Li & Guang Yang

  2. GE Healthcare China, Daxing District, Tongji South Road No.1, Beijing, 100176, China

    Jia-Liang Ren & Yu-Hao Liu

Authors
  1. Xiao-Cui Rong
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  2. Yi-He Kang
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  4. Jia-Liang Ren
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  6. Zhi-Gang Li
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  7. Guang Yang
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Contributions

XCR collected and interpreted the patient data, and was a major contributor in writing the manuscript.YHK collected the data and wrote the first draft. GFS, ZGL and GY proposed and designed the study. JLR and YHL analyzed the data. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yi-He Kang or Gao-Feng Shi.

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Conflict of interest

The authors declare that they have no competing interests.

Ethics approval and consent to participate

The retrospective study was approved by ethics committee of the Fourth Hospital of Hebei Medical University (no. 2020KY271). As patient consent to review their medical records was not required by the ethics committee, because the study is retrospective and does not involve patient privacy, the signed informed consent requirement was waved. This study was conducted in accordance with the declaration of Helsinki and patients’ data confidentiality.

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Rong, XC., Kang, YH., Shi, GF. et al. The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma. J Cancer Res Clin Oncol 149, 11635–11645 (2023). https://doi.org/10.1007/s00432-023-05001-9

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  • DOI: https://doi.org/10.1007/s00432-023-05001-9

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