Literature DB >> 30116958

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging.

Shiteng Suo1, Dandan Zhang1, Fang Cheng1, Mengqiu Cao1, Jia Hua2, Jinsong Lu3, Jianrong Xu4.   

Abstract

OBJECTIVES: To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.
METHODS: One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.
RESULTS: Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.
CONCLUSIONS: Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination. KEY POINTS: • ADC metrics are associated with molecular marker status in IDC. • ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 . • ADC metrics add value to morphologic features in IDC phenotyping.

Entities:  

Keywords:  Breast cancer; Diffusion magnetic resonance imaging; Immunohistochemistry; Phenotype; Prognosis

Mesh:

Year:  2018        PMID: 30116958     DOI: 10.1007/s00330-018-5667-9

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  32 in total

1.  [Influence of b value on the measurement of contrast and apparent diffusion coefficient in 3.0 Tesla breast magnetic resonance imaging].

Authors:  Masako Takanaga; Norio Hayashi; Tosiaki Miyati; Hiroko Kawashima; Takashi Hamaguchi; Naoki Ohno; Shigeru Sanada; Tomoyuki Yamamoto; Osamu Matsui
Journal:  Nihon Hoshasen Gijutsu Gakkai Zasshi       Date:  2012

2.  Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma.

Authors:  Su Kyung Jeh; Sung Hun Kim; Hyeon Sook Kim; Bong Joo Kang; Seung Hee Jeong; Hyeon Woo Yim; Byung Joo Song
Journal:  J Magn Reson Imaging       Date:  2011-01       Impact factor: 4.813

3.  Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade.

Authors:  Kiminori Fujimoto; Tatsuyuki Tonan; Sanae Azuma; Masayoshi Kage; Osamu Nakashima; Takeshi Johkoh; Naofumi Hayabuchi; Koji Okuda; Takumi Kawaguchi; Michio Sata; Aliya Qayyum
Journal:  Radiology       Date:  2011-01-19       Impact factor: 11.105

4.  Estrogen receptor beta is coexpressed with ERalpha and PR and associated with nodal status, grade, and proliferation rate in breast cancer.

Authors:  T A Järvinen; M Pelto-Huikko; K Holli; J Isola
Journal:  Am J Pathol       Date:  2000-01       Impact factor: 4.307

5.  Protein array technology to detect HER2 (erbB-2)-induced 'cytokine signature' in breast cancer.

Authors:  Alejandro Vazquez-Martin; Ramon Colomer; Javier A Menendez
Journal:  Eur J Cancer       Date:  2007-03-26       Impact factor: 9.162

6.  Correlations between diffusion-weighted imaging and breast cancer biomarkers.

Authors:  Laura Martincich; Veronica Deantoni; Ilaria Bertotto; Stefania Redana; Franziska Kubatzki; Ivana Sarotto; Valentina Rossi; Michele Liotti; Riccardo Ponzone; Massimo Aglietta; Daniele Regge; Filippo Montemurro
Journal:  Eur Radiol       Date:  2012-03-13       Impact factor: 5.315

Review 7.  Tumor heterogeneity: causes and consequences.

Authors:  Andriy Marusyk; Kornelia Polyak
Journal:  Biochim Biophys Acta       Date:  2009-11-18

8.  Evaluation of the prognostic role of vascular endothelial growth factor and microvessel density in stages I and II breast cancer patients.

Authors:  V Ludovini; A Sidoni; L Pistola; G Bellezza; V De Angelis; S Gori; A M Mosconi; G Bisagni; R Cherubini; A Rosa Bian; C Rodinò; R Sabbatini; B Mazzocchi; E Bucciarelli; M Tonato; M Colozza
Journal:  Breast Cancer Res Treat       Date:  2003-09       Impact factor: 4.872

9.  Supervised risk predictor of breast cancer based on intrinsic subtypes.

Authors:  Joel S Parker; Michael Mullins; Maggie C U Cheang; Samuel Leung; David Voduc; Tammi Vickery; Sherri Davies; Christiane Fauron; Xiaping He; Zhiyuan Hu; John F Quackenbush; Inge J Stijleman; Juan Palazzo; J S Marron; Andrew B Nobel; Elaine Mardis; Torsten O Nielsen; Matthew J Ellis; Charles M Perou; Philip S Bernard
Journal:  J Clin Oncol       Date:  2009-02-09       Impact factor: 44.544

10.  Triple-negative breast cancer: correlation between MR imaging and pathologic findings.

Authors:  Takayoshi Uematsu; Masako Kasami; Sachiko Yuen
Journal:  Radiology       Date:  2009-03       Impact factor: 11.105

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  12 in total

1.  Volumetric apparent diffusion coefficient histogram analysis of the testes in nonobstructive azoospermia: a noninvasive fingerprint of impaired spermatogenesis?

Authors:  Athina C Tsili; Loukas G Astrakas; Anna C Goussia; Nikolaos Sofikitis; Maria I Argyropoulou
Journal:  Eur Radiol       Date:  2022-04-29       Impact factor: 5.315

2.  Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67.

Authors:  Mustafa Bozdağ; Ali Er; Sümeyye Ekmekçi
Journal:  Neuroradiol J       Date:  2021-10-05

3.  Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions.

Authors:  Alexey Surov; Hans Jonas Meyer; Andreas Wienke
Journal:  BMC Cancer       Date:  2019-10-15       Impact factor: 4.430

4.  Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers.

Authors:  Joao V Horvat; Aditi Iyer; Elizabeth A Morris; Aditya Apte; Blanca Bernard-Davila; Danny F Martinez; Doris Leithner; Olivia M Sutton; R Elena Ochoa-Albiztegui; Dilip Giri; Katja Pinker; Sunitha B Thakur
Journal:  Contrast Media Mol Imaging       Date:  2019-11-22       Impact factor: 3.161

5.  Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging.

Authors:  Weiwei Wang; Xindong Zhang; Laimin Zhu; Yueqin Chen; Weiqiang Dou; Fan Zhao; Zhe Zhou; Zhanguo Sun
Journal:  Front Oncol       Date:  2022-01-31       Impact factor: 6.244

Review 6.  Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Authors:  Toshiki Kazama; Taro Takahara; Jun Hashimoto
Journal:  Life (Basel)       Date:  2022-03-28

Review 7.  Diffusion Breast MRI: Current Standard and Emerging Techniques.

Authors:  Ashley M Mendez; Lauren K Fang; Claire H Meriwether; Summer J Batasin; Stéphane Loubrie; Ana E Rodríguez-Soto; Rebecca A Rakow-Penner
Journal:  Front Oncol       Date:  2022-07-08       Impact factor: 5.738

8.  Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging.

Authors:  Jin Joo Kim; Jin You Kim; Hie Bum Suh; Lee Hwangbo; Nam Kyung Lee; Suk Kim; Ji Won Lee; Ki Seok Choo; Kyung Jin Nam; Taewoo Kang; Heeseung Park
Journal:  Eur Radiol       Date:  2021-08-04       Impact factor: 5.315

9.  Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models.

Authors:  Shiteng Suo; Yan Yin; Xiaochuan Geng; Dandan Zhang; Jia Hua; Fang Cheng; Jie Chen; Zhiguo Zhuang; Mengqiu Cao; Jianrong Xu
Journal:  J Transl Med       Date:  2021-06-02       Impact factor: 5.531

Review 10.  Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends.

Authors:  Mami Iima
Journal:  Magn Reson Med Sci       Date:  2020-06-15       Impact factor: 2.471

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