European Radiology

, Volume 29, Issue 2, pp 485–493 | Cite as

Risk stratification of ductal carcinoma in situ using whole-lesion histogram analysis of the apparent diffusion coefficient

  • Jin You KimEmail author
  • Jin Joo Kim
  • Ji Won Lee
  • Nam Kyung Lee
  • Geewon Lee
  • Taewoo Kang
  • Heesung Park
  • Yo Han Son
  • Robert Grimm



To investigate the value of the whole-lesion histogram apparent diffusion coefficient (ADC) metrics for differentiating low-risk from non-low-risk ductal carcinoma in situ (DCIS).


The authors identified 93 women with pure DCIS who had undergone preoperative MR imaging and diffusion-weighted imaging from 2013 to 2016. Histogram analysis of pixel-based ADC data of the whole tumour volume was performed by two radiologists using a software tool. The results were compared between low-risk and non-low-risk DCIS. Associations between quantitative ADC metrics and low-risk DCIS were evaluated by receiver operating characteristics (ROC) curve and logistic regression analyses.


In whole-lesion histogram analysis, mean ADC and 5th, 50th and 95th percentiles of ADC were significantly different between low-risk and non-low-risk DCIS (1.522, 1.207, 1.536 and 1.854 × 10−3 mm2/s versus 1.270, 0.917, 1.261 and 1.657 × 10−3 mm2/s, respectively; p = .004, p = .003, p = .004 and p = .024, respectively). ROC curve analysis for differentiating low-risk DCIS revealed that 5th percentile ADC yielded the largest area under the curve (0.786) among the metrics of whole-lesion histogram, and the optimal cut-off point was 1.078 × 10−3 mm2/s (sensitivity 80%, specificity 75.9%, p = .001). Multivariate regression analysis revealed that a high 5th percentile of ADC (> 1.078× 10−3 mm2/s; odds ratio [OR] = 10.494, p = .016), small tumour size (≤ 2 cm; OR = 12.692, p = .008) and low Ki-67 status (< 14%; OR = 10.879, p = .046) were significantly associated with low-risk DCIS.


Assessment with whole-lesion histogram analysis of the ADC could be helpful for identifying patients with low-risk DCIS.

Key Points

• Whole-lesion histogram ADC metrics could be helpful for differentiating low-risk from non-low-risk DCIS.

• A high 5th percentile ADC was a significant factor associated with low-risk DCIS.

• Risk stratification of DCIS is important for their management.


Breast neoplasm Magnetic resonance imaging Diffusion magnetic resonance imaging Ductal carcinoma in situ Risk 



Apparent diffusion coefficient


Area under the curve


Confidence interval


Ductal carcinoma in situ


Diffusion-weighted imaging


Oestrogen receptor


Human epidermal growth factor receptor 2


Intraclass correlation coefficient


Odds ratio


Progesterone receptor


Receiver operating characteristic


Regions of interest



This work was based on the Multi Parametric Analysis works-in-progress software package provided by Siemens Healthineers.

This study was presented at the 2017 RSNA Scientific Assembly.


The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Jin You Kim.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.


• retrospective

• observational

• performed at one institution


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Pusan National University HospitalPusan National University School of Medicine and Medical Research InstituteBusanRepublic of Korea
  2. 2.Medical Research InstitutePusan National University School of MedicineBusanRepublic of Korea
  3. 3.Busan Cancer CenterPusan National University HospitalBusanRepublic of Korea
  4. 4.Siemens HealthineersSeoulKorea
  5. 5.Siemens HealthineersMR Application PredevelopmentErlangenGermany

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