Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 2: ADCmin

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Oncotarget. 2018; 9:8675-8680. https://doi.org/10.18632/oncotarget.24006

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Alexey Surov _, Hans Jonas Meyer and Andreas Wienke


Alexey Surov1,*, Hans Jonas Meyer1,* and Andreas Wienke2,*

1Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany

2Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany

*These authors contributed equally to this work

Correspondence to:

Alexey Surov, email: [email protected]

Keywords: diffusion weighted imaging; ADC; KI 67

Received: July 06, 2017     Accepted: November 13, 2017     Published: January 04, 2018


The purpose of this part of the meta-analysis was to summarize data regarding associations between minimum apparent diffusion coefficient (ADCmin) and KI 67 in different tumors.

MEDLINE library was screened for associations between ADCmin and KI 67 in different tumors up to April 2017. Overall, 23 studies with 944 patients were identified. Associations between ADC and KI 67 were analyzed by Spearman's correlation coefficient.

The pooled correlation coefficient between ADCmin and KI 67 for all included tumors was ρ = -0.47. In detail, the correlation coefficients for separate tumors were as follows: cerebral lymphoma: ρ = –0.61 (95% CI = [–0.82; –0.41]); cervical cancer: ρ = –0.56 (95% CI = [–0.68;–0.43]); pituitary adenoma: ρ = –0.55 (95% CI = [–1.31; 0.22]); glioma: ρ = –0.40 (95% CI = [–0.55; –0.24]); breast cancer: ρ = –0.37 (95% CI = [–0.74; –0.01]); meningioma, ρ = –0.15 (95% CI = [–0.38; 0.07]).


Apparent diffusion coefficient (ADC) is a quantitative parameter of water diffusion in tissues [1]. Previously, numerous studies investigated associations between ADC and several histopathological features in different tumors [25]. Some reports indicated that ADC can predict proliferation activity and, therefore, behavior of several malignancies [2, 3, 5]. As already mentioned, ADC can be divided into three sub-parameters: ADC minimum or ADCmin, mean ADC or ADCmean and ADC maximum or ADCmax [5]. As shown in the part 1 of this meta-analysis, several tumors showed different inverse correlations between ADCmean and KI 67 [6]. Overall, the calculated correlation coefficients ranged from –0.22 in breast cancer to –0.62 in ovarian cancer [6].

There were studies, which showed that ADCmin had stronger correlations with KI 67, and can better reflect proliferation potential of malignant lesions [7, 8]. However, the reported data were based on small number of investigated tumors/patients.

The purpose of this part of the meta-analysis was to provide evident data regarding associations between minimum ADC (ADCmin), and KI 67 in different tumors.


Overall, the identified 22 studies [728] contained data about associations between ADCmin and KI 67 for 944 patients (Table 1).

Table 1: Tumor types involved into the meta-analysis




Different breast tumors






Cervical cancer



Lung cancer






Pituary adenoma



Cerebral lymphoma



Prostatic cancer



Neuroendocrine tumor



Thyroid cancer



Head and neck cancer












The pooled correlation coefficient for all patients (Figure 1) was –0.47 (95 % CI = [–0.58; –0.35]), heterogeneity Tau2 = 0.06, Chi2 = 193.62, df = 22 (P < 0.00001), I2 = 89 %, and test for overall effect Z = 7.76 (P < 0.00001).

Forest plots of correlation coefficients between ADCmin and KI 67 in all included studies (n = 22).

Figure 1: Forest plots of correlation coefficients between ADCmin and KI 67 in all included studies (n = 22).

On the next step correlation analysis for every identified entity was performed. Thereby, only primary tumors with more than two reports were included into the analysis. There were 6 entities with 632 patients (Table 2). The calculated correlation coefficients were as follows (Figure 2): -cerebral lymphoma: ρ = –0.61 (95% CI = [–0.82; –0.41]); -cervical cancer: ρ = –0.56 (95% CI = [–0.68;–0.43]); -pituitary adenoma:ρ = –0.55 (95% CI = [–1.31; 0.22]); -glioma: ρ = –0.40 (95% CI = [–0.55; –0.24]); - breast cancer: ρ = –0.37 (95% CI = [–0.74; –0.01]); -meningioma, ρ = –0.15 (95% CI = [–0.38; 0.07]).

Table 2: Tumor entities included into the subgroup analysis



Breast cancer




Cervical carcinoma




Pituary adenoma


Cerebral lymphoma


Forest plots of correlation coefficients between ADCmin and KI 67 in different primary tumors.

Figure 2: Forest plots of correlation coefficients between ADCmin and KI 67 in different primary tumors.


The present meta-analysis summarizes data about associations between ADCmin and KI 67 in different tumors

Previously, some investigations focused on relationships between ADC and histopathology, such as cell count and/or proliferation potential, in several tumors [2, 5]. However, the reported data were inconsistent: while some authors mentioned that ADC fractions can be associated with cellularity and KI 67, others did not confirm this finding [5, 7, 8]. Our previous meta-analysis regarding correlation between ADCmean and tumor cellularity showed that several tumors have different associations between the investigated parameters [29]. In detail, the calculated correlation coefficients ranged significantly and were as follows: ρ = –0.25 in lymphoma, ρ = –0.45 in meningioma, ρ = –0.48 in breast cancer, ρ = –0.53 in renal cell carcinoma, ρ = –0.53 in head and neck squamous cell carcinoma, ρ = –0.56 in prostatic cancer, ρ = –0.57 in uterine cervical cancer, ρ = –0.63 in lung cancer, ρ = –0.64 in ovarian cancer, and ρ = –0.66 in glioma [29]. Almost similar results were also identified for associations between ADCmean and KI 67 in the part 1 of the present work [6]. Because of these findings it can be postulated that ADCmean does not reflect cellularity and proliferation potential in all tumors and tumor-like lesions as assumed previously.

According to some authors, another ADC parameter, namely ADCmin has been reported to be more sensitive in prediction of cell count and proliferation activity than ADCmean [2, 7, 8]. However, a recent meta-analysis showed that ADCmin did not better correlate with tumor cellularity than ADCmean [30].

There were also inconsistent data about correlation between ADCmin and proliferation activity

As seen, in the present analysis, ADCmin correlated moderately with KI 67 expression in overall sample. The calculated correlation coefficient (ρ = –0.47) was almost similar to those reported for ADCmean (ρ = –0.44). However, for the identified tumor entities, it was different in comparison with the coefficients for ADCmean. So, in breast cancer, ADCmin correlated stronger with KI 67 (ρ = –0.37) than ADCmean (ρ = –0.22) [6], although the identified associations were slightly. Also in pituitary adenoma, and cerebral lymphoma, ADCmin tended to be better in comparison to ADCmean: ρ = –0.56 vs ρ = –0.44 [6], and ρ = –0.61 vs ρ = –0.55, respectively [6]. On the other hand, in glioma and meningioma, ADCmin did not better correlate with KI 67 expression than ADCmean: ρ = –0.40 vs ρ = –0.51 [6], and ρ = –0.15 vs ρ = –0.43 [6], respectively.

The exact cause of our findings is unclear. They supported previous suggestions that different ADC fractions reflect different histopathological features [2]. Obviously, there is no general rule regarding ADC parameters and tumor proliferation, i.e. for some tumors ADCmin and for other ADCmean predicts better proliferation potential.

Also for this part of the meta-analysis, already the mentioned limitations [6] do apply: only 6 named above tumor entities were involved into the work. For other malignancies and tumor-like lesions no data could be provided. In addition, the number of patients in the groups of pituitary adenoma, cerebral lymphoma, and meningioma was very small that questions the validity of the estimated correlation coefficients.

In conclusion, there are different inverse correlations between ADCmin and KI 67 in several tumors. In comparison with ADCmean, ADCmin seems to correlate better with proliferation activity in breast cancer, cerebral lymphoma, and pituitary adenoma.

In meningioma and glioma, however, ADCmean reflects better tumor proliferation than ADCmin.


Data acquisition and proving

The search strategy and data acquisition are described precisely in the part 1 of the meta-analysis [6]. For this part, only data regarding associations between ADCmin derived from diffusion weighted imaging (DWI) and expression of KI 67 in different tumors and tumor-like lesions were collected. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research [31].

Overall, 22 studies were included into the present analysis [728]. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.


The methodological quality of the 23 studies was independently checked by two observers (A.S. and H.J.M.) using the Quality Assessment of Diagnostic Studies (QUADAS) instrument according to previous descriptions [32, 33]. The results of QUADAS proving is given in Table 3.

Table 3: Methodological quality of the involved 23 studies according to the QUADAS criteria

QUADAS criteria

Yes (%)

No (%)

Unclear (%)

Patient spectrum

23 (100)

Selection criteria

20 (86.96)

3 (13.04)

Reference standard

23 (100)

Disease progression bias

23 (100)

Partial vertification bias

23 (100)

Differential vertification bias

23 (100)

Incorporation bias

23 (100)

Text details

23 (100)

Reference standard details

23 (100)

Text review details

12 (52.18)

3 (13.04)

8 (34.78)

Diagnostic review bias

15 (65.22)

3 (13.04)

5 (21.74)

Clinical review bias

23 (100)

Uninterpretable results

23 (100)

Withdrawls explained

23 (100)

Associations between ADCmin and KI 67 were analyzed by Spearman’s correlation coefficient. The reported Pearson correlation coefficients in some studies were converted into Spearman correlation coefficients as described previously [34].

The meta-analysis was undertaken by using RevMan 5.3 (Computer program, version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014). Heterogeneity was calculated by means of the inconsistency index I² [35, 36]. In a subgroup analysis, studies were stratified by tumor type. Furthermore, DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction [37].


There are no conflicts of interest.




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