INTRODUCTION {#s1}
============

Apparent diffusion coefficient (ADC) is a quantitative parameter of water diffusion in tissues \[[@R1]\]. Previously, numerous studies investigated associations between ADC and several histopathological features in different tumors \[[@R2]--[@R5]\]. Some reports indicated that ADC can predict proliferation activity and, therefore, behavior of several malignancies \[[@R2], [@R3], [@R5]\]. As already mentioned, ADC can be divided into three sub-parameters: ADC minimum or ADC~min~, mean ADC or ADC~mean~ and ADC maximum or ADC~max~ \[[@R5]\]. As shown in the part 1 of this meta-analysis, several tumors showed different inverse correlations between ADC~mean~ and KI 67 \[[@R6]\]. Overall, the calculated correlation coefficients ranged from --0.22 in breast cancer to --0.62 in ovarian cancer \[[@R6]\].

There were studies, which showed that ADC~min~ had stronger correlations with KI 67, and can better reflect proliferation potential of malignant lesions \[[@R7], [@R8]\]. 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 (ADC~min~), and KI 67 in different tumors.

RESULTS {#s2}
=======

Overall, the identified 22 studies \[[@R7]--[@R28]\] contained data about associations between ADC~min~ and KI 67 for 944 patients (Table [1](#T1){ref-type="table"}).

###### Tumor types involved into the meta-analysis

  Diagnosis                 *n*   \%
  ------------------------- ----- -------
  Different breast tumors   200   34.33
  Glioma                    144   15.25
  Cervical cancer           117   12.39
  Lung cancer               93    9.85
  Meningioma                72    7.63
  Pituary adenoma           50    5.30
  Cerebral lymphoma         49    5.19
  Prostatic cancer          29    3.07
  Neuroendocrine tumor      22    2.33
  Thyroid cancer            14    1.48
  Head and neck cancer      11    1.17
  Ganglioglioma             10    1.06
  Neurocytoma               9     0.95
  Total                     944   100

The pooled correlation coefficient for all patients (Figure [1](#F1){ref-type="fig"}) was --0.47 (95 % CI = \[--0.58; --0.35\]), heterogeneity Tau^2^ = 0.06, Chi^2^ = 193.62, df = 22 (*P* \< 0.00001), I^2^ = 89 %, and test for overall effect Z = 7.76 (*P* \< 0.00001).

![Forest plots of correlation coefficients between ADC~min~ and KI 67 in all included studies (*n* = 22)](oncotarget-09-8675-g001){#F1}

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](#T2){ref-type="table"}). The calculated correlation coefficients were as follows (Figure [2](#F2){ref-type="fig"}): -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 = \[--0.38; 0.07\]).

###### Tumor entities included into the subgroup analysis

  Diagnosis            *n*
  -------------------- -----
  Breast cancer        200
  Glioma               144
  Cervical carcinoma   117
  Meningioma           72
  Pituary adenoma      50
  Cerebral lymphoma    49

![Forest plots of correlation coefficients between ADC~min~ and KI 67 in different primary tumors](oncotarget-09-8675-g002){#F2}

DISCUSSION {#s3}
==========

The present meta-analysis summarizes data about associations between ADC~min~ and KI 67 in different tumors {#s3_1}
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Previously, some investigations focused on relationships between ADC and histopathology, such as cell count and/or proliferation potential, in several tumors \[[@R2], [@R5]\]. 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 \[[@R5], [@R7], [@R8]\]. Our previous meta-analysis regarding correlation between ADC~mean~ and tumor cellularity showed that several tumors have different associations between the investigated parameters \[[@R29]\]. 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 \[[@R29]\]. Almost similar results were also identified for associations between ADC~mean~ and KI 67 in the part 1 of the present work \[[@R6]\]. Because of these findings it can be postulated that ADC~mean~ 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 ADC~min~ has been reported to be more sensitive in prediction of cell count and proliferation activity than ADC~mean~ \[[@R2], [@R7], [@R8]\]. However, a recent meta-analysis showed that ADC~min~ did not better correlate with tumor cellularity than ADC~mean~ \[[@R30]\].

There were also inconsistent data about correlation between ADC~min~ and proliferation activity {#s3_2}
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As seen, in the present analysis, ADC~min~ correlated moderately with KI 67 expression in overall sample. The calculated correlation coefficient (*ρ* = --0.47) was almost similar to those reported for ADC~mean~ (*ρ* = --0.44). However, for the identified tumor entities, it was different in comparison with the coefficients for ADC~mean~. So, in breast cancer, ADC~min~ correlated stronger with KI 67 (*ρ* = --0.37) than ADC~mean~ (*ρ* = --0.22) \[[@R6]\], although the identified associations were slightly. Also in pituitary adenoma, and cerebral lymphoma, ADC~min~ tended to be better in comparison to ADC~mean~: *ρ* = --0.56 vs *ρ* = --0.44 \[[@R6]\], and *ρ* = --0.61 vs *ρ* = --0.55, respectively \[[@R6]\]. On the other hand, in glioma and meningioma, ADC~min~ did not better correlate with KI 67 expression than ADC~mean~: *ρ* = --0.40 vs *ρ* = --0.51 \[[@R6]\], and *ρ* = --0.15 vs *ρ* = --0.43 \[[@R6]\], respectively.

The exact cause of our findings is unclear. They supported previous suggestions that different ADC fractions reflect different histopathological features \[[@R2]\]. Obviously, there is no general rule regarding ADC parameters and tumor proliferation, i.e. for some tumors ADC~min~ and for other ADC~mean~ predicts better proliferation potential.

Also for this part of the meta-analysis, already the mentioned limitations \[[@R6]\] 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 ADC~min~ and KI 67 in several tumors. In comparison with ADC~mean~, ADC~min~ seems to correlate better with proliferation activity in breast cancer, cerebral lymphoma, and pituitary adenoma.

In meningioma and glioma, however, ADC~mean~ reflects better tumor proliferation than ADC~min~.

MATERIALS AND METHODS {#s4}
=====================

Data acquisition and proving {#s4_1}
----------------------------

The search strategy and data acquisition are described precisely in the part 1 of the meta-analysis \[[@R6]\]. For this part, only data regarding associations between ADC~min~ 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 \[[@R31]\].

Overall, 22 studies were included into the present analysis \[[@R7]--[@R28]\]. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.

Meta-analysis {#s4_2}
-------------

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 \[[@R32], [@R33]\]. The results of QUADAS proving is given in Table [3](#T3){ref-type="table"}.

###### 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 ADC~min~ 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 \[[@R34]\].

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 I^2^ \[[@R35], [@R36]\]. 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 \[[@R37]\].

**CONFLICTS OF INTEREST**

There are no conflicts of interest.

**FUNDING**

None.

[^1]: These authors contributed equally to this work
