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Assessment of intravoxel incoherent motion MR imaging for differential diagnosis of breast lesions and evaluation of response: a systematic review

Abstract

Background

The current study aimed to assess the performance for quantitative differentiation and evaluation of response in categorized observations from intravoxel incoherent motion analyses of patients based on breast tumors. To assess the presence of heterogeneity, the Cochran's Q tests for heterogeneity with a significance level of P < 0.1 and I2 statistic with values > 75% were used. A random-effects meta-analysis model was used to estimate pooled sensitivity and specificity. The standardized mean difference (SMD) and 95% confidence intervals of the true diffusivity (D), pseudo-diffusivity (D*), perfusion fraction (f) and apparent diffusion coefficient (ADC) were calculated, and publication bias was evaluated using the Begg's and Egger's tests and also funnel plot. Data were analyzed by STATA v 16 (StataCorp, College Station).

Results

The pooled D value demonstrated good measurement performance showed a sensitivity 86%, specificity 86%, and AUC 0.91 (SMD − 1.50, P < 0.001) in the differential diagnosis of breast lesions, which was comparable to that of the ADC that showed a sensitivity of 76%, specificity 79%, and AUC 0.85 (SMD 1.34, P = 0.01), then by the f it showed a sensitivity 80%, specificity 76%, and AUC 0.85 (SMD 0.89, P = 0.001), and D* showed a sensitivity 84%, specificity 59%, and AUC 0.71 (SMD − 0.30, P = 0.20).

Conclusion

The estimated sensitivity and specificity in the current meta-analysis were acceptable. So, this approach can be used as a suitable method in the differentiation and evaluation response of breast tumors.

Background

As a common health problem of women, breast cancer (BC) has various histological types and therapeutic approaches [1,2,3]. Hence, determining the subtype of the disease is of high value [4, 5]. Gene expression profiling is a useful method to achieve this purpose. Nevertheless, gene expression is not always feasible and the following options have been used for BC subtypes evaluation: estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 labeling indexes using immunohistochemical techniques. In such cases, tissue biopsy can be used to obtain valuable information; however, it may cause stress and suffering for patients. Hence, differentiation between different subtypes of the tumor using noninvasive methods would be useful [6, 7]. Therefore, one of the commonly applied methods to diagnose BC is mammography. While its sensitivity to diagnose dense breast tissue is low, it has good sensitivity for fatty tissue [8]. Due to several reasons, including low cost and being convenient, the application of ultrasound (US) to detect BC is on the rise [9]. However, it should be noted that detection of nonmass BC is difficult in this method.

As a noninvasive technique, magnetic resonance imaging (MRI) is an appropriate radiological method to evaluate BC [10]. According to the literature, diffusion-weighted imaging (DWI) can effectively reflect tumor cellularity and tissue organization [11,12,13]. Due to enhanced cellularity that causes restricted water molecule movement, which roots in decreased extracellular space, diffusion in malignant tumors is restricted. This issue has resulted in an increasing inclination towards using the apparent diffusion coefficient (ADC) to evaluate cellularity [14,15,16,17,18,19].

Intravoxel incoherent motion (IVIM) is a valued imaging technique capable of differentiation between diffusion via a biexponential model analysis based on multiple b-values [20, 21]. In this line, Le Bihan and colleagues [21] developed a technique for IVIM that its effects on microcapillary perfusion are proved by some studies using DWI [22,23,24,25]. In cases that several b-values (usually ranging from 0 to 1500 s/mm2 for body imaging) are applied in DWI, the signal intensity at low b-values (0–200 s/mm2) indicates microcirculation within capillaries. In the same way, the higher the b value (> 200 s/mm2), the better the signal intensity reflects tissue diffusivity [24, 26]. The IVIM technique can provide different quantitative parameters, such as slow ADC, fast ADC, and a fraction of fast ADC values that show the perfusion and diffusion of the tissues. It should be considered that DWI cannot remove the effect of microcirculation. As the slow ADC value removes the impact of blood perfusion, it can show the true diffuse state of water molecules, which, as compared to values obtained using ADC, are more accurate.

Heterogeneity is a prominent characteristic of cancer that negatively affects treatment strategy [27, 28], which is also true for BC [29]. Hence, quantitative analysis of tumor heterogeneity using IVIM parameters and determining their correlation with BC histological characteristics would be of high use. It has been proven that IVIM metrics not only can differentiate malignant and benign lesions [23, 30, 31] but also can determine the correlation between a BC subtype and IVIM factors [25, 32,33,34]. In addition, these metrics can be used to predict the neoadjuvant therapeutic response in cases with BC [37]. Therefore, the current meta-analysis aimed to summarize the available knowledge on intravoxel incoherent motion techniques for quantitative differentiation and response evaluation after neoadjuvant chemotherapy in BC. Besides, the IVIM is potentially able to replace dynamic contrast MRI, so that avoid the cost and side effects of contrast media and improve the diagnostic performance of MRI in patients.

Methods

Search strategy

A meta-analysis search using PubMed (https://pubmed.ncbi.nlm.nih.gov), Embase (https://www.embase.com), Web of Science (https://apps.webofknowledge com), SEMANTIC SCHOLAR, Google Scholar, PROQUEST, and Cochrane Library databases (https://www.cochranelibrary.com) were performed independently by three radiologists to identify articles published before February 2021, using the keywords “breast cancer.” "Intravoxel Incoherent Motion" AND "biexponential" AND "MRI OR magnetic resonance imaging" AND "diffusion-weighted imaging OR DWI" AND "Neoadjuvant Chemotherapy OR NACT" AND "monitoring and response" AND "Breast or Breast Neoplasms, Ductal, Breast or Breast Neoplasms or Breast Diseases or Breast Carcinoma In Situ or Breast Cancer".

Study selection and data extraction

The three radiologists reviewed all 680 abstracts after duplication removal and subsequently the full text of the 90 articles was obtained if the following inclusion criteria were fulfilled: (1) included the diagnostic accuracy of breast lesions underwent diagnostic IVIM-DWI; (2) constituted original research rather than a meta-analysis, a review article, case report or case series; (3) published in English; (4) results are from humans and not animals; (5) included breast lesions IVIM-MRI protocol; (6) included sufficient data, with > 20 patients to calculate true positive (TP), false positive (FP), false negative (FN) and true negative (TN) for constructing a 2 × 2 contingency table; and (7) patients at high risk of breast lesions using pathological analysis (surgical resection, explant and/or biopsy) or imaging from follow-up corresponding to the guidelines for the standardization of breast imaging, diagnosis, classification and reporting of breast carcinoma. In addition, articles from the same institution, which involved an overlap period of patient recruitment, were considered to have an overlapping population. In these cases, the study which had the larger number of BC cases was included. A total of 590 studies were excluded according to the following exclusion criteria: (1) they were not relevant to the present meta-analysis if they fit one of the followings conditions: cancer type involves cancer other than BC; (2) they evaluated previously treated BC; (3) the sensitivity and specificity were not evaluated; (4) there was a lack of sufficient data to construct a 2 × 2 contingency table; and (5) there was study population overlap. A total of 16 studies were included for analysis. In addition, the reference list of these 16 studies was reviewed (Fig. 1).

Fig. 1
figure 1

PRISMA flow diagram for included studies in the current meta-analysis

Quality and risk of bias assessment

The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) was used to assess the quality and risk of bias in included studies. No studies were excluded due to poor quality [63].

Statistical analysis

To assess the presence of heterogeneity, the Cochran's Q tests for heterogeneity with a significance level of P < 0.1 and I2 statistic with values > 75% to the presence of heterogeneity were used. Due to the presence of significant heterogeneity, a random-effects model with 95% CI was used to estimate pooled sensitivity and specificity. To assess the effect of sample size and study year on the heterogeneity of pooled estimations, the simple meta-regression model was used. Publication bias was evaluated using the Begg's and Egger's tests and funnel plot. Data were analyzed by STATA v 11 (StataCorp, College Station, TX, USA).

Results

We obtained 16 related papers through the electronic databases of Medline/PubMed and Science Direct from 2014 to 2021 for studies that reported the percentage of observations was confirmed as breast lesions. We excluded studies that included some papers because of a lack of necessary criteria for patients or methodology. A summary of the details of all included studies is tabulated in Tables 1, 2 and 3.

Table 1 Overview of studies included
Table 2 Findings regarding the response prediction pre and post-therapy based on the IVIM factors value and tumor size
Table 3 The estimated sensitivity and specificity in the included studies in the current meta-analysis

Measurement of ADC value used for of breast tumor

Eight papers about ADC used in distinguishing breast lesions were involved for investigation. The χ2 = 31.73, P < 0.001 of the heterogeneity test (I2 = 78%) was proposed in height heterogeneity between the comprised papers. The plot in Fig. 2 demonstrates the apportionment of the ADC between breast lesions. A random effects pattern made an SMD of − 1.38 (− 1.76, − 1.00) (P < 0.001) between breast tumor for ADC. The Begg’s test proposed no publication bias linking to the ADC (P = 0.428).

Fig. 2
figure 2

The mean value of the apparent diffusion coefficient (ADC) distinguished among breast lesions

Measurement of D value used for of breast tumor

Ten papers about D used in distinguishing breast lesions were involved for investigation. The χ2 = 37.49 and P < 0.001 of the heterogeneity test (I2 = 76%) was proposed in height heterogeneity between the comprised papers. The plot in Fig. 3 demonstrates the apportionment of the D between breast tumor. A random-effects pattern made an SMD of − 1.50 (− 1.85, − 1.14) (P < 0.001) between breast tumor for D. The Begg’s test proposed no publication bias linking to D (P = 0.112).

Fig. 3
figure 3

The mean value of the true diffusivity (D) distinguished among breast lesions

Measurement of D* value used for of breast tumor

Twelve papers about D* used in distinguishing breast lesions were involved for investigation. The χ2 = 123.02 and P < 0.001 of the heterogeneity test (I2 = 91%) was proposed in height heterogeneity between the comprised papers. The plot in Fig. 4 demonstrates the apportionment of the D* between breast tumor. A random-effects pattern made an SMD of − 0.30 (− 0.76, 0.16) (P = 0.20) between breast tumor for D*. The Begg’s test proposed no publication bias linking to D* (P = 0.208).

Fig. 4
figure 4

The mean value of the pseudo-diffusivity (D*) distinguished among breast lesions

Measurement of f* value used for of breast tumor

Twelve papers about f used in distinguishing breast lesions were involved for investigation. The χ2 = 20.07 and P < 0.04 of the heterogeneity test (I2 = 45%) was proposed in height heterogeneity between the comprised papers. The plot in Fig. 5 demonstrates the apportionment of the D* between breast tumor. A random-effects pattern made an SMD of 0.89 (0.75, 1.02) (P < 0.001) between breast tumor for f. The Begg’s test proposed no publication bias linking to f (P = 0.880).

Fig. 5
figure 5

The mean value of the perfusion volume fraction (f) distinguished among breast lesions

Measurement performance

The measurement performance as evaluated by pooling sensitivity, specificity, the ADC, D, D*, and f values is recorded in Table 4. The D value demonstrated good measurement performance showed a sensitivity 86%, specificity 86%, and AUC 0.91 in the differential diagnosis of breast lesions, which was comparable to that of the ADC that showed a sensitivity of 76%, specificity 79%, and AUC 0.85, then by the f it showed a sensitivity 80%, specificity 76%, and AUC 0.85, and D* showed a sensitivity 84%, specificity 59%, and AUC 0.71.

Table 4 The pooled estimation of sensitivity and specificity of breast lesions

Meta-regression

To identify the cause of heterogeneity between studies, the effect of variables like years of study and sample size of different studies on pooled sensitivity and specificity was assessed. The effect of the year of study (P: 0.80) and sample size on heterogeneity between studies in the estimation of pooled sensitivity was not statistically significant (P: 0.49). Also, the effect of the year of study (P: 0.17) and sample size on heterogeneity between studies in the estimation of pooled specificity was not statistically significant (P: 0.72). The distribution of sensitivity and specificity according to different sample sizes is shown in Fig. 6.

Fig. 6
figure 6

The distribution of estimated sensitivity (a) and specificity (b) according to different sample sizes

Publication bias

According to the results of Begg's and Egger's test, there was a significant publication bias about the reported sensitivity (Begg's test P: 0.001, and Egger’s test P: 0.001). Also according to the results of Begg's and Egger's test, there was a significant publication bias about the reported specificity (Begg’s test P < 0.001, and Egger’s test P < 0.001) (Fig. 7).

Fig. 7
figure 7

The funnel plot to assess publication bias in estimated sensitivity (a) and specificity (b)

Quantitative analysis evaluation of response

The performance of IVIM in the prediction of the therapy response (mainly neoadjuvant chemotherapy [NAC]) in BC has been recently explored. Cho and colleagues found that the pretreatment average, skewness, and K of Dp were significant differentiators of responders from nonresponders in 32 lesions [37]. Kim and colleagues assessed 46 cases with stage II or III BC and found that pretreatment IVIM histogram parameters, including the mean, 25th percentile, 50th percentile, and 75th percentile of D obtained from the histogram of the whole tumor (Dmean, D25, D50, and D75, respectively), were significantly higher in good responders than in poorer responders [36]. Che et al. showed that the f value before NAC of patients with a pathologic complete response (pCR) was significantly larger than that of non-pCR patients and the change in f after two cycles of NAC were also significantly larger than that in the non-pCR group in 28 patients with locally advanced breast cancer [38]. These IVIM values and histogram metrics might serve as prognostic biomarkers for the selection of neoadjuvant treatment.

In three articles on IVIM as an MRI parameter, there were pre-treatment differences among responders and nonresponders. Bedair et al. [39] reported that prior to NAC, nonresponders had a higher mean Dt than responders (0.85 ± 0.05 × 10−3mm2/s and 1.02 ± 0.05 × 10−3mm2/s, respectively) (P = 0.02). In addition, responders had a better function concerning the ƒ fraction, which was not statistically significant (P = 0.09). Also, the f was significantly lower in nonresponders of the TNBC subtype (12.4 ± 4.1% vs. 10.9 ± 1.2%, P = 0.01). Following NAC, enhanced mean values in Dt were not associated with a significant difference between response groups (36% vs. 23%, P = 0.14). Moreover, decreased ƒ fraction in responders (29%) was considerably different from the increase found in ƒ in pNCR (5%, P = 0.05). Che et al. found similar results [38]. At the mid-treatment period, the D presented excellent diagnostic prediction performance by the area of the curve 0.851 (95% CI 0.666–0.956), which is a bit higher than the D* value (AUC = 0.579, 95% CI 0.379–0.762, P = 0.025). Nevertheless, the f value presented an acceptable diagnostic performance (AUC = 0.772, 95% CI 0.575–0.908). The optimal cutoff of D during the NAC to differentiate pCR from non-pCR was 0.971 × 10–3mm2/s, which showed a sensitivity of 100% (95% CI 66.4%–100%) and a specificity of 63.2% (95% CI 38.4%–83.7%). At the beginning of the follow-up, Kim et al. [36] recommended the administration of IVIM-DW imaging factors of good and minor responders pre and post NAC. Prior to NAC, while Dmean was lower in poor responders versus good responders (P ≤ 0.043). After NAC, Dmean, was lower in poor responders (P ≤ 0.037). We found no difference between the study groups concerning D* and f values both prior to and following NAC (P ≥ 0.07). While in Cho et al. [37] the values of average Dt of responders were lower than before NAC was 0.99 (0.55, 2.16) μm2/ms, the average values fp and Dp for responders were 8.7 (4.8, 19.3)% and 25.54 (15.99, 37.14) μm2/ms while 1.05 (0.96, 1.21) μm2/ms, 11.7 (5.2, 14.2)%, and 17.16 (16.9, 25.79) μm2/ms for nonresponders. The results for all parameters are summarized in Table 4.

Discussion

Individual patients and treatment regimens might have a wide range of responses to NAC. Breast MRI could be a useful imaging tool for determining whether there are changes in MRI parameters between (histopathological) responders and nonresponders before and after treatment. Intravoxel incoherent motion imaging can quantify both real molecular diffusion and motion of water molecules in the capillary network using a single diffusion-weighted acquisition technique. Utilizing the IVIM imaging model and numerous b values, IVIM imaging is used to reflect tissue diffusivity and microcapillary perfusion, as opposed to standard DWI using a pair of b values. Biexponential IVIM imaging modeling can yield three parameters, like D, the diffusion-related parameter (that shows the true molecular diffusion of the nonvascular compartment related to Brown movement)t; D*, the pseudo-diffusion coefficient (that macroscopically shows the incoherent movement of blood in the microvascular compartment); and f, the perfusion fraction (that shows the percentage of incoherent signal due to the vascular compartment in each voxel as a proportion of the total incoherent signal) [23]. Furthermore, tumor diameter and volume cannot be used to distinguish chemotherapy final responders and nonresponders. Several studies, on the other hand, revealed significant alternations in diameter and/or volume of the tumor following the initial cycle of NAC, which can be used to differentiate between patient groups.

In our study, the SMDs proposed breast tumors confirmed ADC and D lower values and f values higher than did benign lesions. BC usually has dense cellularity with a high ability for propagation, which may decrease the extracellular space and boundary the diffusion of water molecules thus causing a reduction in the diffusion coefficient.

Captivatingly, breast tumors proved a significantly greater f value but a nonsignificantly greater D* value than the benign tissue. This mostly arose from improved angiogenesis in BC [14]. The f value also confirmed a greater specificity of 0.76 and an AUC of 0.85 related with the specificity of 0.59 and AUC of 0.71 for the D* value.

Kim et al. [36], for example, proved the mean, 25th, 50th, and 75th percentiles of ADC and D enhanced in the total study group after two cycles of NAC, which might indicate reduced cellularity owing to treatment effects. The majority of these variables were likewise related to NAC response. These findings support earlier findings that good vs poor responders had considerably greater post-NAC ADC or D values [38, 51]. Meanwhile, in the entire trial population, D* and f values did not change substantially before and after NAC and were not effective for predicting tumor response. Unlike ADC and D, which have reduced signal attenuation fluctuation, D* and f have poor measurement repeatability [52, 53]. According to Che et al., significant swings in D* values may overpower minor alternations in D* [38]. Intratumoral heterogeneity and noise fluctuations might potentially have an impact on the outcomes [52]. To improve repeatability, more research should be conducted.

In addition, Cho et al. found that in their pathologic complete response (pCR) group, the post-NAC f value was significantly lower [37]. They reported a negative association between microvascular structures and f value in good responders. Nevertheless, we did not find significant results in f values to predict therapy response. In contrast to the other DW parameters, f values exhibited very poor inter-observer agreement, with a large range of values across the cases. No study used the f values to evaluate chemo-radiation therapy response in breast cancer patients [54]. In this context, Kim et al. [36] found that pre-NAC Dmean, D50, and D75 were significantly lower in poor responders. There are controversies regarding the administration of DW imaging parameters as pretreatment predictors for pathologic response. Some studies reported that ow pre-NAC ADC or D tends to respond better to NAC in BC [39, 55]; however, some also reported no difference in pre-NAC ADC or D between pCR and non-pCR groups [16, 17]. There are studies that found differences in the predictive value of pre-NACADC based on the subtypes of BC [56]. The findings revealed no difference concerning other pre-NAC DW values, i.e., ADC, D*, or f, between good and minor responders. We found that good responders tended to have higher changes in ADC and D values, which was not statistically significant. Some articles found a significantly higher lower in ADC or D in nonresponders compared to responders [38, 39].

Cho et al. [37] reported that particular IVIM factors could differentiate between RECIST responders and nonresponders. Initial measurements revealed that Dp and VTT% had the highest level of prognosis, as high vascularity with slow and heterogeneous pseudo-diffusion offering poor prognosis; in the same vein, for all dual-scanned responders, Dp was decreased. Furthermore, the heterogeneity metrics of Dp revealed surprising findings and showed the administration of advanced metrics within IVIM analysis. Histogram investigation presented the potential to detect dissimilarities in tumor heterogeneity between the two groups. The findings regarding Dp and VTT% showed that the vascular entities from lesions can have a potential role to predict the response to NAT, and heterogeneity in the distribution of blood volume may be an optimal parameter to predict the response. Recently conducted studies on cancer patients showed IVIM differences between responders and nonresponders [38, 54], indicating the potential predicting role of vascular, along with cellular, IVIM parameters [38].

Che et al. [38] reported that an increased level of D following 2 cycles of NAC in cases under tumor treatment may indicate decreased cellularity because of necrosis and fibrotic alternation caused by the treatment, which is consistent with some of the previous studies [44, 57]. We found a significant decline in the f value of all subjects during the treatment. Following chemotherapy, consumption of cytotoxic drugs causes apoptosis of tumor cells, which in turn led to decreased cell density and immature endothelial cells; the extracellular spaces will expand that fades the restrictions for water molecules movement and weakening the process of perfusion [58, 59]. In a study, Liu et al. [49] reported insignificant association D* values with reason the depressed SNR ratio and the low measure reproducibility. Xiao and colleagues [59] found a significantly higher D and lower D* value following NAC in nasopharyngeal cancer; however, they reported a f value that is more consistent. According to the findings, D value was significantly higher and lower, respectively, following NAC, which is in line with the study by Li et al. [60]. Nevertheless, there was no significant difference concerning the decrease of D* [61].

Chemotherapy's cytotoxic and anti-angiogenic effects may cause tumor cell and microvessel density to decrease. On the one hand, the cytotoxic impact of chemotherapeutic drugs causes an excessive amount of micro necrosis in tumor cells. Because of the larger extracellular and extravascular gaps, pure water diffusion motion becomes more unconstrained, increasing the D value. As a result of the improved chemotherapeutic response to NAC, the D value has significantly increased. The f value, on the other hand, is primarily linked to the volume fraction of microcirculation [61]. In breast cancers, successful chemotherapy causes death of cytotoxic tumors, which in turn led to a reduction of the proportion of immature microvessel density [62].

As a result, microvascular structures would diminish more significantly, resulting in a higher f value and a greater chemotherapeutic response to NAC. Simultaneously, the pCR group patients showed a trend toward a larger change in D* value, though the two groups were not significantly different. These findings imply that changes in IVIM-MRI characteristics can be applied for the prediction of chemotherapeutic responsiveness in cases with BC at an early stage of NAC. The link between parameter values and NAC effects, as measured by mass shrinkage, thus supports the usefulness of IVIM parameters to forecast and monitor events early. Middle treatment D and change of D and f measurements were found to be the most sensitive to mass shrinking among pre-and mid-treatment, and change of D and f evaluations, but pretreatment D exhibited no statistically significant link with mass shrinkage.

Our study had some flaws that needed to be addressed. First, evaluate the small number of IVIM-DWI studies that have been reported in the same patient group. Second, the majority of studies have a small sample size, and the bulk of them are single-center research. If sample sizes are expanded in (future) multi-center research to determine the genuine precision of MRI in the NAC scenario with higher confidence, statistical noise will be reduced. Third, we were unable to conduct a meta-analysis because of the heterogeneity among studies, which could be due to the diverse types and stages of breast cancer. As a result, rather than completing a meta-analysis that employs statistical models to address such heterogeneities to some extent, the research team decided to conduct a systematic review to develop a descriptive presentation. Finally, we admit that there are additional potential limitations, such as selector bias, which was caused by the research selection, publication, and verification.

Conclusions

The estimated sensitivity and specificity in the current meta-analysis were acceptable, so can help radiologists achieve the needed sensitivity and specificity, while also ensuring consistent reporting and communication between radiologists and other physicians within an institution, between different institutions, and worldwide.

During cancer therapy, these techniques can give useful clinical, noninvasive biomarkers. We show that various IVIM indicators have the potential to be used as predictors of NAT treatment response, as well as spatially dependent physiological alterations that occur after therapy. Also the results of this review confirmed that the IVIM conclusion is significantly superior in malignant breast tissues than in benign tissues also normal breast, and the IVIM parameters may advance the accuracy of breast tumors differentiation from tumors tissues. In the advanced breast, IVIM-derived metrics, particularly the D and f values, had a major contribution to the evaluation, pre-treatment prognosis, and monitoring of early responses to NAC. Patients with lower baseline D value and a high f value were found to respond better to neoadjuvant chemotherapy after treatment. The present overall studies showed that the parameters D and f were more reliable predictors of pretreatment in pathological response than the other parameters, the D value was significantly higher in the pCR than that in the non-pCR and also described that as the microvascular structures decrease more in the pCR, a greater decrease in the f-value might be observed. The IVIM model's D and f values suggested that they could be administered for early therapy prediction and monitoring the response. More research is needed to confirm the IVIM biomarkers' predictive utility in longitudinal BC research for both therapy and outcome monitoring.

Availability of data and material

Not applicable.

Abbreviations

ADC:

Apparent diffusion coefficient

BC:

Breast cancer

D*:

Pseudo-diffusivity

D :

Tissue diffusivity

DTI:

Diffusion tensor imaging

DWMRI:

Diffusion-weighted magnetic resonance imaging

f :

Perfusion volume fraction

IVIM:

Intravoxel incoherent motion

MRI:

Magnetic resonance imaging

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M.S, A.A, and N.A contributed to study concept and design; M.S, R.A, and H.D collected the data; A.A and N.A carried out analysis and interpretation of data; M.S performed drafting of the manuscript; M.S, R.A, and H.D performed critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.

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Correspondence to Arian Arvin.

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Sahib, M.A., Arvin, A., Ahmadinejad, N. et al. Assessment of intravoxel incoherent motion MR imaging for differential diagnosis of breast lesions and evaluation of response: a systematic review. Egypt J Radiol Nucl Med 53, 99 (2022). https://doi.org/10.1186/s43055-022-00770-8

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Keywords

  • Intravoxel incoherent motion (IVIM)
  • Quantitative of breast tumors
  • Diffusion-weighted imaging (DWI)
  • Evaluation of response
  • Meta-analysis