Diagnostic efficacy and correlation of Intravoxel incoherent motion (IVIM) and Contrast Enhanced (CE) MRI perfusion parameters in oncology imaging: A systematic review and meta-analysis

Diagnostic efficacy and correlation of Intravoxel incoherent motion (IVIM) and Contrast Enhanced (CE) MRI perfusion parameters in oncology imaging: A systematic review and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diagnostic efficacy and correlation of Intravoxel incoherent motion (IVIM) and Contrast Enhanced (CE) MRI perfusion parameters in oncology imaging: A systematic review and meta-analysis Abhijith S, Saikiran P, Rajagopal K V, Dharmesh Singh, Priya P S, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5236350/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Intravoxel incoherent motion (IVIM) MRI is a non-contrast technique that estimates diffusion and perfusion parameters using multiple b-values. This systematic review and meta-analysis aimed to compare the diagnostic accuracy of IVIM with contrast-enhanced (CE) perfusion MRI in oncology imaging. Following PRISMA guidelines, a comprehensive literature search across five databases identified studies that compared IVIM and CE MRI in patients with brain, breast, and rectal cancers. Meta-analyses were performed using R software. A total of 18 studies met the inclusion criteria, encompassing 123 gliomas, 231 breast, and 208 rectal cancer lesions. IVIM demonstrated comparable diagnostic performance to dynamic susceptibility contrast (DSC) MRI in gliomas, with a pooled AUC of 0.84, sensitivity of 92.27%, and specificity of 74.06%. In breast cancer, IVIM outperformed dynamic contrast-enhanced (DCE) MRI, with AUCs of 0.856 compared to 0.786. For rectal cancer, IVIM and DCE both showed moderate diagnostic accuracy, with AUCs of 0.62 and 0.64, respectively. Correlation analysis showed moderate relationships between IVIM and DSC/DCE parameters, particularly in gliomas and rectal cancer. IVIM MRI presents a promising non-contrast alternative to CE techniques, especially in gliomas and breast cancer, where it matched or exceeded CE perfusion performance. Further studies are needed to validate IVIM’s efficacy across more cancer types and to strengthen its correlation with CE parameters. Biological sciences/Cancer/Cancer epidemiology Biological sciences/Cancer/Cancer imaging Biological sciences/Cancer/Cns cancer Intravoxel incoherent motion Dynamic susceptibility contrast Dynamic contrast-enhanced Neoplasm Diagnostic Efficacy Correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Magnetic Resonance Imaging (MRI) is the modality of choice for oncology imaging due to its superior soft tissue contrast and non-ionizing radiation. 1 – 4 To enhance lesion characterization and measure the perfusion parameters, contrast-enhanced (CE) perfusion techniques such as Dynamic contrast-enhanced imaging (DCE) and Dynamic susceptibility contrast (DSC) imaging play a crucial role. 5 , 6 DCE uses a T1-weighted sequence to capture changes in signal intensity as a gadolinium-based contrast agent (GBCA) circulates through tissue. The increase in signal intensity reflects the concentration of the contrast agent, which enables the calculation of perfusion metrics like Ktrans (volume transfer constant), ve (extracellular-extravascular volume fraction), and vp (plasma volume fraction), which provide insights into tissue vascularity, permeability, and perfusion respectively. 7 DSC uses T2*-weighted sequences to monitor signal loss caused by susceptibility effects from a bolus injection of GBCAs. As the agent passes through the microvasculature, it induces changes in the magnetic susceptibility of tissues, leading to a signal drop. Perfusion metrics obtained from DSC-MRI include BV (Blood Volume), BF (Blood Flow), TTP (Time to Peak), and MTT (Mean Transit Time). 8 However, GBCAs used in CE imaging carry risks, including gadolinium deposition in tissues like the brain with uncertain long-term effects and nephrogenic systemic fibrosis (NSF) in patients with severe renal impairment (eGFR < 30 mL/min/1.73m²). GBCAs can also trigger hypersensitivity reactions, especially in individuals with allergies. Their use requires cautious risk-benefit evaluation in vulnerable populations, such as those with renal dysfunction and pregnant or breastfeeding women. 9 , 10 In addition to CE imaging, Diffusion-weighted imaging (DWI) significantly differentiates malignant from benign lesions by estimating cellularity and vascularity. 11 , 12 The concept of intravoxel incoherent motion (IVIM) MRI was introduced by Le Bihan et al., 13 which separates diffusion and microvascular perfusion effects in tissues without using contrast agents. It leverages the signal decay in diffusion-weighted imaging (DWI) at different b-values to capture both molecular water diffusion and perfusion-related pseudo-diffusion in capillaries. The critical parameters obtained from IVIM include the true diffusion coefficient (D), reflecting pure tissue diffusion; the pseudo-diffusion coefficient (D*), representing perfusion-related diffusion; and the perfusion fraction (f), which quantifies the proportion of signal attributed to blood flow. 14 IVIM obtains perfusion parameters without using contrast agents; hence, it may replace CE perfusion, which is beneficial for patients with renal impairment and who need frequent follow-up scans. 14 IVIM also provides diffusion parameters, which may eliminate the need for routine DWI scans, potentially shortening overall scan time. The literature search identified a lack of comprehensive evidence evaluating the diagnostic accuracy of tumor characterization and the correlation of perfusion metrics between IVIM and CE. Therefore, our systematic review and meta-analysis investigated perfusion parameters' diagnostic accuracy and correlation between IVIM and CE Perfusion parameters in oncology imaging. Method Study registration This systematic review and meta-analysis followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Supplementary file 1). 15 The protocol of this review is registered under PROSPERO (International Prospective Register of Systematic Reviews, Review ID: CRD42024568365). Databases and Search Strategies A literature search was conducted among the following databases: PubMed, Scopus, Embase, Web of Science, and Cochrane Library. The search term used for literature search were as follows: “Meningioma”, “Glioma”, “Brain Neoplasms”, “Breast Neoplasms", “Breast Cancer Lymphedema,” “Carcinoma, Hepatocellular”, “Liver Neoplasms”, “Carcinoma, Renal Cell”, “Renal Neoplasms", “Rectal Neoplasms”, “IVIM”, “Intravoxel incoherent motion”, “Multi b value”, “biexponential” “DWI”, “Diffusion magnetic resonance imaging”, “Dynamic contrast enhanced”, “DCE”, Dynamic susceptibility contrast,” “DSC”, “Contrast enhanced MR”, “Multiparametric”, “Efficacy”, “Sensitivity”, “Specificity”, “Correlation”, “Effectiveness”. For each database, specific search terms and combinations of Boolean operators are provided in Supplementary File 2. Selection criteria The review employed the Participants, Intervention, Comparison, and Outcomes (PICO) methodology to select articles, as detailed in Table 1 . Inclusion criteria The study included articles that compared the efficacy of IVIM with CE Perfusion MRI for characterizing histopathology-confirmed lesions in oncology imaging. Studies that correlated perfusion metrics obtained from IVIM and CE MRI were also included. Exclusion criteria Articles were excluded if they did not report Receiver operating characteristic (ROC) curve analysis or correlation coefficient between both techniques. Other articles, including meta-analyses, reviews, or surveys, were excluded. Data extraction Search strategies for the literature review were conducted independently by two reviewers. Initially, titles and abstracts were screened for the edibility criteria, and eligible articles were sought for full-text availability. The eligible articles were extracted with the following parameters: Title, author, year of publication, country, scanner and field strength, b-values, sample size, IVIM and CE Perfusion metrics, Sensitivity, Specificity, AUC, and Correlation coefficients. Any discrepancies in the results of the two reviewers were discussed and resolved through consensus. Quality Assessment The quality of the studies and the risk of bias assessment were evaluated using the Diagnostic Study Appraisal Worksheet created by the Centre for Evidence-Based Medicine at the University of Oxford. 16 The screening was performed by two reviewers independently, and any discrepancies in the decision were resolved by discussion. Statistical analysis We conducted meta-analyses using the meta package in R. We first analyzed AUC, sensitivity, and specificity separately across IVIM and DSC/DCE diagnostic accuracy. We calculated standard errors from 95% confidence intervals and pooled results using a random-effects model. Forest plots were generated to display individual and pooled estimates of diagnostic accuracy. The second meta-analysis evaluated various IVIM and DSC/DCE parameters using multiple studies' Pearson correlation coefficients. A random-effects model with the DerSimonian-Laird method was applied, and heterogeneity was assessed using the I² statistic. Forest plots were created for both analyses to visualize the results. Correlation coefficients were scaled weak (< 0.39), moderate (0.4–0.59), and Strong ( ≥ 0.6). Table 1 PICO methodology used Characteristics Criteria Population Patients diagnosed with histopathology-confirmed brain, breast, liver, renal, and rectal cancers. Intervention Intravoxel incoherent Motion MRI Comparator Dynamic Contrast-Enhanced (DCE) / Dynamic Susceptibility Contrast (DSC) MRI Outcomes AUC, Sensitivity, Specificity, and Correlation coefficients of IVIM matrices with Contrast-enhanced Perfusion Metrices Result A total of 1,234 articles were identified across various databases. Of these, 499 were duplicates and were removed. The remaining 735 articles were screened based on title and abstract according to the study's inclusion criteria, leading to the exclusion of 654 articles. Subsequently, 81 articles were selected for full-text retrieval, but 11 were unavailable. The 70 available full texts were screened for inclusion, resulting in the exclusion of 41. Finally, 29 articles underwent critical appraisal, and 18 were included in the review. 17 – 34 Fig. 1 outlines the PRISMA flow diagram used for the study selection process for the review. Study characteristics and Quality Assessment In the final analysis, 18 studies were included, with seven on the brain, six on the breast, and five on the rectum. A comprehensive overview of the baseline characteristics for all studies is provided in Table 2 . Quality assessment was conducted across three key domains: study validity, study outcomes, and the applicability of findings. Each domain was rated on a scale from 0 to 3. The total score for each study was divided by the maximum possible score to calculate a percentage. Based on these percentages, the studies were classified into three categories: poor ( 70%). Among the 30 studies evaluated, 10 were categorized as poor, 7 as average, and 13 as good quality. The quality assessment score of each study included can be found in Supplementary Table S1 . Table 2 Study Characteristics Sl No Author and Year Part Scanned Pathology Scanner Specifications Number of lesions B values Comparator Technique used Gold standard Quality Assessment Score (%) 1 Cao et al., 2017 Brain Glioma 3T, Signa HDxt; GE 50 (19 low and 31 high grade) 0, 20, 50, 80, 150, 200, 300, 500, 800, 1000 DCE Histopathology 88.88 2 Dolgorsuren et al, 2019 Brain Glioma & Lymphoma 3T, Discovery 750; GE 24 10, 20, 30, 40, 60, 80, 100, 200, 400, 800, 1000 DSC Histopathology & Clinical 55.55 3 Federau et al, 2014 Brain Glioma 3T, Trio, Verio or Sykra; Siemens 21 (5 low and 16 high grade) 0, 10, 20, 40, 80, 110, 140, 170, 200, 300, 400, 500, 600, 700, 800, 900 DSC Histopathology 66.66 4 Puig et al., 2015 Brain Glioma 1.5 T Hyroscan Intera; Philips 15 0, 10, 20, 30, 50, 100, 150, 200, 350, 500, 650, 800, and 1000 DSC Histopathology 88.88 5 Togao et al., 2016 Brain Glioma 3 T, Achieva TX, Philips Healthcare 45 (16 low and 29 high grade) 0, 10, 20, 30, 50, 80, 100, 200, 300, 400, 600, 800, 1000 DSC Histopathology 88.88 6 Catenese et al., 2018 Brain Glioma 3T, Signa Excite, GE 28 (6 low and 22 high grade) 0, 10, 20, 30, 40, 60, 80, 90, 100, 200, 700, 1000, 1300 DSC Histopathology 88.88 7 Besides et al., 2014 Brain Glioma 3 T, Magnetom Verio; Siemens 20 0, 5, 10, 20, 30, 40, 60, 80, 90, 100, 200, 700, 1000, 1300 DSC & DCE Histopathology 44.44 Breast 8 Dijkstra et al 2016 Breast Benign vs Malignant lesion 1.5 T, Magnetom Avanto; Siemens 139 0, 50, 200, 500, 800, 1000 DCE Histopathology 77.77 9 Jiang et al., 2017 Breast Benign vs Malignant lesion 3T, Discovery 750; GE 66 (35 benign, 31 malignant) 0, 10, 30, 50, 70, 100, 150, 200, 400, 600, 1000, 1500 DCE Histopathology 77.77 10 Liu et al., 2015 Breast Benign vs Malignant lesion 1.5 T, Philips 59 (23 benign, 36 malignant) 0, 10, 20, 30, 50, 70, 100, 150, 200, 400, 600, 1000 DCE Histopathology 100 11 Ma et al, 2016 Breast Benign vs Malignant lesion 3T, Skyra; Siemens 117 (47 benign, 81 malignant) 0, 50, 100, 150, 200, 250, 300, 400, 600, 800, 1000, 1200 DCE Histopathology 55.55 12 Tao et al, 2019 Breast Benign vs Ducatl Carcinoma 3T, Verio; Siemens 47 (22 benign, 25 Ductal carcinoma) 0, 50, 100, 150, 200, 400, 600, 1000; DCE Histopathology 66.66 13 Zheng et al., 2024 Breast Benign vs Malignant lesion 3T, Ingenia CX; Philips 59 (40 malignant, 22 benign lesions) 0, 10, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1200 DCE Histopathology 88.88 Rectal 14 Bekke et al., 2018 Rectal Rectal Cancer 1.5 T, Achieva; Philips 94 0, 25, 50, 100, 500, 1000, 1300 DSC & DCE Histopathology 55.55 15 Chen et al, 2021 Rectal Rectal Cancer 3T, Prisma; Siemens 110 0, 600, 1000, 2000, 3000 DCE Histopathology 77.77 16 Li et al., 2021 Rectal Rectal Cancer 3T, Inter Achieva; Philips 98 0, 10, 20, 50, 100, 200, 500, 800, 1000 DCE Histopathology 77.77 17 Sun et al., 2018 Rectal Rectal Cancer 3T, Ingenia; Philips 97 0, 10, 20, 30, 40, 60, 80, 100, 150, 200, 400, 800, 1000, 1200, 1500, 2000 DCE Histopathology 77.77 18 Yang et al., 2019 Rectal Rectal Cancer 3T, Magnetom Veria; Siemens 47 0, 5, 10, 20, 30, 40, 60, 80, 100, 150, 200, 400, 600, 1000, 1500, 2000 DCE Histopathology 55.55 Note: DCE - Dynamic contrast-enhanced, DSC - Dynamic susceptibility contrast The literature search included studies involving brain, breast, liver, renal, and rectal cancers. After screening, only studies on brain, breast, and rectal cancers met the selection criteria and were included in this review. The meta-analysis of diagnostic performance in brain tumors was limited to gliomas, as no other tumor types fulfilled the selection criteria. Diagnostic Performance of IVIM and DSC in Glioma The meta-analysis included 123 and 73 Gliomas for IVIM and DSC, respectively. Figure 2 shows meta-analysis results for IVIM parameters 19 , 20 , 23 within a pooled AUC of 0.84 [0.75; 0.93], Sensitivity of 92.27 [86.88; 97.65]%, and Specificity of 74.06 [60.51; 87.61]% with I 2 (heterogeneity index) of 85 %, 48 %and 8 % respetively. The meta-analysis result for DSC parameters 17 , 18 with a pooled Sensitivity of 95.71 [90.86; 100] % and Specificity of 92.91 [75.06; 100] % with I 2 of 0% and 68%, respectively. Diagnostic Performance of IVIM and DCE in Breast Cancer The meta-analysis included 231 breast lesions in total for IVIM and DCE. Figure 3 shows results for IVIM parameters 17 , 18 , 30 , 33 with pooled AUC of 0.856 [0.817; 0.895], Sensitivity of 81.42 [76.23; 86.61] % and Specificity of 85.08 [77.05; 93.1] % with I 2 of 0%, 29% and 72% respectively. The result for DCE parameters 17 , 18 , 30 , 33 showed pooled AUC of 0.786 [0.713; 0.858], Sensitivity 86.51 [80.41; 92.62] % and Specificity 69.89 [61.36; 78.42] % with I 2 of 74%, 77% and 71% respectively. Diagnostic Performance of IVIM and DCE in Rectal Cancer The meta-analysis included a total of 208 lesions. Figure 4 shows the result for IVIM parameters 32 , 34 with pooled AUC of 0.62 [0.53; 0.71], Sensitivity of 70.9 [51.22; 90.59] % and Specificity of 56.2 [37.75; 74.64] % with I 2 of 67%, 96% and 95% respectively. The result for DCE parameters 32 , 34 shows pooled AUC of 0.64 [0.55; 0.74], Sensitivity of 58.11 [30.44; 85.77] %, and Specificity of 72.49 [57.71; 87.26] % with I 2 of 78%, 99% and 94% respectively. The result is shown in Fig. 4 . Correlation between IVIM and DSC parameters in Brain The meta-analysis for correlation coefficients in brain shows pooled Pearson’s correlation coefficient between D* and CBF was 0.41 [-0.07; 0.74] (I 2 = 77%), between D* and CBV was 0.48 [0.16; 0.63] (I 2 = 8%) and between D* and MTT was 0.13 [-0.33; 0.54] (I 2 = 68%) (Fig. 6 ). 21 , 24 , 25 Pooled Pearson’s Correlation Coefficient between f and CBF was 0.25 [-0.33; 0.7] (I 2 = 84%), between f and CBV was 0.34 [-0.04; 0.63] (I 2 = 76%) and between f and MTT was − 0.13 [-0.61; 0.43] (I 2 = 79%). Figure 5 shows the pooled correlation coefficients with heterogeneity index and forest plot. 20 , 21 , 24 – 26 Correlation between IVIM and DCE parameters in Rectum The meta-analysis for correlation coefficients in rectal cancer shows pooled Pearson’s correlation coefficient between D* - Ktrans was 0.15 [-0.23; 0.49] (I 2 = 88%), D* - Kep was 0.03 [-0.12; 0.18] (I 2 = 26%) and D* - Ve was − 0.15 [-0.27; -0.02] (I 2 = 0%). Between f – Ktrans was 0.10 [-0.26; 0.44] (I 2 = 87%), f – Kep was 0.13 [-0.18; 0.43] (I 2 = 88%), and f – Ve was 0.09 [-0.09; 0.27] (I 2 = 47%). Between fD* - Ktrans 0.25 [-0.32; 0.68] (I 2 = 95%), fD* - Kep was 0.06 [-0.22; 0.32] (I 2 = 77%) and fD* - Ve was 0.02 [-0.11; 0.15] (I 2 = 0%). Figure 6 shows the pooled correlation coefficients with heterogeneity index and forest plot. Discussion Diffusion and Perfusion MR widely used in oncology imaging due to its non-invasive and non-ionizing, for tumor structure, perfusion, and permeability assesment. 35 , 36 CE perfusion MRI plays a significant role in functional characterization, whereas diffusion imaging will provide the structural information of the tissue. The IVIM sequence, a non-contrast technique that quantifies and separates the true diffusion from the perfusion by using multiple b values, the majority, including low b values, has gained increasing prominence in oncology imaging. 14 However, there is a notable lack of consolidated evidence regarding the overall efficacy of IVIM compared to CE perfusion MRI in this context. This systematic review and meta-analysis aimed to compare the diagnostic performance of perfusion metrics derived from IVIM with those obtained from CE perfusion MRI. The meta-analysis suggests that the diagnostic accuracy of IVIM imaging in glioma diagnosis was comparable to that of CE perfusion imaging, particularly DSC MRI. Wang et al. 37 investigated the prediction of isocitrate dehydrogenase (IDH) mutation status, which offers valuable insights into the biological behavior, treatment approaches, and prognosis of gliomas. Their study concluded that perfusion and diffusion parameters derived from IVIM imaging could predict IDH mutation status with high Sensitivity and Specificity, especially the perfusion fraction (f). Further extending the application of IVIM, Lu et al. 38 demonstrated its potential in predicting the methylation status of O 6 -methylguanine-DNA-methyltransferase (MGMT) in gliomas, with IVIM parameters showing Sensitivity and Specificity comparable to or exceeding those of DSC imaging. Additionally, Puig et al. 24 found that IVIM parameters effectively predict overall survival in patients with glioblastoma. In the brain, the correlation between IVIM and DSC parameters was moderate. Specifically, D* showed a correlation of 0.41 and 0.48 with CBF and CBV, respectively. Additionally, the f had a correlation coefficient of 0.34 with CBV. Significant heterogeneity was observed across the results, potentially due to conflicting findings from the study by Bisdas et al., which reported positive correlations between the two techniques in tumor tissues but no correlation or opposite trends in healthy gray and white matter. As a relatively low-perfused organ, the brain may experience signal under-sampling in low-perfusion regions. In contrast, highly perfused tumor tissue will likely yield a higher signal-to-noise ratio, facilitating more accurate perfusion estimation. 25 , 39 Additionally, the acquisition parameters, such as the number of b-values and the number of excitations (NEX) for IVIM, varied significantly across studies. These parameters also substantially influence the estimation of IVIM parameters, which requires careful consideration. 40 , 41 Breast cancer, the most common cancer in women worldwide, is best detected using MRI due to its high sensitivity, with critical roles played by CE perfusion and diffusion imaging for diagnosis. 42 , 43 Our meta-analysis compared a non-contrast IVIM approach with DCE perfusion MRI; IVIM outperformed DCE in differentiating between benign and malignant breast lesions, showing superior diagnostic performance. Human epidermal growth factor receptor 2 (HER2)-enriched breast cancers are considered aggressive and less easily distinguishable forms of breast cancer. 44 Triple-negative breast cancer (TNBC), another aggressive and highly malignant subtype, lacks estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. 45 IVIM alone was able to effectively differentiate these subtypes of breast cancer, with even better results when combined with CE perfusion MRI. 46 A meta-analysis was not feasible due to the limited number of studies; however, Jiang et al., 33 reported a moderate correlation between f and Kep, while Liu et al., 30 found moderate f correlations with Ktrans, Kep, and Vp. Overall, IVIM demonstrated superior performance in distinguishing benign from malignant breast lesions, as evidenced by the results of this meta-analysis. While some emerging evidence suggests IVIM's potential in diagnosing aggressive subtypes, such as HER2-enriched cancers and TNBC, further research is needed to fully validate its efficacy in these contexts. 47 , 48 The current evidence indicates that the diagnostic accuracy of IVIM improves when combined with CE perfusion MRI. Furthermore, insufficient reports were found to assess the correlation of perfusion metrics between IVIM and DCE techniques. We conducted a meta-analysis to compare the diagnostic performance of IVIM and DCE imaging in rectal cancer. The results indicated that IVIM had moderate diagnostic performance in rectal cancer. Although IVIM's performance was moderate, it was not inferior to the DCE techniques. 32 , 34 The results also indicate that the correlation between IVIM and DCE parameters ranged from weak to moderate. This could be attributed to the differing underlying principles of the two techniques in quantifying perfusion. 22 , 27 , 28 , 34 However, further detailed comparisons are required to compare with DCE in rectal cancer diagnosis to understand the relative advantages of IVIM better. Limitations The limited number of studies for each cancer type restricts the generalizability of our findings. Additionally, only studies on brain (glioma), breast, and rectal cancers were included, highlighting a gap that warrants further investigation. Variability in imaging protocols, scanner types, and analytical methods across studies may have introduced inconsistencies in the reported diagnostic performance, complicating direct comparisons. In the case of glioma, the number of cases included in the meta-analysis differed between IVIM and DSC due to reporting bias in the selected studies. Furthermore, a lack of studies evaluating the correlation between IVIM and DCE parameters in breast tissue prevented their inclusion in our analysis. Conclusion This systematic review and meta-analysis suggest that IVIM imaging is a viable alternative to traditional DSC/DCE perfusion MRI for cancer diagnosis. In gliomas, it offers comparable diagnostic accuracy. It can predict key molecular biomarkers, while in breast cancer, it may outperform DCE in distinguishing benign from malignant lesions and identifying aggressive subtypes. In rectal cancer, its diagnostic performance is moderate but comparable to DCE. Overall, IVIM shows promise as a non-contrast perfusion imaging method. However, its correlations with DSC/DCE vary across cancer types, reflecting potential differences in perfusion mechanisms and the need for further validation. Declarations Author Contributions: Abhijith S Conceptualization, Literature Search and Data Collection, Methodology, Data Analysis, Manuscript Writing and Drafting Dr Saikiran P Conceptualization, Literature Search and Data Collection, Methodology, Data Analysis, Manuscript Writing and Drafting Dr Rajagopal K V Conceptualization, Supervision, Manuscript Revision Dr Dharmesh Singh Manuscript Writing and Drafting, Manuscript Revision Dr Priya P S Supervision, Manuscript Revision Dr Priyanka Conceptualization, Manuscript Revision Tancia Pires Data Analysis, Manuscript Writing and Drafting Dileep Kumar Manuscript Writing and Drafting, Manuscript Revision Data availability: PRISMA checklist used for the meta-analysis can be found in Supplementary file 1 Search strategies used for each data base are supplied in Supplementary file 2 Quality assessment scores process and scores of each study are in Supplementary Table S1 The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests: No competing interests were disclosed Ethics declarations: This systematic review and meta-analysis did not involve human participants or the use of patient data. All data was obtained from previously published studies, where ethical approval was granted. Therefore, no additional ethical approval was required. Grant information: The author(s) declared that no grants were involved in supporting this work. References Arya, S., Das, D., Engineer, R. & Saklani, A. Imaging in rectal cancer with emphasis on local staging with MRI. Indian J. Radiol. Imaging . 25 , 148 (2015). Arif-Tiwari, H. et al. MRI of hepatocellular carcinoma: an update of current practices. Diagn. Interventional Radiol. 20 , 209 (2014). Gao, Y. et al. Magnetic Resonance Imaging in Screening of Breast Cancer. Radiol. Clin. North. Am. 59 , 85 (2021). Villanueva-Meyer, J. E., Mabray, M. C. & Cha, S. Curr. Clin. Brain Tumor Imaging Neurosurg. 81 , 397 (2017). 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Preclinical Applications of Magnetic Resonance Imaging in Oncology. Recent Results Cancer Res. 216 , 405–437 (2020). Baboli, M., Zhang, J. & Kim, S. G. Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging. Curr. Pathobiol Rep. 7 , 129–141 (2019). Wang, C. & Dong, H. Intravoxel incoherent motion magnetic resonance imaging in predicting IDH1 gene mutations in high-grade gliomas. Acta radiol. 62 , 1412–1417 (2021). Lu, J., Li, X. & Li, H. Perfusion parameters derived from MRI for preoperative prediction of IDH mutation and MGMT promoter methylation status in glioblastomas. Magn. Reson. Imaging . 83 , 189–195 (2021). Meeus, E. M. et al. Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue. J. Magn. Reson. Imaging . 45 , 1325 (2017). Chabert, S. et al. Impact of b-Value Sampling Scheme on Brain IVIM Parameter Estimation in Healthy Subjects. Magn. Reson. Med. Sci. 19 , 216 (2020). Hu, Y. C. et al. Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain? BMC Med. Imaging 20 , (2020). Simon, A., Robb, K., Breast & Cancer Cambridge Handbook of Psychology, Health and Medicine, Second Edition 577–580 doi: (2024). 10.1017/CBO9780511543579.131 Mann, R. M., Cho, N., Moy, L. & Breast, M. R. I. State of the art. Radiology . 292 , 520–536 (2019). Schettini, F. et al. HER2-enriched subtype and pathological complete response in HER2-positive breast cancer: A systematic review and meta-analysis. Cancer Treat. Rev. 84 , 101965 (2020). Aysola, K. et al. Triple Negative Breast Cancer – An Overview. Hereditary Genet (2012). (2013). Sun, Z. et al. IVIM and DCE-MRI in Predicting Phenotypic Subtypes and Nottingham Prognostic Index of Breast Cancer. J. Coll. Physicians Surg. Pak . 34 , 400–406 (2024). Sun, Z. et al. IVIM and DCE-MRI in Predicting Phenotypic Subtypes and Nottingham Prognostic Index of Breast Cancer. J. Coll. Physicians Surg. Pak . 34 , 400–406 (2024). Liang, J. et al. Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis. Front. Oncol. 10 , 585486 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiles.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5236350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377665025,"identity":"f8d2d480-8fd3-4651-a5a2-5167bffd31fa","order_by":0,"name":"Abhijith S","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Abhijith","middleName":"","lastName":"S","suffix":""},{"id":377665026,"identity":"6676f63e-fbb6-4b36-bc9d-f1bb3a7caae7","order_by":1,"name":"Saikiran P","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYFACxmaGBDCD+QCQkGBgYAdSPMRpYUuAaGEmqAWsBAR4DBB8fFrk2w83GzzcY5fHIN3zdcPPHRZ5/M0MjA/etuHWYnAmsTkh4VlyMYPM2W03e89IFEscZmA2nItPC0Ni84GEA8yJDRK5227wtkkkNhxmYJPmxaNFvv8hSEs9UEvOs5t/gVrmH2Zg/41PC8MNkMMOHAZpYbsNsmUD0BZmfFoMbjxsNkg4cDyxTeaY2W3ZNoliw8OMzZJzzuFzWPpjyR8HqhP7pZuf3XzbVpcnd7z54Ic3ZXgcBgNsEhA6ARi5DUSoBwGEllEwCkbBKBgFqAAAOblUqHE+pygAAAAASUVORK5CYII=","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Saikiran","middleName":"","lastName":"P","suffix":""},{"id":377665027,"identity":"2651c6ae-bb86-4740-b8f0-d64c4f2aa53b","order_by":2,"name":"Rajagopal K V","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Rajagopal","middleName":"K","lastName":"V","suffix":""},{"id":377665028,"identity":"a49630cf-d236-4bfb-88f9-d77bebbd4329","order_by":3,"name":"Dharmesh Singh","email":"","orcid":"","institution":"Central Research Institute – Global Scientific Collaboration, United Imaging Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Dharmesh","middleName":"","lastName":"Singh","suffix":""},{"id":377665029,"identity":"cc2d13cd-c7a7-43f1-9c86-8ec00386cc61","order_by":4,"name":"Priya P S","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Priya","middleName":"P","lastName":"S","suffix":""},{"id":377665030,"identity":"6074d375-47f9-4fc2-85c2-aa5fc96a0594","order_by":5,"name":"Priyanka .","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Priyanka","middleName":"","lastName":".","suffix":""},{"id":377665031,"identity":"69ac35b4-5247-46bb-a0e2-c510c9a3a55f","order_by":6,"name":"Tancia Pires","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Tancia","middleName":"","lastName":"Pires","suffix":""},{"id":377665032,"identity":"af2bafd1-e4e5-4851-a59b-256fff6e488a","order_by":7,"name":"Dileep Kumar","email":"","orcid":"","institution":"Central Research Institute – Global Scientific Collaboration, United Imaging Healthcare","correspondingAuthor":false,"prefix":"","firstName":"Dileep","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-10-10 04:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5236350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5236350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70405874,"identity":"70d245a8-3207-44a9-9181-7b39ce7be4cc","added_by":"auto","created_at":"2024-12-02 23:17:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":585050,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow chart\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/216f10c5e6cf6e0300094f09.jpg"},{"id":70405279,"identity":"7949d5d5-214a-4d79-8c85-d703024215d8","added_by":"auto","created_at":"2024-12-02 23:09:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":662679,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic Performance of IVIM and DSC for Glioma\u003c/p\u003e\n\u003cp\u003e[(i) \u003cstrong\u003ea, b\u003c/strong\u003e, and \u003cstrong\u003ec\u003c/strong\u003e illustrate the AUC, sensitivity, and specificity of IVIM parameters, respectively. (ii) \u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e illustrate the sensitivity and specificity of DSC parameters, respectively]\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/6b4be9619afdd48650f1f710.jpg"},{"id":70405284,"identity":"f9701597-24d8-43f3-ba57-f74979924439","added_by":"auto","created_at":"2024-12-02 23:09:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":972350,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic performance of IVIM and DCE for Breast cancer\u003c/p\u003e\n\u003cp\u003e[(i) \u003cstrong\u003ea, b,\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e illustrate the AUC, sensitivity, and specificity of IVIM parameters, respectively. (ii) \u003cstrong\u003ed, e\u003c/strong\u003e and \u003cstrong\u003ef \u003c/strong\u003eillustrate the AUC, sensitivity and specificity of DCE parameters, respectively]\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/193641abc9a11d7662ae0a07.jpg"},{"id":70405285,"identity":"acec48c2-259e-48d5-a763-a94a5a4e732c","added_by":"auto","created_at":"2024-12-02 23:09:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":879312,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic performance of IVIM and DCE for Rectal cancer\u003c/p\u003e\n\u003cp\u003e[(i) \u003cstrong\u003ea, b,\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e illustrate the AUC, sensitivity, and specificity of IVIM parameters, respectively. (ii)\u003cstrong\u003e d, e\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e illustrate the AUC, sensitivity and specificity of DCE parameters, respectively]\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/afa1ffca7c13017023867e0c.jpg"},{"id":70405282,"identity":"61beab26-65df-488b-a5f7-5dab3c7deae3","added_by":"auto","created_at":"2024-12-02 23:09:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":739084,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for Meta-analysis of Pearson’s Correlation coefficients between IVIM and DSC\u003c/p\u003e\n\u003cp\u003e[(i)\u003cstrong\u003ea, b\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e illustrates correction of D* with CBF, CBV and MTT respectively. (ii) \u003cstrong\u003ed, e\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e illustrates correlation of f with CBF, CBV and MTT]\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/733f7bdfccb4eddb1fc7a424.jpg"},{"id":70405876,"identity":"ff4e716b-204f-44c5-b945-2899939d3e11","added_by":"auto","created_at":"2024-12-02 23:17:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":798929,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for Meta-analysis of Pearson’s Correlation coefficients between IVIM and DCE\u003c/p\u003e\n\u003cp\u003e[(i) \u003cstrong\u003ea, b\u003c/strong\u003e and\u003cstrong\u003e c\u003c/strong\u003e illustrates correction of D* with Ktrans, Kep and Ve respectively. (ii) \u003cstrong\u003ed, e\u003c/strong\u003e and \u003cstrong\u003ef \u003c/strong\u003eillustrates correlation of f with Ktrans, Kep and Ve. (iii) \u003cstrong\u003eg, h\u003c/strong\u003e and \u003cstrong\u003ei\u003c/strong\u003e illustrates correlation of f with Ktrans, Kep and Ve]\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/de95833344bdbabefd53f96b.jpg"},{"id":72819016,"identity":"3837a552-95db-450d-abbc-749933b663fc","added_by":"auto","created_at":"2025-01-02 13:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5477135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/b658eb36-48d6-4a7f-bba9-b5bea74e014f.pdf"},{"id":70405875,"identity":"5c400cc4-854a-44b5-b873-aa2b06fb303a","added_by":"auto","created_at":"2024-12-02 23:17:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":230261,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5236350/v1/41a71db86536fc7d578fa4e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic efficacy and correlation of Intravoxel incoherent motion (IVIM) and Contrast Enhanced (CE) MRI perfusion parameters in oncology imaging: A systematic review and meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMagnetic Resonance Imaging (MRI) is the modality of choice for oncology imaging due to its superior soft tissue contrast and non-ionizing radiation.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e To enhance lesion characterization and measure the perfusion parameters, contrast-enhanced (CE) perfusion techniques such as Dynamic contrast-enhanced imaging (DCE) and Dynamic susceptibility contrast (DSC) imaging play a crucial role.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e DCE uses a \u003cem\u003eT1-weighted sequence\u003c/em\u003e to capture changes in signal intensity as a gadolinium-based contrast agent (GBCA) circulates through tissue. The increase in signal intensity reflects the concentration of the contrast agent, which enables the calculation of perfusion metrics like Ktrans (volume transfer constant), ve (extracellular-extravascular volume fraction), and vp (plasma volume fraction), which provide insights into tissue vascularity, permeability, and perfusion respectively.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e DSC uses \u003cem\u003eT2*-weighted sequences\u003c/em\u003e to monitor signal loss caused by susceptibility effects from a bolus injection of GBCAs. As the agent passes through the microvasculature, it induces changes in the magnetic susceptibility of tissues, leading to a signal drop. Perfusion metrics obtained from DSC-MRI include BV (Blood Volume), BF (Blood Flow), TTP (Time to Peak), and MTT (Mean Transit Time).\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, GBCAs used in CE imaging carry risks, including gadolinium deposition in tissues like the brain with uncertain long-term effects and nephrogenic systemic fibrosis (NSF) in patients with severe renal impairment (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 mL/min/1.73m\u0026sup2;). GBCAs can also trigger hypersensitivity reactions, especially in individuals with allergies. Their use requires cautious risk-benefit evaluation in vulnerable populations, such as those with renal dysfunction and pregnant or breastfeeding women. \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition to CE imaging, Diffusion-weighted imaging (DWI) significantly differentiates malignant from benign lesions by estimating cellularity and vascularity.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The concept of intravoxel incoherent motion (IVIM) MRI was introduced by Le Bihan et al.,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e which separates diffusion and microvascular perfusion effects in tissues without using contrast agents. It leverages the signal decay in diffusion-weighted imaging (DWI) at different b-values to capture both molecular water diffusion and perfusion-related pseudo-diffusion in capillaries. The critical parameters obtained from IVIM include the true diffusion coefficient (D), reflecting pure tissue diffusion; the pseudo-diffusion coefficient (D*), representing perfusion-related diffusion; and the perfusion fraction (f), which quantifies the proportion of signal attributed to blood flow.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIVIM obtains perfusion parameters without using contrast agents; hence, it may replace CE perfusion, which is beneficial for patients with renal impairment and who need frequent follow-up scans.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e IVIM also provides diffusion parameters, which may eliminate the need for routine DWI scans, potentially shortening overall scan time.\u003c/p\u003e \u003cp\u003eThe literature search identified a lack of comprehensive evidence evaluating the diagnostic accuracy of tumor characterization and the correlation of perfusion metrics between IVIM and CE. Therefore, our systematic review and meta-analysis investigated perfusion parameters' diagnostic accuracy and correlation between IVIM and CE Perfusion parameters in oncology imaging.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy registration\u003c/h2\u003e \u003cp\u003eThis systematic review and meta-analysis followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Supplementary file 1).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The protocol of this review is registered under PROSPERO (International Prospective Register of Systematic Reviews, Review ID: CRD42024568365).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDatabases and Search Strategies\u003c/h3\u003e\n\u003cp\u003eA literature search was conducted among the following databases: PubMed, Scopus, Embase, Web of Science, and Cochrane Library. The search term used for literature search were as follows: \u0026ldquo;Meningioma\u0026rdquo;, \u0026ldquo;Glioma\u0026rdquo;, \u0026ldquo;Brain Neoplasms\u0026rdquo;, \u0026ldquo;Breast Neoplasms\", \u0026ldquo;Breast Cancer Lymphedema,\u0026rdquo; \u0026ldquo;Carcinoma, Hepatocellular\u0026rdquo;, \u0026ldquo;Liver Neoplasms\u0026rdquo;, \u0026ldquo;Carcinoma, Renal Cell\u0026rdquo;, \u0026ldquo;Renal Neoplasms\", \u0026ldquo;Rectal Neoplasms\u0026rdquo;, \u0026ldquo;IVIM\u0026rdquo;, \u0026ldquo;Intravoxel incoherent motion\u0026rdquo;, \u0026ldquo;Multi b value\u0026rdquo;, \u0026ldquo;biexponential\u0026rdquo; \u0026ldquo;DWI\u0026rdquo;, \u0026ldquo;Diffusion magnetic resonance imaging\u0026rdquo;, \u0026ldquo;Dynamic contrast enhanced\u0026rdquo;, \u0026ldquo;DCE\u0026rdquo;, Dynamic susceptibility contrast,\u0026rdquo; \u0026ldquo;DSC\u0026rdquo;, \u0026ldquo;Contrast enhanced MR\u0026rdquo;, \u0026ldquo;Multiparametric\u0026rdquo;, \u0026ldquo;Efficacy\u0026rdquo;, \u0026ldquo;Sensitivity\u0026rdquo;, \u0026ldquo;Specificity\u0026rdquo;, \u0026ldquo;Correlation\u0026rdquo;, \u0026ldquo;Effectiveness\u0026rdquo;. For each database, specific search terms and combinations of Boolean operators are provided in Supplementary File 2.\u003c/p\u003e\n\u003ch3\u003eSelection criteria\u003c/h3\u003e\n\u003cp\u003eThe review employed the Participants, Intervention, Comparison, and Outcomes (PICO) methodology to select articles, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInclusion criteria\u003c/strong\u003e \u003cp\u003eThe study included articles that compared the efficacy of IVIM with CE Perfusion MRI for characterizing histopathology-confirmed lesions in oncology imaging. Studies that correlated perfusion metrics obtained from IVIM and CE MRI were also included.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExclusion criteria\u003c/strong\u003e \u003cp\u003eArticles were excluded if they did not report Receiver operating characteristic (ROC) curve analysis or correlation coefficient between both techniques. Other articles, including meta-analyses, reviews, or surveys, were excluded.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eSearch strategies for the literature review were conducted independently by two reviewers. Initially, titles and abstracts were screened for the edibility criteria, and eligible articles were sought for full-text availability. The eligible articles were extracted with the following parameters: Title, author, year of publication, country, scanner and field strength, b-values, sample size, IVIM and CE Perfusion metrics, Sensitivity, Specificity, AUC, and Correlation coefficients. Any discrepancies in the results of the two reviewers were discussed and resolved through consensus.\u003c/p\u003e\n\u003ch3\u003eQuality Assessment\u003c/h3\u003e\n\u003cp\u003eThe quality of the studies and the risk of bias assessment were evaluated using the Diagnostic Study Appraisal Worksheet created by the Centre for Evidence-Based Medicine at the University of Oxford.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The screening was performed by two reviewers independently, and any discrepancies in the decision were resolved by discussion.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe conducted meta-analyses using the meta package in R. We first analyzed AUC, sensitivity, and specificity separately across IVIM and DSC/DCE diagnostic accuracy. We calculated standard errors from 95% confidence intervals and pooled results using a random-effects model. Forest plots were generated to display individual and pooled estimates of diagnostic accuracy. The second meta-analysis evaluated various IVIM and DSC/DCE parameters using multiple studies' Pearson correlation coefficients. A random-effects model with the DerSimonian-Laird method was applied, and heterogeneity was assessed using the I\u0026sup2; statistic. Forest plots were created for both analyses to visualize the results. Correlation coefficients were scaled weak (\u0026lt;\u0026thinsp;0.39), moderate (0.4\u0026ndash;0.59), and Strong (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;0.6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePICO methodology used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients diagnosed with histopathology-confirmed brain, breast, liver, renal, and rectal cancers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntravoxel incoherent Motion MRI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Contrast-Enhanced (DCE) / Dynamic Susceptibility Contrast (DSC) MRI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC, Sensitivity, Specificity, and Correlation coefficients of IVIM matrices with Contrast-enhanced Perfusion Metrices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eA total of 1,234 articles were identified across various databases. Of these, 499 were duplicates and were removed. The remaining 735 articles were screened based on title and abstract according to the study's inclusion criteria, leading to the exclusion of 654 articles. Subsequently, 81 articles were selected for full-text retrieval, but 11 were unavailable. The 70 available full texts were screened for inclusion, resulting in the exclusion of 41. Finally, 29 articles underwent critical appraisal, and 18 were included in the review.\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the PRISMA flow diagram used for the study selection process for the review.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStudy characteristics and Quality Assessment\u003c/h3\u003e\n\u003cp\u003eIn the final analysis, 18 studies were included, with seven on the brain, six on the breast, and five on the rectum. A comprehensive overview of the baseline characteristics for all studies is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Quality assessment was conducted across three key domains: study validity, study outcomes, and the applicability of findings. Each domain was rated on a scale from 0 to 3. The total score for each study was divided by the maximum possible score to calculate a percentage. Based on these percentages, the studies were classified into three categories: poor (\u0026lt;\u0026thinsp;35%), average (35\u0026ndash;69%), and good quality (\u0026gt;\u0026thinsp;70%). Among the 30 studies evaluated, 10 were categorized as poor, 7 as average, and 13 as good quality. The quality assessment score of each study included can be found in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor and Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePart Scanned\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScanner Specifications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of lesions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eComparator Technique used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGold standard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQuality Assessment Score (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCao et al., 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Signa HDxt; GE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (19 low and 31 high grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 20, 50, 80, 150, 200, 300, 500, 800, 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDolgorsuren et al, 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma \u0026amp; Lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Discovery 750; GE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10, 20, 30, 40, 60, 80, 100, 200, 400, 800, 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology \u0026amp; Clinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFederau et al, 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Trio, Verio or Sykra; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (5 low and 16 high grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 40, 80, 110, 140, 170, 200, 300, 400, 500, 600, 700, 800, 900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuig et al., 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 T Hyroscan Intera; Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 30, 50, 100, 150, 200, 350, 500, 650, 800, and 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTogao et al., 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 T, Achieva TX, Philips Healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (16 low and 29 high grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 30, 50, 80, 100, 200, 300, 400, 600, 800, 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatenese et al., 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Signa Excite, GE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (6 low and 22 high grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 30, 40, 60, 80, 90, 100, 200, 700, 1000, 1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBesides et al., 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 T, Magnetom Verio; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 5, 10, 20, 30, 40, 60, 80, 90, 100, 200, 700, 1000, 1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC \u0026amp; DCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e44.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDijkstra et al 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign vs Malignant lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 T, Magnetom Avanto; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 50, 200, 500, 800, 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJiang et al., 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign vs Malignant lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Discovery 750; GE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (35 benign, 31 malignant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 30, 50, 70, 100, 150, 200, 400, 600, 1000, 1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiu et al., 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign vs Malignant lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 T, Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (23 benign, 36 malignant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 30, 50, 70, 100, 150, 200, 400, 600, 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMa et al, 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign vs Malignant lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Skyra; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117 (47 benign, 81 malignant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 50, 100, 150, 200, 250, 300, 400, 600, 800, 1000, 1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTao et al, 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign vs Ducatl Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Verio; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (22 benign, 25 Ductal carcinoma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 50, 100, 150, 200, 400, 600, 1000;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e66.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZheng et al., 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign vs Malignant lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Ingenia CX; Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (40 malignant, 22 benign lesions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBekke et al., 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 T, Achieva; Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 25, 50, 100, 500, 1000, 1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDSC \u0026amp; DCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChen et al, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Prisma; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 600, 1000, 2000, 3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLi et al., 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Inter Achieva; Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 50, 100, 200, 500, 800, 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSun et al., 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Ingenia; Philips\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 10, 20, 30, 40, 60, 80, 100, 150, 200, 400, 800, 1000, 1200, 1500, 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYang et al., 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3T, Magnetom Veria; Siemens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0, 5, 10, 20, 30, 40, 60, 80, 100, 150, 200, 400, 600, 1000, 1500, 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHistopathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e DCE - Dynamic contrast-enhanced, DSC - Dynamic susceptibility contrast\u003c/p\u003e \u003cp\u003eThe literature search included studies involving brain, breast, liver, renal, and rectal cancers. After screening, only studies on brain, breast, and rectal cancers met the selection criteria and were included in this review. The meta-analysis of diagnostic performance in brain tumors was limited to gliomas, as no other tumor types fulfilled the selection criteria.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Performance of IVIM and DSC in Glioma\u003c/h2\u003e \u003cp\u003eThe meta-analysis included 123 and 73 Gliomas for IVIM and DSC, respectively. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows meta-analysis results for IVIM parameters\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e within a pooled AUC of 0.84 [0.75; 0.93], Sensitivity of 92.27 [86.88; 97.65]%, and Specificity of 74.06 [60.51; 87.61]% with \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e(heterogeneity index) of 85 %, 48 %and 8 % respetively. The meta-analysis result for DSC parameters\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e with a pooled Sensitivity of 95.71 [90.86; 100] % and Specificity of 92.91 [75.06; 100] % with I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0% and 68%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Performance of IVIM and DCE in Breast Cancer\u003c/h2\u003e \u003cp\u003eThe meta-analysis included 231 breast lesions in total for IVIM and DCE. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows results for IVIM parameters\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e with pooled AUC of 0.856 [0.817; 0.895], Sensitivity of 81.42 [76.23; 86.61] % and Specificity of 85.08 [77.05; 93.1] % with \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e of 0%, 29% and 72% respectively. The result for DCE parameters\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e showed pooled AUC of 0.786 [0.713; 0.858], Sensitivity 86.51 [80.41; 92.62] % and Specificity 69.89 [61.36; 78.42] % with \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e of 74%, 77% and 71% respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Performance of IVIM and DCE in Rectal Cancer\u003c/h2\u003e \u003cp\u003eThe meta-analysis included a total of 208 lesions. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the result for IVIM parameters\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e with pooled AUC of 0.62 [0.53; 0.71], Sensitivity of 70.9 [51.22; 90.59] % and Specificity of 56.2 [37.75; 74.64] % with \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e of 67%, 96% and 95% respectively. The result for DCE parameters\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e shows pooled AUC of 0.64 [0.55; 0.74], Sensitivity of 58.11 [30.44; 85.77] %, and Specificity of 72.49 [57.71; 87.26] % with \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e of 78%, 99% and 94% respectively. The result is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between IVIM and DSC parameters in Brain\u003c/h2\u003e \u003cp\u003eThe meta-analysis for correlation coefficients in brain shows pooled Pearson\u0026rsquo;s correlation coefficient between D* and CBF was 0.41 [-0.07; 0.74] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;77%), between D* and CBV was 0.48 [0.16; 0.63] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8%) and between D* and MTT was 0.13 [-0.33; 0.54] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;68%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Pooled Pearson\u0026rsquo;s Correlation Coefficient between f and CBF was 0.25 [-0.33; 0.7] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;84%), between f and CBV was 0.34 [-0.04; 0.63] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;76%) and between f and MTT was \u0026minus;\u0026thinsp;0.13 [-0.61; 0.43] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;79%). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the pooled correlation coefficients with heterogeneity index and forest plot.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between IVIM and DCE parameters in Rectum\u003c/h2\u003e \u003cp\u003eThe meta-analysis for correlation coefficients in rectal cancer shows pooled Pearson\u0026rsquo;s correlation coefficient between D* - Ktrans was 0.15 [-0.23; 0.49] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;88%), D* - Kep was 0.03 [-0.12; 0.18] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;26%) and D* - Ve was \u0026minus;\u0026thinsp;0.15 [-0.27; -0.02] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%). Between f \u0026ndash; Ktrans was 0.10 [-0.26; 0.44] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;87%), f \u0026ndash; Kep was 0.13 [-0.18; 0.43] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;88%), and f \u0026ndash; Ve was 0.09 [-0.09; 0.27] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;47%). Between fD* - Ktrans 0.25 [-0.32; 0.68] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;95%), fD* - Kep was 0.06 [-0.22; 0.32] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;77%) and fD* - Ve was 0.02 [-0.11; 0.15] (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0%). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the pooled correlation coefficients with heterogeneity index and forest plot.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiffusion and Perfusion MR widely used in oncology imaging due to its non-invasive and non-ionizing, for tumor structure, perfusion, and permeability assesment.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e CE perfusion MRI plays a significant role in functional characterization, whereas diffusion imaging will provide the structural information of the tissue. The IVIM sequence, a non-contrast technique that quantifies and separates the true diffusion from the perfusion by using multiple b values, the majority, including low b values, has gained increasing prominence in oncology imaging.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e However, there is a notable lack of consolidated evidence regarding the overall efficacy of IVIM compared to CE perfusion MRI in this context. This systematic review and meta-analysis aimed to compare the diagnostic performance of perfusion metrics derived from IVIM with those obtained from CE perfusion MRI.\u003c/p\u003e \u003cp\u003eThe meta-analysis suggests that the diagnostic accuracy of IVIM imaging in glioma diagnosis was comparable to that of CE perfusion imaging, particularly DSC MRI. Wang et al.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e investigated the prediction of isocitrate dehydrogenase (IDH) mutation status, which offers valuable insights into the biological behavior, treatment approaches, and prognosis of gliomas. Their study concluded that perfusion and diffusion parameters derived from IVIM imaging could predict IDH mutation status with high Sensitivity and Specificity, especially the perfusion fraction (f). Further extending the application of IVIM, Lu et al. \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e demonstrated its potential in predicting the methylation status of O\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e-methylguanine-DNA-methyltransferase (MGMT) in gliomas, with IVIM parameters showing Sensitivity and Specificity comparable to or exceeding those of DSC imaging. Additionally, Puig et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e found that IVIM parameters effectively predict overall survival in patients with glioblastoma.\u003c/p\u003e \u003cp\u003eIn the brain, the correlation between IVIM and DSC parameters was moderate. Specifically, D* showed a correlation of 0.41 and 0.48 with CBF and CBV, respectively. Additionally, the f had a correlation coefficient of 0.34 with CBV. Significant heterogeneity was observed across the results, potentially due to conflicting findings from the study by Bisdas et al., which reported positive correlations between the two techniques in tumor tissues but no correlation or opposite trends in healthy gray and white matter. As a relatively low-perfused organ, the brain may experience signal under-sampling in low-perfusion regions. In contrast, highly perfused tumor tissue will likely yield a higher signal-to-noise ratio, facilitating more accurate perfusion estimation.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Additionally, the acquisition parameters, such as the number of b-values and the number of excitations (NEX) for IVIM, varied significantly across studies. These parameters also substantially influence the estimation of IVIM parameters, which requires careful consideration.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBreast cancer, the most common cancer in women worldwide, is best detected using MRI due to its high sensitivity, with critical roles played by CE perfusion and diffusion imaging for diagnosis.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Our meta-analysis compared a non-contrast IVIM approach with DCE perfusion MRI; IVIM outperformed DCE in differentiating between benign and malignant breast lesions, showing superior diagnostic performance. Human epidermal growth factor receptor 2 (HER2)-enriched breast cancers are considered aggressive and less easily distinguishable forms of breast cancer.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Triple-negative breast cancer (TNBC), another aggressive and highly malignant subtype, lacks estrogen receptor (ER), progesterone receptor (PR), and HER2 expression.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e IVIM alone was able to effectively differentiate these subtypes of breast cancer, with even better results when combined with CE perfusion MRI.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e A meta-analysis was not feasible due to the limited number of studies; however, Jiang et al.,\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e reported a moderate correlation between f and Kep, while Liu et al.,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e found moderate f correlations with Ktrans, Kep, and Vp.\u003c/p\u003e \u003cp\u003eOverall, IVIM demonstrated superior performance in distinguishing benign from malignant breast lesions, as evidenced by the results of this meta-analysis. While some emerging evidence suggests IVIM's potential in diagnosing aggressive subtypes, such as HER2-enriched cancers and TNBC, further research is needed to fully validate its efficacy in these contexts.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e The current evidence indicates that the diagnostic accuracy of IVIM improves when combined with CE perfusion MRI. Furthermore, insufficient reports were found to assess the correlation of perfusion metrics between IVIM and DCE techniques.\u003c/p\u003e \u003cp\u003eWe conducted a meta-analysis to compare the diagnostic performance of IVIM and DCE imaging in rectal cancer. The results indicated that IVIM had moderate diagnostic performance in rectal cancer. Although IVIM's performance was moderate, it was not inferior to the DCE techniques.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e The results also indicate that the correlation between IVIM and DCE parameters ranged from weak to moderate. This could be attributed to the differing underlying principles of the two techniques in quantifying perfusion.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e However, further detailed comparisons are required to compare with DCE in rectal cancer diagnosis to understand the relative advantages of IVIM better.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe limited number of studies for each cancer type restricts the generalizability of our findings. Additionally, only studies on brain (glioma), breast, and rectal cancers were included, highlighting a gap that warrants further investigation. Variability in imaging protocols, scanner types, and analytical methods across studies may have introduced inconsistencies in the reported diagnostic performance, complicating direct comparisons. In the case of glioma, the number of cases included in the meta-analysis differed between IVIM and DSC due to reporting bias in the selected studies. Furthermore, a lack of studies evaluating the correlation between IVIM and DCE parameters in breast tissue prevented their inclusion in our analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review and meta-analysis suggest that IVIM imaging is a viable alternative to traditional DSC/DCE perfusion MRI for cancer diagnosis. In gliomas, it offers comparable diagnostic accuracy. It can predict key molecular biomarkers, while in breast cancer, it may outperform DCE in distinguishing benign from malignant lesions and identifying aggressive subtypes. In rectal cancer, its diagnostic performance is moderate but comparable to DCE. Overall, IVIM shows promise as a non-contrast perfusion imaging method. However, its correlations with DSC/DCE vary across cancer types, reflecting potential differences in perfusion mechanisms and the need for further validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbhijith S\u003c/p\u003e\n\u003cp\u003eConceptualization, Literature Search and Data Collection, Methodology, Data Analysis, Manuscript Writing and Drafting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr Saikiran P\u003c/p\u003e\n\u003cp\u003eConceptualization, Literature Search and Data Collection, Methodology, Data Analysis, Manuscript Writing and Drafting\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr Rajagopal K V\u003c/p\u003e\n\u003cp\u003eConceptualization, Supervision, Manuscript Revision\u003c/p\u003e\n\u003cp\u003eDr Dharmesh Singh\u003c/p\u003e\n\u003cp\u003eManuscript Writing and Drafting, Manuscript Revision\u003c/p\u003e\n\u003cp\u003eDr Priya P S\u003c/p\u003e\n\u003cp\u003eSupervision, Manuscript Revision\u003c/p\u003e\n\u003cp\u003eDr Priyanka\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization, Manuscript Revision\u003c/p\u003e\n\u003cp\u003eTancia Pires\u003c/p\u003e\n\u003cp\u003eData Analysis, Manuscript Writing and Drafting\u003c/p\u003e\n\u003cp\u003eDileep Kumar\u003c/p\u003e\n\u003cp\u003eManuscript Writing and Drafting, Manuscript Revision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRISMA checklist used for the meta-analysis can be found in Supplementary file 1\u003c/p\u003e\n\u003cp\u003eSearch strategies used for each data base are supplied in Supplementary file 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuality assessment scores process and scores of each study are in Supplementary Table S1\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNo competing interests were disclosed\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations:\u0026nbsp;\u003c/strong\u003eThis systematic review and meta-analysis did not involve human participants or the use of patient data. All data was obtained from previously published studies, where ethical approval was granted. Therefore, no additional ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrant information:\u003c/strong\u003e The author(s) declared that no grants were involved in supporting this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArya, S., Das, D., Engineer, R. \u0026amp; Saklani, A. Imaging in rectal cancer with emphasis on local staging with MRI. \u003cem\u003eIndian J. Radiol. Imaging\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 148 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArif-Tiwari, H. et al. MRI of hepatocellular carcinoma: an update of current practices. \u003cem\u003eDiagn. Interventional Radiol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 209 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, Y. et al. 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Oncol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 585486 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intravoxel incoherent motion, Dynamic susceptibility contrast, Dynamic contrast-enhanced, Neoplasm, Diagnostic Efficacy, Correlation","lastPublishedDoi":"10.21203/rs.3.rs-5236350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5236350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntravoxel incoherent motion (IVIM) MRI is a non-contrast technique that estimates diffusion and perfusion parameters using multiple b-values. This systematic review and meta-analysis aimed to compare the diagnostic accuracy of IVIM with contrast-enhanced (CE) perfusion MRI in oncology imaging. Following PRISMA guidelines, a comprehensive literature search across five databases identified studies that compared IVIM and CE MRI in patients with brain, breast, and rectal cancers. Meta-analyses were performed using R software. A total of 18 studies met the inclusion criteria, encompassing 123 gliomas, 231 breast, and 208 rectal cancer lesions. IVIM demonstrated comparable diagnostic performance to dynamic susceptibility contrast (DSC) MRI in gliomas, with a pooled AUC of 0.84, sensitivity of 92.27%, and specificity of 74.06%. In breast cancer, IVIM outperformed dynamic contrast-enhanced (DCE) MRI, with AUCs of 0.856 compared to 0.786. For rectal cancer, IVIM and DCE both showed moderate diagnostic accuracy, with AUCs of 0.62 and 0.64, respectively. Correlation analysis showed moderate relationships between IVIM and DSC/DCE parameters, particularly in gliomas and rectal cancer. IVIM MRI presents a promising non-contrast alternative to CE techniques, especially in gliomas and breast cancer, where it matched or exceeded CE perfusion performance. Further studies are needed to validate IVIM\u0026rsquo;s efficacy across more cancer types and to strengthen its correlation with CE parameters.\u003c/p\u003e","manuscriptTitle":"Diagnostic efficacy and correlation of Intravoxel incoherent motion (IVIM) and Contrast Enhanced (CE) MRI perfusion parameters in oncology imaging: A systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 23:09:40","doi":"10.21203/rs.3.rs-5236350/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f28004a7-9a13-4e86-a23e-1e80082bd6c7","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40210089,"name":"Biological sciences/Cancer/Cancer epidemiology"},{"id":40210090,"name":"Biological sciences/Cancer/Cancer imaging"},{"id":40210091,"name":"Biological sciences/Cancer/Cns cancer"}],"tags":[],"updatedAt":"2025-01-02T12:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 23:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5236350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5236350","identity":"rs-5236350","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}