Estimating morphological, diffusion and susceptibility perfusion criteria in discrimination between the perplexing orbital lymphocytic mimickers: Lymphoma versus inflammatory pseudotumor

Estimating morphological, diffusion and susceptibility perfusion criteria in discrimination between the perplexing orbital lymphocytic mimickers: Lymphoma versus inflammatory pseudotumor | 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 Research Article Estimating morphological, diffusion and susceptibility perfusion criteria in discrimination between the perplexing orbital lymphocytic mimickers: Lymphoma versus inflammatory pseudotumor Lamyaa Abdel-Galil Eissa, Nadia Ahmed Abdelfattah, Eiman Ahmed El-Bakoury, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7282417/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → Version 1 posted You are reading this latest preprint version Abstract BACKGROUND Characterization of orbital masses is crucial in the therapeutic strategy planning owing to the fact that patient management greatly differs depending on the dignity of the orbital lesion. However, it is often difficult to differentiate malignant orbital masses from inflammatory pseudo tumors (IPT) due to their comparable clinical presentation with proptosis in terms of most common symptoms. Recently, magnetic resonance imaging (MRI) has become essential for the pre-treatment delineation of orbital tumors. PATIENTS & METHODS Retrospective study for 58 patients being retrieved form available records of Alexandria University Hospital between August 2021 to August 2023, diagnosed with either lymphoma or inflammatory pseudo-tumor. I) Conventional MR protocol had been tailored to include the orbits and brain The standard MR brain acquisition-parameters were as following: a) Rapid scout images, b) multi-planar axial, coronal, sagittal T1 and T2-weighed (with and without STIR), c) Diffusion weighted imaging had been obtained using single shot spin echo planar imaging in axial plane d) Dynamic T2* Perfusion: conventional post-contrast MRI fat-suppressed images are made in axial, sagittal and coronal planes, using same parameters as non-contrast axialT1 images, then subtraction is provided at axial images. Perfusion color maps images are interpreted on workstation. RESULTS Results demonstrated a wide ADC range = 0.53–1.20 x 10 − 3 cm 2 /sec, with mean value of 0.73. The lymphomas had an ADC range = 0.53–0.82 x 10 − 3 cm 2 /sec, and mean value is0.6482x 10 − 3 cm 2 /sec. The IPTs had slightly higher ranges and values, showing ADC range of 0.63–1.20 x 10 − 3 cm 2 /sec, and mean ADC was 0.90x 10 − 3 cm 2 /sec. ADC differences yielded a statistically significant difference (p < 0.001*). Using a cut-off value of 0.82 x10 − 3 cm 2 /sec yielded a sensitivity of 60%, 100% specificity, PPV = 100%, NPV = 62%, and accuracy of 76%. Lymphomas showed predominantly hyper-perfused pattern in susceptibility perfusion –seen in 21 lesions (= 91.3%), and only two (= 7.8%) showed iso- perfusion, and none of lymphomas was hypo-perfused. On the contrary, IPTs were predominantly hypo-perfused (n = 31; 88.6%), 2 were iso-perfused (5.7%) and 2 hyper- perfused (= 5.7%). CONCLUSIONS The combination of DWI and DCE MRI can improve diagnostic performance in differentiating lymphoma from in IPT, and are recommended to be used in appropriate clinical setting. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Despite the limited anatomical space of the orbit, it harbors wide range of different cellular origin structures which have the capability to give rise to a variety of tumors and tumor-like conditions in both adults as well as pediatrics (1). The clinical presentation of such masses is widely variable depending on the nature, location, and extent of the disease. The symptoms can involve proptosis, globe displacement, eyelid swelling, and restricted eye motility (2). In spite of the fact that improvement in radiological imaging techniques with wide range of differential diagnosis, orbital masses often present considerable diagnostic challenges (2). So, cross sectional imaging especially MRI with fat signal intensity suppression, contrast enhancement pattern and its novel techniques play a pivotal role in detection, characterization and management of these lesions as well as distinguishing malignant from benign lesions (2). Orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP) represent the most common lymphoproliferative disorders affecting the orbit, accounting nearly 20% of all orbital mass lesions (3). Lymphoma is the most common orbital malignancy in the adult. “Orbital lymphoma” refers to intraorbital and ocular surface lymphomas (i.e., conjunctiva, eyelid, and lacrimal gland). It accounts for 10% of all orbital tumors and approximately 2% of all nodal and extra-nodal lymphomas (4). It can be mistaken for other diseases due to overlapping clinical and imaging features. Definitive diagnosis is conducted via: histopathology, immune-histochemical staining, in addition to flow cytometry phenotype examination (5). Orbital pseudotumor is an acute non-infective inflammatory condition of the orbital soft tissues which often manifests borderline morphological characters. It is one of the most common causes of unilateral exophthalmos. It responds to steroids or immune-suppressive therapy (6). Precise differentiation of orbital lymphoma and IOIP remains challenging in clinical practice, because of the nonspecific clinical presentations and the overlapping conventional MR findings. Therefore, it is of great importance to improve the performance of differentiating orbital lymphoma from IOI for therapeutic decisions and prognosis (7). Fine-needle aspiration is the gold standard for diagnosis of lymphoma and IOIP. However, it is technically challenging when it comes to posteriorly far located lesions in the orbit. Moreover, diffuse lymphocytic infiltrative IOIP, which is considered as the most common pathological subtype, could not be easily differentiated pathologically from orbital lymphoma (6). MRI still poses its advantages of inherent high soft-tissue resolution, multi-planar capability with various technical innovations which in turn improves final image quality. The diagnostic performance of anatomical images for ocular tumors can be further enhanced through provision of functional data by diffusion weighed images which study micro-structure of tumors, meanwhile perfusion weighted imaging (PWI) can detect angiogenesis to discriminate benign from malignant tumors (8). Previous studies have indicated some MRI features, such as signal intensity on T2W sequence, presence of flow void sign, and degree of contrast enhancement, have the potential to discriminate between lymphoma and IOIP. Nevertheless, overall diagnostic efficacy of morphological features was still limited. In addition to that, the qualitative assessment of MRI features might be observer-dependent (6). Diffusion-weighted imaging (DWI) showed promising results in diagnosing orbital lesions. It proved accuracy when distinguishing benign from malignant orbital lesions, particularly in the cases of lymphoproliferative disorders. Low apparent diffusion coefficient (ADC) values were reported to predict overall malignancy or to distinguish orbital lymphoma from orbital inflammation (9). The aim of this study is to evaluate morphological, diffusion, and susceptibility perfusion criteria on MRI in terms of distinguishing orbital lymphoma from inflammatory pseudotumor. Methods Study Design and Patient Selection Retrospective study included 58 patients who underwent orbital MRI at our main tertiary university hospital between August 2022 and August 2024, while Institutional Review Board (IRB) approval was obtained, and informed consent was waived due to the retrospective nature of the research. MRI Acquisition Protocol All imaging was performed using a 1.5T or 3.0T MRI scanner (e.g., Siemens Magnetom Avanto) with a dedicated head/neck coil. The MRI protocol was optimized to cover the orbits and upper fascial neck spaces. Conventional Imaging Sequences a) T1-weighted (T1WI): axial, coronal, and sagittal planes. Parameters: TR 400–700 ms; TE 10–114 ms; slice thickness 3.5 mm,b) T2-weighted (T2WI): Axial and coronal with and without fat suppression (STIR). TR 302700–5000 ms; TE 76–120 ms.• Scout localizers: For anatomical planning. Diffusion-Weighted Imaging (DWI) : • Axial single-shot spin-echo echo-planar imaging (EPI).• b-values: 0 and 1000 s/mm². Parameters: TR ~ 4000 ms; TE ~ 80 ms; slice thickness 3.5 mm; FOV 180–220 mm. ADC maps were automatically generated using mono-exponential fitting. Dynamic Susceptibility Contrast (DSC) T2-Weighted Perfusion Imaging* T2*-weighted gradient-echo echo-planar imaging (GRE*-EPI) was used for dynamic susceptibility contrast (DSC) perfusion, b) After diffusion, dynamic MRI sequence was obtained by injection of “ Gadopentate/-Dimeglumine” using a dose of 0.1 mmol/kg) and at a rate of 2 ml/s by means of a power injector traced by 20 ml saline-flush, c) Sequential images were then obtained through maximum area of lesion in axial plane, and with variegated time intervals at 30, 60, 90, 120, 150, 180, 250, and 300” ms following onset of injection. Then, conventional post-contrast MRI fat-suppressed images are obtained in axial, sagittal and coronal planes, using same parameters as non-contrast axial T1 images, d) Subtraction is thereafter provided at axial images, and perfusion subjective color maps images are interpreted on a dedicated qualified workstation, e)Post-processing and image analysis for DCE-MRI technique made as follows: Generated color maps - on dedicated software or on Macbook-pro (version 10.12.2; Early 2011-Model year ), made after drawing ROI on solid enhancing portion; Image Analysis: A lesion was described regarding: Note: All perfusion analysis in this study was performed on T2-weighted DSC images* Images Evaluation Two very well experienced subspecialized radiologists independently reviewed the generated anatomical and functional images, blinded to histopathology; they have 18 and 14 years of experiences. ADC Analysis: Sufficient ROIs were manually located on ADC maps overlying the solid, non-necrotic portion of each lesion, avoiding cystic/necrotic or hemorrhagic components. ADC values were reported in ×10⁻³ mm²/s as a descriptive unit. Perfusion Categorization: Lesions were classified based on T2*-weighted perfusion maps as (Wholly subjective method to facilitate comparison with other perfusion studies utilizing different techniques): a) Hyperperfused: Marked signal drop (increased capillary perfusion), b) Iso-perfused: Comparable signal intensity to adjacent muscle, c) Hypo-perfused: Minimal or no signal drop. Statistical Analysis : The statistical analyses were performed by specialized personnel, using SPSS v26 (IBM Corp., Armonk, NY). Continuous variables were presented as mean ± SD or median (range); categorical data as counts and percentages; a) Mann–Whitney U test: comparing continuous variables between two groups, b) Chi-square test, Fisher’s exact-test, or Monte-Carlo-correction: for categorical data, c) ROC analysis: This is used to assess diagnostic performance of ADC values and determine optimal cutoff thresholds, reporting AUC, sensitivity, specificity, PPV, NPV, and accuracy. A p value ≤ 0.05 was considered statistically significant. Pathological final diagnosis All collected patients were proven by pathology at least FNAC and not merely medial follow up post cortico-steroid. Results This study included 58 patients, comprising 23 cases of orbital lymphoma and 35 cases of inflammatory pseudotumor (IPT). The results were classified into two groups in terms of anatomical distribution, imaging features, and functional MRI parameters Anatomical and Morphological MRI Findings (Table 1) • Site of Lesion: Lymphomas were predominantly located in both extra- and intra-conal compartments (60.9%), whereas IPTs were more frequently conal in location (42.9%), yielding a statistically significant difference (p = 0.001). • Shape : Lymphomas were more likely to demonstrate glandular and infiltrative morphologies, while IPTs showed more fusiform and elongated patterns (p < 0.001). • Signal Intensity : On T1-weighted images, hypo-intensity was more common in IPTs (82.9%) compared to lymphomas (34.8%) (p < 0.001). On T2-weighted images, lymphomas tended to be iso-intense (73.9%) while IPTs were more often hypo-intense or deeply hypo-intense (p = 0.007). • Muscle Involvement : Diffuse extraocular muscle involvement ("all muscles") was significantly associated with lymphomas (17.4% vs. 0%; p = 0.021). Table (1): Correlation between final diagnosis (Lymphoma vs. IPT) with various morphological parameters Total sample (n=58) Final Diagnosis Test of sig. p Lymphoma (n=23) PTX (n=35) No. % No. % No. % Laterality Unilateral 50 86.2 19 82.6 31 88.6 c 2 = 0.415 0.519 Bilateral 8 13.8 4 17.4 4 11.4 Site Conal 15 25.9 0 0.0 15 42.9 Extra-conal 19 32.8 9 39.1 10 28.6 c 2 = 13.82* 0.001* Extra-&-Intra-conal 24 41.4 14 60.9 10 28.6 Size Min. – Max. 1.20-8.50 1.90-8.50 1.20-5.40 Mean ± SD 3.41±1.58 4.01±2.0 3.01±1.10 Median 3.20 3.50 2.90 U = 297.0 0.093 Shape Elongated 4 6.9 0 0.0 4 11.4 Elongated and infiltrative 10 17.2 0 0.0 10 28.6 Elongated and lobulated 2 3.4 2 8.7 0 0.0 Fusiform 13 22.4 2 8.7 11 31.4 Fusiform and infiltrative 2 3.4 2 8.7 0 0.0 MC p Glandular 9 15.5 7 30.4 2 5.7 c 2 = 35.0* <0.001* Globular 2 3.4 2 8.7 0 0.0 Globular and infiltrative 2 3.4 2 8.7 0 0.0 Infiltrative 2 3.4 2 8.7 0 0.0 Lenticular 10 17.2 2 8.7 8 22.9 Lobulated 2 3.4 2 8.7 0 0.0 T1 Hypo-intense 37 63.8 8 34.8 29 82.9 So-intense 21 36.2 15 65.2 6 17.1 c 2 = 13.88* <0.001* T2 Deeply hypo-intense 4 6.9 0 0.0 4 11.4 Hypo-intense 25 43.1 6 26.1 19 54.3 c 2 = 9.001* 0.007* Iso-intense 29 50.0 17 73.9 12 34.3 Which muscle FE p No muscle involvement 34 58.6 11 47.8 23 65.7 c 2 = 1.831 0.176 “All muscles” involvement 4 6.9 4 17.4 0 0.0 c 2 = 6.538* 0.021* LR 14 24.1 6 26.1 8 22.9 c 2 = 0.079 0.779 SR 6 10.3 2 8.7 4 11.4 c 2 = 0.112 1.000 MR 4 6.9 0 0.0 4 11.4 c 2 = 2.823 0.144 SO 2 3.4 0 0.0 2 5.7 c 2 = 1.361 0.243 IR 4 6.9 2 8.7 2 5.7 c 2 = 0.192 1.000 Diffusion-Weighted Imaging (DWI) Table 2 Apparent diffusion coefficient (ADC) values showed a statistically significant difference between the two groups: •Lymphomas had lower ADC values (range: 0.53–0.82 × 10⁻³ mm²/s; mean: 0.65 ± 0.09). •Meanwhile IPTs demonstrated higher ADC values (range: 0.63–1.20 × 10⁻³ mm²/s; mean: 0.91 ± 0.20) (p < 0.001). Receiver Operating Characteristic (ROC) analysis revealed : Table 3 •An AUC of 0.882 (p 0.67 × 10⁻³ mm²/s (Youden index), sensitivity was 88.6%, specificity 73.9%, and accuracy 82.8%. •A more specific cutoff of >0.82 × 10⁻³ mm²/s yielded a higher diagnostic achievement in terms of 100% specificity and 60% sensitivity, with an accuracy of 75.9%. Table (2): Relation between final diagnosis (Lymphoma vs. IPT) with variegated functional parameters. AUC p 95% CI Cutoff# Sensitivity Specificity PPV NPV Accuracy ADC 0.882 0.67 88.6 73.9 83.8 81.0 82.8 >0.82 60.0 100.0 100.0 62.2 75.9 0.88 54.3 100.0 100.0 59.0 72.4 AUC: Area Under The Curve NPV: Negative predictive value PPV: Positive predictive value *: Statistically significant at p ≤ 0.05 Cutoff recommended by Youden index Contrast-Enhanced MRI and Perfusion Table 2 • Enhancement Patterns: Homogeneous avid enhancement was more common in IPTs (42.9%) than in lymphomas (8.7%) (p = 0.002). • Perfusion Patterns: Lymphomas were predominantly hyperperfused (91.3%), with no cases of hypoperfusion. IPTs showed predominantly hypoperfusion (88.6%), a statistically significant distinction (p < 0.001). Table (3): Measurements of sensitivity/ Specificity/ PPV/ NPV and diagnostic accuracy) For ADC cut-off value. Total sample (n=58) Final Diagnosis Test of sig. p Lymphoma (n=23) PTX (n=35) No. % No. % No. % ADC Min. – Max. 0.53-1.20 0.53-0.82 0.63-1.20 Mean ± SD 0.81±0.21 0.65±0.09 0.91±0.20 Median 0.73 0.64 0.90 U = 95.0* <0.001* GAD Homogenous hypo-enhanced 10 17.2 8 34.8 2 5.7 Homogenous iso-enhanced 31 53.4 13 56.5 18 51.4 c 2 = 12.39* 0.002* Homogenous avid 17 29.3 2 8.7 15 42.9 Perfusion Hypo-perfused 31 53.4 0 0.0 31 88.6 MC p Iso-perfused 4 6.9 2 8.7 2 5.7 c 2 = 54.07* <0.001* Hyper-perfused 23 39.7 21 91.3 2 5.7 c 2 : Chi square test MC: Monte Carlo U: Mann Whitney test *: Statistically significant at p ≤ 0.05 Discussion Orbital lymphoma and inflammatory pseudotumor (PT) can present with comparable clinical and imaging features, making their differentiation depending solely on conventional MRI a challenging approach. Advanced MRI techniques offer potential for better discrimination [ 10 ]. Therefore, this study aims to evaluate morphological, diffusion, and susceptibility perfusion criteria on MRI in distinguishing orbital lymphoma from inflammatory pseudotumor. In the present study, significant differences were observed between lymphoma and pseudotumor groups in terms of lesion site, shape, signal intensity, and muscle involvement. Conal lesions appeared only in inflammatory pseudotumor, whereas lymphoma more commonly involved both extra- and intra-conal compartments. inflammatory pseudotumor lesions tended to have elongated, infiltrative, or fusiform shapes, while glandular morphology was more typical of lymphoma. inflammatory pseudotumor was also characterized by hypo-intense signals on T1 and T2-weighted images, whereas lymphoma more often showed iso-intense signals. Complete involvement of all extraocular muscles was found exclusively in lymphoma cases insert figure (1) and figure (2) about here. Parallel to our findings, Priego et al.[ 11 ] investigated 19 patients with orbital lymphoma using CT and MRI and reported predominant involvement of the extra- and intra-conal compartments (47%), followed by extra-conal (42%) and intra-conal (11%) locations. The superior-lateral quadrant was most frequently affected (59%), and unilateral presentation was seen in 95% of cases. They also noted frequent involvement of the superior rectus (74%), lateral rectus (59%), eyelid (53%), and lacrimal gland (47%). In the current study, diffusion characteristics, enhancement patterns, and perfusion behavior showed clear differences between lymphoma and pseudotumor. Lymphoma demonstrated lower ADC values, more frequent homogenous hypo-enhancement, and predominant hyper-perfusion. Insert figure (3) and figure (4) about here. In agreement with our results, Ren et al. [ 6 ] retrospectively analyzed 40 patients (18 with orbital lymphoma and 22 with idiopathic orbital inflammatory pseudotumor) to assess the diagnostic value of conventional MRI and ADC histogram analysis. They reported significantly lower ADC mean in lymphoma (0.719 ± 0.216 ×10⁻³ mm²/s) compared to IOIP (1.131 ± 0.317 ×10⁻³ mm²/s) (P < 0.001). Similarly, Abdelgawad et al. [ 12 ] assessed 53 patients (21 with orbital lymphoma and 32 with idiopathic orbital inflammatory pseudotumor) and demonstrated significantly lower ADC values in lymphoma (mean 0.56 ± 0.29 ×10⁻³ mm²/s) compared to IOIP (mean 1.15 ± 0.37 ×10⁻³ mm²/s), with P < 0.0001. Also, Eissa et al.[ 13 ] retrospectively evaluated 37 untreated patients using diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) to differentiate IOIP from orbital lymphoma. They reported significantly lower ADC values in lymphoma (0.69 ± 0.10 and 0.72 ± 0.11 ×10⁻³ mm²/s) compared to IOIP (1.04 ± 0.19 and 1.12 ± 0.23 ×10⁻³ mm²/s) (P = 0.001). Jing Li et al.[ 14 ] evaluated the utility of multimodal MRI parameters—including DWI and DCE-MRI—in differentiating lacrimal gland lymphoma (LGL) from IgG4-related lacrimal disease and lacrimal gland inflammatory pseudotumor (LGIP). They found significantly lower ADC ratios in LGL (0.88 ± 0.11) compared to LGIP (1.40 ± 0.27) (P < 0.001). Furthermore, Ozturk et al.[ 15 ] assessed 43 patients with orbital lesions using conventional MRI and diffusion-weighted imaging and reported significantly lower ADC mean values in malignant tumors (median 0.99 ×10⁻³ mm²/s) compared to benign ones (median 1.23 ×10⁻³ mm²/s, P = 0.012). They further demonstrated that ADC min and ADC ratios (ADC mean ratio and ADC min ratio) were also significantly lower in malignant lesions. Fatima et al. [ 16 ] assessed 39 orbital masses using DWI and reported significantly lower ADC values in malignant lesions (mean 0.77 ± 0.38 ×10⁻³ mm²/s) compared to benign ones (mean 1.23 ± 0.42 ×10⁻³ mm²/s), with P ≤ 0.0001. In addition to that, Sepahdari et al.[ 17 ] analyzed 202 orbital lesions across multiple institutions and reported that lymphomas had markedly low ADC values (mean 0.66 ± 0.09 ×10⁻³ mm²/s), while inflammatory lesions showed higher ADCs (mean 1.40 ± 0.31 ×10⁻³ mm²/s). Also supporting our results, Politi et al. [ 18 ] conducted a prospective study involving 114 subjects and demonstrated that ocular adnexal lymphomas exhibited significantly lower ADC values than normal orbital structures and other orbital lesions (P < 0.001). In strong agreement with our findings, Abdel Razek et al. [ 19 ] evaluated 47 patients with orbital tumors using 3-T diffusion-weighted imaging and reported a significantly lower mean ADC in malignant lesions (0.84 ± 0.34 ×10⁻³ mm²/s) compared to benign ones (1.57 ± 0.33 ×10⁻³ mm²/s, P = 0.001). Specifically, lymphomas had a mean ADC of 0.67 ± 0.08 ×10⁻³ mm²/s, significantly lower than pseudo-tumors (1.29 ± 0.5 ×10⁻³ mm²/s, P = 0.001). Using a threshold of 1.15 ×10⁻³ mm²/s, they achieved 95% sensitivity, 91% specificity, and 93% accuracy in distinguishing malignant from benign lesions. It is suspected that the uniformly and atypical lymphocyte infiltrations in orbital lymphoma lead to higher cellularity and less extracellular space with subsequent low ADC value [ 20 ]. Regarding the benign IOIP, interstitial edematous changes in this lesion give rise to high ADC value promoting a significant ADC value difference [ 21 ] insert figure (5) and figure (6) about here. In the present study, ROC analysis showed that ADC had excellent diagnostic performance for differentiating lymphoma from pseudotumor, with an AUC of 0.882 (P 0.67 ×10⁻³ mm²/s) provided 88.6% sensitivity, 73.9% specificity, and 82.8% accuracy. Higher cutoffs improved specificity but reduced sensitivity. In accordance, Ren et al. [ 6 ] found that ADC mean achieved high area under the curve (AUC) of 0.871 in the diagnostic differentiation of orbital lymphoma from IOIP with a sensitivity of 72.70% and specificity of 94.40%. Also, Abdelgawad et al. [ 12 ] found that ascribing a cutoff of 0.876 ×10⁻³ mm²/s, their ROC analysis reported 100% sensitivity, 99% specificity, and 90.5% accuracy for differentiating the two entities. Furthermore, Eissa et al. [ 13 ] found that ADC thresholds of 0.84 and 0.86 ×10⁻³ mm²/s achieved AUCs of 0.933 and 0.920 and diagnostic accuracy of 89% and 90%, respectively differentiating lymphoma from IOIP. Jing Li et al. [ 14 ] found that ADC ratio demonstrated strong diagnostic performance with an AUC of 0.87 for differentiating LGL from LGIP. Also, Ozturk et al. [ 15 ] found that using a cutoff of 0.97 ×10⁻³ mm²/s, ADC mean values achieved 75% sensitivity and 74% specificity differentiating malignant and benign lesions. They further demonstrated that ADC mean ratio showed high diagnostic performance (AUC = 0.743). Additionally, Fatima et al.[ 16 ] found that using a cutoff of 0.84 ×10⁻³ mm²/s, ADC achieved a sensitivity of 83.33% and specificity of 85.71% differentiating malignant lesions from benign ones. Also, Sepahdari et al.[ 17 ] found in their research that using a threshold of < 0.92 ×10⁻³ mm²/s, ADC achieved 100% sensitivity and 100% specificity in distinguishing lymphoma from inflammation. This study had several limitations. Its retrospective design may have introduced selection bias, and the relatively small sample size could limit the generalizability of the findings. Limitations and Future Directions This study’s limitations include its retrospective design, relatively small cohort, secondly, adequate comparisons of perfusion parameters/values could not be achieved because we used another different perfusion techniques based on T2*-DSC susceptibility parameter which is different from previous literatures using more quantitative techniques (mostly DCE-MRI) in head and neck regions, either orbital or neck soft tissue tumors and reliance on semi-quantitative perfusion assessment rather than full pharmacokinetic modeling. Future studies should integrate parameters like K^trans^, K_ep, and V_e from DCE MRI, as well as explore histogram-based ADC analysis or more recent techniques like ASL. Conclusion Our findings support the growing consensus that multiparametric MRI imaging pakcage—particularly the combination of DWI and DCE—substantially improves diagnostic differentiation of orbital lymphoma and IPT. These tools can aid in clinical decision-making, reduce diagnostic uncertainty, and limit unnecessary invasive procedures. Declarations Ethics Approval and Consent to Participate: All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (Institutional Review Board (IRB)” of Alexandria General Hospital on 14 th February 2024) and with the Helsinki Declaration of 1964 and later versions. Committee’s reference number is unavailable (NOT applicable). Consent for publication: All patients included in this research gave written informed consent to publish the data contained within this study. Not needed in setting of retrospective studies according to our institutional preferences and legislations of ethics committee. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This study had no funding from any resource. Authors’ contributions : LE provided the cases and final diagnoses, with detailed description of results . NE gave the idea, wrote the section of introduction and EE provided the whole references for introduction and discussion with making of figure legends. “All authors read and approved the final manuscript”. EE made the whole final supervision on conducted research and on the written consent. “ All authors have read and approved the manuscript”. Acknowledgments: Not Applicable. References Purohit BS, Vargas MI, Ailianou A, Merlini L, Poletti PA, Platon A, Delattre BM, Rager O, Burkhardt K, Becker M. Orbital tumours and tumour-like lesions: exploring the armamentarium of multiparametric imaging. Insights into imaging. 2016;7(1):43–68. Mombaerts I, Ramberg I, Coupland SE, Heegaard S. 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Orbital masses: the usefulness of diffusion-weighted imaging in lesion categorization. Clin Neuroradiol. 2014;24:129–34. Sepahdari AR, Politi LS, Aakalu VK, Kim HJ, Razek AA. Diffusion-weighted imaging of orbital masses: multi-institutional data support a 2-ADC threshold model to categorize lesions as benign, malignant, or indeterminate. AJNR Am J Neuroradiol. 2014;35:170–5. Politi LS, Forghani R, Godi C, Resti AG, Ponzoni M, Bianchi S, et al. Ocular adnexal lymphoma: diffusion-weighted mr imaging for differential diagnosis and therapeutic monitoring. Radiology. 2010;256:565–74. Razek AA, Elkhamary S, Mousa A. Differentiation between benign and malignant orbital tumors at 3-T diffusion MR-imaging. Neuroradiology. 2011;53:517–22. Wu X, Pertovaara H, Dastidar P, Vornanen M, Paavolainen L, Marjomäki V, et al. ADC measurements in diffuse large B-cell lymphoma and follicular lymphoma: a DWI and cellularity study. European journal of radiology. 2013;82:e158-e64. Mundhada P, Rawat S, Acharya U, Raje D. Role of Quantitative Diffusion-Weighted Imaging in Differentiating Benign and Malignant Orbital Masses. Indian J Radiol Imaging. 2021;31:102–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → 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. 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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-7282417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508855672,"identity":"b801d205-4982-4ff8-bfc8-d2d19c1c6f1f","order_by":0,"name":"Lamyaa Abdel-Galil Eissa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACPijNA8SMD0AMPtyKIYANSQuzAYjBhkc1ihYwWwJdBLsW9tNpEj8YbGTk+w8/q/yaYyfDxsD88NENfFp4crdJ9jCk8RjcSDO7LbstGegwNmPjHLwOy90mwcNwmMdAgsHstuQ2ZqAWHjZpvFr4326T/MPwn0e+//i3Yslt9URokcjdJs3DcACIcswYP247TIyWt5utZQySgX7JKZZm3Hach42ZgF/4+XM33nxTYWcPdNjGjz+3Vdvzszc/fIxPCxCwSDAYQFjMPGASv3Kwkg8wFuMPwqpHwSgYBaNgBAIAGaU8Ti7zpF0AAAAASUVORK5CYII=","orcid":"","institution":"Alexandria University","correspondingAuthor":true,"prefix":"","firstName":"Lamyaa","middleName":"Abdel-Galil","lastName":"Eissa","suffix":""},{"id":508855673,"identity":"a4a072e6-eeae-40b0-aa31-1b4b67ab0269","order_by":1,"name":"Nadia Ahmed Abdelfattah","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"Ahmed","lastName":"Abdelfattah","suffix":""},{"id":508855674,"identity":"40d4464e-76c9-4edc-8bc1-1c2c23d985cf","order_by":2,"name":"Eiman Ahmed El-Bakoury","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"prefix":"","firstName":"Eiman","middleName":"Ahmed","lastName":"El-Bakoury","suffix":""},{"id":508855675,"identity":"7e839323-2662-41f9-8da0-ef8fd6688c8a","order_by":3,"name":"Aya Mohammed Abdel Aziz","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"prefix":"","firstName":"Aya","middleName":"Mohammed Abdel","lastName":"Aziz","suffix":""}],"badges":[],"createdAt":"2025-08-03 09:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7282417/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7282417/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43055-026-01750-y","type":"published","date":"2026-04-10T15:58:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90882390,"identity":"eb7116b2-2a3f-46e0-acf4-781199a59dfb","added_by":"auto","created_at":"2025-09-09 09:52:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":442214,"visible":true,"origin":"","legend":"\u003cp\u003eA 56 years old female, with history of progressively increasing bilateral lacrimal gland masses, more on right side (for one year); Not responding to CST. Axial T1 (a) shows bilateral nearly symmetrical glandular-conforming enlargement of both lacrimal glands displaying T1 iso-intense signal and also T2 iso-intense signal on T2- STIR (b). Axial diffusion (c) shows hyper-intensity with restricted diffusion and low ADC of only 0.62x10 - 3 cm 2 /sec (d). DSC perfusion on (e) shows areas of marked hyper- perfusion (pointed by arrows), with dynamic curve (f) showing marked recirculation as compared to normal parenchyma. Axial T1+GAD showed homogenous iso-enhancement (g). Pathology was proven as bilateral lacrimal gland lymphoma (NHL).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/ed5fa4d9bfc4b3c6cf46f46e.jpg"},{"id":90882392,"identity":"fbfb0832-e55c-4193-b30e-aa1ef7a8e8e9","added_by":"auto","created_at":"2025-09-09 09:52:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":360401,"visible":true,"origin":"","legend":"\u003cp\u003eA 52 years old male with indolent course of orbital mass for 2.5 years. Clinically eye pain, proptosis and redness of conjunctiva. Only short remissions with systemic steroids and longer remission with intra-lesional CST by direct injection. Axial T1 (a) shows fusiform mass enlargement of the medial rectus with homogenous hypo- intense signal and iso-intense signal on T2 in (b). Axial diffusion-ADC shows low ADC = 0.72x10 -3 cm 2 /sec. Axial T1+GAD (d) shows avid homogenous enhancement. DSC- perfusion-generated map in (e) shows marked hypo-perfusion of mass even much lower than lowest normal parenchyma making diagnosis of non-neoplastic inflammatory pseudo-tumour. The curve shows recirculation of lesion equivalent with normal parenchyma (f). Diagnosis was made of aggressive atypical pattern of orbital inflammatory pseudotumor.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/592a08c1260a2c9e18dba3b8.jpg"},{"id":90884407,"identity":"e90b945b-f6f2-47fe-a619-5f8bd647daf9","added_by":"auto","created_at":"2025-09-09 10:00:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":597524,"visible":true,"origin":"","legend":"\u003cp\u003eA 72 years old female. Axial T1 and T2 (a \u0026amp;;b) showed left globular mass with iso-intense signal and encasement of optic nerve, MR and IR. Coronal STIR shows lesion more clearly. Axial diffusion (d) and ADC map (e) reveal restricted diffusion and very low ADC of only 0.55 x10 -3 cm 2 /sec. Axial T1+GAD (f) revealed homogenous hypo-to-iso-enhancement with perplexing finding of iso-perfusion map (g). However, final diagnosis conformed with morphological data favouring lymphoma.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/e3c705a8a325d1d3a2c52263.jpg"},{"id":90884409,"identity":"305e893f-60e8-4a52-b8ac-b8ae266dd7f8","added_by":"auto","created_at":"2025-09-09 10:00:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":541260,"visible":true,"origin":"","legend":"\u003cp\u003eA 42 years old male with superior extra conal mass showing lenticular shaped mass with T1 and T2 iso-intense signal (a, and b) and more caudal axial T2 (c) showing extension along lateral wall, while coronal T2 (d) showing more clearly shape and spared SR. Axial T1+GAD (e) shows homogenous avid enhancement. ADC image not available, value was 0.69 x10 -3 cm 2 /sec), while perfusion image showed marked hypo- perfusion (f) documented in mean curve (g) revealing a very poorly hypo-perfusion curve with no re-circulation at any point; nearly parallel/similar to baseline normal curve. The perfusion data was more suggestive of non-neoplastic pseudotumor, despite morphological data seeming atypical. Final diagnosis was already inflammatory pseudotumor.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/54186f20330847abed789ee7.jpg"},{"id":90885714,"identity":"01e3d0f6-ab0f-4ab9-a0af-a23d4f440542","added_by":"auto","created_at":"2025-09-09 10:08:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":335199,"visible":true,"origin":"","legend":"\u003cp\u003eA 36 years old pre-septal lenticular shaped mass with T1 and T2 hypo - intense signal (a, and b). Axial T1+gAd shows homogenous iso-enhancement (c). An ADC image showing a low value was 0.67 x10 -3 cm 2 /sec in (d) w The perfusion map shows iso-perfusion with focal middle spots of marked hyper-perfusion reaching 10 times normal parenchyma, which could be perplexing of neoplastic despite morphological data of being inflammatory. This was more suggestive of neoplastic mass (Lymphoma versus Meibomian adenocarcinoma). Final diagnosis was already inflammatory pseudotumor.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/8eaa8e1d52383b27e7ee9701.jpg"},{"id":90884415,"identity":"7e758209-00f3-44de-b042-715b6ef46171","added_by":"auto","created_at":"2025-09-09 10:00:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":952620,"visible":true,"origin":"","legend":"\u003cp\u003eA 42 years old female with proven IgG4 disease of V2. Coronal T1 n (a) and (b) shows cord like and oblong shaped structures along V2-related foramina: IOC (in a), and along Vidian canal (VC) and foramen Rotundum (FR). Coronal T2 (c) revealed posterior component at CS effacing the MC showing T2 iso-to-hypo-intense signal. Axial ADC map (d) revealed low ADC= 0.7 x10 -3 cm2 /sec. Axial post GAD (e) shows elongated bizarre dural based mass involving the CS, MC and MCF, with iso-to-hypo- enhancement. Axial post GAD (f) shows component in PPF through MF foramina, also extending through naso-palatine foramina into nasal cavity. Figure (g) revealed coronal GDA Fat Sat showing IOC, F.R and VC components. Coronal GAD+FAT Sat (g) shows hypo-enhanced CS portion. Additional for interest DSC showed low tumoral blood volume (TBV=1.2 normal). Axial perfusion (j) map showed low flow of only 0.8 relative of normal parenchyma. The curve shows smaller recirculation peak of lesion compared to normal parenchyma.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/d9845f27b24aee377e1440e3.jpg"},{"id":106809173,"identity":"c215eed7-5e65-4bc2-9de5-c88e81a63c01","added_by":"auto","created_at":"2026-04-13 16:07:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4421446,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7282417/v1/b45592c3-b7c4-4e2e-8648-db330b64684d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating morphological, diffusion and susceptibility perfusion criteria in discrimination between the perplexing orbital lymphocytic mimickers: Lymphoma versus inflammatory pseudotumor","fulltext":[{"header":"Background","content":"\u003cp\u003eDespite the limited anatomical space of the orbit, it harbors wide range of different cellular origin structures which have the capability to give rise to a variety of tumors and tumor-like conditions in both adults as well as pediatrics (1).\u003c/p\u003e\u003cp\u003eThe clinical presentation of such masses is widely variable depending on the nature, location, and extent of the disease. The symptoms can involve proptosis, globe displacement, eyelid swelling, and restricted eye motility (2).\u003c/p\u003e\u003cp\u003eIn spite of the fact that improvement in radiological imaging techniques with wide range of differential diagnosis, orbital masses often present considerable diagnostic challenges (2).\u003c/p\u003e\u003cp\u003eSo, cross sectional imaging especially MRI with fat signal intensity suppression, contrast enhancement pattern and its novel techniques play a pivotal role in detection, characterization and management of these lesions as well as distinguishing malignant from benign lesions (2).\u003c/p\u003e\u003cp\u003eOrbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP) represent the most common lymphoproliferative disorders affecting the orbit, accounting nearly 20% of all orbital mass lesions (3).\u003c/p\u003e\u003cp\u003eLymphoma is the most common orbital malignancy in the adult. \u0026ldquo;Orbital lymphoma\u0026rdquo; refers to intraorbital and ocular surface lymphomas (i.e., conjunctiva, eyelid, and lacrimal gland). It accounts for 10% of all orbital tumors and approximately 2% of all nodal and extra-nodal lymphomas (4).\u003c/p\u003e\u003cp\u003eIt can be mistaken for other diseases due to overlapping clinical and imaging features. Definitive diagnosis is conducted via: histopathology, immune-histochemical staining, in addition to flow cytometry phenotype examination (5).\u003c/p\u003e\u003cp\u003eOrbital pseudotumor is an acute non-infective inflammatory condition of the orbital soft tissues which often manifests borderline morphological characters. It is one of the most common causes of unilateral exophthalmos. It responds to steroids or immune-suppressive therapy (6).\u003c/p\u003e\u003cp\u003ePrecise differentiation of orbital lymphoma and IOIP remains challenging in clinical practice, because of the nonspecific clinical presentations and the overlapping conventional MR findings. Therefore, it is of great importance to improve the performance of differentiating orbital lymphoma from IOI for therapeutic decisions and prognosis (7).\u003c/p\u003e\u003cp\u003eFine-needle aspiration is the gold standard for diagnosis of lymphoma and IOIP. However, it is technically challenging when it comes to posteriorly far located lesions in the orbit. Moreover, diffuse lymphocytic infiltrative IOIP, which is considered as the most common pathological subtype, could not be easily differentiated pathologically from orbital lymphoma (6).\u003c/p\u003e\u003cp\u003eMRI still poses its advantages of inherent high soft-tissue resolution, multi-planar capability with various technical innovations which in turn improves final image quality. The diagnostic performance of anatomical images for ocular tumors can be further enhanced through provision of functional data by diffusion weighed images which study micro-structure of tumors, meanwhile perfusion weighted imaging (PWI) can detect angiogenesis to discriminate benign from malignant tumors (8).\u003c/p\u003e\u003cp\u003ePrevious studies have indicated some MRI features, such as signal intensity on T2W sequence, presence of flow void sign, and degree of contrast enhancement, have the potential to discriminate between lymphoma and IOIP. Nevertheless, overall diagnostic efficacy of morphological features was still limited. In addition to that, the qualitative assessment of MRI features might be observer-dependent (6).\u003c/p\u003e\u003cp\u003eDiffusion-weighted imaging (DWI) showed promising results in diagnosing orbital lesions. It proved accuracy when distinguishing benign from malignant orbital lesions, particularly in the cases of lymphoproliferative disorders. Low apparent diffusion coefficient (ADC) values were reported to predict overall malignancy or to distinguish orbital lymphoma from orbital inflammation (9).\u003c/p\u003e\u003cp\u003eThe aim of this study is to evaluate morphological, diffusion, and susceptibility perfusion criteria on MRI in terms of distinguishing orbital lymphoma from inflammatory pseudotumor.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Patient Selection\u003c/b\u003e Retrospective study included 58 patients who underwent orbital MRI at our main tertiary university hospital between August 2022 and August 2024, while Institutional Review Board (IRB) approval was obtained, and informed consent was waived due to the retrospective nature of the research.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMRI Acquisition Protocol\u003c/b\u003e All imaging was performed using a 1.5T or 3.0T MRI scanner (e.g., Siemens Magnetom Avanto) with a dedicated head/neck coil. The MRI protocol was optimized to cover the orbits and upper fascial neck spaces.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConventional Imaging Sequences a)\u003c/b\u003e T1-weighted (T1WI): axial, coronal, and sagittal planes. Parameters: TR 400\u0026ndash;700 ms; TE 10\u0026ndash;114 ms; slice thickness 3.5 mm,b) T2-weighted (T2WI): Axial and coronal with and without fat suppression (STIR). TR 302700\u0026ndash;5000 ms; TE 76\u0026ndash;120 ms.\u0026bull; Scout localizers: For anatomical planning.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiffusion-Weighted Imaging (DWI)\u003c/b\u003e: \u0026bull; Axial single-shot spin-echo echo-planar imaging (EPI).\u0026bull; b-values: 0 and 1000 s/mm\u0026sup2;. Parameters: TR\u0026thinsp;~\u0026thinsp;4000 ms; TE\u0026thinsp;~\u0026thinsp;80 ms; slice thickness 3.5 mm; FOV 180\u0026ndash;220 mm. ADC maps were automatically generated using mono-exponential fitting.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDynamic Susceptibility Contrast (DSC) T2-Weighted Perfusion Imaging*\u003c/h2\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-alpha;\"\u003e\n\u003cspan\u003e\u003cli\u003e\u003cp\u003eT2*-weighted gradient-echo echo-planar imaging (GRE*-EPI) was used for dynamic susceptibility contrast (DSC) perfusion, b) After diffusion, dynamic MRI sequence was obtained by injection of \u0026ldquo; Gadopentate/-Dimeglumine\u0026rdquo; using a dose of 0.1 mmol/kg) and at a rate of 2 ml/s by means of a power injector traced by 20 ml saline-flush, c) Sequential images were then obtained through maximum area of lesion in axial plane, and with variegated time intervals at 30, 60, 90, 120, 150, 180, 250, and 300\u0026rdquo; ms following onset of injection. Then, conventional post-contrast MRI fat-suppressed images are obtained in axial, sagittal and coronal planes, using same parameters as non-contrast axial T1 images, d) Subtraction is thereafter provided at axial images, and perfusion subjective color maps images are interpreted on a dedicated qualified workstation, e)Post-processing and image analysis for DCE-MRI technique made as follows: Generated color maps - on dedicated software or on Macbook-pro (version 10.12.2; Early 2011-Model year ), made after drawing ROI on solid enhancing portion; Image Analysis: A lesion was described regarding: Note: All perfusion analysis in this study was performed on T2-weighted DSC images*\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eImages Evaluation\u003c/strong\u003e\u003cp\u003eTwo very well experienced subspecialized radiologists independently reviewed the generated anatomical and functional images, blinded to histopathology; they have 18 and 14 years of experiences.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eADC Analysis: Sufficient\u003c/b\u003e ROIs were manually located on ADC maps overlying the solid, non-necrotic portion of each lesion, avoiding cystic/necrotic or hemorrhagic components. ADC values were reported in \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s as a descriptive unit. Perfusion Categorization: Lesions were classified based on T2*-weighted perfusion maps as (Wholly subjective method to facilitate comparison with other perfusion studies utilizing different techniques): a) Hyperperfused: Marked signal drop (increased capillary perfusion), b) Iso-perfused: Comparable signal intensity to adjacent muscle, c) Hypo-perfused: Minimal or no signal drop.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analysis\u003c/b\u003e: The statistical analyses were performed by specialized personnel, using SPSS v26 (IBM Corp., Armonk, NY). Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (range); categorical data as counts and percentages; a) Mann\u0026ndash;Whitney U test: comparing continuous variables between two groups, b) Chi-square test, Fisher\u0026rsquo;s exact-test, or Monte-Carlo-correction: for categorical data, c) ROC analysis: This is used to assess diagnostic performance of ADC values and determine optimal cutoff thresholds, reporting AUC, sensitivity, specificity, PPV, NPV, and accuracy. A p value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePathological final diagnosis\u003c/strong\u003e\u003cp\u003eAll collected patients were proven by pathology at least FNAC and not merely medial follow up post cortico-steroid.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 58 patients, comprising 23 cases of orbital lymphoma and 35 cases of inflammatory pseudotumor (IPT). The results were classified into two groups in terms of anatomical distribution, imaging features, and functional MRI parameters\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnatomical and Morphological MRI Findings\u0026nbsp;\u003c/strong\u003e(Table 1)\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eSite of Lesion:\u003c/strong\u003e Lymphomas were predominantly located in both extra- and intra-conal compartments (60.9%), whereas IPTs were more frequently conal in location (42.9%), yielding a statistically significant difference (p = 0.001).\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eShape\u003c/strong\u003e: Lymphomas were more likely to demonstrate glandular and infiltrative morphologies, while IPTs showed more fusiform and elongated patterns (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eSignal Intensity\u003c/strong\u003e: On T1-weighted images, hypo-intensity was more common in IPTs (82.9%) compared to lymphomas (34.8%) (p \u0026lt; 0.001). On T2-weighted images, lymphomas tended to be iso-intense (73.9%) while IPTs were more often hypo-intense or deeply hypo-intense (p = 0.007).\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003eMuscle Involvement\u003c/strong\u003e: Diffuse extraocular muscle involvement (\u0026quot;all muscles\u0026quot;) was significantly associated with lymphomas (17.4% vs. 0%; p = 0.021).\u003c/p\u003e\n\u003cp\u003eTable (1): Correlation between final diagnosis (Lymphoma vs. IPT) with various\nmorphological parameters\n\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003cbr\u003e\u0026nbsp;(n=58)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of sig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphoma\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n=23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTX\u003cbr\u003e\u0026nbsp;(n=35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaterality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eUnilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e86.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e82.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eBilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eConal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eExtra-conal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e39.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 13.82*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eExtra-\u0026amp;-Intra-conal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMin. \u0026ndash; Max.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.20-8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.90-8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.20-5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.41\u0026plusmn;1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e4.01\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.01\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMedian\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eU = 297.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShape\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eElongated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eElongated and infiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eElongated and lobulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eFusiform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eFusiform and infiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003csup\u003eMC\u003c/sup\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eGlandular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 35.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eGlobular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eGlobular and infiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eInfiltrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eLenticular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eLobulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eHypo-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e63.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e82.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSo-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e65.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 13.88*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDeeply hypo-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eHypo-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 9.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eIso-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e73.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhich muscle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003csup\u003eFE\u003c/sup\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eNo muscle involvement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e58.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e47.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 1.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026ldquo;All muscles\u0026rdquo; involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 6.538*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 2.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 1.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiffusion-Weighted Imaging (DWI)\u003c/strong\u003e Table 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApparent diffusion coefficient (ADC) values showed a statistically significant difference between the two groups:\u003c/p\u003e\n\u003cp\u003e\u0026bull;Lymphomas had lower ADC values (range: 0.53\u0026ndash;0.82 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s; mean: 0.65 \u0026plusmn; 0.09).\u003c/p\u003e\n\u003cp\u003e\u0026bull;Meanwhile IPTs demonstrated higher ADC values (range: 0.63\u0026ndash;1.20 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s; mean: 0.91 \u0026plusmn; 0.20) (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eReceiver Operating Characteristic (ROC) analysis revealed\u003cstrong\u003e: Table 3\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull;An AUC of 0.882 (p \u0026lt; 0.001), indicating excellent diagnostic performance of ADC values.\u003c/p\u003e\n\u003cp\u003e\u0026bull;Using an ADC cutoff of \u0026gt;0.67 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s (Youden index), sensitivity was 88.6%, specificity 73.9%, and accuracy 82.8%.\u003c/p\u003e\n\u003cp\u003e\u0026bull;A more specific cutoff of \u0026gt;0.82 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s yielded a higher diagnostic achievement in terms of 100% specificity and 60% sensitivity, with an accuracy of 75.9%.\u003c/p\u003e\u003cp\u003eTable (2): Relation between final diagnosis (Lymphoma vs. IPT) with variegated \nfunctional parameters.\n\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutoff#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.797-0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026gt;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e73.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e83.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e82.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026gt;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e72.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC: Area Under The Curve\u003c/p\u003e\n\u003cp\u003eNPV: Negative predictive value \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPV: Positive predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*: Statistically significant at p \u0026le; 0.05\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCutoff recommended by Youden index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContrast-Enhanced MRI and Perfusion\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u0026nbsp; Enhancement Patterns: Homogeneous avid enhancement was more common in IPTs (42.9%) than in lymphomas (8.7%) (p = 0.002).\u003c/p\u003e\n\u003cp\u003e\u0026bull; \u003cstrong\u003ePerfusion Patterns:\u003c/strong\u003e\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eLymphomas were predominantly hyperperfused (91.3%), with no cases of hypoperfusion.\u003c/li\u003e\n \u003cli\u003eIPTs showed predominantly hypoperfusion (88.6%), a statistically significant distinction (p \u0026lt; 0.001).\u003c/li\u003e\n\u003c/ul\u003e\u003cp\u003eTable (3): Measurements of sensitivity/ Specificity/ PPV/ NPV and diagnostic accuracy) For\nADC cut-off value.\n\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sample\u003cbr\u003e\u0026nbsp;(n=58)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest of sig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphoma\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n=23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTX\u003cbr\u003e\u0026nbsp;(n=35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMin. \u0026ndash; Max.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.53-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.53-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.63-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.65\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.91\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMedian\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eU = 95.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHomogenous hypo-enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHomogenous iso-enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 12.39*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.002*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHomogenous avid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerfusion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHypo-perfused\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003csup\u003eMC\u003c/sup\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eIso-perfused\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e= 54.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eHyper-perfused\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003ec\u003csup\u003e2\u003c/sup\u003e: \u0026nbsp;Chi square test \u0026nbsp; MC: Monte Carlo \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eU: Mann Whitney test\u003c/p\u003e\n\u003cp\u003e*: Statistically significant at p \u0026le; 0.05\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOrbital lymphoma and inflammatory pseudotumor (PT) can present with comparable clinical and imaging features, making their differentiation depending solely on conventional MRI a challenging approach. Advanced MRI techniques offer potential for better discrimination [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, this study aims to evaluate morphological, diffusion, and susceptibility perfusion criteria on MRI in distinguishing orbital lymphoma from inflammatory pseudotumor.\u003c/p\u003e\u003cp\u003eIn the present study, significant differences were observed between lymphoma and pseudotumor groups in terms of lesion site, shape, signal intensity, and muscle involvement. Conal lesions appeared only in inflammatory pseudotumor, whereas lymphoma more commonly involved both extra- and intra-conal compartments. inflammatory pseudotumor lesions tended to have elongated, infiltrative, or fusiform shapes, while glandular morphology was more typical of lymphoma. inflammatory pseudotumor was also characterized by hypo-intense signals on T1 and T2-weighted images, whereas lymphoma more often showed iso-intense signals. Complete involvement of all extraocular muscles was found exclusively in lymphoma cases insert figure (1) and figure (2) about here.\u003c/p\u003e\u003cp\u003eParallel to our findings, Priego et al.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] investigated 19 patients with orbital lymphoma using CT and MRI and reported predominant involvement of the extra- and intra-conal compartments (47%), followed by extra-conal (42%) and intra-conal (11%) locations. The superior-lateral quadrant was most frequently affected (59%), and unilateral presentation was seen in 95% of cases. They also noted frequent involvement of the superior rectus (74%), lateral rectus (59%), eyelid (53%), and lacrimal gland (47%).\u003c/p\u003e\u003cp\u003eIn the current study, diffusion characteristics, enhancement patterns, and perfusion behavior showed clear differences between lymphoma and pseudotumor. Lymphoma demonstrated lower ADC values, more frequent homogenous hypo-enhancement, and predominant hyper-perfusion. Insert figure (3) and figure (4) about here.\u003c/p\u003e\u003cp\u003eIn agreement with our results, Ren et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] retrospectively analyzed 40 patients (18 with orbital lymphoma and 22 with idiopathic orbital inflammatory pseudotumor) to assess the diagnostic value of conventional MRI and ADC histogram analysis. They reported significantly lower ADC mean in lymphoma (0.719\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) compared to IOIP (1.131\u0026thinsp;\u0026plusmn;\u0026thinsp;0.317 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eSimilarly, Abdelgawad et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] assessed 53 patients (21 with orbital lymphoma and 32 with idiopathic orbital inflammatory pseudotumor) and demonstrated significantly lower ADC values in lymphoma (mean 0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) compared to IOIP (mean 1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s), with P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e\u003cp\u003eAlso, Eissa et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] retrospectively evaluated 37 untreated patients using diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) to differentiate IOIP from orbital lymphoma. They reported significantly lower ADC values in lymphoma (0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 and 0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) compared to IOIP (1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 and 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) (P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eJing Li et al.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] evaluated the utility of multimodal MRI parameters\u0026mdash;including DWI and DCE-MRI\u0026mdash;in differentiating lacrimal gland lymphoma (LGL) from IgG4-related lacrimal disease and lacrimal gland inflammatory pseudotumor (LGIP). They found significantly lower ADC ratios in LGL (0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11) compared to LGIP (1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFurthermore, Ozturk et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] assessed 43 patients with orbital lesions using conventional MRI and diffusion-weighted imaging and reported significantly lower ADC mean values in malignant tumors (median 0.99 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) compared to benign ones (median 1.23 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P\u0026thinsp;=\u0026thinsp;0.012). They further demonstrated that ADC min and ADC ratios (ADC mean ratio and ADC min ratio) were also significantly lower in malignant lesions.\u003c/p\u003e\u003cp\u003eFatima et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] assessed 39 orbital masses using DWI and reported significantly lower ADC values in malignant lesions (mean 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) compared to benign ones (mean 1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s), with P\u0026thinsp;\u0026le;\u0026thinsp;0.0001.\u003c/p\u003e\u003cp\u003eIn addition to that, Sepahdari et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] analyzed 202 orbital lesions across multiple institutions and reported that lymphomas had markedly low ADC values (mean 0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s), while inflammatory lesions showed higher ADCs (mean 1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s).\u003c/p\u003e\u003cp\u003eAlso supporting our results, Politi et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] conducted a prospective study involving 114 subjects and demonstrated that ocular adnexal lymphomas exhibited significantly lower ADC values than normal orbital structures and other orbital lesions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn strong agreement with our findings, Abdel Razek et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] evaluated 47 patients with orbital tumors using 3-T diffusion-weighted imaging and reported a significantly lower mean ADC in malignant lesions (0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) compared to benign ones (1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P\u0026thinsp;=\u0026thinsp;0.001). Specifically, lymphomas had a mean ADC of 0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, significantly lower than pseudo-tumors (1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P\u0026thinsp;=\u0026thinsp;0.001). Using a threshold of 1.15 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, they achieved 95% sensitivity, 91% specificity, and 93% accuracy in distinguishing malignant from benign lesions.\u003c/p\u003e\u003cp\u003eIt is suspected that the uniformly and atypical lymphocyte infiltrations in orbital lymphoma lead to higher cellularity and less extracellular space with subsequent low ADC value [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Regarding the benign IOIP, interstitial edematous changes in this lesion give rise to high ADC value promoting a significant ADC value difference [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] insert figure (5) and figure (6) about here.\u003c/p\u003e\u003cp\u003eIn the present study, ROC analysis showed that ADC had excellent diagnostic performance for differentiating lymphoma from pseudotumor, with an AUC of 0.882 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal cutoff (\u0026gt;\u0026thinsp;0.67 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s) provided 88.6% sensitivity, 73.9% specificity, and 82.8% accuracy. Higher cutoffs improved specificity but reduced sensitivity.\u003c/p\u003e\u003cp\u003eIn accordance, Ren et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] found that ADC mean achieved high area under the curve (AUC) of 0.871 in the diagnostic differentiation of orbital lymphoma from IOIP with a sensitivity of 72.70% and specificity of 94.40%.\u003c/p\u003e\u003cp\u003eAlso, Abdelgawad et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] found that ascribing a cutoff of 0.876 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, their ROC analysis reported 100% sensitivity, 99% specificity, and 90.5% accuracy for differentiating the two entities.\u003c/p\u003e\u003cp\u003eFurthermore, Eissa et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] found that ADC thresholds of 0.84 and 0.86 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s achieved AUCs of 0.933 and 0.920 and diagnostic accuracy of 89% and 90%, respectively differentiating lymphoma from IOIP.\u003c/p\u003e\u003cp\u003eJing Li et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found that ADC ratio demonstrated strong diagnostic performance with an AUC of 0.87 for differentiating LGL from LGIP.\u003c/p\u003e\u003cp\u003eAlso, Ozturk et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found that using a cutoff of 0.97 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, ADC mean values achieved 75% sensitivity and 74% specificity differentiating malignant and benign lesions. They further demonstrated that ADC mean ratio showed high diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.743).\u003c/p\u003e\u003cp\u003eAdditionally, Fatima et al.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] found that using a cutoff of 0.84 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, ADC achieved a sensitivity of 83.33% and specificity of 85.71% differentiating malignant lesions from benign ones. Also, Sepahdari et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] found in their research that using a threshold of \u0026lt;\u0026thinsp;0.92 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, ADC achieved 100% sensitivity and 100% specificity in distinguishing lymphoma from inflammation.\u003c/p\u003e\u003cp\u003eThis study had several limitations. Its retrospective design may have introduced selection bias, and the relatively small sample size could limit the generalizability of the findings.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s limitations include its retrospective design, relatively small cohort, secondly, adequate comparisons of perfusion parameters/values could not be achieved because we used another different perfusion techniques based on T2*-DSC susceptibility parameter which is different from previous literatures using more quantitative techniques (mostly DCE-MRI) in head and neck regions, either orbital or neck soft tissue tumors and reliance on semi-quantitative perfusion assessment rather than full pharmacokinetic modeling. Future studies should integrate parameters like K^trans^, K_ep, and V_e from DCE MRI, as well as explore histogram-based ADC analysis or more recent techniques like ASL.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings support the growing consensus that multiparametric MRI imaging pakcage\u0026mdash;particularly the combination of DWI and DCE\u0026mdash;substantially improves diagnostic differentiation of orbital lymphoma and IPT. These tools can aid in clinical decision-making, reduce diagnostic uncertainty, and limit unnecessary invasive procedures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (Institutional Review Board \u003cstrong\u003e\u003cem\u003e(IRB)\u0026rdquo;\u003c/em\u003e\u003c/strong\u003e of Alexandria General Hospital on 14\u003csup\u003eth\u003c/sup\u003e February 2024) and with the Helsinki Declaration of 1964 and later versions. Committee\u0026rsquo;s reference number is unavailable (NOT applicable).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll patients included in this research gave written informed consent to publish the data contained within this study.\u0026nbsp;\u003cstrong\u003eNot needed\u0026nbsp;\u003c/strong\u003ein setting of retrospective studies according to our institutional preferences and legislations of ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study had no funding from any resource.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e:\u0026nbsp;\u003cstrong\u003eLE\u0026nbsp;\u003c/strong\u003eprovided the cases and final diagnoses, with detailed\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edescription of results\u003cstrong\u003e. NE\u0026nbsp;\u003c/strong\u003egave the idea,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewrote the section of introduction and EE provided the whole references for introduction and discussion with making of figure legends. \u0026ldquo;All authors read and approved the final manuscript\u0026rdquo;. \u003cstrong\u003eEE\u0026nbsp;\u003c/strong\u003emade the whole final supervision on conducted research and on the written consent. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;\u003cstrong\u003e\u003cem\u003eAll authors have read and approved the manuscript\u0026rdquo;.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Not Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePurohit BS, Vargas MI, Ailianou A, Merlini L, Poletti PA, Platon A, Delattre BM, Rager O, Burkhardt K, Becker M. Orbital tumours and tumour-like lesions: exploring the armamentarium of multiparametric imaging. Insights into imaging. 2016;7(1):43\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMombaerts I, Ramberg I, Coupland SE, Heegaard S. Diagnosis of orbital mass lesions: clinical, radiological, and pathological recommendations. survey of ophthalmology. 2019;64(6):741\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi EY, Yuen HK, Cheuk W. Lymphoproliferative disease of the orbit. Asia-Pacific Journal of Ophthalmology. 2015;4(2):106\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzizmirliler D, Karakaya B, Yaman A, Bajin MS, Men S, Demirkan F, Guner HO, Utine CA. Retrospective Evaluation of Clinical, Demographic, and Radiological Data of Orbital Lymphoma Patients: A Single Tertiary Center Experience. Beyoglu Eye Journal. 2025;10(1):25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBriscoe D, Safieh C, Ton Y, Shapiro H, Assia EI, Kidron D. Characteristics of orbital lymphoma: a clinicopathological study of 26 cases. International Ophthalmology. 2018;38(1):271\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen J, Yuan Y, Wu Y, Tao X. Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps. BMC medical imaging. 2018;18(1):6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang H, Wang S, Li Z, Xie L, Wei W, Ma J, Xian J. Improving diagnostic performance of differentiating ocular adnexal lymphoma and idiopathic orbital inflammation using intravoxel incoherent motion diffusion-weighted MRI. European Journal of Radiology. 2020;130:109191.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKeraliya AR, Krajewski KM, Braschi-Amirfarzan M, Tirumani SH, Shinagare AB, Jagannathan JP (2015) Extracutaneous melanomas: A primer for the radiologist. Insights Imaging; 6 (6):707\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShor N, Sen\u0026eacute; T, Zuber K, Zmuda M, Berg\u0026egrave;s O, Savatovsky J, Lecler A. Discriminating between IgG4-related orbital disease and other causes of orbital inflammation with intra voxel incoherent motion (IVIM) MR imaging at 3T. Diagnostic and Interventional Imaging. 2021;102(12):727\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou M, Tan H, Zhou Y. Multi-modal for the diagnosis and management of ocular adnexal lymphoma. Holistic Integrative Oncology. 2025;4:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePriego G, Majos C, Climent F, Muntane A. Orbital lymphoma: imaging features and differential diagnosis. Insights Imaging. 2012;3:337\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdelgawad MS, Mohamed WMA, Aly RA. Value of diffusion-weighted magnetic resonance imaging (DWI) in differentiating orbital lymphoma from idiopathic orbital inflammatory pseudotumor. Egyptian Journal of Radiology and Nuclear Medicine. 2022;53:235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEissa L, Razek AAKA, Helmy E. Arterial spin labeling and diffusion-weighted MR imaging: utility in differentiating idiopathic orbital inflammatory pseudotumor from orbital lymphoma. Clinical Imaging. 2021;71:63\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Lin Y, Yang B. A comparative study of multimodal magnetic resonance imaging parameters for distinguishing lacrimal gland lymphoma from immune globulin G4-related lacrimal disease and lacrimal gland inflammatory pseudotumor. Chinese Journal of Academic Radiology. 2025:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzturk M, Sayıt AT, \u0026Ccedil;elenk C, Yeter V. Diagnostic value of diffusion-weighted MRI and conventional MRI in the differentiation of benign and malignant orbital lesions. Cukurova Medical Journal. 2022;47:34\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFatima Z, Ichikawa T, Ishigame K, Motosugi U, Waqar AB, Hori M, et al. Orbital masses: the usefulness of diffusion-weighted imaging in lesion categorization. Clin Neuroradiol. 2014;24:129\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSepahdari AR, Politi LS, Aakalu VK, Kim HJ, Razek AA. Diffusion-weighted imaging of orbital masses: multi-institutional data support a 2-ADC threshold model to categorize lesions as benign, malignant, or indeterminate. AJNR Am J Neuroradiol. 2014;35:170\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoliti LS, Forghani R, Godi C, Resti AG, Ponzoni M, Bianchi S, et al. Ocular adnexal lymphoma: diffusion-weighted mr imaging for differential diagnosis and therapeutic monitoring. Radiology. 2010;256:565\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRazek AA, Elkhamary S, Mousa A. Differentiation between benign and malignant orbital tumors at 3-T diffusion MR-imaging. Neuroradiology. 2011;53:517\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu X, Pertovaara H, Dastidar P, Vornanen M, Paavolainen L, Marjom\u0026auml;ki V, et al. ADC measurements in diffuse large B-cell lymphoma and follicular lymphoma: a DWI and cellularity study. European journal of radiology. 2013;82:e158-e64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMundhada P, Rawat S, Acharya U, Raje D. Role of Quantitative Diffusion-Weighted Imaging in Differentiating Benign and Malignant Orbital Masses. Indian J Radiol Imaging. 2021;31:102\u0026ndash;8.\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":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7282417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7282417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCharacterization of orbital masses is crucial in the therapeutic strategy planning owing to the fact that patient management greatly differs depending on the dignity of the orbital lesion. However, it is often difficult to differentiate malignant orbital masses from inflammatory pseudo tumors (IPT) due to their comparable clinical presentation with proptosis in terms of most common symptoms. Recently, magnetic resonance imaging (MRI) has become essential for the pre-treatment delineation of orbital tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePATIENTS \u0026amp; METHODS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRetrospective study for 58 patients being retrieved form available records of Alexandria University Hospital between August 2021 to August 2023, diagnosed with either lymphoma or inflammatory pseudo-tumor. I) Conventional MR protocol had been tailored to include the orbits and brain The standard MR brain acquisition-parameters were as following: a) Rapid scout images, b) multi-planar axial, coronal, sagittal T1 and T2-weighed (with and without STIR), c) Diffusion weighted imaging had been obtained using single shot spin echo planar imaging in axial plane d) Dynamic T2* Perfusion: conventional post-contrast MRI fat-suppressed images are made in axial, sagittal and coronal planes, using same parameters as non-contrast axialT1 images, then subtraction is provided at axial images. Perfusion color maps images are interpreted on workstation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults demonstrated a wide ADC range = 0.53–1.20 x 10 \u003csup\u003e− 3\u003c/sup\u003e cm \u003csup\u003e2\u003c/sup\u003e /sec, with mean value of 0.73. The lymphomas had an ADC range = 0.53–0.82 x 10 \u003csup\u003e− 3\u003c/sup\u003e cm \u003csup\u003e2\u003c/sup\u003e /sec, and mean value is0.6482x 10 \u003csup\u003e− 3\u003c/sup\u003e cm \u003csup\u003e2\u003c/sup\u003e /sec. The IPTs had slightly higher ranges and values, showing ADC range of 0.63–1.20 x 10 \u003csup\u003e−\u003c/sup\u003e3 cm \u003csup\u003e2\u003c/sup\u003e /sec, and mean ADC was 0.90x 10 \u003csup\u003e− 3\u003c/sup\u003e cm \u003csup\u003e2\u003c/sup\u003e /sec. ADC differences yielded a statistically significant difference (p \u0026lt; 0.001*). Using a cut-off value of 0.82 x10 \u003csup\u003e− 3\u003c/sup\u003e cm \u003csup\u003e2\u003c/sup\u003e /sec yielded a sensitivity of 60%, 100% specificity, PPV = 100%, NPV = 62%, and accuracy of 76%. Lymphomas showed predominantly hyper-perfused pattern in susceptibility perfusion –seen in 21 lesions (= 91.3%), and only two (= 7.8%) showed iso- perfusion, and none of lymphomas was hypo-perfused. On the contrary, IPTs were predominantly hypo-perfused (n = 31; 88.6%), 2 were iso-perfused (5.7%) and 2 hyper- perfused (= 5.7%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe combination of DWI and DCE MRI can improve diagnostic performance in differentiating lymphoma from in IPT, and are recommended to be used in appropriate clinical setting.\u003c/p\u003e","manuscriptTitle":"Estimating morphological, diffusion and susceptibility perfusion criteria in discrimination between the perplexing orbital lymphocytic mimickers: Lymphoma versus inflammatory pseudotumor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:52:23","doi":"10.21203/rs.3.rs-7282417/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":"7e0b19ff-55af-4424-9ab1-ee1dbce8913c","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:03:32+00:00","versionOfRecord":{"articleIdentity":"rs-7282417","link":"https://doi.org/10.1186/s43055-026-01750-y","journal":{"identity":"egyptian-journal-of-radiology-and-nuclear-medicine","isVorOnly":false,"title":"Egyptian Journal of Radiology and Nuclear Medicine"},"publishedOn":"2026-04-10 15:58:51","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2025-09-09 09:52:23","video":"","vorDoi":"10.1186/s43055-026-01750-y","vorDoiUrl":"https://doi.org/10.1186/s43055-026-01750-y","workflowStages":[]},"version":"v1","identity":"rs-7282417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7282417","identity":"rs-7282417","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}