Soft-tissue masses

Journal of Clinical Imaging 30 (2006) 37 – 42 Soft-tissue masses Use of a scoring system in differentiation of benign and malignant lesions Hakan MutluT, Emir Silit, Zekai Pekkafali, C. Cinar Basekim, Ersin Ozturk, Onur Sildiroglu, Esref Kizilkaya, A. Fevzi Karsli Department of Radiology, Gulhane Military Medical Academy Haydarpasa Teaching Hospital, Istanbul, Turkey Received 23 May 2005; received in revised form 23 May 2005 Abstract The purpose of this study was to show the qualitative and quantitative MRI characteristics of soft-tissue masses in differentiation of benign and malignant lesions. A total of 90 soft-tissue lesions were reviewed in this study. The scoring system presents a more objective diagnostic performance in the prediction of benign or malignant masses. With the use of this scoring system, unnecessary biopsy can be precluded in benign lesions. D 2006 Elsevier Inc. All rights reserved. Keywords: MRI; Dynamic; Soft-tissue tumor; Scoring system 1. Introduction 2. Materials and methods Since the introduction of magnetic resonance (MR) imaging of the soft tissue, its role as a sensitive tool in the diagnosis of malignant soft-tissue masses has been confirmed. Despite a variable specificity, contrast-enhanced MR imaging of the soft tissue can improve diagnostic accuracy if it is used as an adjunct to other imaging modalities. MR imaging is valuable not only in detecting occult soft-tissue tumor, but also in assessing tumor extension. Because of the technique’s limited specificity for diagnosis of malignant soft-tissue masses, MR imaging reveals a considerable number of enhancing lesions that are benign. These MR imaging findings result in a number of unnecessary biopsies. To avoid these unnecessary biopsies, we attempted to find a more precise way to differentiate between benign and malignant masses. We performed this study to assess whether a scoring system can be used for the differentiation of benign and malignant soft-tissue masses. Therefore, the purpose of this study was to correlate qualitative and quantitative MR imaging characteristics of soft-tissue masses with histology. A total of 90 soft-tissue masses (54 benign and 36 malignant) were reviewed in this retrospective study. Inasmuch as the surgical changes could alter the morphological and dynamic features of the masses, recurrent tumors were excluded. Diagnoses of all malignant and 45 benign masses were histologically confirmed. Diagnoses of the remaining 12 benign masses were hemangioma and arteriovenous malformation. MR images were performed with a 1.5-T MR system (Vision; Siemens, Erlangen, Germany). A large flex coil or a knee coil was used, depending on the location and size of the lesion. After, T1-, STIR-, and T2-weighted axial, coronal, and sagittal sequences dynamic MR imaging sequence was performed, followed by static postcontrast T1-weighted sequences. The dynamic imaging was performed with a three-dimensional fast low-angle shot (3D FLASH) sequence. The slice was located through the largest sectional area of the lesion. The imaging parameter for both coils was the following: flip angle, 208; matrix, 192256; number of acquisition, 1; TR, 8.1 ms; TE, 4 ms. Afterwards, 3D FLASH sequence in the axial plane was repeated six times, and consecutive contrast-enhanced five images were obtained with a temporal resolution of 10 –20 s/image during a bolus injection of 0.1 mmol/kg of body weight gadolinium was given through a needle in the antecubital vein and was T Corresponding author. GATA Haydarpasa Egt. Hst. RadyolojV Servisi., 81327 Uskudar, Istanbul, Turkey. Tel.: +90 216 542 28 85; fax: +90 216 330 43 89. E-mail address: hakanmutlu1@yahoo.com (H. Mutlu). 0899-7071/06/$ – see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.clinimag.2005.06.027 38 H. Mutlu et al. / Journal of Clinical Imaging 30 (2006) 37 – 42 Table 1 Criteria for evaluating soft-tissue masses on contrast-enhanced MR imaging Criterion Points Initial peak signal intensity increase (initial enhancement rate) b 50% 0 50 –100% 1 N 100% 2 Type of time–signal intensity curve Type 1 0 Type II 1 Type III 2 Margin type Well-defined 0 Ill-defined 1 Enhancement pattern Homogeneous 0 Heterogeneous 1 Peripheral (rim-vs.-center enhancement) 2 followed with flushing of isotonic saline solution. The total injection time was 10 s. The analysis of the relation of the signal intensity to time was performed with the region of interest (ROI) technique. The ROI was placed within the tumor area with the highest signal intensity enhancement. The evaluation criterion was the peak percentage of signal intensity increase within the first 3 min after contrast material administration relative to the precontrast signal intensity (initial enhancement rate). The rate of signal intensity increase is defined as SI increase =[(SI post SI pre)/SI pre]100; where SI is signal intensity and bpreQ and bpostQ mean before and after contrast material administration, respectively. We evaluated the behavior of the signal intensity curve from the 3rd to the 6th min: A signal intensity increase of more than 10% within this interval relative to the peak enhancement in the first 3 min was defined as continued signal intensity increase (Type I signal intensity curve). A signal intensity similar to the peak signal intensity was considered as a plateau (Type II signal intensity curve), and a decrease of more than 10% was defined as a washout (Type III signal intensity curve). The morphology of the contrast-enhancing lesions and the kinetic aspects of the contrast material enhancement were evaluated according to the established scoring system that was first described by Fischer et al. [1] for breast lesions. We modified the scoring system for soft-tissue masses, as shown in Table 1. Each criterion for diagnosing malignancy was given a score of 0 –2 points, depending on the suspicion of malignancy (high score indicated high suspicion). The masses were interpreted by a musculoskeletal radiologist who was blinded with regard to diagnosis, clinical history, and results of other imaging studies. He was Fig. 1. A 23-year-old man with primitive neuroectodermal tumor metastasis to muscle. (A) Subtracted axial 3D gradient-echo T1-weighted echo MR image shows a nodular mass. (B) Time–intensity curve shows Type III enhancement. (C) Fat-suppressed, contrast-enhanced coronal T1-weighted spin-echo MR image shows uniform enhancement. H. Mutlu et al. / Journal of Clinical Imaging 30 (2006) 37 – 42 39 given a checklist with which he evaluated each mass for margins, enhancement pattern, initial enhancement ratio, and type of time–signal intensity curve. The total number of points for all criteria was summed and a score was defined for each lesion. A cut-off value between the benign and malignant softtissue masses was determined with ROC analysis. The chisquare test for independent variables was used to assess the lesion features and to determine whether any of the lesion features showed a significant correlation with the histopathologic result. The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were analyzed with ROC analysis. The results were correlated with histopathologic findings. 3. Results Ninety soft-tissue masses (54 benign and 36 malignant) were reviewed in this retrospective study. All malignant and 45 benign masses were histologically confirmed. Diagnoses of the remaining 12 benign soft-tissue masses were hemangioma and arteriovenous malformation at clinical follow-up. The most frequent malignant masses were malignant fibrous histiocytoma (6/36), followed by metastasis (6/36; Fig. 1), fibrosarcoma (6/36), and rhabdomyosarcoma (6/36). The Fig. 3. A 20-year-old woman with hemangioma. (A) Subtracted axial 313 gradient-echo, T1-weighted echo MR image shows a mass. (B) Time– intensity curve shows Type I enhancement. Fig. 2. A 35-year-old man with schwannoma of femoral region who presents with mass. (A) Coronal T1-weighted spin-echo MR image shows a fusiform shaped mass: in the level of mid third of the femur, with signal intensity identical to that of the surrounding muscle. (B) Coronal STIR and (C) axial T2-weighted MR images show a well-defined mass, with high signal intensity. most frequent benign masses were benign neural tumor (21/54; Fig. 2), hemangioma and arteriovenous malformation (12/54; Fig. 3), benign fibrous histiocytoma (6/54), and fibroma (3/54; Fig. 4). The diagnoses of all the masses are summarized in Table 2. The majority of both benign and malignant masses had inhomogeneous signal intensity. Malignant masses uncommonly had smooth borders and homogeneous signal intensity. Lesions with ill-defined margins had a higher positive predictive value for malignancy (60%) than did those with well-defined margins (20%). Although the P value was less than .05 ( P =.025), margin type was not a suitable parameter in differentiating benign from malignant soft-tissue lesions relative to signal intensity curve type ( P = .0001) or initial enhancement rate ( P =.0001). Enhancement pattern was the only parameter that was not significantly ( P =.1) associated with malignant histology. There was a considerable overlap for benign and malignant masses according to heterogeneous and rim enhancement. The distribution of the benign and malignant masses according to morphological and kinetic features is shown in Table 3. 40 H. Mutlu et al. / Journal of Clinical Imaging 30 (2006) 37 – 42 Fig. 4. A 22-year-old man with fibroma. (A) Axial T1-weighted spin-echo MR image shows a mass in the volar aspect of the hand, with signal intensity similar to that of skeletal muscle. (B) Corresponding contrast-enhanced axial T1-weighted spin-echo MR image shows uniform enhancement. (C) Corresponding fatsuppressed T2-weighted MR image shows a mass, with high signal intensity that of skeletal muscle. The scores of the benign and malignant masses are shown in Table 4. The most suitable cut-off value to distinguish benign and malignant lesions was found to be 3 in the ROC analysis. The lesions with at least 3 points were considered to be suspicious of malignancy. We achieved rather a high accuracy ratio of 87%. With the cut-off value mentioned above, the sensitivity, specificity, PPV, and NPV were 100%, 78%, 75%, and 100%, respectively. The sensitivity, specificity, PPV, NPV, and accuracy according to the variable cut-off value are shown in Table 5. Table 2 Diagnoses of the soft-tissue masses MR imaging is assuming a major role in the recognition, staging, and treatment planning of soft-tissue tumors due to excellent soft-tissue contrast and multiplanar imaging capability. Sagittal, axial, and coronal images from MR imaging increase the accuracy of assessment of the relation between tumors and adjacent structures. MR imaging is superior to other modalities in delineating the extent of tumor and its relation to surrounding structures in all cases. Despite the presence of contradictive reports [2 – 5], there is a general agreement on the value of some features of soft-tissue masses as tumor margin, enhancement pattern, time–signal intensity curve, and enhancement rate in differentiating malignant lesions from benign ones with MR imaging [6,7]. The margin type was not a suitable parameter in differentiating benign from malignant masses relative to time–signal intensity type and initial enhancement rate. Benign soft-tissue masses Number of masses Malignant soft-tissue masses Number of masses Benign neural tumor Fibromatosis/ Desmoid tumor Hemangioma/ Arteriovenous malformation Fibroma Ganglion cyst Benign fibrous histiocytoma 21 6 9 Malignant fibrous histiocytoma Metastasis 6 12 Synovial sarcoma 3 Rhabdomyosarcoma Leimyosarcoma Liposarcoma 6 3 3 Fibrosarcoma Primitive neuroectodermal tumor 6 3 3 3 6 4. Discussion 41 H. Mutlu et al. / Journal of Clinical Imaging 30 (2006) 37 – 42 Table 3 Distribution of the benign and malignant masses according to the morphologic and kinetic features Morphologic features Enhancement pattern Margin type Number of benign masses Number of malignant masses Well-defined Ill-defined Homogeneous Heterogeneous Peripheral 36 9 18 27 24 3 24 27 6 6 Kinetic features Type of time–signal intensity curve Number of benign masses Number of malignant masses Initial enhancement rate Type I Type II Type III b 50% 50 –100% N 100% 45 3 3 6 6 27 39 – 6 15 9 21 The enhancement patterns of benign and malignant masses are different [8 –10]. Malignant lesions tend to proliferate rapidly, developing a vascular rim due to both the neovascularity and parasitization of neighboring normal blood vessels. The center of malignant masses often has a decreased blood supply, and the central interstitial pressure within malignant tumors is high compared with that of normal tissues, with a sudden drop at the rim of the tumor [9]. Benign lesions are much less likely to outgrow their vital blood supply, have well-vascularized central areas, and do not exhibit neovascularity. Ma et al. [11] used the rim-to-center enhancement ratio to differentiate between benign and malignant musculoskeletal masses and suggested that it has potential as an additional parameter for the MR imaging differentiation of indeterminate musculoskeletal masses. In addition, Van der Woude et al. [12] reported that initial peripheral enhancement was a specific sign for differentiating malignant from benign soft-tissue tumors. Although we gave 2 points to the lesions that showed rim-to-center enhancement because of high risk of malignancy, only 6 of 36 malignant lesions showed this pattern. Moreover, 6 of 54 benign lesions showed peripheral enhancement. Benign lesion such as hemangioma, which shows peripheral enhancement, may have a dense network of vessels, a large extravascular space, and a high permeability of capillaries. Totty et al. [3] suggested that differences in signal intensity did not help to distinguish benign from malignant tumors, and the majority of both benign malignant masses showed inhomogeneous signal intensity. In our study, most of the malignant and benign masses had inhomogeneous signal intensity, as in the study of Crim et al. [2]. It showed that the enhancement pattern was not a useful parameter in distinguishing benign from malignant tumors. The difference between the enhancement pattern of the benign and malignant lesions was not statistically significant ( P N.05). In our study, 45 of 54 benign soft-tissue tumors showed a Type I curve (Fig. 3), while 27 of 36 malignant soft-tissue tumors showed a Type III curve (Fig. 1). The difference was statistically significant between benign and malignant Table 4 Scores of the benign and malignant masses Score Number of benign masses (n = 54) Number of malignant masses (n = 36) 0 1 2 3 4 5 6 7 15 – 12 – 15 – 3 6 6 3 – 12 3 12 – 3 Table 5 Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy according to the variable cut-off value Score TP TN FP FN Sensitivity (%) Specificity (%) PPV (%) NPV (%) A (%) 0 1 2 3 4 5 6 7 36 36 36 36 30 27 15 3 0 15 27 42 45 51 51 54 54 39 27 12 9 3 3 0 0 0 0 0 6 9 21 33 100 100 100 100 83.3 75 41.6 8.3 l 27.7 50 77.7 83.3 94.4 94.4 100 40 48 57 75 76.9 90 83.3 100 l 100 100 100 88.2 85 70.8 62 40 57 70 87 83.3 86.6 73.3 63.3 TP: true positive; TN: true negative; FP: false positive; FN: false negative; PPV: positive predictive value; NPV: negative predictive value; A: accuracy. 42 H. Mutlu et al. / Journal of Clinical Imaging 30 (2006) 37 – 42 lesions ( P b.05). Our result is in accord with the results of the study by Van der Woude et al. [12]. They achieved a sensitivity of 86% and a specificity of 81% based on the type of signal intensity curve. Dynamic contrast-enhanced MR imaging is very useful, especially in these equivocal mass lesions, because there is a different pattern of enhancement during and immediately after the first pass of the contrast medium through tumor and reactive tissue [13]. Tumor will enhance fast during the first pass, whereas reactive tissue will enhance later and more slowly (= equal to or even slower than reference tissue, i.e., normal muscle). Initial enhancement rate (increase of signal intensity relative to the precontrast signal intensity within the first 3 min) was below 50% in 39 of 54 benign lesions. There was no malignant lesion in this group. Majority of the malignant lesions (21/36) enhanced more than at least two times (initial enhancement rate N 100%) within the first 3 min. Different features of soft-tissue tumors have been evaluated in MR imaging to differentiate benign and malignant lesions. The findings of these studies were based on a single feature and sensitivities or specificities that were defined one by one for each feature. All the features were used together in our study, and sensitivity and specificity were 100% and 78% (score =3), respectively. Kransdorf et al. [5] evaluated 112 soft-tissue masses (85 benign and 27 malignant) in a retrospective study. Smooth margins, homogenous signal intensity, or characteristics findings of lipoma, hematoma, or hemangioma were used for benignity criteria. Sensitivity and specificity were 50% and 85% for benign masses and 41% and 84% for malignant masses, respectively. Einarsdottir et al. [14] evaluated 33 soft-tissue lesions regarding start, pattern, and progression of enhancement and achieved 87% sensitivity and 70% specificity. The differentiation of benign from malignant soft-tissue masses with unenhanced MR imaging is difficult because of similar morphological and kinetic characteristics of benign and malignant soft-tissue masses. Even if the rate of enhancement of malignant tumors is greater than that of benign solid masses, substantial overlap occurs with dynamic contrast-enhanced MR imaging. In addition, some lesions could show both benign and malignant features at the same time. In this situation, a radiologist could misdiagnose a malignant mass as benign or vice versa. The scoring system presents a more objective diagnostic performance in the prediction of benign or malignant masses. With the use of this scoring system, unnecessary biopsy can be precluded in benign lesions. References [1] Fischer U, Kopka L, Grabbe E. Breast carcinoma: effect of preoperative contrast-enhanced MR imaging on the therapeutic approach. Radiology 1999;213:881 – 8. [2] Crim JR, Seeger LL, Yao L, Chandnani V, Eckardt JJ. 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