How to differentiate benign from malignant myometrial tumours using MR imaging

Purpose

To retrospectively evaluate the ability of magnetic resonance imaging (MRI) to differentiate malignant from benign myometrial tumours.

Methods

Fifty-one women underwent MRI before surgery for evaluation of a solitary myometrial tumour. At histopathology, there were 25 uncertain or malignant mesenchymal tumours and 26 benign leiomyomas. Conventional morphological MRI criteria were recorded in addition to b _1,000 signal intensity and apparent diffusion coefficient (ADC). Odds ratios (OR) were calculated for each criterion. A multivariate analysis was performed to construct an interpretation model.

Results

The significant criteria for prediction of malignancy were high b _1,000 signal intensity (OR?=?+∞), intermediate T2-weighted signal intensity (OR?=?+∞), mean ADC (OR?=?25.1), patient age (OR?=?20.1), intra-tumoral haemorrhage (OR?=?21.35), endometrial thickening (OR?=?11), T2-weighted signal heterogeneity (OR?=?10.2), menopausal status (OR?=?9.7), heterogeneous enhancement (OR?=?8) and non-myometrial origin on MRI (OR?=?4.9). In the recursive partitioning model, using b _1,000 signal intensity, T2 signal intensity, mean ADC, and patient age, the model correctly classified benign and malignant tumours in 47 of the 51 cases (92.4 %).

Conclusion

We have developed an interpretation model usable in routine practice for myometrial tumours discovered at MRI including T2 signal, b _1,000 signal and ADC measurement.

Key Points

? MRI is widely used to differentiate benign from malignant myometrial tumours. ? By combining T2-weighted, b _1,000 and ADC features, MRI is 92.4?% accurate. ? DWI may limit misdiagnoses of uterine sarcoma as benign leiomyoma. ? Patient age is important when considering a solitary myometrial tumour.     

MRI of soft-tissue masses: the relationship between lesion size, depth, and diagnosis

AIMS: To identify the relationship between depth and size of soft-tissue mass lesions relative to histological diagnosis in a range of malignant neoplastic, benign neoplastic, and non-neoplastic conditions on magnetic resonance imaging (MRI). METHOD: The MRI findings of 571 consecutive patients referred to a supra-regional orthopaedic oncology unit with a suspected soft-tissue neoplasm were reviewed and included in the study. The patient age, histological diagnosis, lesion size, anatomical location, and lesion depth (superficial or deep to fascia) were recorded. RESULTS: There were 288 males and 283 females (mean age 48 years, age range 2-92 years). The mean age was 54.1 years for malignant neoplastic lesions compared with 40.1 years for benign neoplastic and 45.4 years for non-neoplastic conditions. There was a significant age difference when malignant lesions were compared with benign neoplastic and non-neoplastic lesions (p<0.001). No significant relationship was present between lesion depth (480 deep, 91 superficial) and diagnosis (288 malignant neoplastic, 197 benign neoplastic and 86 non-neoplastic lesions). However, a significant relationship was identified between lesion size and diagnosis (p<0.001). Furthermore, a significant relationship was identified when lesion size greater than 5 cm, lesion depth, and diagnosis were analysed. CONCLUSION: Current guidelines suggest the most important variables for assessing risk of malignancy in a soft-tissue lesion include size, depth in relation to the fascia, increasing size, and pain. The current study suggests that relationship to fascia is less important as a predictor of malignant potential in a patient cohort treated at a supra-regional centre. Significant risk factors include increasing patient age and lesion size greater than or equal to 5 cm.     

Strain elastography features of epidermoid tumours in superficial soft tissue: differences from other benign soft-tissue tumours and malignant tumours

Objective:

We evaluated ultrasonographic features of superficial epidermoid tumour with a focus on strain elastography (SE) features that will help in the differential diagnosis of epidermoid tumour from other benign and malignant soft-tissue tumours.

Methods:

We retrospectively evaluated ultrasonographic and SE data of 103 surgically confirmed superficial soft-tissue tumours and tumour-like lesions: 29 cases of epidermoid tumour, 46 cases of other benign tumours and 28 cases of malignant tumour. SE and B-mode imaging were performed at the same time. SE characteristics were assigned into four grades (1–4) according to their elasticity. Interobserver agreement for the four SE scores between the two radiologists was analysed using kappa statistics. We classified each SE finding as a hard lesion (SE Score 3–4) or soft lesion (SE Score 1–2) and compared these findings using the χ² test to identify whether a significant difference in mass hardness existed among epidermoid tumour, other benign tumour and malignant tumour.

Results:

Overall interobserver agreement according to the four SE scores was moderate (κ = 0.540), and overall agreement for the hardness [soft (Score 1–2) or hard (Score 3–4)] was almost perfect (κ = 0.825). Malignant tumours showed higher SE scores (3–4, hard nature) than did epidermoid tumour or other benign soft-tissue tumours. There were no differences in SE score between epidermoid tumour and other benign tumours.

Conclusion:

Superficial epidermoid tumour exhibits a softer nature than does malignant tumour but does not have a different SE pattern from other benign tumours.

Advances in knowledge:

SE features of epidermoid tumour might be helpful in differentiating from other benign and malignant tumours.Epidermoid tumour is a slow-growing dermal or subcutaneous epithelial cyst that contains keratin material within the lesion and is lined by epidermis.¹ Epidermoid tumour may result from traumatic implantation of the epidermis into the dermis or subcutaneous fat layer.² Although most epidermoid tumours remain asymptomatic, some lesions may grow large enough to compress the surrounding structures, become infected or rupture, and thus become symptomatic.³ Common ultrasonographic findings of epidermoid tumour are a circumscribed solid or mixed nature mass with variable echogenicity.⁴ Repeated trauma or enlargement of the epidermoid tumour can precipitate rupture, and ruptured epidermoid tumours elicit inflammation with abundant foreign body giant cells.^3,5 The rate of malignant transformation into squamous cell carcinoma has been reported to range from 0.01% to 2.00%.^1,6–8 Therefore, correct diagnosis of epidermoid tumour is very important for the establishment of the treatment plan. Strain elastography (SE) has been introduced for the diagnosis of soft-tissue tumours and is used to evaluate tissue stiffness.^9,10 Hard lesions are more resistant to extrinsic compression and show smaller strain, whereas soft lesions show larger strain.¹⁰ Patel et al¹¹ reported that most epidermoid tumours show a predominant blue colour, which represents their hard nature. However, it is not possible to ascribe a characteristic SE pattern to epidermoid tumour that differentiates it from malignancy. To the best of our knowledge, there has not been a study of SE-applied differential diagnosis among superficial epidermoid tumour, other benign soft-tissue tumours and malignant soft-tissue tumours. In this study, we evaluated ultrasonographic features of superficial epidermoid tumour with an emphasis on SE features that might help in the differential diagnosis of epidermoid tumour from other benign soft-tissue tumours and malignant soft-tissue tumours.     

Application of a quantitative model to differentiate benign from malignant breast lesions detected by dynamic,gadolinium-enhanced MRI

Both benign and malignant breast lesions may exhibit intense contrast enhancement when imaged using gadolinium-enhanced MRI. We propose a quantitative approach for fitting dynamic signal intensity (SI) data that may distinguish benign from malignant lesions. We studied 78 lesions in 75 women (18 malignancies, 16 fibroadenomas, and 44 other benign breast lesions) to determine the potential of this model for decreasing false-positive MR results. Twenty-eight lesions showed no enhancement; all were benign. One lesion showed a complex pattern not amenable to region-of-interest analysis and was considered a false positive. SI versus time data for the remaining 49 lesions were fit to the proposed model. We found that one parameter, M, the normalized slope of the SI enhancement profile evaluated at half the maximal signal intensity, seemed to be highly correlated with malignancy and offered improved discrimination between malignant and benign lesions compared to a previously published two-point slope method.     

Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas

PurposeDifferentiating WHO grade I–III of meningioma by non-invasive imaging is challenging. This study investigated the potential of MR arterial spin labeling (ASL) to establish tumor grade in meningioma patients.Material and methodsPseudo-continuous ASL with 3D background suppressed gradient and spin echo (GRASE) was acquired on 54 patients with newly diagnosed or recurrent intracranial meningioma. Perfusion patterns characterized in CBF color maps were independently evaluated by three neuroradiologists blinded to patient history, and correlated with tumor grade from histo-pathological review.ResultsThree perfusion patterns could be discerned by visual evaluation of CBF maps. Pattern 1 consisted of homogeneous hyper-perfusion of the entire tumor; pattern 2 demonstrated heterogeneous hyper-perfusion; pattern 3 showed no substantial hyper-perfusion. Evaluation of the perfusion patterns was highly concordant among the three readers (Kendall W = 0.9458, P < 0.0001). Pattern 1 was associated with WHO Grade I meningioma of (P < 0.0001). Patterns 2 and 3 were predictive of WHO Grade II and III meningioma (P < 0.0001), with an odds ratio (OR, versus pattern 1) of 49.6 (P < 0.01) in a univariate analysis, and an OR of 186.4 (P < 0.01) in a multivariate analysis.ConclusionQualitative evaluation of ASL CBF maps can help differentiate benign (WHO Grade I) from higher grade (WHO Grade II and III) intracranial meningiomas, potentially impacting therapeutic strategy.     

Magnetic resonance imaging of soft-tissue tumors of the extremities: A practical approach

Diagnosis of extremity soft-tissue tumors can be challenging. Characteristics of tumor margins can help precisely identify locally aggressive or non-aggressive behavior for surgical planning, but cannot differentiate benign from malignant lesions. Most malignant tumors can have inhomogeneous signals on T2-weighted images. Although a uniform signal on T2-weighted images can be a reliable indication of a benign lesion, a well-defined mass with homogeneous internal signal intensity does not definitively identify a benign lesion. Some common and distinctive soft-tissue lesions can have specific clinical and imaging features allowing a diagnosis without biopsy. These are known as determinate lesions. This illustrative report presents a diagnostic guide for extremity soft-tissue tumors based on tissue signal and morphological characteristics on magnetic resonance images. It is important for clinicians to be familiar with the imaging characteristics of common determinate lesions.     

The value of intratumoral heterogeneity of 18F-FDG uptake to differentiate between primary benign and malignant musculoskeletal tumours on PET/CT

Objective:

The cumulative standardized uptake value (SUV)–volume histogram (CSH) was reported to be a novel way to characterize heterogeneity in intratumoral tracer uptake. This study investigated the value of fluorine-18 fludeoxyglucose (¹⁸F-FDG) intratumoral heterogeneity in comparison with SUV to discriminate between primary benign and malignant musculoskeletal (MS) tumours.

Methods:

The subjects comprised 85 pathologically proven MS tumours. The area under the curve of CSH (AUC-CSH) was used as a heterogeneity index, with lower values corresponding with increased heterogeneity. As 22 tumours were indiscernible on ¹⁸F-FDG positron emission tomography, maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean) and AUC-CSH were obtained in 63 positive tumours. The Mann–Whitney U test and receiver operating characteristic (ROC) analysis were used for analyses.

Results:

The difference between benign (n = 35) and malignant tumours (n = 28) was significant in AUC-CSH (p = 0.004), but not in SUV_max (p = 0.168) and SUV_mean (p = 0.879). The sensitivity, specificity and accuracy for diagnosing malignancy were 61%, 66% and 64% for SUV_max (optical threshold value, >6.9), 54%, 60% and 57% for SUV_mean (optical threshold value, >3) and 61%, 86% and 75% for AUC-CSH (optical threshold value, ≤0.42), respectively. The area under the ROC curve was significantly higher in AUC-CSH (0.71) than SUV_max (0.60) (p = 0.018) and SUV_mean (0.51) (p = 0.005).

Conclusion:

The heterogeneity index, AUC-CSH, has a higher diagnostic accuracy than SUV analysis in differentiating between primary benign and malignant MS tumours, although it is not sufficiently high enough to obviate histological analysis.

Advances in knowledge:

AUC-CSH can assess the heterogeneity of ¹⁸F-FDG uptake in primary benign and malignant MS tumours, with significantly greater heterogeneity associated with malignant MS tumours. AUC-CSH is more diagnostically accurate than SUV analysis in differentiating between benign and malignant MS tumours.     

肾上腺素药物血管造影鉴别良恶性疾病的价值

目的探讨肾上腺素药物血管造影鉴别良恶性病变的价值。材料与方法收集直径≤3cm富血管性病变17个,良性10个,恶性7个。通过DSA了解良恶性病变对肾上腺素局部动脉内注射的不同反应。结果良性病变对肾上腺素反应明显,病灶缩小＞50％;恶性病灶反应不明显,病灶缩小≤25％,并因周围正常血管的收缩而使病灶更清楚(P＜0．05)。结论本研究表明肾上腺素药物血管造影对鉴别良恶性病变较有价值,在使用时应注意肾上腺素的剂量、导管的位置和摄片方法。     

Diagnosis of soft-tissue masses with MR imaging: can benign masses be differentiated from malignant ones?

A blinded, retrospective review of 83 soft-tissue masses (49 benign and 34 malignant) was performed to evaluate the ability to distinguish benign from malignant soft-tissue masses with magnetic resonance (MR) imaging. The correct histologic diagnosis was reached in 31% of cases by one reader and in 16% of cases by the second reader. Mean sensitivity was 50% for benign masses and 80% for malignant masses. The majority of both benign and malignant masses had inhomogeneous signal intensity and at least partially irregular borders. Malignant masses uncommonly had smooth borders and homogeneous signal intensity. MR imaging can be used to evaluate the extent of soft-tissue masses, but most masses will require biopsy to determine if they are benign or malignant.     

肾皮质肿瘤:应用MR多期增强扫描鉴别良恶性肿瘤

摘要目的评价MR定量多期增强扫描在常见肾皮质良恶性肿瘤鉴别诊断中的应用。材料与方法本回顾性研究获伦理审查委员会批准无需病人知情同意,并符合HIPAA规定。2004年1月-2008年12月,     

MR imaging of the seminal vesicles--correlations between age, size and signal intensity]

MR findings of 82 cases without seminal vesicle lesions were retrospectively evaluated to assess the mutual correlations between age, size and signal intensity of the seminal vesicle. MR findings of 31 cases of carcinomatous invasion to the seminal vesicle were also evaluated as a control group for low signal intensity area-study. 1.5T superconductive unit (Toshiba 2000FX-II) was used to obtain T1-weighted (SE500/13) and T2-weighted (SE2500/80) images. Significant correlations were noted between age, short-axis diameter of the seminal vesicle and global signal intensity of the seminal vesicle on T2-weighted images. In the study of low signal intensity area on T2-weighted images, diffuse strand-like low signal intensity areas were occasionally seen in cases without seminal vesicle lesions. On the other hand, low signal intensity areas in cases of carcinomatous invasion to the seminal vesicle tended to be more localized and nodular. MR evaluation of cases without seminal vesicle lesions is important not only to delineate physiological changes of the seminal vesicle, but also to avoid erroneous MR-diagnosis of seminal vesicle lesions.     

Differentiation of malignant from benign soft tissue tumours: use of additive qualitative and quantitative diffusion-weighted MR imaging to standard MR imaging at 3.0 T

Objectives

To determine the added value of diffusion-weighted imaging (DWI) to standard magnetic resonance imaging (MRI) to differentiate malignant from benign soft tissue tumours at 3.0 T.

Methods

3.0 T MR images including DWI in 63 patients who underwent surgery for soft tissue tumours were retrospectively analyzed. Two readers independently interpreted MRI for the presence of malignancy in two steps: standard MRI alone, standard MRI and DWI with qualitative and quantitative analysis combined.

Results

There were 34 malignant and 29 non-malignant soft tissue tumours. In qualitative analysis, hyperintensity relative to skeletal muscle was more frequent in malignant than benign tumours on DWI (P=0.003). In quantitative analysis, ADCs of malignant tumours were significantly lower than those of non-malignant tumours (P≤0.002): 759±385 vs. 1188±423 μm²/sec minimum ADC value, 941±440 vs. 1310±440 μm²/sec average ADC value. The mean sensitivity, specificity and accuracy of both readers were 96 %, 72 %, and 85 % on standard MRI alone and 97 %, 90 %, and 94 % on standard MRI with DWI.

Conclusions

The addition of DWI to standard MRI improves the diagnostic accuracy for differentiation of malignant from benign soft tissue tumours at 3.0 T.

Key Points

? DWI has added value for differentiating malignant from benign soft tissue tumours. ? Addition of DWI to standard MRI at 3.0 T improves the diagnostic accuracy. ? Measurements of both ADC _min within solid portion and ADC _av are helpful.

     

Osseous change adjacent to soft-tissue hemangiomas of the extremities: correlation with lesion size and proximity to bone

OBJECTIVE: The purpose of this study was to describe the findings of MR imaging and radiographic changes that occur in osseous structures adjacent to soft-tissue hemangiomas of the extremities and to correlate them with patient symptomatology, the size of the hemangiomas, and their proximity to adjacent bone. MATERIALS AND METHODS: We retrospectively reviewed the radiographs and MR images of 35 patients with soft-tissue hemangiomas of the extremities. The pattern and extent of the osseous change were categorized as periosteal, cortical, or medullary. Symptomatology, size, and proximity of the hemangioma to the adjacent bone were compared with the presence or absence of osseous change. Statistical analysis was performed using the Student's t test. RESULTS: Osseous change was noted on radiographs in 13 (37%) of 35 patients and on MR images in 11 (31%) of 35 patients with a total of 14 patients (40%) showing osseous change on at least one study. Periosteal change was present in eight (23%) of 35 patients; cortical change, in 11 (31%) of 35 patients; and medullary change, in 10 (29%) of 35 patients. Direct contact between the soft-tissue hemangioma and the adjacent bone was seen in 13 of 14 patients with osseous change. In those patients without osseous change, the average distance between the soft-tissue hemangioma and bone was 1.06 cm (range, 0-4 cm). No correlation was found between symptoms and the presence of osseous change. CONCLUSION: Soft-tissue hemangiomas of the extremities frequently result in adjacent osseous change that can be categorized as either periosteal, cortical, or medullary. Only medullary changes correspond with hemangioma size, whereas all three categories of change correlate with the proximity of the hemangioma to the adjacent bone. The presence of osseous change does not correlate with patient symptomatology.     

Dynamic MRI of the gallbladder lesions: Differentiation of benign from malignant

Forty-nine pathologically proven gallbladder lesions were evaluated in 45 patients using dynamic MRI with a spoiled gradient pulse sequence (SPGR), to access the ability of this technique to differentiate benign from malignant gallbladder lesions. The studies were reviewed retrospectively. Signal intensity of the lesions were measured. Twenty-one malignant and 28 benign lesions were classified into three categories: polypoid, diffuse wall thickening, and exophytic. Early and delayed enhancement patterns were evaluated. For the polypoid masses, malignant lesions (n = 9) demonstrated early and prolonged enhancements, whereas benign lesions (n = 14) had early enhancement with subsequent washout (P < .05). For diffuse gallbladder wall thickening, malignant lesions (n = 6) demonstrated early and prolonged enhancement and benign lesions (n = 14) showed relatively slow, prolonged enhancement (P < .05). The exophytic masses (n = 6) all were malignant and demonstrated early and prolonged enhancement. Dynamic MRI can help differentiate benign from malignant gallbladder lesions.     

Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement

Purpose

To investigate methods developed for the characterisation of the morphology and enhancement kinetic features of both mass and non-mass lesions, and to determine their diagnostic performance to differentiate between malignant and benign lesions that present as mass versus non-mass types.

Methods

Quantitative analysis of morphological features and enhancement kinetic parameters of breast lesions were used to differentiate among four groups of lesions: 88 malignant (43 mass, 45 non-mass) and 28 benign (19 mass, 9 non-mass). The enhancement kinetics was measured and analysed to obtain transfer constant (K ^trans) and rate constant (k _ep). For each mass eight shape/margin parameters and 10 enhancement texture features were obtained. For the lesions presenting as nonmass-like enhancement, only the texture parameters were obtained. An artificial neural network (ANN) was used to build the diagnostic model.

Results

For lesions presenting as mass, the four selected morphological features could reach an area under the ROC curve (AUC) of 0.87 in differentiating between malignant and benign lesions. The kinetic parameter (k _ep) analysed from the hot spot of the tumour reached a comparable AUC of 0.88. The combined morphological and kinetic features improved the AUC to 0.93, with a sensitivity of 0.97 and a specificity of 0.80. For lesions presenting as non-mass-like enhancement, four texture features were selected by the ANN and achieved an AUC of 0.76. The kinetic parameter k _ep from the hot spot only achieved an AUC of 0.59, with a low added diagnostic value.

Conclusion

The results suggest that the quantitative diagnostic features can be used for developing automated breast CAD (computer-aided diagnosis) for mass lesions to achieve a high diagnostic performance, but more advanced algorithms are needed for diagnosis of lesions presenting as non-mass-like enhancement.