Focal splenic lesions: Imaging spectrum of diseases on CT, MRI and PET/CT

Diagnostic ability of [18F]FDG PET/CT for distinguishing benign from malignant spleen lesions

OBJECTIVES

[18F]Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is a non-invasive imaging modality used in the differential diagnosis of splenic lesions, although ideal parameters and thresholds remain unclear. The present study evaluated the ability of [18F]FDG PET/CT, including its visual and quantitative parameters, to differentiate between benign and malignant splenic lesions.

METHODS

Patients who underwent [18F]FDG PET/CT following the detection of splenic lesions on contrast-enhanced CT were retrospectively analysed. Visual parameters assessed on [18F]FDG PET/CT included whole spleen uptake intensity, lesion multiplicity, and lesion uptake, and quantitative parameters included maximum standardised uptake value (SUVmax), lesion-to-background ratio (LBR), metabolic tumour volume (MTV), total lesion glycolysis (TLG), and lesion size. Parameters differentiating between benign and malignant lesions were evaluated by Pearson's chi-square test, Mann-Whitney U-test, and receiver operating characteristics (ROC) curve analysis.

RESULTS

Splenic lesion uptake (p = 0.001) was the only visual parameter significantly distinguishing between benign and malignant lesions. ROC curve analysis demonstrated that SUVmax had the largest area under the ROC, 0.91 (p < 0.001), with an optimal cut-off > 5.3 having a sensitivity of 90.3% and a specificity of 80.6%. Subgroup analysis of malignant lesions showed that SUVmax (p = 0.013), LBR (p = 0.012), and TLG (p  = 0.034) were significantly higher in splenic lymphomas than in splenic metastases.

CONCLUSION

Of the [18F]FDG PET/CT parameters investigated, SUVmax had the highest accuracy in diagnosing malignant splenic lesions and was significantly higher in splenic lymphomas than in splenic metastases. Visual determination of [18F]FDG uptake by splenic lesions may be an easily evaluated parameter.

CLINICAL RELEVANCE STATEMENT

SUVmax and visual grade of [18F]FDG PET/CT help to differentiate spleen lesions. [18F]FDG PET/CT is useful for discriminating between benign and malignant spleen lesions.

KEY POINTS

Many splenic lesions are difficult to diagnose on anatomical imaging, with histopathologic analyses are required. SUVmax of PET/CT provided the diagnostic ability to differentiate between benign and malignant splenic lesions. More than normal spleen uptake can be a convenient parameter to diagnose malignant spleen lesions.

Differentiating malignant from benign splenic lesions: a meta-analysis and pictorial review of imaging features

OBJECTIVES

Splenic lesions might exhibit overlapping imaging features, varying from benign entities like cysts and hemangiomas to malignancies such as lymphoma and angiosarcoma. This meta-analysis aims to delineate imaging characteristics that distinguish malignant from benign splenic lesions.

METHODS

Adhering to PRISMA guidelines, we searched PubMed, Scopus, and Web of Science for studies on imaging features differentiating malignant from benign splenic lesions. We extracted data on splenic pathology and imaging characteristics and assessed the methodological quality via QUADAS-2. Odds ratio meta-analyses were performed using STATA (Version 17.0, Stata Corp, College Station, TX).

RESULTS

Portal phase hypoenhancement, hypovascular enhancement pattern, diffusion restriction, and late phase hypoenhancement, with odds ratios above 10, highly indicate malignancy. Other features suggestive of malignancy include solid morphology, lymphadenopathy, presence of perisplenic fluid, arterial hypoenhancement, hypoechogenicity on ultrasound, splenomegaly, and presence of multiple lesions. In contrast, cystic morphology, hypervascular-washout and hypervascular-persistent pattern of enhancement, late phase hyperenhancement, anechogenicity on ultrasound, portal phase hyperenhancement, well-defined borders, and calcification are in favour of benign pathology.

CONCLUSION

The study underscores the critical role of contrast-enhanced and diffusion-weighted imaging in distinguishing malignant from benign splenic lesions, emphasizing the role of features like portal phase hypoenhancement and restricted diffusion in diagnosing malignancies. Additionally, the study emphasizes the value of contrast-enhanced ultrasound, which allows for the visualization of key contrast-enhancement patterns without the risk of ionizing radiation exposure.

Primary splenic angiosarcoma: a case series of a rare oncological entity and diagnostic challenge

BACKGROUND AND PURPOSE

Primary angiosarcoma of the spleen (PAS), an exceptionally rare and aggressive neoplasm with high metastatic risk (70%-85%), is frequently diagnosed in an advanced or metastatic stage. It presents diagnostic challenges due to its nonspecific symptomatology and resemblance to benign vascular lesions in various imaging modalities.

PATIENTS AND METHODS

This case series aims to clarify the diagnostic difficulties by comparing imaging characteristics (CT-scan, MRI, and [18F]FDG-PET/CT) as well as pathological findings of three PAS cases diagnosed in different stages of the diseases (localized, metastatic, and metastatic with organ failure). Furthermore, a brief review on diagnostic and therapeutic features is included.

RESULTS AND INTERPRETATION

We suggest [18F]FDG-PET/CT as a differentiating tool between benign and malignant splenic lesions and propose a flowchart of a diagnostic algorithm for PAS. For treatment, we advocate for early splenectomy and when systemic therapy is warranted, paclitaxel emerges as a viable first-line option. While it is crucial to acknowledge that further trial data is required to evaluate the efficacy of emerging treatment regimens, designing and conducting trials for PAS is challenging given its scarcity and aggressive behavior. Therefore case reporting remains important.

CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence

Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.

Evaluation and Classification of Incidentally Detected Splenic Lesions Based on B-Mode and Contrast-Enhanced Ultrasound

PURPOSE

To evaluate B-mode ultrasound (B-US) and contrast-enhanced ultrasound (CEUS) patterns of focal splenic incidentalomas (FSIs), and to correlate ultrasound patterns with benignity and malignancy via histologic examination and/or the clinical course.

MATERIALS AND METHODS

Between 2004 and 2021, 139 consecutive patients with an FSI detected by B-US were investigated additionally with CEUS. On CEUS, the arterial enhancement (AE) of the FSI (hyperenhancement, isoenhancement, hypoenhancement, and absent enhancement) was analyzed. Subsequently, the malignancy rate according to different B-US echo patterns and CEUS perfusion patterns was determined.

RESULTS

The final diagnosis of FSI was malignant in 9/139 (6.5%) and benign in 130/139 (93.5%) cases. The hypoechoic and hyperechoic lesions on B-US with arterial hyperenhancement on CEUS and the echogenic cystic or complex lesions on B-US with predominantly absent enhancement on CEUS were benign in 54/54 (100%) cases. 6/37 (16.2%) hypoechoic lesions on B-US with arterial hypo-/isoenhancement on CEUS and 3/48 (6.3%) of hyperechoic lesions on B-US with an arterial hypo-/isoenhancement on CEUS were malignant.

CONCLUSION

Based on these results, FSIs reveal different malignancy rates depending on the B-US und CEUS patterns, and classification according to these B-US and CEUS patterns may be helpful in further evaluation of an FSI.

A case of symptomatic intramesenteric accessory spleen: The diagnostic role of contrast-enhanced ultrasound

Introduction

Accessory spleen is a congenital defect characterised by a separated ectopic splenic parenchyma usually located in the splenic hilum and the tail of the pancreas. It is present in about 10%-30% of the population and, generally, does not cause any symptoms.

Case report

We report an interesting case of a woman with symptomatic intramesenteric accessory spleen detected and characterised by contrast-enhanced ultrasound. The patient experienced a long history of intermittent pain in the left upper abdomen. The diagnosis was confirmed by post-operative pathology examination.

Discussion

Accessory spleen usually appears as a well-circumscribed ovoid mass, 1-3 cm in diameter, infrequently located in the mesentery. It may rarely become symptomatic because of complications. Diagnosis of this condition as a cause of abdominal is difficult and rarely has been made pre-operatively. Computed tomography and magnetic resonance imaging might help, but they should be performed with intravenous contrast injection, and they cannot provide direct evidence between the pain of the patient and the lesion. Conversely, real-time ultrasound can assess and diagnose the lesion showing the exact correspondence with abdominal pain of the patient. Furthermore, ultrasound and contrast-enhanced ultrasound are widely available, safe and relatively inexpensive.

Conclusion

Apart from the rarity of this condition, this case report demonstrates the ability of ultrasound to localise the intramesenteric accessory spleen, assess the relationship between the lesion and the symptoms of the patient, and characterise the lesion.

Acoustic Radiation Force Impulse (ARFI) Elastography of Focal Splenic Lesions: Feasibility and Diagnostic Potential

PURPOSE

Nontraumatic focal splenic lesions (FSL) are rare, and the need for tissue diagnosis must be weighed against the very high risk of bleeding after a splenic biopsy. The aim of this study was to explore the feasibility and diagnostic potential of acoustic radiation force impulse (ARFI) elastography as a noninvasive method for different benign and malignant FSLs. No human studies on the elastographic characteristics of FSL exist.

METHODS

This was a retrospective analysis of 34 patients with FSLs, who underwent abdominal B-mode ultrasound (B-US), contrast-enhanced ultrasound (CEUS), and standardized ARFI examinations between October 2021 and December 2022 at our university hospital. The inclusion criteria were: (i) FSL size ≥ 1 cm; (ii) 10 valid ARFI measurements of the FSL, as well as of the normal splenic parenchyma (NSP) as an in vivo reference; and (iii) diagnostic confirmation of FSL etiology based on histological examination (8/34; 23.5%) or clinical evaluation, which included a clinical and sonographic follow-up (FU), CEUS morphology, and/or morphology on cross-sectional imaging (26/34; 76.5%). CEUS was performed on all patients and the FSLs were classified according to the current guidelines; cross-sectional imaging was available for 29/34 (85.3%). The mean FU duration was 25.8 ± 30.5 months. The mean ARFI velocity (MAV) of the FSL (MAVL), the NSP (MAVP), and the ratio of the MAVL to the MAVP (MAVL/P) were calculated and compared.

RESULTS

Of the 34 FSLs, 13 (38.2%) were malignant (mFSL) and 21 (61.8%) were benign (bFSL). The MAVL of all 34 FSLs (2.74 ± 0.71 m/s) was lower than the MAVP (3.20 ± 0.59 m/s), p = 0.009, with a mean MAVL/P ratio of 0.90 ± 0.34. No significant differences in the MAVL were observed between the mFSL (2.66 ± 0.67 m/s) and bFSL (2.79 ± 0.75 m/s). There were also no significant differences between the MAVP in patients with mFSL (3.24 ± 0.68 m/s) as compared to that in the patients with bFSL (3.18 ± 0.55 m/s). Likewise, the MAV L/P ratio did not differ between the mFSL (0.90 ± 0.41 m/s) and bFSL (0.90 ± 0.30 m/s) groups.

CONCLUSION

ARFI elastography is feasible in evaluating the stiffness of FSLs. The lesions' stiffness was lower than that of the NSP, regardless of the FSL etiology. However, differentiation between benign and malignant FSL with the help of this elastographic method does not appear possible. Larger prospective studies are needed to validate these findings.

Incidental Focal Spleen Lesions: Integrated Imaging and Pattern Recognition Approach to the Differential Diagnosis

Spleen lesions and pseudolesions, detected incidentally in imaging, are not uncommon and may require further work-up. The imaging appearance of focal splenic lesions (FSLs) may not be pathognomonic, because of considerably overlapping features. Consequently, all imaging techniques lack specificity to fully characterize FSLs. Clinical correlation is mandatory, so as, first of all, to categorize the patient as having or not having a history of solid or hematologic malignancy. Nowadays, many patients have old imaging studies available for comparison and, consequently, it is important to understand if the lesion was previously present or not, and if the size is the same or has changed. In the absence of comparison studies, and with a lack of imaging features of benignity, further investigation may be necessary, using PET, biopsy, or short-term follow-up. Some algorithms have been proposed to manage incidental FSLs; however, none of these strategies has been validated by prospective studies to date. In this review we illustrate the topic of incidental FSLs and we analyze a number of published algorithms.

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features

The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen.

Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation

Simple Summary

In malignant lymphoma an early and accurate diagnosis is essential for therapy initiation and patient outcome. Within the diagnostic process, imaging plays a crucial role in disease staging. However, an invasive biopsy is required for subtype classification. Involvement of the spleen, a major lymphoid organ, is frequent in malignant lymphoma; this may be reactive or due to infiltration by malignant cells. Using radiomics features of the spleen in a machine learning approach, we investigated the possibility of distinguishing malignant lymphoma patients from other cancer patients and to classify lymphoma subtypes in the case of disease presence. Recent studies have proven the value of radiomics analysis in differentiating lymphoma from non-lymphoma groups on involved sites. Supported by machine learning, imaging could gain importance as a noninvasive diagnostic tool for future lymphoma classification, offering more precise radiological information for an interdisciplinary approach regarding treatment planning.

Abstract

The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence—in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/− 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 (p < 0.01); HL vs. NHL AUC = 0.75 (p < 0.01); DLBCL vs. other NHL AUC = 0.65 (p < 0.01); MCL vs. FL AUC = 0.67 (p < 0.01). Classifying malignant lymphoma vs. non-lymphoma was also possible using only shape features AUC = 0.77 (p < 0.01), with the most important feature being sphericity. Based on only shape features, a significant AUC could be achieved for all tasks, however, best results were achieved combining shape and textural features. This study demonstrates the value of splenic imaging and radiomic analysis in the diagnostic process in malignant lymphoma detection and subtype classification.