Image-Guided Core Needle Biopsy of Adipocytic Tumors: Diagnostic Accuracy and Concordance With Final Surgical Pathology

A national survey of the current management of nonmalignant lipomatous tumours and a proposal for unified UK guidelines for practice

Although there exists clear guidance to determine which lipomatous lesions should be referred to a sarcoma multidisciplinary team (MDT) for discussion, subsequent guidelines for management advice are only focused on malignant lesions. This study aimed to address the management of benign and intermediate lipomatous lesions with reference to the available literature, precedent from 15 of the 17 UK sarcoma MDTs, and local experience. Fifteen mock cases were presented to each MDT to determine local management pathways. This revealed significant heterogeneity within the management of these benign and intermediate lipomatous lesions across the UK. After combining this data with the available evidence in the literature, we developed recommendations for management of these lesions with the aim to reduce national variability. We propose the first set of national management guidelines specific to benign lipomatous lesions, which stratifies resources according to lipomatous tumour type.

Minimally invasive thermal ablation techniques for neck lipomas: RFA vs. MWA

Lipomas, common benign tumors, may cause discomfort and cosmetic concerns. Radiofrequency ablation (RFA) and microwave ablation (MWA) is a minimally invasive treatment gaining popularity. This report examines the efficacy of RFA and MWA in treating neck lipomas. Two cases of RFA and MWA for neck lipomas were evaluated over 6 months. RFA achieved 53% volume reduction with partial ablation, while MWA resulted in remarkable reductions (81%, 86%, and 90% at 1, 3, and 6 months). Thermal ablation techniques are possibly used for managing lipomas. MWA is a more effective and safer minimally invasive option for treating neck lipomas compared to RFA, offering significant volume reduction and potential for broader clinical application.

Characterizing the Transformation and Diagnosis of Atypical Lipomatous Tumor to Dedifferentiated Liposarcoma: Single Institutional Outcomes

INTRODUCTION

Atypical lipomatous tumor (ALT) in the extremities is a locally aggressive adipocytic tumor with the potential risk of transformation into dedifferentiated liposarcoma (DDLS). Studies seldom differentiate whether DDLS was diagnosed on initial biopsy, final resected specimen, or subsequent recurrence. Our study seeks to characterize how and when patients received their ALT or DDLS diagnoses to better understand the relationship between the two neoplasms.

METHODS

We performed a retrospective review of patients diagnosed with ALT or DDLS of the extremities. Clinical characteristics, including the method of diagnosis of an ALT or DDLS, time between diagnoses, and tumor recurrence was recorded. Univariate/multivariate analysis was performed to identify risk factors.

RESULTS

Forty-five patients were diagnosed with ALT after core needle biopsy (CNB) and 41 of them received marginal en bloc excision. Three (7.3%) of these patients had a heterogeneous tumor on final resection, pathology revealed both ALT and DDLS. Four patients (8.2%) were diagnosed with DDLS from CNB and received negative margin en bloc excision. One of these tumors was identified as heterogeneous ALT/DDLS after resection. Fifty-three patients received marginal en bloc resection without CNB after a benign lipomatous mass was suspected on CT/MRI. Among these, one (1.9%) had a tumor with a heterogeneous composition of both ALT and DDLS on pathology. There were 11 (11.7%) ALT recurrences and 1 (1.0%) DDLS recurrence after ALT resection.

CONCLUSION

Obtaining a proper diagnosis whether ALT or DDLS is critical. Our cohort found that amongst those concerning lipomatous lesions biopsied, 7.84% will show biopsy proven DDLS. Additionally, 6.67% of the biopsies will be false negatives and show DDLS on final pathology. Furthermore, our local recurrence for ALT was 11.7% recurring as ALT and 1.0% recurring as DDLS.

Diagnostic and Predictive Immunocytochemistry in Head and Neck Lesions

BACKGROUND

The application of immunocytochemistry (ICC) as a diagnostic and predictive tool in the workup of head and neck lesions has followed the changes and progresses in the surgical pathology evaluation. The contribution of ICC has shown a significant role in head and neck cytology, demonstrating as its contribution can support the diagnosis of many lesions. Furthermore, its role has been evolving as an important adjuvant tool in targeted therapies. An additional useful role is defined by the recent introduction of ICC markers related to genetic alterations, which has opened the door to the adoption of a surrogate for molecular evaluation also on cytological material.

SUMMARY

The current review article analyzes the role of ICC in the field of head and neck cytology, showing that it might represent a valid diagnostic tool in difficult cases. The review will include all the different head and neck lesions, demonstrating how we could rely on organ-specific ICC markers but also on ICC markers able to discriminate between benign and malignant lesions.

KEY MESSAGES

The role of ICC represents a valid additional tool in the management of several difficult lesions, especially when morphology alone is not able to make a conclusive diagnosis. The support of ICC is likely to support the morphological findings leading to the definition of the diagnosis and the most appropriate management.

Integrated lipidomics and RNA-seq reveal prognostic biomarkers in well-differentiated and dedifferentiated retroperitoneal liposarcoma

BACKGROUND

Retroperitoneal liposarcoma (RLPS) is a mesenchymal malignant tumor characterized by different degrees of adipocytic differentiation. Well-differentiated liposarcoma (WDLPS) and dedifferentiated liposarcoma (DDLPS) are two of the most common subtypes of RLPS, exhibiting clear differences in biological behaviors and clinical prognosis. The metabolic features and genomic characteristics remain unclear.

METHODS

This study employed lipidomic and RNA-seq analyses of RLPS tissues from 19 WDLPS and 29 DDLPS patients. Western blot and immunohistochemistry staining were performed to verify the tumor tissue protein levels of TIMP1, FN1, MMP11, GPNMB, and ECM1. Enzyme-linked immunosorbent assay (ELISA) was performed to evaluate different serum protein levels in 128 blood samples from patients with RLPS. Multivariate analysis was performed to identify the most crucial variables associated with overall survival (OS) and recurrence-free survival (RFS) of the RLPS patients.

RESULTS

Lipidomic analysis revealed a significant difference in lipid metabolism, particularly in phosphatidylcholines and triacylglycerides metabolism. RNA sequencing analysis revealed that 1,630 differentially expressed genes (DEGs) were significantly enriched in lipid metabolism, developmental process, and extracellular matrix (ECM) pathways. Integrated lipidomic and transcriptomic analysis identified 29 genes as potential biomarkers between WDLPS and DDLPS. Among the 29 DEGs, we found that TIMP1, FN1, MMP11, GPNMB, and ECM1 were increased in DDLPS tumor tissues than in WDLPS tumor tissues. The receiver operating characteristic (ROC) curve showed high specificity and sensitivity in diagnosing patients using a five-gene combination (AUC = 0.904). ELISA revealed a significant increase in the serum levels of ECM1 and GPNMB in patients with DDLPS compared to patients with WDLPS. ECM1 increased progressively across different FNCLCC Grades, correlating negatively with RFS (P = 0.043). GPNMB levels showed a negative correlation with OS (P = 0.019).

CONCLUSIONS

Our study reveals different lipid metabolism, several transcriptional pathways between WDLPS and DDLPS, and examines several serum markers associated with the prognosis of RLPS. These findings provide a vital basis for future endeavors in diagnosing and predicting the prognosis of retroperitoneal liposarcoma with different differentiations.

[Translated article] Comparative study of deep lipomas and atypical lipomatous tumours: Malignancy risk factors

BACKGROUND AND OBJECTIVES

The diagnostic suspicion of an atypical lipomatous tumour (ALT) is difficult. The aim of this study is to delve into the most controversial diagnostic aspects of the subject.

MATERIAL AND METHOD

Observational, longitudinal and retrospective study of a series of 96 deep adipose tumours (75 lipomas and 21 TLA) from 2006 to 2016: demographic, clinical, imaging and pathological variables were analysed and compared, as well as other variables related to treatment and oncological outcomes of the patients. A descriptive analysis of the collected variables was performed for the statistical study. To evaluate the potential predictor variables of malignancy, a multivariate logistic regression analysis was performed, including those that were statistically significant in the univariate analysis.

RESULTS

Older age at diagnosis, lower limb location and larger size were significantly more frequent in ALTs. MRI findings showed no statistically significant differences between the two groups. In multivariate analysis, the same clinical variables were confirmed as predictors of malignancy. In the ROC curve, an optimal cut-off point of 134.0mm was used as a predictor of malignancy.

CONCLUSIONS

Advanced age, location in the lower limbs and larger size are risk factors for malignancy in the differential diagnosis of deep lipomas and atypical lipomatous tumours. No radiological variable on MRI reached significance as a predictor of malignancy in our series.

Comparative study of deep lipomas and atypical lipomatous tumours: Malignancy risk factors

BACKGROUND AND OBJECTIVES

The diagnostic suspicion of an atypical lipomatous tumour (ALT) is difficult. The aim of this study is to delve into the most controversial diagnostic aspects of the subject.

MATERIAL AND METHOD

Observational, longitudinal, and retrospective study of a series of 96 deep adipose tumours (75 lipomas and 21 TLA) from 2006 to 2016: demographic, clinical, imaging and pathological variables were analysed and compared, as well as other variables related to treatment and oncological outcomes of the patients. A descriptive analysis of the collected variables was performed for the statistical study. To evaluate the potential predictor variables of malignancy, a multivariate logistic regression analysis was performed, including those that were statistically significant in the univariate analysis.

RESULTS

Older age at diagnosis, lower limb location and larger size were significantly more frequent in ALTs. MRI findings showed no statistically significant differences between the two groups. In multivariate analysis, the same clinical variables were confirmed as predictors of malignancy. In the ROC curve, an optimal cut-off point of 134.0 mm was used as a predictor of malignancy.

CONCLUSIONS

Advanced age, location in the lower limbs and larger size are risk factors for malignancy in the differential diagnosis of deep lipomas and atypical lipomatous tumours. No radiological variable on MRI reached significance as a predictor of malignancy in our series.

Routine radiology-pathology concordance evaluation of CT-guided percutaneous lung biopsies increases the number of cancers identified

BACKGROUND

Routine concordance evaluation between pathology and imaging findings was introduced for CT-guided biopsies.

PURPOSE

To analyze malignancy rate in concordant, discordant, and indeterminate non-malignant results of CT-guided lung biopsies.

METHODS

Concordance between pathology results and imaging findings of consecutive patients undergoing CT-guided lung biopsy between 7/1/2016 and 9/30/2021 was assessed during routine meetings by procedural radiologists. Concordant was defined as pathology consistent with imaging findings; discordant was used when pathology could not explain imaging findings; indeterminate when pathology could explain imaging findings but there was concern for malignancy. Recommendations for discordant and indeterminate were provided. All the malignant results were concordant. Pathology of repeated biopsy, surgical sample, or follow-up was considered reference standard.

RESULTS

Consecutive 828 CT-guided lung biopsies were performed on 795 patients (median age 70 years, IQR 61-77), 423/828 (51%) women. On pathology, 224/828 (27%) were non-malignant. Among the non-malignant, radiology-pathology concordance determined 138/224 (62%) to be concordant with imaging findings, 54/224 (24%) discordant, and 32/224 (14%) indeterminate. When compared to the reference standard, 33/54 (61%) discordant results, 6/30 (20%) indeterminate, and 3/133 (2%) concordant were malignant. The prevalence of malignancy in the three groups was significantly different (p < 0.001). Time to diagnosis was significantly different between patients who reached the diagnosis with imaging follow-up (median 114 days, IQR 69-206) compared to repeat biopsy (33 days, IQR 18-133) (p = 0.01).

CONCLUSION

Routine radiology-pathology concordance evaluation of CT-guided lung biopsy correctly identifies patients at high risk for missed diagnosis of malignancy. Repeat biopsy is the fastest method to reach diagnosis.

CLINICAL RELEVANCE STATEMENT

A routine radiology-pathology concordance assessment identifies patients with non-malignant CT-guided lung biopsy result who are at greater risk of missed diagnosis of malignancy.

KEY POINTS

• A routine radiology-pathology concordance evaluation of CT-guided lung biopsies classified 224 non-malignant results as concordant, discordant, or indeterminate. • The percentage of malignancy on follow-up was significantly different in concordant (2%), discordant (61%), and indeterminate (20%) (p < 0.001). • Time to definitive diagnosis was significantly shorter with repeat biopsy (33 days), compared to imaging follow-up (114 days), p = 0.01.

Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis

OBJECTIVES

This systematic review and meta-analysis evaluated the diagnostic accuracy of CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from benign lipomatous lesions.

METHODS

MEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to January 2022 were systematically evaluated. Original studies with > 5 patients evaluating the accuracy of CT and/or MRI for detecting liposarcomas with a histopathological reference standard were included. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). This study is registered on PROSPERO, number CRD42022306479.

RESULTS

Twenty-six studies with a total of 2613 patients were included. Mean/median reported patient ages ranged between 50 and 63 years. The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. Deep depth to fascia, thickened septations, enhancing components, and lesion size (≥ 10 cm) all demonstrated sensitivities ≥ 85%. Other imaging characteristics including heterogenous/amorphous signal intensity, irregular tumor margin, and nodules present demonstrated lower sensitivities ranging from 43 to 65%. Inter-reader reliability for radiologist gestalt within studies ranged from fair to substantial (k = 0.23-0.7). Risk of bias was predominantly mixed for patient selection, low for index test and reference standard, and unclear for flow and timing.

CONCLUSION

Higher sensitivities for detecting liposarcomas were achieved with radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size. Combined clinical and imaging scoring and/or radiomics both show promise for optimal performance, though require further analysis with prospective study designs.

CLINICAL RELEVANCE

This pooled analysis evaluates the accuracy of CT and MRI for detecting atypical lipomatous tumors and malignant liposarcomas. Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size demonstrate the highest overall sensitivities.

KEY POINTS

• The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. • Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large tumor size (≥ 10 cm) showed the highest sensitivities for detecting atypical lipomatous tumors/well-differentiated liposarcomas and malignant liposarcomas. • A combined clinical and imaging scoring system and/or radiomics is likely to provide the best overall diagnostic accuracy, although currently proposed scoring systems and radiomic feature analysis require further study with prospective study designs.

MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities

PURPOSE

To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities.

MATERIAL AND METHODS

This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort.

RESULTS

Eight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474).

CONCLUSION

MRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.

Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

Background

To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists.

Methods

The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test.

Results

There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05).

Conclusions

The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-023-03718-4.

Analysis on diagnostic failure of US-guided core needle biopsy for soft tissue tumors

Purpose

To evaluate the diagnostic yield of ultrasonography (US)-guided core needle biopsy (CNB) in the diagnosis of soft tissue tumors (STTs) and to analyze the failure factors.

Methods

139 patients with STTs that underwent both US-guided CNB and surgical resection were collected retrospectively. Compared with the histopathological results of surgical resection, the biopsy failure was defined as the following conditions: indefinitive diagnosis, including insufficient samples and unknown subtypes with correct biological potential classification; wrong diagnosis, including wrong biological potential classification and wrong subtypes with correct biological potential classification. Univariate and multivariate analyses from the perspectives of histopathological, demographic and US features together with biopsy procedures were performed to determine risk factors for diagnostic failure.

Results

The diagnostic yield of US-guided CNB for STTs in our study was 78.4%, but when only considering the correct biological potential classification of STTs, the diagnostic yield was 80.6%. The multivariate analysis showed that adipocytic tumors (odds ratio (OR) = 10.195, 95% confidence interval (CI): 1.062 - 97.861, p = 0.044), vascular tumors (OR = 41.710, 95% CI: 3.126 - 556.581, p = 0.005) and indeterminate US diagnosis (OR = 8.641, 95% CI: 1.852 - 40.303, p = 0.006) were correlated with the diagnostic failure. The grade III vascular density (OR = 0.019, 95% CI: 0.001 - 0.273, p = 0.007) enabled a higher diagnostic accuracy.

Conclusion

US-guided CNB can be an effective modality for the diagnosis of STTs. The diagnostic yield can be increased when the tumor vascular density was grade III in Color Doppler US, but can be decreased in adipocytic tumors, vascular tumors and masses with indeterminate US diagnosis.

Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors

This retrospective study aimed to compare the intra- and inter-observer manual-segmentation variability in the feature reproducibility between two-dimensional (2D) and three-dimensional (3D) magnetic-resonance imaging (MRI)-based radiomic features. The study included patients with lipomatous soft-tissue tumors that were diagnosed with histopathology and underwent MRI scans. Tumor segmentation based on the 2D and 3D MRI images was performed by two observers to assess the intra- and inter-observer variability. In both the 2D and the 3D segmentations, the radiomic features were extracted from the normalized images. Regarding the stability of the features, the intraclass correlation coefficient (ICC) was used to evaluate the intra- and inter-observer segmentation variability. Features with ICC > 0.75 were considered reproducible. The degree of feature robustness was classified as low, moderate, or high. Additionally, we compared the efficacy of 2D and 3D contour-focused segmentation in terms of the effects of the stable feature rate, sensitivity, specificity, and diagnostic accuracy of machine learning on the reproducible features. In total, 93 and 107 features were extracted from the 2D and 3D images, respectively. Only 35 features from the 2D images and 63 features from the 3D images were reproducible. The stable feature rate for the 3D segmentation was more significant than for the 2D segmentation (58.9% vs. 37.6%, p = 0.002). The majority of the features for the 3D segmentation had moderate-to-high robustness, while 40.9% of the features for the 2D segmentation had low robustness. The diagnostic accuracy of the machine-learning model for the 2D segmentation was close to that for the 3D segmentation (88% vs. 90%). In both the 2D and the 3D segmentation, the specificity values were equal to 100%. However, the sensitivity for the 2D segmentation was lower than for the 3D segmentation (75% vs. 83%). For the 2D + 3D radiomic features, the model achieved a diagnostic accuracy of 87% (sensitivity, 100%, and specificity, 80%). Both 2D and 3D MRI-based radiomic features of lipomatous soft-tissue tumors are reproducible. With a higher stable feature rate, 3D contour-focused segmentation should be selected for the feature-extraction process.

A Large Intraoral Lipoma: Case Report

Lipomas of the oral cavity are uncommon. Here we report a case of 85 year old female presenting with a progressively increasing large growth in the oropharynx which was diagnosed as lipoma on histopathology. The clinicoradiological and histopathological findings are discussed. To the best of our knowledge; this is one of the largest intraoral lipoma reported in India till date. The present case highlights the need to be aware of intraoral lipomas which can present as large growths at this unusual site so as to avoid any unwarranted aggressive surgery.