Dataset Information

Lipomatous Tumors: A Comparison of MRI-Reported Diagnosis with Histological Diagnosis.

ABSTRACT: Lipomatous tumors are among the most common soft tissue tumors (STTs). Magnetic resonance imaging (MRI) is a state-of-the-art diagnostic tool used to differentiate and characterize STTs. Radiological misjudgment can lead to incorrect treatment. This was a single-center retrospective study. Two hundred and forty lipomatous tumors were included. MRI diagnoses were categorized as benign, intermediate, or malignant and were compared with histological diagnoses. Tumor volumes were measured by MRI and from surgical specimens. The tumor was correctly categorized 73.3% of the time. A total of 21.7% of tumors were categorized as more malignant in MRI reports than they were by histology, and vice versa for 5.0% of tumors. Volume measured by MRI was not different from actual tumor size in pathology. Atypical lipomatous tumors (ALTs) and liposarcomas (LPSs) were larger when compared with lipomata and occurred in older patients. Based on the MRI-suspected tumor entity, surgical treatment can be planned. Large lipomatous tumors in elderly patients are more likely to be ALTs. However, a safe threshold size or volume for ALTs cannot be determined.

SUBMITTER: Ballhause TM

PROVIDER: S-EPMC9141562 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Lipomatous Tumors: A Comparison of MRI-Reported Diagnosis with Histological Diagnosis.

Ballhause Tobias M TM Korthaus Alexander A Jahnke Martin M Frosch Karl-Heinz KH Yamamura Jin J Dust Tobias T Schlickewei Carsten W CW Priemel Matthias H MH

Diagnostics (Basel, Switzerland) 20220521 5

Lipomatous tumors are among the most common soft tissue tumors (STTs). Magnetic resonance imaging (MRI) is a state-of-the-art diagnostic tool used to differentiate and characterize STTs. Radiological misjudgment can lead to incorrect treatment. This was a single-center retrospective study. Two hundred and forty lipomatous tumors were included. MRI diagnoses were categorized as benign, intermediate, or malignant and were compared with histological diagnoses. Tumor volumes were measured by MRI and ...[more]

PMID: 35626435

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Project description:BackgroundBoth meningiomas and schwannomas are the most common Meckel's cave (MC) tumors in terms of distinct imaging features. When they are small, they may present with similar imaging characteristics that make their diagnosis difficult. The aim of this study was to diagnose small meningiomas and schwannomas of the MC on the basis of their clinical and MRI findings.MethodsThe clinical data of 33 patients who were diagnosed with small MC tumors (SMCTs) (17 schwannomas, 16 meningiomas) between August 2002 and August 2023 were retrospectively evaluated. SMCTs were defined as MC tumors that were less than 3 cm in size. We analyzed their clinical and MRI findings, including demographic features, lesion morphologies and changes in adjacent structures.ResultsThe rate of subtotal resection of meningiomas less than 3 cm in size was significantly lower than that of schwannomas less than 3 cm in size (43.8% vs. 100%, p = 0.032). The MRI features of meningiomas and schwannomas were as follows: 1) a prominent dura tail sign (8/16 [50%] vs. 0/17 [0%], p < 0.001); 2) few cystic components (0/16 [0%] vs. 9/17 [52.94%], p < 0.001); 3) lower minimum ADC (ADCmin) values (820.575 ± 302.545 [86.1-1144.4] vs. 1372.424 ± 561.337 [355.7-2616.6], p < 0.001); and 4) minimal ipsilateral masticatory muscle atrophy (-6.71% ± 22.43% [-85.71% ~ 13.79%] vs. 11.24% ± 11.98% [-14% ~ 38%], p < 0.001). Very small MC tumors (VSMCTs) were ≤ 2 cm in size, and the subgroup analysis of very small meningiomas and schwannomas revealed no differences in terms of ipsilateral masticatory muscle atrophy (p = 0.078), prominence of the dural tail (p = 0.236), or the presence of cystic components (p = 0. 364). However, the ADCmin values were significantly lower for very small meningiomas than for very small schwannomas (p = 0.009).ConclusionMRI features such as a prominent dural tail appearance, the presence of fewer cystic components, and less masticatory muscle atrophy may aid in differentiating meningiomas from schwannomas less than 3 cm in size. The ADC and DWI parameters provided additional critical insights, particularly for VSMCTs, thus facilitating preoperative diagnoses.

Project description:BackgroundAs differentiating between lipomas and atypical lipomatous tumors (ALTs) based on imaging is challenging and requires biopsies, radiomics has been proposed to aid the diagnosis. This study aimed to externally and prospectively validate a radiomics model differentiating between lipomas and ALTs on MRI in three large, multi-center cohorts, and extend it with automatic and minimally interactive segmentation methods to increase clinical feasibility.MethodsThree study cohorts were formed, two for external validation containing data from medical centers in the United States (US) collected from 2008 until 2018 and the United Kingdom (UK) collected from 2011 until 2017, and one for prospective validation consisting of data collected from 2020 until 2021 in the Netherlands. Patient characteristics, MDM2 amplification status, and MRI scans were collected. An automatic segmentation method was developed to segment all tumors on T1-weighted MRI scans of the validation cohorts. Segmentations were subsequently quality scored. In case of insufficient quality, an interactive segmentation method was used. Radiomics performance was evaluated for all cohorts and compared to two radiologists.FindingsThe validation cohorts included 150 (54% ALT), 208 (37% ALT), and 86 patients (28% ALT) from the US, UK and NL. Of the 444 cases, 78% were automatically segmented. For 22%, interactive segmentation was necessary due to insufficient quality, with only 3% of all patients requiring manual adjustment. External validation resulted in an AUC of 0.74 (95% CI: 0.66, 0.82) in US data and 0.86 (0.80, 0.92) in UK data. Prospective validation resulted in an AUC of 0.89 (0.83, 0.96). The radiomics model performed similar to the two radiologists (US: 0.79 and 0.76, UK: 0.86 and 0.86, NL: 0.82 and 0.85).InterpretationThe radiomics model extended with automatic and minimally interactive segmentation methods accurately differentiated between lipomas and ALTs in two large, multi-center external cohorts, and in prospective validation, performing similar to expert radiologists, possibly limiting the need for invasive diagnostics.FundingHanarth fonds.

Project description:Background: Lung cancer is a complex disease composed of neuroendocrine (NE) and non-NE tumors. Accurate diagnosis of lung cancer is essential in guiding therapeutic management. Several transcriptional signatures have been reported to distinguish between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) belonging to non-NE tumors. This study aims to identify a transcriptional panel that could distinguish the histological subtypes of NE tumors to complement the morphology-based classification of an individual. Methods: A public dataset with NE subtypes, including 21 small-cell lung cancer (SCLC), 56 large-cell NE carcinomas (LCNECs), and 24 carcinoids (CARCIs), and non-NE subtypes, including 85 ADC and 61 SCC, was used as a training set. In the training set, consensus clustering was first used to filter out the samples whose expression patterns disagreed with their histological subtypes. Then, a rank-based method was proposed to develop a panel of transcriptional signatures for determining the NE subtype for an individual, based on the within-sample relative gene expression orderings of gene pairs. Twenty-three public datasets with a total of 3,454 samples, which were derived from fresh-frozen, formalin-fixed paraffin-embedded, biopsies, and single cells, were used for validation. Clinical feasibility was tested in 10 SCLC biopsy specimens collected from cancer hospitals via bronchoscopy. Results: The NEsubtype-panel was composed of three signatures that could distinguish NE from non-NE, CARCI from non-CARCI, and SCLC from LCNEC step by step and ultimately determine the histological subtype for each NE sample. The three signatures achieved high average concordance rates with 97.31%, 98.11%, and 90.63%, respectively, in the 23 public validation datasets. It is worth noting that the 10 clinic-derived SCLC samples diagnosed via immunohistochemical staining were also accurately predicted by the NEsubtype-panel. Furthermore, the subtype-specific gene expression patterns and survival analyses provided evidence for the rationality of the reclassification by the NEsubtype-panel. Conclusion: The rank-based NEsubtype-panel could accurately distinguish lung NE from non-NE tumors and determine NE subtypes even in clinically challenging samples (such as biopsy). The panel together with our previously reported signature (KRT5-AGR2) for SCC and ADC would be an auxiliary test for the histological diagnosis of lung cancer.

Project description:Suspicion of malignant change within a lipoma is a common and increasing workload within the UK Sarcoma multidisciplinary team (MDT) network, and a source of considerable patient anxiety. Currently, there is no lipoma-specific data, with regard to which clinical or radiographic features predict non-benign histology, or calculate an odds-ratio specific to a lipomatous lesion being non-benign. We performed a 9-year, double-blind, unmatched cohort study, comparing post-operative histology outcomes (benign versus non-benign) versus 15 signs across three domains: Clinical (size of tumour, depth, growth noticed by patient, previous lipoma, patient felt pain), Ultrasonographic (size, depth, vascularity, heterogenous features, septae) and MRI (size, depth, vascularity, heterogenous features, septae, complete fat signal suppression). Receiver operating characteristic (ROC) analysis, odds ratios and binary logistic regression analysis was performed double-blind. When each sign is considered independently, (ROC analysis, followed by binary logistic regression) only Ultrasound depth is a significant predictor (p = 0.044) of a histologically non-benign lipoma. Ultrasonographically determined vascularity and septation were not statistically significant predictors. None of the clinical signs were statistically significant (p > 0.05). Of the MRI signs none was statistically significant (p > 0.05). However, heterogeneous MRI features fared better than MRI depth. Ultrasound signs (Pseudo R-Square = 0.105) are more predictive of the post-operation histology outcome than Clinical signs (Pseudo R-Square = 0.082) or MRI tests (Pseudo R-Square = 0.052) Ultrasound and Clinical tests combined (Pseudo R-Square = 0.147) are more predictive of the post-operation histology outcome than MRI tests (Pseudo R-Square = 0.052). This work challenges the traditional perception of "red-flag" signs when applied to lipomatous tumours. We provide accurate data upon which an informed choice can be made, and provides a robust bases for expedited risk/benefit. The importance of an experienced and cohesive MDT network is emphasised.

Dataset Information

Lipomatous Tumors: A Comparison of MRI-Reported Diagnosis with Histological Diagnosis.

Publications

Lipomatous Tumors: A Comparison of MRI-Reported Diagnosis with Histological Diagnosis.

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