Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis

Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance.

METHODS

In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test.

RESULTS

Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment.

CONCLUSION

Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials.

CLINICAL RELEVANCE STATEMENT

Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.

AI applications in musculoskeletal imaging: a narrative review

This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.

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.

Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives

This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.

Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases.

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.

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

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.

Bone and soft tissue tumors at the borderlands of malignancy

This review examines findings of musculoskeletal neoplasms whose equivocal imaging and/or histopathologic features make it difficult to determine if they will show aggressive behavior. We include both intermediate tumors as defined by the World Health Organization (WHO), and a single low-grade malignancy, low-grade central osteosarcoma, which mimics a benign lesion on imaging and histology. Intermediate tumors are a broad category and are subdivided into tumors that have risk of local recurrence only, and ones that have a risk of distant limb and pulmonary metastases. Difficult intermediate musculoskeletal lesions include atypical cartilaginous tumor/grade 1 chondrosarcoma, atypical lipomatous tumor/grade 1 liposarcoma, and solitary fibrous tumor. We review diagnostic criteria, differential diagnosis, and recommendations for surveillance.

Differences in MRI findings of superficial spindle cell lipoma and atypical lipomatous tumor/well-differentiated liposarcoma

OBJECTIVE

This study aimed to evaluate the efficacy of using MRI findings to differentiate superficial spindle cell lipomas (SCLs) from atypical lipomatous tumor/well-differentiated liposarcomas (ALT/WDLs).

METHODS

This study included 12 patients with histopathologically proven superficial SCLs and 11 with ALT/WDLs. MRI findings for both pathologies were retrospectively reviewed and compared between the two pathologies.

RESULTS

The neck, upper back, and shoulder regions were more frequent locations of SCLs than of ALT/WDLs (100% vs 55%, p < 0.05), whereas no significant differences were observed in age and sex. The median maximum diameter of the lesion was smaller in SCLs than in ALT/WDLs (44 mm [interquartile range (IQR): 35-63] vs 102 mm [IQR: 86-119], p < 0.05). On T 1 weighted images, non-fatty area was more frequently observed in SCLs than in ALT/WDLs (73% vs 25%, p < 0.05), and the median rate of non-fatty area was larger in SCLs than in ALT/WDLs (7.5% [IQR: 1.0-53] vs 0% [IQR: 0-0.2], p < 0.05). On fat-suppressed T 2 weighted images, a solid hyperintense area was more frequently observed in SCLs than in ALT/WDLs (83% vs 27%, p < 0.05).

CONCLUSION

The maximum diameter, non-fatty area on T 1 weighted images, and solid hyperintense area on fat-suppressed T 2 weighted images were useful imaging features for differentiating superficial SCLs from ALT/WDLs.

ADVANCES IN KNOWLEDGE

In superficial lipomatous tumors, small tumor size and non-fatty solid area were valuable findings for diagnosing SCLs.

A case of liposarcoma with osteolysis of the maxilla as the first symptom and review of the literature

Liposarcomas are extremely rare in the oral cavity and mainly observed in the buccal mucosa, tongue, floor of the mouth, gingiva, and lips, while those occurring in the jaws have not been reported so far. Clinically, the initial presentation of liposarcoma is usually a painless soft tissue mass. The aim of this study was to report a case of liposarcoma with osteolysis of the maxilla as the first symptom, and review the relevant literature to summarize its clinical features, imaging features, pathological features and treatment methods.

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.

MRI findings to differentiate musculoskeletal dedifferentiated liposarcoma from atypical lipomatous tumor

OBJECTIVE

This study aimed to assess the efficacy of using MRI findings for differentiating musculoskeletal dedifferentiated liposarcoma (DDLP) from atypical lipomatous tumor (ALT).

MATERIALS AND METHODS

This study included 22 patients with histopathologically proven DDLP and 35 with ALT in the musculoskeletal areas. All DDLPs were immunohistochemically positive for MDM2. MRI findings for both pathologies were retrospectively reviewed and compared.

RESULTS

The maximum lesion diameter was significantly lower in DDLPs than in ALTs (p < 0.01). Ill-defined margin, peritumoral edema, and tail sign were more frequently observed in DDLPs than in ALTs (p < 0.01, respectively). The fatty component was less frequently observed in DDLPs than in ALTs (27 vs. 100%; p < 0.01), whereas the non-fatty component was more frequently observed in DDLPs than in ALTs (100 vs. 11%; p < 0.01). The occupation rate by non-fatty components was significantly higher in DDLPs than in ALTs (p < 0.01). No significant differences were observed in imaging findings associated with fatty component; however, necrosis within the non-fatty component on the contrast-enhanced image was more frequently observed in DDLPs than in ALTs (72 vs. 0%, p < 0.05).

CONCLUSION

DDLPs always had a non-fatty component, whereas ALTs always had fatty component. Ill-defined margin, peritumoral edema, tail sign, and necrosis within non-fatty components were useful MRI features for differentiating musculoskeletal DDLP from ALT.

Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review

BACKGROUND

Differentiating atypical lipomatous tumors (ALTs) and well-differentiated liposarcomas (WDLs) from benign lipomatous lesions is important for guiding clinical management, though conventional visual analysis of these lesions is challenging due to overlap of imaging features. Radiomics-based approaches may serve as a promising alternative and/or supplementary diagnostic approach to conventional imaging.

PURPOSE

The purpose of this study is to review the practice of radiomics-based imaging and systematically evaluate the literature available for studies evaluating radiomics applied to differentiating ALTs/WDLs from benign lipomas.

REVIEW

A background review of the radiomic workflow is provided, outlining the steps of image acquisition, segmentation, feature extraction, and model development. Subsequently, a systematic review of MEDLINE, EMBASE, Scopus, the Cochrane Library, and the grey literature was performed from inception to June 2022 to identify size studies using radiomics for differentiating ALTs/WDLs from benign lipomas. Radiomic models were shown to outperform conventional analysis in all but one model with a sensitivity ranging from 68 to 100% and a specificity ranging from 84 to 100%. However, current approaches rely on user input and no studies used a fully automated method for segmentation, contributing to interobserver variability and decreasing time efficiency.

CONCLUSION

Radiomic models may show improved performance for differentiating ALTs/WDLs from benign lipomas compared to conventional analysis. However, considerable variability between radiomic approaches exists and future studies evaluating a standardized radiomic model with a multi-institutional study design and preferably fully automated segmentation software are needed before clinical application can be more broadly considered.

The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy

Soft-tissue sarcomas (STS) represent a group of rare and heterogeneous tumors associated with several challenges, including incorrect or late diagnosis, the lack of clinical expertise, and limited therapeutic options. Digital pathology and radiomics represent transformative technologies that appear promising for improving the accuracy of cancer diagnosis, characterization and monitoring. Herein, we review the potential role of the application of digital pathology and radiomics in managing patients with STS. We have particularly described the main results and the limits of the studies using radiomics to refine diagnosis or predict the outcome of patients with soft-tissue sarcomas. We also discussed the current limitation of implementing radiomics in routine settings. Standard management approaches for STS have not improved since the early 1970s. Immunotherapy has revolutionized cancer treatment; nonetheless, immuno-oncology agents have not yet been approved for patients with STS. However, several lines of evidence indicate that immunotherapy may represent an efficient therapeutic strategy for this group of diseases. Thus, we emphasized the remarkable potential of immunotherapy in sarcoma treatment by focusing on recent data regarding the immune landscape of these tumors. We have particularly emphasized the fact that the development of immunotherapy for sarcomas is not an aspect of histology (except for alveolar soft-part sarcoma) but rather that of the tumor microenvironment. Future studies investigating immunotherapy strategies in sarcomas should incorporate at least the presence of tertiary lymphoid structures as a stratification factor in their design, besides including a strong translational program that will allow for a better understanding of the determinants involved in sensitivity and treatment resistance to immune-oncology agents.

Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning

Objectives

Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep learning (DL) to predict malignancy in patients with lipomas oratypical lipomatous tumours.

Methods

Cohort include 145 patients affected by lipomatous soft tissue tumours with histology and fat-suppressed gadolinium contrast-enhanced T1-weighted MRI pulse sequence. Images were collected between 2010 and 2019 over 78 centres with non-uniform protocols (three different magnetic field strengths (1.0, 1.5 and 3.0 T) on 16 MR systems commercialised by four vendors (General Electric, Siemens, Philips, Toshiba)).

Two approaches have been compared: (i) ML from radiomic features with and without batch correction; and (ii) DL from images. Performances were assessed using 10 cross-validation folds from a test set and next in external validation data.

Results

The best DL model was obtained using ResNet50 (resulting into an area under the curve (AUC) of 0.87 ± 0.11 (95% CI 0.65−1). For ML/radiomics, performances reached AUCs equal to 0.83 ± 0.12 (95% CI 0.59−1) and 0.99 ± 0.02 (95% CI 0.95−1) on test cohort using gradient boosting without and with batch effect correction, respectively. On the external cohort, the AUC of the gradient boosting model was equal to 0.80 and for an optimised decision threshold sensitivity and specificity were equal to 100% and 32% respectively.

Conclusions

In this context of limited observations, batch-effect corrected ML/radiomics approaches outperformed DL-based models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-022-00295-9.

Radiomics of Musculoskeletal Sarcomas: A Narrative Review

Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

Background

Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability.

Results

Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies.

Conclusions

The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.

The recurrence of well-differentiated liposarcoma from benign giant intramuscular lipoma: A case (CARE-compliant) report

RATIONALE

Recurrent liposarcoma, previously confirmed as lipoma, has rarely been reported. However, the risk factors for recurrence and the correlation between benign lipoma and malignant liposarcoma remain unclear. In this case study, we suggest a precise diagnostic strategy to minimize recurrence and malignant transformation.

PATIENT CONCERNS

A 60-year-old male patient with a history of left chest wall swelling without any symptoms underwent excisional surgery, and the mass was confirmed as a benign lipoma in 2015. In 2019, the patient returned to the hospital with symptoms of a palpable mass on the left chest wall.

DIAGNOSIS

The mass was considered a recurrent lipomatous tumor with the possibility of malignant transformation. Magnetic resonance imaging (MRI) revealed a deep-seated, septate, intramuscular, irregular margin, and large lipomatous tumor invading the ribs, pleura, and adjacent muscle, suggestive of malignancy. The MRI findings were similar to those 4 years ago, except for margin irregularity and invasion to adjacent tissue.

INTERVENTIONS

Wide en bloc excisions encompassing the 5th to 7th ribs, pleura, and adjacent muscle were followed by reconstruction with a pedicled latissimus dorsi muscle flap.

OUTCOMES

The recurrent large lipomatous tumor was confirmed as well-differentiated liposarcomas through histological and MDM2-FISH immunohistochemical staining. Postoperatively, follow-up visits continued for 1.5 years without recurrence.

LESSONS

We suggest that deep-seated, septate, and giant lipomatous tumors should be considered as risk factors for recurrence with the possibility of malignancy and misdiagnosis. It is important to inform patients of all these possibilities and plan close and long-term follow-up.

Inflammatory Variant of Atypical Lipomatous Tumor/Well-Differentiated Liposarcoma of the Buccal Mucosa: An Overview and Case Report with a 10-Year Follow-Up

Liposarcomas of the oral cavity are rare. Those originating in the buccal mucosa cause challenging diagnostic and therapeutic issues since less than 40 cases of liposarcomas of the buccal mucosa and cheek have been reported in the worldwide literature. Herein, we present a case of atypical lipomatous tumor/well-differentiated liposarcoma affecting a 45-year-old female patient. Ultrasonography and magnetic resonance imaging confirmed a well-defined mass located in the right buccal mucosa, extending to the submucosal layers of the cheek. Histopathologically, a well-differentiated fatty neoplasm with presence of prominent stromal inflammatory cells was observed. Multifocally scattered bizarre hyperchromatic stromal cells, some of which multinucleated, were also observed. An immunohistochemical panel comprising vimentin, S-100, CD10, CD34, CD20, CD3, CD68, CD138, MDM2, Ki-67, and P53 was employed to better characterize the lesion. A local recurrence event occurred during a 10-year follow-up period. Surgical resection was performed during both episodes. We also provided an overview of demographic and clinicopathological characteristics, immunohistochemical features, imaging findings, and the differential diagnosis of liposarcoma of the oral cavity. Knowledge of the etiopathological and clinical aspects of this rare neoplasm is fundamental in order to rule out other conditions, including lipomatous lesions that affect the buccal mucosa.