Radiomics of Musculoskeletal Sarcomas: A Narrative Review

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics

OBJECTIVES

Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI.

MATERIALS AND METHODS

The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors.

RESULTS

Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation.

CONCLUSION

This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.

Relative Wash-In Rate in Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a New Prognostic Biomarker for Event-Free Survival in 82 Patients with Osteosarcoma: A Multicenter Study

BACKGROUND

The decreased perfusion of osteosarcoma in dynamic contrast-enhanced (DCE) MRI, reflecting a good histological response to neoadjuvant chemotherapy, has been described.

PURPOSE

In this study, we aim to explore the potential of the relative wash-in rate as a prognostic factor for event-free survival (EFS).

METHODS

Skeletal high-grade osteosarcoma patients, treated in two tertiary referral centers between 2005 and 2022, were retrospectively included. The relative wash-in rate (rWIR) was determined with DCE-MRI before, after, or during the second cycle of chemotherapy (pre-resection). A previously determined cut-off was used to categorize patients, where rWIR < 2.3 was considered poor and rWIR ≥ 2.3 a good radiological response. EFS was defined as the time from resection to the first event: local recurrence, new metastases, or tumor-related death. EFS was estimated using Kaplan-Meier's methodology. Multivariate Cox proportional hazard model was used to estimate the effect of histological response and rWIR on EFS, adjusted for traditional prognostic factors.

RESULTS

Eighty-two patients (median age: 17 years; IQR: 14-28) were included. The median follow-up duration was 11.8 years (95% CI: 11.0-12.7). During follow-up, 33 events occurred. Poor histological response was not significantly associated with EFS (HR: 1.8; 95% CI: 0.9-3.8), whereas a poor radiological response was associated with a worse EFS (HR: 2.4; 95% CI: 1.1-5.0). In a subpopulation without initial metastases, the binary assessment of rWIR approached statistical significance (HR: 2.3; 95% CI: 1.0-5.2), whereas its continuous evaluation demonstrated a significant association between higher rWIR and improved EFS (HR: 0.7; 95% CI: 0.5-0.9), underlining the effect of response to chemotherapy. The 2- and 5-year EFS for patients with a rWIR ≥ 2.3 were 85% and 75% versus 55% and 50% for patients with a rWIR < 2.3.

CONCLUSION

The predicted poor chemo response with MRI (rWIR < 2.3) is associated with shorter EFS even when adjusted for known clinical covariates and shows similar results to histological response evaluation. rWIR is a potential tool for future response-based individualized healthcare in osteosarcoma patients before surgical resection.

The impact of radiomics in the management of soft tissue sarcoma

INTRODUCTION

Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation.

MATERIALS AND METHODS

We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: 'radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])'. Only original articles, referring to humans, were included.

RESULTS

A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response.

CONCLUSIONS

With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.

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

OBJECTIVE

To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.

METHODS

A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.

RESULTS

Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.

CONCLUSIONS

Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage.

CRITICAL RELEVANCE STATEMENT

An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage.

KEY POINTS

• 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

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.

Radiomics in Musculoskeletal Tumors

Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

How AI May Transform Musculoskeletal Imaging

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

Emerging innovations and advancements in the treatment of extremity and truncal soft tissue sarcomas

In this special edition update on soft tissue sarcomas (STS), we cover classifications, emerging technologies, prognostic tools, radiation schemas, and treatment disparities in extremity and truncal STS. We discuss the importance of enhancing local control and reducing complications, including the role of innovative imaging, surgical guidance, and hypofractionated radiation. We review advancements in systemic and immunotherapeutic treatments and introduce disparities seen in this vulnerable population that must be considered to improve overall patient care.

Radiomics model based on intravoxel incoherent motion and diffusion kurtosis imaging for predicting histopathological grade and Ki-67 expression level of soft tissue sarcomas

BACKGROUND

Accurate identification of the histopathological grade and the Ki-67 expression level is important in clinical cases of soft tissue sarcomas (STSs).

PURPOSE

To explore the feasibility of a radiomics model based on intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) MRI parameter maps in predicting the histopathological grade and Ki-67 expression level of STSs.

MATERIAL AND METHODS

In total, 42 patients diagnosed with STSs between May 2018 and January 2020 were selected. The MADC software in Functool of GE ADW 4.7 workstation was used to obtain standard apparent diffusion coefficient (ADC), D, D*, f, mean diffusivity, and mean kurtosis (MK). The histopathological grade and Ki-67 expression level of STSs were identified. The radiomics features of IVIM and DKI parameter maps were used as the dataset. The area under the receiver operating characteristic curve (AUC) and F1-score were calculated.

RESULTS

D-SVM achieved the best diagnostic performance for histopathological grade. The AUC in the validation cohort was 0.88 (sensitivity: 0.75 [low level] and 0.83 [high level]; specificity: 0.83 [low level] and 0.75 [high level]; F1-score: 0.75 [low level] and 0.83 [high level]). MK-SVM achieved the best diagnostic performance for Ki-67 expression level. The AUC in the validation cohort was 0.83 (sensitivity: 0.83 [low level] and 0.50 [high level; specificity: 0.50 [low level] and 0.83 [high level]; F1-score: 0.77 [low level] and 0.57 [high level]).

CONCLUSION

The proposed radiomics classifier could predict the pathological grade of STSs and the Ki-67 expression level in STSs.

Radiomic applications in upper gastrointestinal cancer surgery

INTRODUCTION

Cross-sectional imaging plays an integral role in the management of upper gastrointestinal (UGI) cancer, from initial diagnosis and staging to determining appropriate treatment strategies. Subjective imaging interpretation has known limitations. The field of radiomics has evolved to extract quantitative data from medical imaging and relate these to biological processes. The key concept behind radiomics is that the high-throughput analysis of quantitative imaging features can provide predictive or prognostic information, with the goal of providing individualised care.

OBJECTIVE

Radiomic studies have shown promising utility in upper gastrointestinal oncology, highlighting a potential role in determining stage of disease and degree of tumour differentiation and predicting recurrence-free survival. This narrative review aims to provide an insight into the concepts underpinning radiomics, as well as its potential applications for guiding treatment and surgical decision-making in upper gastrointestinal malignancy.

CONCLUSION

Outcomes from studies to date have been promising; however, further standardisation and collaboration are required. Large prospective studies with external validation and evaluation of radiomic integration into clinical pathways are needed. Future research should now focus on translating the promising utility of radiomics into meaningful patient outcomes.

Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions

Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.

MSRA-Net: Tumor segmentation network based on Multi-scale Residual Attention

Automatic Medical segmentation of medical images is an important part in the field of computer medical diagnosis, among which tumor segmentation is an important branch of medical image segmentation. Accurate automatic segmentation method is very important in medical diagnosis and treatment. Positron emission computed tomography (PET) and X-ray computed tomography (CT) images are widely used in medical image segmentation to help doctors accurately locate information such as tumor location and shape, providing metabolic and anatomical information, respectively. At present, PET/CT images have not been effectively combined in the research of medical image segmentation, and the complementary semantic information between the superficial and deep layers of neural network has not been ensured. To solve the above problems, this paper proposed a Multi-scale Residual Attention network (MSRA-Net) for tumor segmentation of PET/CT. We first use an attention-fusion based approach to automatically learn the tumor-related areas of PET images and weaken the irrelevant area. Then, the segmentation results of PET branch are processed to optimize the segmentation results of CT branch by using attention mechanism. The proposed neural network (MSRA-Net) can effectively fuse PET image and CT image, which can improve the precision of tumor segmentation by using complementary information of the multi-modal image, and reduce the uncertainty of single modal image segmentation. Proposed model uses multi-scale attention mechanism and residual module, which fuse multi-scale features to form complementary features of different scales. We compare with state-of-the-art medical image segmentation methods. The experiment showed that the Dice coefficient of the proposed network in soft tissue sarcoma and lymphoma datasets increased by 8.5% and 6.1% respectively compared with UNet, showing a significant improvement.

A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy

Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (D_RBE) and dose-averaged Linear Energy Transfer (LET_d) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan-Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LET_d maps, with D_RBE models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LET_d maps show potential as a prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).

3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction

OBJECTIVE

The extent of response to neoadjuvant chemotherapy predicts survival in Ewing sarcoma. This study focuses on MRI radiomics of skeletal Ewing sarcoma and aims to investigate feature reproducibility and machine learning prediction of response to neoadjuvant chemotherapy.

MATERIALS AND METHODS

This retrospective study included thirty patients with biopsy-proven skeletal Ewing sarcoma, who were treated with neoadjuvant chemotherapy before surgery at two tertiary sarcoma centres. 7 patients were poor responders and 23 were good responders based on pathological assessment of the surgical specimen. On pre-treatment T1-weighted and T2-weighted MRI, 2D and 3D tumour segmentations were manually performed. Features were extracted from original and wavelet-transformed images. Feature reproducibility was assessed through small geometrical transformations of the regions of interest mimicking multiple manual delineations, and intraclass correlation coefficient >0.75 defined feature reproducibility. Feature selection also consisted of collinearity and significance analysis. After class balancing in the training cohort, three machine learning classifiers were trained and tested on unseen data using hold-out cross-validation.

RESULTS

1303 (77%) 3D and 620 (65%) 2D radiomic features were reproducible. 4 3D and 4 2D features passed feature selection. Logistic regression built upon 3D features achieved the best performance with 85% accuracy (AUC=0.9) in predicting response to neoadjuvant chemotherapy.

CONCLUSION

Compared to 2D approach, 3D MRI radiomics of Ewing sarcoma had superior reproducibility and higher accuracy in predicting response to neoadjuvant chemotherapy, particularly when using logistic regression classifier.

Research in Musculoskeletal Radiology: Setting Goals and Strategic Directions

The future of musculoskeletal (MSK) radiology is being built on research developments in the field. Over the past decade, MSK imaging research has been dominated by advancements in molecular imaging biomarkers, artificial intelligence, radiomics, and novel high-resolution equipment. Adequate preparation of trainees and specialists will ensure that current and future leaders will be prepared to embrace and critically appraise technological developments, will be up to date on clinical developments, such as the use of artificial tissues, will define research directions, and will actively participate and lead multidisciplinary research. This review presents an overview of the current MSK research landscape and proposes tangible future goals and strategic directions that will fortify the future of MSK radiology.