T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma: predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival

MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas From Benign Soft Tissue Musculoskeletal Myxomas

BACKGROUND

Differentiation of benign myxomas and malignant myxoid sarcomas can be difficult with an overlapping spectrum of morphologic MR findings.

PURPOSE

To assess the diagnostic utility of MRI radiomics in the differentiation of musculoskeletal myxomas and myxoid sarcomas.

STUDY TYPE

Retrospective.

POPULATION

A total of 523 patients were included; histologically proven myxomas (N = 201) and myxoid sarcomas (N = 322), randomly divided (70:30) into training:test subsets.

SEQUENCE/FIELD STRENGTH

T1-weighted (T1W), T2-weighted fat-suppressed (fluid-sensitive), and T1-weighted post-contrast (T1W + C) sequences at 1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Seven semantic (qualitative) tumor features were assessed in each case. Manual 3D tumor segmentations performed with radiomics features extracted from T1W, fluid-sensitive, and T1W + C acquisitions. Models were constructed based on radiomic features from individual sequences and from their combination, both with and without the addition of qualitative tumor features.

STATISTICAL TESTS

Intraclass correlation evaluated in 60 cases segmented by three readers. Features with intraclass correlation <0.7 excluded from further analysis. Boruta feature selection and Random Forest modeling performed using the training-dataset, with resultant models used to assess class discrimination (myxoma vs. myxoid sarcoma) in the test dataset. Radiomics score defined as probability class = myxoma. Logistic regression modeling employed to estimate performance of the radiomics score. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance, and DeLong's test to assess performance between constructed models. A P-value <0.05 was considered significant.

RESULTS

Four qualitative semantic features showed significant predictive power in class discrimination. Radiomic models demonstrated excellent differentiation of myxomas from myxoid sarcomas: AUC of 0.9271 (T1W), 0.9049 (fluid-sensitive), and 0.9179 (T1W + C). Incorporation of multiparametric data or semantic features did not significantly improve model performance (P ≥ 0.08) compared to radiomic models derived from any individual MRI sequence alone.

DATA CONCLUSION

MRI radiomics appears to be accurate in the differentiation of myxomas from myxoid sarcomas. Classification performance did not improve when incorporating qualitative features or multiparametric imaging data.

PLAIN LANGUAGE SUMMARY

Accurately distinguishing between benign soft tissue myxomas and malignant myxoid sarcomas is essential for guiding appropriate management but remains challenging with conventional MRI interpretation. This study utilized radiomics, a method that extracts quantitative mathematically derived features from images, to develop predictive models based on routine MRI examination. Analyzing over 500 cases, MRI radiomics demonstrated excellent diagnostic accuracy in differentiating between benign myxomas and malignant myxoid sarcomas, highlighting the potential of the technique, as a powerful non-invasive tool that could complement current diagnostic approaches, and enhance clinical decision-making in patients with soft tissue myxoid tumors of the musculoskeletal system.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Radiomics-based machine learning in prediction of response to neoadjuvant chemotherapy in osteosarcoma: A systematic review and meta-analysis

BACKGROUND AND AIMS

Osteosarcoma (OS) is the most common primary bone malignancy, and neoadjuvant chemotherapy (NAC) improves survival rates. However, OS heterogeneity results in variable treatment responses, highlighting the need for reliable, non-invasive tools to predict NAC response. Radiomics-based machine learning (ML) offers potential for identifying imaging biomarkers to predict treatment outcomes. This systematic review and meta-analysis evaluated the accuracy and reliability of radiomics models for predicting NAC response in OS.

METHODS

A systematic search was conducted in PubMed, Embase, Scopus, and Web of Science up to November 2024. Studies using radiomics-based ML for NAC response prediction in OS were included. Pooled sensitivity, specificity, and AUC for training and validation cohorts were calculated using bivariate random-effects modeling, with clinical-combined models analyzed separately. Quality assessment was performed using the QUADAS-2 tool, radiomics quality score (RQS), and METRICS scores.

RESULTS

Sixteen studies were included, with 63 % using MRI and 37 % using CT. Twelve studies, comprising 1639 participants, were included in the meta-analysis. Pooled metrics for training cohorts showed an AUC of 0.93, sensitivity of 0.89, and specificity of 0.85. Validation cohorts achieved an AUC of 0.87, sensitivity of 0.81, and specificity of 0.82. Clinical-combined models outperformed radiomics-only models. The mean RQS score was 9.44 ± 3.41, and the mean METRICS score was 60.8 % ± 17.4 %.

CONCLUSION

Radiomics-based ML shows promise for predicting NAC response in OS, especially when combined with clinical indicators. However, limitations in external validation and methodological consistency must be addressed.

Multimodal Imaging of Osteosarcoma: From First Diagnosis to Radiomics

Osteosarcoma is a primary malignant bone tumor characterized by the production of an osteoid matrix. Although histology remains the definitive diagnostic standard, imaging plays a crucial role in diagnosis, therapeutic planning, and follow-up. Conventional radiography serves as the initial checkpoint for detecting this pathology, which often presents diagnostic challenges due to vague and nonspecific symptoms, especially in its early stages. Today, the integration of different imaging techniques enables an increasingly personalized diagnosis and management, with each contributing unique and complementary information. Conventional radiography typically initiates the imaging assessment, and the Bone Reporting and Data System (Bone-RADS) of the Society of Skeletal Radiology (SSR) is a valuable tool for stratifying the risk of suspicious bone lesions. CT is the preferred modality for evaluating the bone matrix, while bone scans and PET/CT are effective for detecting distant metastases. MRI reveals the extent of the lesion in adjacent soft tissues, the medullary canal, and joints, as well as its relationship to neurovascular structures and the presence of skip lesions. Advanced techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), and perfusion MRI help characterize the tumor environment and assess treatment response. Osteosarcoma comprises a range of subtypes with differing clinical and imaging characteristics, some of which are particularly distinctive, such as in the case of telangiectatic osteosarcoma. Knowledge of these variants can guide radiologists in the differential diagnosis, which includes both central and surface forms, ranging from highly aggressive to more indolent types. In this review, we present a wide range of representative cases from our hospital case series to illustrate both typical and atypical imaging presentations. Finally, we discuss recent advancements and challenges in applying artificial intelligence approaches to the imaging of osteosarcoma.

Magnetic Resonance Imaging Radiomics Predicts Histological Response to Neoadjuvant Chemotherapy in Localized High-grade Osteosarcoma of the Extremities

RATIONALE AND OBJECTIVES

Research involving radiomics models based on magnetic resonance imaging (MRI) has mainly used radiomics features derived from a single MRI sequence at a single time point to develop predictive models. This study aimed to construct radiomics models based on before and after neoadjuvant chemotherapy (NAC) MRI for predicting the histological response to NAC in patients with high-grade osteosarcoma.

MATERIALS AND METHODS

We included 109 patients with localized high-grade osteosarcomas of the extremities, who underwent pre- and post-NAC MRI examinations, from which radiomics features were extracted. According to the tumor necrosis rate, all patients were classified as good responders (GRs) or poor responders (PRs) and were randomly allocated into training and test sets at a 7:3 ratio. Radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted imaging (T1CE) of the two MRI scans to construct three models: pre-NAC, post-NAC, and combined pre-NAC and post-NAC (combined model).

RESULTS

In total, 1175 radiomics features were extracted from each sequence. Following feature selection, nine radiomics features were selected for each model to construct radiomics signatures. The radiomics signatures of the pre-NAC, post-NAC, and combined models demonstrated good predictive performance for chemotherapy response in osteosarcoma. The combined model achieved the highest areas under the receiver operating curve (AUC) values of 0.999 and 0.915 in the training and test sets, respectively. The AUCs of the post-NAC model were higher than those of the pre-NAC model.

CONCLUSION

MRI-based radiomics models demonstrate excellent performance in predicting the histological response to NAC in patients with high-grade osteosarcoma.

Imaging of Osteosarcoma: Presenting Findings, Metastatic Patterns, and Features Related to Prognosis

Background: Osteosarcomas are rare malignancies (<1% of all cancers) that produce an osteoid matrix. Osteosarcomas are the second most frequent type of primary bone tumor after multiple myeloma and the most prevalent primary bone tumor in children. The spectrum of imaging findings of these malignancies varies significantly, reflecting different histological subtypes. For instance, conventional osteosarcoma typically presents with a mixed radiological pattern (lytic and bone mineralization) or with a completely eburneous one; aggressive periosteal reactions such as sunburst, Codman triangle, and soft-tissue components are frequently displayed. On the other hand, telangiectatic osteosarcoma usually presents as a purely lytic lesion with multiple fluid-fluid levels on MRI fluid-sensitive sequences. Other typical and atypical radiological patterns of presentation in other subtypes of osteosarcomas are described in this review. In addition to the characteristics associated with osteosarcoma subtyping, this review article also focuses on imaging features that have been associated with patient outcomes, namely response to chemotherapy and event-free and overall survivals. This includes simple semantic radiological features (such as tumor dimensions, anatomical location with difficulty of radical surgery, occurrence of pathological fractures, and presence of distant metastases), but also quantitative imaging parameters from diffusion-weighted imaging, dynamic contrast-enhanced MRI, and 18F-FDG positron emission tomography and radiomics approaches. Other particular features are described in the text. Overall, this comprehensive literature review aims to be a practical tool for oncologists, pathologists, surgeons, and radiologists involved in these patients' care.

Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery

This review article focuses on the applications of deep learning with neural networks and multimodal neural networks in the orthopaedic domain. By providing practical examples of how artificial intelligence (AI) is being applied successfully in orthopaedic surgery, particularly in the realm of imaging data sets and the integration of clinical data, this study aims to provide orthopaedic surgeons with the necessary tools to not only evaluate existing literature but also to consider AI's potential in their own clinical or research pursuits. We first review standard deep neural networks which can analyze numerical clinical variables, then describe convolutional neural networks which can analyze image data, and then introduce multimodal AI models which analyze various types of different data. Then, we contrast these deep learning techniques with related but more limited techniques such as radiomics, describe how to interpret deep learning studies, and how to initiate such studies at your institution. Ultimately, by empowering orthopaedic surgeons with the knowledge and know-how of deep learning, this review aspires to facilitate the translation of research into clinical practice, thereby enhancing the efficacy and precision of real-world orthopaedic care for patients.

Ct-based intratumoral and peritumoral radiomics for predicting prognosis in osteosarcoma: A multicenter study

PURPOSE

To evaluate the performance of CT-based intratumoral, peritumoral and combined radiomics signatures in predicting prognosis in patients with osteosarcoma.

METHODS

The data of 202 patients (training cohort:102, testing cohort:100) with osteosarcoma admitted to the two hospitals from August 2008 to February 2022 were retrospectively analyzed. Progression free survival (PFS) and overall survival (OS) were used as the end points. The radiomics features were extracted from CT images, three radiomics signatures（RSintratumoral, RSperitumoral, RScombined）were constructed based on intratumoral, peritumoral and combined radiomics features, respectively, and the radiomics score (Rad-score) were calculated. Kaplan-Meier survival analysis was used to evaluate the relationship between the Rad-score with PFS and OS, the Harrell's concordance index (C-index) was used to evaluate the predictive performance of the radiomics signatures.

RESULTS

Finally, 8, 6, and 21 features were selected for the establishment of RSintratumoral, RSperitumoral, and RScombined, respectively. Kaplan-Meier survival analysis confirmed that the Rad-scores of the three RSs were significantly correlated with the PFS and OS of patients with osteosarcoma. Among the three radiomics signatures, RScombined had better predictive performance, the C-index of PSF prediction was 0.833 in the training cohort and 0.814 in the testing cohort, the C-index of OS prediction was 0.796 in the training cohort and 0.764 in the testing cohort.

CONCLUSIONS

CT-based intratumoral, peritumoral and combined radiomics signatures can predict the prognosis of patients with osteosarcoma, which may assist in individualized treatment and improving the prognosis of osteosarcoma patients.

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.

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.