Diffusion-weighted imaging in differentiating mid-course responders to chemotherapy for long-bone osteosarcoma compared to the histologic response: an update

The presence of a fat layer after neoadjuvant chemotherapy as an indicator of prognosis in osteosarcoma

Objective

This study aimed to evaluate the potential of magnetic resonance imaging (MRI) to monitor the response in patients with osteosarcoma receiving chemotherapy and to assess the correlation between the presence of a fat layer surrounding the tumor after neoadjuvant chemotherapy and prognosis.

Methods

In total, 28 patients with osteosarcoma were included in this retrospective study. All patients underwent chemotherapy and surgery. MRI scans of the patients were evaluated before and after neoadjuvant chemotherapy. The prognostic factors included histological response and alkaline phosphatase (ALP) level. Relapse and survival at follow-up were defined as patient outcomes. The log-rank test was used to compare these factors with various MRI characteristics (e.g. change in maximum lesion length before and after chemotherapy, change in maximum edema, and fat layer presence after chemotherapy).

Results

The median time of follow-up was 64.3 ± 41.5 months. The 3- and 5-year event-free survival rates were 75.0% and 67.9%, respectively. ALP levels after chemotherapy were associated with tumor necrosis (p = 0.01). Change in maximum lesion length [p = 0.044; odds ratio (OR) = 0.035; confidence interval (CI): 0.01-0.911] was a predictor of survival. Changes in edema on T2-weighted sequences (p = 0.979; OR = 0.989, CI: 0.437-2.242) were not significant. The presence of a fat layer (p = 0.013; OR = 0.000; confidence CI: 0.000-0.018) predicted good event-free survival.

Conclusions

The presence of a fat layer correlated with good prognosis in patients with osteosarcoma. MRI characteristics in the early stages could help to inform decision-making about treatment strategy.

Ewing sarcoma of the temporal bone with aneurysmal bone cyst-like changes: A rare case report with an unusual radiological presentation

Ewing sarcoma (ES) is a malignant small round cell tumor, accounting for 10-15% of all primary bone tumors and approximately 3% of all pediatric cancers. Primary ES of the cranial bone is unusual with reported incidence from 1% to 6% of all ES cases. This report shows a rare case of primary ES of the squamous temporal bone in a 12-year-old boy with a history of swelling of the right temporal region and symptoms of increased intracranial pressure. We illustrate the extremely unusual radiological presentation of this primary ES of temporal bone associated with large aneurysmal bone cyst-like (ABC-like) changes. The boy was successfully treated according to Euro Ewing 2012 protocol. He is alive with no evidence of recurrence and metastasis after 16 months of completed treatment.

Evaluation of the neoadjuvant chemotherapy response in osteosarcoma using the MRI DWI-based machine learning radiomics nomogram

Objective

To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma.

Methods

A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA).

Results

Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram.

Conclusion

The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.

Evaluation of response to neoadjuvant chemotherapy in osteosarcoma using dynamic contrast-enhanced MRI: development and external validation of a model

OBJECTIVE

To identify which dynamic contrast-enhanced (DCE-)MRI features best predict histological response to neoadjuvant chemotherapy in patients with an osteosarcoma.

METHODS

Patients with osteosarcoma who underwent DCE-MRI before and after neoadjuvant chemotherapy prior to resection were retrospectively included at two different centers. Data from the center with the larger cohort (training cohort) was used to identify which method for region-of-interest selection (whole slab or focal area method) and which change in DCE-MRI features (time to enhancement, wash-in rate, maximum relative enhancement and area under the curve) gave the most accurate prediction of histological response. Models were created using logistic regression and cross-validated. The most accurate model was then externally validated using data from the other center (test cohort).

RESULTS

Fifty-five (27 poor response) and 30 (19 poor response) patients were included in training and test cohorts, respectively. Intraclass correlation coefficient of relative DCE-MRI features ranged 0.81-0.97 with the whole slab and 0.57-0.85 with the focal area segmentation method. Poor histological response was best predicted with the whole slab segmentation method using a single feature threshold, relative wash-in rate <2.3. Mean accuracy was 0.85 (95%CI: 0.75-0.95), and area under the receiver operating characteristic curve (AUC-index) was 0.93 (95%CI: 0.86-1.00). In external validation, accuracy and AUC-index were 0.80 and 0.80.

CONCLUSION

In this study, a relative wash-in rate of <2.3 determined with the whole slab segmentation method predicted histological response to neoadjuvant chemotherapy in osteosarcoma. Consistent performance was observed in an external test cohort.

Imaging Assessment of the Efficacy of Chemotherapy in Primary Malignant Bone Tumors: Recent Advances in Qualitative and Quantitative Magnetic Resonance Imaging and Radiomics

Recent studies have shown that MRI demonstrates promising results for evaluating the chemotherapy efficacy in bone sarcomas. This article reviews current methods for evaluating the efficacy of malignant bone tumors and the application of MRI in this area, and emphasizes the advantages and limitations of each modality. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Pitfalls of Diffusion-Weighted Imaging: Clinical Utility of T2 Shine-through and T2 Black-out for Musculoskeletal Diseases

Diffusion-weighted imaging (DWI) with an apparent diffusion coefficient (ADC) value is a relatively new magnetic resonance imaging (MRI) sequence that provides functional information on the lesion by measuring the microscopic movement of water molecules. While numerous studies have evaluated the promising role of DWI in musculoskeletal radiology, most have focused on tumorous diseases related to cellularity. This review article aims to summarize DWI-acquisition techniques, considering pitfalls such as T2 shine-through and T2 black-out, and their usefulness in interpreting musculoskeletal diseases with imaging. DWI is based on the Brownian motion of water molecules within the tissue, achieved by applying diffusion-sensitizing gradients. Regardless of the cellularity of the lesion, several pitfalls must be considered when interpreting DWI with ADC values in musculoskeletal radiology. This review discusses the application of DWI in musculoskeletal diseases, including tumor and tumor mimickers, as well as non-tumorous diseases, with a focus on lesions demonstrating T2 shine-through and T2 black-out effects. Understanding these pitfalls of DWI can provide clinically useful information, increase diagnostic accuracy, and improve patient management when added to conventional MRI in musculoskeletal diseases.

Effect of Paclitaxel Combined with Doxorubicin Hydrochloride Liposome Injection in the Treatment of Osteosarcoma and MRI Changes before and after Treatment

Objective

The aim of this study is to investigate the effect of paclitaxel combined with doxorubicin hydrochloride liposome injection (DHLI) in the treatment of osteosarcoma and the MRI changes before and after treatment.

Methods

A total of 108 osteosarcoma patients treated in our hospital (January 2020–April 2022) were selected to carry out a single-center retrospective study. Among them, 54 patients receiving the combination chemotherapy (MDT) with high-dose methotrexate, ifosfamide, cisplatin, and ADM were selected as the control group (COG), while 54 patients receiving MDT with high-dose methotrexate, ifosfamide, cisplatin, paclitaxel, and DHLI were chosen as the study group (STG). The COG and STG had the same dose intensity and chemotherapy cycles, and clinical and MRI evaluations were performed after treatment.

Results

The evaluation of postoperative clinical efficacy showed that the disease control rate (DCR) of the STG was markedly higher than that of the COG (P < 0.05). The incidence of cardiac toxicity was remarkably lower in the STG than that in the COG (P < 0.05), with no between-group differences in the incidence of fever, abnormal liver function, myelosuppression, stomatitis, and alopecia (P > 0.05). Obvious differences were found in the semiquantitative parameters of MRI in the STG before and after chemotherapy (P < 0.05) and were also found in the SI_max, TTP, SEE, PPE, WOR, and R values in the COG before and after chemotherapy (P < 0.05). After chemotherapy, statistical differences were observed in the semiquantitative parameters of MRI between the two groups, with lower parameters such as Slope, SI_max, SEE, and R values and higher parameters such as TTP, PPE, and WOR values in the STG than those in the COG (P < 0.05).

Conclusion

Paclitaxel combined with DHLI has definite efficacy in osteosarcoma chemotherapy, which is conducive to narrowing the lesion, controlling the disease, and reducing the occurrence of cardiac-related risk events. In addition, the semiquantitative parameters of dynamic contrast-enhanced MRI (DCE-MRI) have a high predictive value for the efficacy of chemotherapy, which can reflect the degree of tumor necrosis and contribute to a timely and objective assessment of the efficacy of osteosarcoma chemotherapy.

Multiparametric MRI evaluation of bone sarcomas in children

Osteosarcoma and Ewing sarcoma are the most common bone sarcomas in children. Their clinical presentation is very variable depending on the age of the patient and tumor location. MRI is the modality of choice to assess these bone sarcomas and has an important function at diagnosis and also for monitoring recurrence or tumor response. Anatomic sequences include T1- and T2-weighted images and provide morphological assessment that is crucial to localize the tumor and describe anatomical boundaries. Multiparametric MRI provides functional information that helps in the assessment of tumor response to therapy by using different imaging sequences and biomarkers. This review manuscript illustrates the role of MRI in osteosarcoma and Ewing sarcoma in the pediatric population, with emphasis on a functional perspective, highlighting the use of diffusion-weighted imaging and dynamic contrast-enhanced MRI at diagnosis, and during and after treatment.