Imaging of Bone Sarcomas and Soft-Tissue Sarcomas

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

The role of imaging in extremity sarcoma surgery

The surgical management of extremity bone and soft tissue sarcomas has evolved significantly over the last 50 years. The introduction and refinement of high-resolution cross-sectional imaging has allowed accurate assessment of anatomy and tumor extent, and in the current era more than 90% of patients can successfully undergo limb-salvage surgery. Advances in imaging have also revolutionized the clinician's ability to assess treatment response, detect metastatic disease, and perform intraoperative surgical navigation. This review summarizes the broad and essential role radiology plays in caring for sarcoma patients from diagnosis to post-treatment surveillance. Present evidence-based imaging paradigms are highlighted along with key future directions.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Beyond Clinical Examination: Utilizing MRI Surveillance to Detect Recurrence of Soft Tissue Sarcomas and Differentiate from Posttherapeutic Changes

BACKGROUND

Early detection of soft tissue sarcoma (STS) recurrence is essential; however, the role and timeline of Magnetic resonance imaging (MRI) surveillance are still under debate. The aim of this study was to determine whether local recurrence (LR) could be identified via clinical examination alone and to assess the MRI morphology of primary STS and LR.

METHODS

This retrospective study included all patients with STS recurrence after surveillance for at least five years from the tumor database of the Medical University of Vienna from 2000 until December 2023. The characteristics of primary STS and LR and the time interval to recurrence and clinical detectability were assessed. The MRIs of LR and posttherapeutic changes (PTC) were compared with the initial MRIs.

RESULTS

A total of 57 patients (60% male; mean age 58.5 ± 18.0 years) with STS and histologically confirmed LR were included. The mean time interval to LR was 2.3 ± 1.8 years (range 108 to 3037 days). The clinically detectable recurrences were significantly larger than the inapparent ones (71.9 cm3 vs. 7.0 cm3; p < 0.01). The MRI morphology of all LRs (26/26) closely resembled the initial STS. For comparison, nine patients were included with clinically suspected LRs, which were histologically proven to be PTC. None of these resembled the primary STS.

CONCLUSION

Based on clinical symptoms alone, especially small and early recurrences can be missed, which supports the importance of MRI surveillance.

Experience in utilizing a novel 3D digital model with CT and MRI fusion data in sarcoma evaluation and surgical planning

OBJECTIVE

To assess sarcoma margins with more accuracy and aid surgical planning, we constructed three-dimensional (3D) digital models with computed tomography(CT) and magnetic resonance imaging (MRI) image fusion data and validated the preciseness of the models by comparing them with 3D models constructed with CT only data.

MATERIALS AND METHODS

We retrospectively reviewed a consecutive set of patients treated in our center who were preoperatively evaluated with the fusion image model. Models based on fusion images or CT-only data were constructed. Volumes of both tumors were calculated and the tumors were overlapped to see the location of differences between the two models.

RESULTS

A consecutive 12 cases (4 male vs. 8 female) were included in this study. Most of the tumors were located in the pelvic bone or spine. The volume of the two tumor models was different and the differences were mainly in the peripheral region of the tumor.

CONCLUSION

CT and MRI fusion image 3D models are more accurate than models with CT-only data and can be very helpful in preoperative planning of sarcoma patients.

Primary spindle cell sarcoma of gallbladder: An unusual case report and a literature review

INTRODUCTION

Primary spindle cell sarcoma of the gallbladder is a rare condition.

PATIENT CONCERNS

A 67-year-old woman was admitted to a local hospital with a chief complaint of abdominal pain in the right upper quadrant for the past 2 months.

DIAGNOSIS AND INTERVENTION

Surgical resection was performed following the diagnosis of primary gallbladder sarcoma with local hepatic metastasis. Histological examination confirmed a diagnosis of primary spindle cell sarcoma and hepatic metastasis with simultaneous cholecystolithiasis.

OUTCOMES

Adjuvant chemoradiation therapy was not performed because the patient refused treatment. Three months after the surgery, a relapsed lesion was diagnosed. The patient underwent transcatheter arterial chemoembolization.

CONCLUSIONS

The disease should be differentially diagnosed from gallbladder carcinoma or carcinosarcoma with hepatic metastasis. An aggressive surgical approach should be based on a balance between the risk of surgery and the outcome.