Leiomyosarcoma in the extremities and trunk wall: systematic review and meta-analysis of the oncological outcomes

CT-based quantification of intratumoral heterogeneity for predicting distant metastasis in retroperitoneal sarcoma

OBJECTIVES

Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk.

METHODS

We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits.

RESULTS

The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for DM (p < 0.05).

CONCLUSIONS

The combined model demonstrated promising potential for accurately predicting the DM risk and stratifying the DMFS risk in RPS patients undergoing complete surgical resection, providing a valuable tool for guiding treatment decisions and follow-up strategies.

CRITICAL RELEVANCE STATEMENT

The intratumoral heterogeneity analysis facilitates the identification of high-risk retroperitoneal sarcoma patients prone to distant metastasis and poor prognoses, enabling the selection of candidates for more aggressive surgical and post-surgical interventions.

KEY POINTS

Preoperative identification of retroperitoneal sarcoma (RPS) with a high potential for distant metastasis (DM) is crucial for targeted interventional strategies. Quantitative assessment of intratumoral heterogeneity achieved reasonable performance for predicting DM. The integrated model combining clinicoradiological variables, ITH radiomics, and deep-learning features effectively predicted distant metastasis-free survival.

Primary leiomyosarcoma of the scalp: a case report and review of the literature

Background and importance

Leiomyosarcoma is a rare and aggressive malignant tumor with a high potential for relapse and metastasis. Correct and timely diagnosis is critical for effective treatment, yet it is often challenging due to the diverse clinical presentations. This case report highlights the significance of early identification and the consequences of delayed diagnosis in scalp leiomyosarcoma.

Clinical presentation

We present the case of a 39-year-old woman with a scalp neoplasm. Initially, the diagnosis was missed, leading to a delay in surgical intervention. The tumor demonstrated a locally aggressive course, infiltrating the skull and dura mater. Upon admission, the scalp tumor was promptly excised. This case provides valuable insights into the varied symptoms and presentations of scalp leiomyosarcoma, which can aid in the recognition of this condition.

Conclusion

This report underscores the importance of considering leiomyosarcoma in the differential diagnosis of scalp masses, particularly when the etiology is unclear. Early recognition and intervention are essential to prevent locally invasive growth and potential metastasis, emphasizing the need for a high index of suspicion among healthcare professionals.

Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma

PURPOSE

Machine learning (ML) models have been used to predict cancer survival in several sarcoma subtypes. However, none have investigated extremity leiomyosarcoma (LMS). ML is a powerful tool that has the potential to better prognosticate extremity LMS.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologic extremity LMS (n = 634). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of extremity LMS patients (n = 46).

RESULTS

All ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.75-0.76 at the 5-year time point. The Random Forest (RF) model was the best performing model and used for external validation. This model also performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.90 and 0.87, respectively. The RF model was well calibrated on external validation. This model has been made publicly available at https://rachar.shinyapps.io/lms_app/ CONCLUSIONS: ML models had excellent performance for survival prediction of extremity LMS. Future studies incorporating a larger institutional cohort may be needed to further validate the ML model for LMS prognostication.

Histology-Tailored Approach to Soft Tissue Sarcoma

Soft tissue sarcomas are a diverse and heterogeneous group of cancers of mesenchymal origin. Each histological type of soft tissue sarcoma has unique clinical particularities, which makes them challenging to diagnose and treat. Multidisciplinary management of these rare diseases is thus key for improved survival. The role of surgery has been well established, and it represents the cornerstone curative treatment for soft tissue sarcomas. To date, local recurrence is the leading cause of death in low-grade sarcomas located at critical sites, and distant metastasis in high-grade sarcomas, regardless of the site of origin. Management must be tailored to each individual histologic type. We describe the most common types of extremity, trunk, abdominal, and retroperitoneal soft tissue sarcoma along with characteristics to consider for optimized management.

Outcomes and Follow-Up Trends in Adrenal Leiomyosarcoma: A Comprehensive Literature Review and Case Report

Background: Leiomyosarcoma (LMS) originating from the adrenal gland is exceedingly rare, constituting a minute fraction of soft tissue sarcomas. Due to its rarity, with less than 50 documented cases in English medical literature, the diagnosis and management of adrenal LMS remain challenging. The aim of this study was to perform a review of the literature, in order to evaluate the prognosis of these rare cancers and report our specific case. Methods: A systematic review of the literature was conducted using PubMed, Web of Science, Google Scholar, and Scopus databases, up to December 2020. The search utilized MeSH terms such as "Adrenal Gland Neoplasms," "Leiomyosarcoma," "Adrenalectomy," and "Smooth Muscle Tumor." The inclusion criteria focused on studies reporting patients with a histopathological diagnosis of adrenal leiomyosarcoma. The PRISMA guidelines were followed to ensure a comprehensive analysis. Results: Out of 63 identified studies, 43 met the inclusion criteria and were reviewed. These studies highlighted the rarity and aggressive behavior of adrenal leiomyosarcoma. Surgical excision remains the cornerstone of treatment, often complemented by adjuvant therapies. The reviewed case involved a 52-year-old woman who underwent a right laparoscopic adrenalectomy for a 9 × 7 × 6 cm grade 3 leiomyosarcoma. Despite subsequent adjuvant chemotherapy, hepatic metastases were detected, illustrating the aggressive nature of the disease. The literature underscores the importance of histopathological analysis and long-term surveillance for managing disease progression. Conclusions: Optimal management of adrenal leiomyosarcoma requires a multidisciplinary approach and meticulous follow-up. The rarity of the disease poses challenges for standardizing treatment, but surgical excision and tailored adjuvant therapies show promise. Further research is essential to refine treatment strategies and improve prognosis for this rare malignancy.

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study

BACKGROUND

Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.

METHODS

A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.

RESULTS

The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649-0.923] vs. 0.822 [0.692-0.952] vs. 0.733 [0.573-0.892] vs. 0.511 [0.359-0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.

CONCLUSIONS

The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.

TRIAL REGISTRATION

Not applicable.

A Giant, Neglected Leiomyosarcoma on the Left Shoulder

This paper examines the impact of delayed diagnosis and treatment on the prognosis of patients with leiomyosarcomas (LMS). We present a case study highlighting the consequences of neglected LMS, focusing on vascular involvement and metastatic potential. Our findings underscore the importance of early detection and intervention in improving patient outcomes. Additionally, we discuss the challenges associated with diagnosing rare skin LMS and the implications of limited access to medical screening. Through a comprehensive analysis of the literature, we elucidate the critical role of routine surveillance in detecting these malignancies at an earlier stage, thus facilitating timely intervention and potentially curative treatment. This study underscores the urgency of raising awareness among both healthcare providers and the general population about the significance of early detection and prompt management in mitigating the adverse outcomes associated with neglected LMS.

Primary Pulmonary Leiomyosarcoma Managed With Pazopanib: A Case Report and Literature Review

Primary pulmonary leiomyosarcoma (PPL) arises from pulmonary smooth muscle tissue, with less than 0.5% incidence among pulmonary malignancies and 30% among primary pulmonary sarcomas. Here, we present a case of PPL managed with pazopanib. Moreover, a brief review of relevant literature was conducted. A 65-year-old female presented with chronic cough, weight loss, and recurrent pneumonia, with a medical history including diabetes mellitus, hypertension, and hyperlipidemia, as well as past surgical colectomy and hysterectomy (due to abnormal uterine bleeding with normal pathology and no evidence of uterine leiomyosarcoma). Upon admission, she exhibited fever, cough, dyspnea, and decreased breath sounds over the upper lung lobe. Diagnostic workups revealed a large pulmonary mass, diagnosed as leiomyosarcoma via core needle biopsy and subsequent immunohistochemical staining. Neoadjuvant treatment with ifosfamide and adriamycin followed by chemoradiotherapy was attempted, but surgery for tumor resection was not feasible. Then, gemcitabine and docetaxel were chosen as the new treatment after ifosfamide and adriamycin were not effective. Imaging revealed tumor not reacting to latest treatment, either. Given the disease's persistence and the patient's diminished capacity for chemotherapy, the patient is presently undergoing pazopanib treatment, with ongoing monitoring of its effects. After 3 months of treatment with pazopanib (administered orally at 200 mg twice daily), the patient experienced a significant reduction in tumor size, with a notable decrease from 135 mm to 80 mm, approximating one-third of the initial size, indicating a positive therapeutic effect. This case report provides preliminary evidence suggesting that pazopanib, an oral multi-tyrosine kinase inhibitor, may be a promising therapeutic option for the management of PPL. However, further in-depth and long-term studies are warranted to evaluate the clinical efficacy and superiority of this treatment.

Establishment and characterization of NCC-LMS3-C1: a novel patient-derived cell line of leiomyosarcoma

Leiomyosarcoma (LMS) is an aggressive mesenchymal malignancy, which originates from the smooth muscle cells or from the precursor mesenchymal stem cells that potentially differentiate into smooth muscle cells. LMS is one of the most common sarcomas. LMS has genomic instability, reflecting complex and unbalanced karyotypes, and the cytogenetic and molecular changes in LMS are not consistent. The standard treatment of the primary LMS is complete resection, and the metastasis is often observed even after curative surgery. Patient-derived cancer models are a key bioresource to develop a novel therapy, and we aimed to establish and characterize a novel cell line for LMS. We established a cell line from tumor tissues of the patient with LMS and named it NCC-LMS3-C1. We maintained NCC-LMS3-C1 cells for 12 months and passed them more than 30 times. Genome-wide copy number analysis demonstrated that NCC-LMS3-C1 cells harbored genetic abnormalities. NCC-LMS3-C1 cells exhibited aggressive phenotypes such as continuous growth, spheroid formation, and invasion in the tissue culture condition, which may reflect the clinical behaviors of LMS. We performed a drug screening using NCC-LMS3-C1 cells and found that four anti-cancer agents, such as bortezomib, dasatinib, mitoxantrone, and romidepsin, had remarkable anti-proliferative effects on NCC-LMS3-C1 cells. We conclude that NCC-LMS3-C1 cells will be a useful resource for the study of LMS.

Effect of electrochemotherapy on myogenesis of mouse C2C12 cells in vitro

Electrochemotherapy (ECT) is a local ablative therapy for the treatment of different skin and subcutaneous tumors and certain tumors in internal organs. Skeletal muscle represents a major tumor- surrounding tissue, exposed to side effects of ECT. At the cellular level, side-effects of ECT on skeletal muscle and underlying mechanisms have not been examined yet. Thus, we aimed to determine the effect of ECT in the mouse muscle cell line C2C12 during in vitro myogenesis. We evaluated the electroporation efficiency and viability of C2C12 myotubes at increasing voltages (200-1300 V/cm) using propidium iodide (PI). Permeabilization of PI into the cells was voltage-dependent accounting up to 97 % efficiency at the highest voltage. High cell viability and myotube integrity were maintained until 4 days after electroporation. ECT with the cytostatic drugs bleomycin and cisplatin decreased the viability of C2C12 myoblasts and myotubes in a dose-dependent manner. However, myoblasts were more sensitive to ECT than myotubes. Increased secretion of IL-6, observed 3 days after ECT, confirming its effects on early myogenesis. Only minor effects of ECT were observed in treated myotubes. These results contribute to the safety profile of ECT in tumor treatment.

Two simple-to-use web-based nomograms to predict overall survival and cancer-specific survival in patients with extremity fibrosarcoma

Background

Fibrosarcoma is a rare sarcoma of the soft tissue in adults, occurring most commonly in the extremities. This study aimed to construct two web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with extremity fibrosarcoma (EF) and validate it with multicenter data from the Asian/Chinese population.

Method

Patients with EF in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were included in this study and were randomly divided into a training cohort and a verification cohort. The nomogram was developed based on the independent prognostic factors determined by univariate and multivariate Cox proportional hazard regression analyses. The predictive accuracy of the nomogram was validated with the Harrell's concordance index (C-index), receiver operating curve, and calibration curve. Decision curve analysis (DCA) was utilized to compare the clinical usefulness between the novel model and the existing staging system.

Result

A total of 931 patients finally were obtained in our study. Multivariate Cox analysis determined five independent prognostic factors for OS and CSS, namely, age, M stage, tumor size, grade, and surgery. The nomogram and the corresponding web-based calculator were developed to predict OS (https://orthosurgery.shinyapps.io/osnomogram/) and CSS (https://orthosurgery.shinyapps.io/cssnomogram/) probability at 24, 36, and 48 months. The C-index of the nomogram was 0.784 in the training cohort and 0.825 in the verification cohort for OS and 0.798 in the training cohort and 0.813 in the verification cohort for CSS, respectively, indicating excellent predictive performance. The calibration curves showed excellent agreement between the prediction by the nomogram and actual outcomes. Additionally, the results of DCA showed that the newly proposed nomogram was significantly better than the conventional staging system with more clinical net benefits. The Kaplan-Meier survival curves showed that patients assigned into the low-risk group had a more satisfactory survival outcome than the high-risk group.

Conclusion

In this study, we constructed two nomograms and web-based survival calculators including five independent prognostic factors for the survival prediction of patients with EF, which could help clinicians make personalized clinical decisions.

Interdisciplinary Surgical Therapy of Extremity Soft-Tissue Sarcomas: A Personalized Resection and Reconstruction Algorithm

Soft-tissue sarcomas (STS) are rare, but potentially life-threatening malignancies. STS can occur anywhere in the human body with the limbs being the most common site. Referral to a specialized sarcoma center is crucial to guarantee prompt and appropriate treatment. STS treatment strategies should be discussed in an interdisciplinary tumor board to involve expertise from all available resources, including an experienced reconstructive surgeon for an optimal outcome. In many cases, extensive resection is needed to achieve R0 resection, resulting in large defects after surgery. Hence, an evaluation of whether plastic reconstruction might be required is mandatory to avoid complications due to insufficient primary wound closure. In this retrospective observational study, we present data of patients with extremity STS treated at the Sarcoma Center, University Hospital Erlangen, in 2021. We found that complications were more frequent in patients who received secondary flap reconstruction after insufficient primary wound closure compared to patients who received primary flap reconstruction. Additionally, we propose an algorithm for an interdisciplinary surgical therapy of soft-tissue sarcomas regarding resection and reconstruction and present two problematic cases to emphasize the complexity of surgical sarcoma therapy.