Leiomyosarcoma: investigation of prognostic factors for risk-stratification model

Prediction of 5-year survival in soft tissue leiomyosarcoma using a machine learning model algorithm

BACKGROUND AND OBJECTIVES

Leiomyosarcoma (LMS) is associated with one of the poorest overall survivals among soft tissue sarcomas. We sought to develop and externally validate a model for 5-year survival prediction in patients with appendicular or truncal LMS using machine learning algorithms.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used for development and internal validation of the models; external validation was assessed using our institutional database. Five machine learning algorithms were developed and then tested on our institutional database. Area under the receiver operating characteristic curve (AUC) and Brier score were used to assess model performance.

RESULTS

A total of 2209 patients from the SEER database and 81 patients from our tertiary institution were included. All models had excellent calibration with AUC 0.84-0.85 and Brier score 0.15-0.16. After assessing the performance indicators according to the TRIPOD model, we found that the Elastic-Net Penalized Logistic Regression outperformed other models. The AUCs of the institutional data were 0.83 (imputed) and 0.85 (complete-case analysis) with a Brier score of 0.16.

CONCLUSION

Our study successfully developed five machine learning algorithms to assess 5-year survival in patients with LMS. The Elastic-Net Penalized Logistic Regression retained performance upon external validation with an AUC of 0.85 and Brier score of 0.15.

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients

Background

Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.

Methods

Patients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots.

Results

118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves.

Conclusion

The nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.

Leiomyosarcoma of the chest wall mimicking schwannoma resected by a video-assisted thoracoscopic approach: a case report

Chest wall sarcomas account for <20% of all soft tissue sarcomas of which leiomyosarcomas represent only 1–4%. We report a case of thoracic leiomyosarcoma that resembled schwannoma in preoperative image studies. A 79-year-old man presented to our hospital with a chest wall tumor that increased in size over 3 months. Computed tomography of the chest revealed a 3-cm mass arising from the chest wall. Thoracic magnetic resonance imaging showed a solid tumor that was hypo-intense on T1-weighted imaging and iso-intense on T2-weighted imaging. Chest wall resection was performed using a video-assisted thoracoscopic approach after a frozen section examination revealed sarcoma. The histological diagnosis was leiomyosarcoma. Liver and multiple lung metastases were detected 5 years after surgery. Malignant tumors should be considered in any patient with chest wall tumors. The thoracoscopic approach could be an optimal treatment for chest wall tumor.

Eribulin versus dacarbazine in patients with leiomyosarcoma: subgroup analysis from a phase 3, open-label, randomised study

BACKGROUND

This subgroup analysis of a phase 3 study compares outcomes for eribulin versus dacarbazine in patients with leiomyosarcoma.

METHODS

Patients ≥18 years old with advanced liposarcoma or leiomyosarcoma, ECOG PS ≤2, and ≥2 prior treatment regimens were randomly assigned (1:1) to eribulin mesylate (1.4 mg/m² intravenously on day 1 and day 8) or dacarbazine (either 850, 1000, or 1200 mg/m² intravenously) every 21 days until disease progression. The primary end point was OS; additional end points were progression-free survival (PFS) and objective response rate (ORR).

RESULTS

309 Patients with leiomyosarcoma were included (eribulin, n = 157; dacarbazine, n = 152). Median age was 57 years; 42% of patients had uterine disease and 57% had nonuterine disease. Median OS was 12.7 versus 13.0 months for eribulin versus dacarbazine, respectively (hazard ratio [HR] = 0.93 [95% CI 0.71-1.20]; P = 0.57). Median PFS (2.2 vs 2.6 months, HR = 1.07 [95% CI 0.84-1.38]; P = 0.58) and ORR (5% vs 7%) were similar between eribulin- and dacarbazine-treated patients. Grade ≥3 TEAEs occurred in 69% of patients receiving eribulin and 59% of patients receiving dacarbazine.

CONCLUSIONS

Efficacy of eribulin in patients with leiomyosarcoma was comparable to that of dacarbazine. Both agents had manageable safety profiles.

Development and Validation of a Prognostic Nomogram for Extremity Soft Tissue Leiomyosarcoma

Background: Extremity soft tissue leiomyosarcoma (LMS) is a rare disease with a poor prognosis. The aim of this study is to develop nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity soft tissue LMS.

Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, 1,528 cases of extremity soft tissue LMS diagnosed between 1983 and 2015 were included. Cox proportional hazards regression modeling was used to analyze prognosis and obtain independent predictors. The independent predictors were integrated to develop nomograms predicting 5- and 10-year OS and CSS. Nomogram performance was evaluated by a concordance index (C-index) and calibration plots using R software version 3.5.0.

Results: Multivariate analysis revealed that age ≥60 years, high tumor grade, distant metastasis, tumor size ≥5 cm, and lack of surgery were significantly associated with decreased OS and CSS. These five predictors were used to construct nomograms for predicting 5- and 10-year OS and CSS. Internal and external calibration plots for the probability of 5- and 10-year OS and CSS showed excellent agreement between nomogram prediction and observed outcomes. The C-index values for internal validation of OS and CSS prediction were 0.776 (95% CI 0.752–0.801) and 0.835 (95% CI 0.810–0.860), respectively, whereas those for external validation were 0.748 (95% CI 0.721–0.775) and 0.814 (95% CI 0.785–0.843), respectively.

Conclusions: The proposed nomogram is a reliable and robust tool for accurate prognostic prediction in patients with extremity soft tissue LMS.

A Case of "en bloc" Excision of a Chest Wall Leiomyosarcoma and Closure of the Defect with Non-Cross-Linked Collagen Matrix (Egis®)

Sarcomas arising from the chest wall account for less than 20% of all soft tissue sarcomas, and at this site, primitive tumors are the most frequent to occur. Leiomyosarcoma is a malignant smooth muscle tumor and the best outcomes are achieved with wide surgical excision. Although advancements have been made in treatment protocols, leiomyosarcoma remains one of the more difficult soft tissue sarcoma to treat. Currently, general local control is obtained with surgical treatment with wide negative margins. We describe the case of a 50-year-old man who underwent a chest wall resection involving a wide portion of the pectoralis major and minor muscle, the serratus and part of the second, third and fourth ribs of the left side. The full-thickness chest wall defect of 10 × 8 cm was closed using a non-cross-linked acellular dermal matrix (Egis^®) placed in two layers, beneath the rib plane and over it. A successful repair was achieved with no incisional herniation and with complete tissue regeneration, allowing natural respiratory movements. No complications were observed in the postoperative course. Biological non-cross-linked matrix, derived from porcine dermis, behaves like a scaffold supporting tissue regeneration; it can be successfully used as an alternative to synthetic mesh for chest wall reconstruction.