Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

Incidence of Undifferentiated Pleomorphic Sarcoma (UPS) in the United States

The classification of undifferentiated pleomorphic sarcoma (UPS) has been evolving with advances in immunohistochemistry and genomic profiling over the past 20 years. There is a lack of current information on UPS incidence. Due to the lack of designated histology codes for UPS in the Surveillance, Epidemiology, and End Results (SEERs) program, we estimated UPS incidence by three different definitions based on clinical opinions using the 2000-2020 data from 22 registries of the SEER program. The incidence varied widely across the three definitions with 0.06 per 100,000 persons for the least inclusive definition and 0.67 per 100,000 persons for the most inclusive definition in 2016-2020, making it challenging to estimate the exact incidence of UPS. Regardless, all the incidences decreased between 2000 and 2020. Guidelines in UPS diagnosis and classification need to be better implemented in the US.

Development and validation of a machine learning-based model to predict postoperative overall survival in patients with soft tissue sarcoma: a retrospective cohort study

BACKGROUND

The aim of this study is to develop a machine learning-based model to predict postoperative overall survival (OS) in patients with soft tissue sarcoma (STS) that demonstrates superior comprehensive performance.

METHODS

This analysis leveraged data from the SEER database spanning 2010-2020, alongside a STS cohort from the National Cancer Center. Machine learning methods were applied for predictor selection by wrapper methods and the development of the predictive model. The optimal model was determined using the concordance index (C-index), time-dependent calibration curves, time dependent receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).

RESULTS

Six machine learning learners identified six feature subsets. Subsequently, six feature subsets and six machine learning learners were combined, resulting in the development of 36 prognostic models. The CAM model, exhibiting the highest prediction performance, was selected. The CAM model achieved a C-index of 0.849 (95% CI 0.837-0.859) in the training cohort and 0.837 (95% CI 0.809-0.871) in the validation cohort. Furthermore, time-dependent calibration curves, time-dependent ROC curves, and DCA indicate that the PAM demonstrates excellent calibration, predictive accuracy, and clinical net benefit. A publicly accessible web tool was developed for the CAM. Notably, CAM's performance exceeds that of all existing STS prognostic nomograms and prediction models.

CONCLUSIONS

The CAM has the potential to identify postoperative OS in STS patients. This can assist clinicians in assessing the severity of the disease, facilitating patient follow-up, and aiding in the formulation of adjuvant treatment strategies.

A Detailed Examination of Retroperitoneal Undifferentiated Pleomorphic Sarcoma: A Case Report and Review of the Existing Literature

This detailed review focuses on retroperitoneal undifferentiated pleomorphic sarcoma (UPS), a particularly aggressive soft-tissue sarcoma that poses unique diagnostic and therapeutic challenges due to its rarity and complex presentation. By documenting a new case of retroperitoneal UPS and conducting a comprehensive review of all known cases, this article aims to expand the existing body of knowledge on the epidemiology, molecular pathogenesis, and treatment strategies associated with this rare disease. The complexity of diagnosing UPS is emphasized given that it rarely occurs in the retroperitoneal space and its histological and molecular complexity often complicates its recognition. This review highlights the need for specialized diagnostic approaches, including advanced imaging techniques and histopathological studies, to accurately diagnose and stage the disease. In terms of treatment, this paper advocates a multidisciplinary approach that combines surgery, radiotherapy and chemotherapy and tailors it to individual patients to optimize treatment outcomes. This review highlights case studies that illustrate the effectiveness of surgical intervention in the treatment of these tumors and emphasize the importance of achieving clear surgical margins to prevent recurrence. Furthermore, this review discusses the potential of new molecular targets and the need for innovative therapies that could bring new hope to patients affected by this challenging sarcoma.

Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma

PURPOSE

Machine learning (ML) algorithms to predict cancer survival have recently been reported for a number of sarcoma subtypes, but none have investigated undifferentiated pleomorphic sarcoma (UPS). ML is a powerful tool that has the potential to better prognosticate UPS.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologically confirmed undifferentiated pleomorphic sarcoma (UPS) (n = 665). 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 UPS patients (n = 151).

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 0.67-0.69 at the 5-year time point. The Multi-Layer Perceptron Neural Network (MLP) model was the best performing model and used for external validation. Similarly, the MLP model performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.85 and 0.81, respectively. The MLP model was well calibrated on external validation. The MLP model has been made publicly available at https://rachar.shinyapps.io/ups_app/ .

CONCLUSION

Machine learning models perform well for survival prediction in UPS, though this sarcoma subtype may be more difficult to prognosticate than other subtypes. Future studies are needed to further validate the machine learning approach for UPS prognostication.

Emerging innovations and advancements in the treatment of extremity and truncal soft tissue sarcomas

In this special edition update on soft tissue sarcomas (STS), we cover classifications, emerging technologies, prognostic tools, radiation schemas, and treatment disparities in extremity and truncal STS. We discuss the importance of enhancing local control and reducing complications, including the role of innovative imaging, surgical guidance, and hypofractionated radiation. We review advancements in systemic and immunotherapeutic treatments and introduce disparities seen in this vulnerable population that must be considered to improve overall patient care.

A Rare Presentation of Undifferentiated Pleomorphic Sarcoma in the Subpectoral Space

Soft tissue sarcomas (STS) are often described as asymptomatic, rapidly expanding masses, particularly in the extremities or trunk. Undifferentiated pleomorphic sarcoma (UPS), a high-grade variant of STS, ranks as the second most prevalent subtype in the United States. It predominantly affects males between their fifth and seventh decades. Its often benign symptomatology, however, can lead to initial misdiagnosis and subsequent mismanagement. We present the case of a 57-year-old Caucasian male, previously in good health, who experienced a recurring subpectoral lesion causing discomfort and mass-related effects. Initial management included incision and drainage, which provided temporary relief. The biopsy revealed a diagnosis of grade 3 UPS. The lesion's recurrence two months later was accompanied by local invasion into adjacent skin and musculature as well as metastasis to the right hemiliver. A comprehensive understanding of UPS among medical professionals is vital for accurate diagnosis and facilitating prompt intervention to prevent avoidable complications and optimize patient outcomes.

Retroperitoneal undifferentiated pleomorphic sarcoma with total nephrectomy: a case report and literature review

Background

Undifferentiated pleomorphic sarcoma (UPS) is a highly malignant soft tissue sarcoma with a poor prognosis and no clear effective clinical means for treatment, and there has been no significant progress in research within this field in recent years. This study aimed to investigate the epidemiology, etiology, clinical features, diagnostic modalities, various treatment modalities, and prognosis of retroperitoneal undifferentiated pleomorphic sarcoma and to contribute to the clinical management of this type of disease. In this study, we report a case of undifferentiated pleomorphic sarcoma with a primary origin in the retroperitoneum. Undifferentiated pleomorphic sarcoma occurring in the retroperitoneum is rarely reported.

Case description

A 59-year-old man with abdominal distension and pain for 4 months presented to our hospital after the failure of conservative treatment. A 9.6 cm by 7.4 cm mass in the left retroperitoneum was found on a CT scan of the whole abdomen with three degrees of enhancement. After surgical treatment, the tumor and the left kidney were completely removed, and pathological examination and genetic sequencing showed an apparent undifferentiated pleomorphic sarcoma. The patient subsequently declined follow-up treatment and is currently alive and well.

Conclusions

At the current level of clinical technology, the treatment of undifferentiated pleomorphic sarcoma is still in the exploratory stage, and the scarcity of clinical cases of this disease may have hindered the acquisition of clinical trials and research data for this disease. At present, the first choice of treatment for undifferentiated pleomorphic sarcoma is still radical resection. In the existing clinical studies, there are no strong data to support the effect of preoperative neoadjuvant chemoradiotherapy and adjuvant chemoradiotherapy in clinical practice. Similar to other diseases, the use of radiotherapy and chemotherapy before and after surgery may be a potential treatment for this disease in the future. Targeted therapy for this disease still needs further exploration, and we need more reports on related diseases to promote future treatment and research on this disease.

Epidemiology and prediction model of patients with carcinosarcoma in the United States

Background

Carcinosarcoma is a rare biphasic tumor composed of both carcinoma and sarcoma elements, which occurs at various sites. Most studies are case reports or small population-based studies for a single disease site, so comprehensive evaluations of epidemiology and prognostic factors for carcinosarcoma are needed.

Methods

Surveillance, Epidemiology, and End Results (SEER)-8 (1975-2019) provided data for the epidemiological analysis. SEER-17 (2000-2019) provided data on the primary tumor sites, initial treatment, construction, and validation of the nomogram.

Results

The age-adjusted incidence per 100,000 persons of carcinosarcoma increased significantly from 0.46 to 0.91 [1975-2019; average annual percent change (AAPC): 1.3%, P = 0.006], with localized stage increasing from 0.14 to 0.26 [2005-2015; annual percent change (APC): 4.2%]. The 20-year limited-duration prevalence per 100,000 increased from 0.47 to 3.36 (1999-2018). The mortality per 100,000 increased significantly from 0.16 to 0.51 (1975-2019; AAPC: 1.9%, P < 0.001). The 5-year relative survival was 32.8%. The greatest number of carcinosarcomas were from the uterus (68.7%), ovary (17.8%), lung and bronchus (2.3%). The main treatment is comprehensive treatment based on surgery; however, surgery alone is preferred in older patients. In multivariate analysis (N = 11,424), age, sex, race, year of diagnosis, disease stage, tumor site, and treatment were associated with survival. A nomogram was established to predict 1-, 3-, and 5-year survival, and the C-indexes were 0.732 and 0.748 for the training and testing sets, respectively. The receiver operating characteristic curve demonstrated that the nomogram provided a comprehensive and accurate prediction [1-year area under the curve (AUC): 0.782 vs. 0.796; 3-year AUC: 0.771 vs. 0.798; 5-year AUC: 0.777 vs. 0.810].

Conclusions

In this study, the incidence, prevalence, and mortality of carcinosarcoma have increased over the past decades. There was a rapid rise in the incidence of localized stage in recent years, which reflected improved early detection. The prognosis of carcinosarcoma remains poor, signifying the urgency of exploring targeted cancer control treatments. Explicating distribution and gender disparities of carcinosarcoma may facilitate disease screening and medical surveillance. The nomogram demonstrated good predictive capacity and facilitated clinical decision-making.

Establishment and Validation of a Nomogram Prognostic Model for Epithelioid Hemangioendothelioma

Background. Epithelioid hemangioendothelioma (EHE) is an ultrarare vascular sarcoma. At present, the epidemiological and clinical characteristics and prognostic factors are still unclear. Our study attempted to describe clinical features, investigate the prognostic indicators, and establish the nomogram prediction model based on the Surveillance, Epidemiology, and End Results (SEER) database for EHE patients. Methods. The patients diagnosed with EHE from 1986 to 2018 were collected from the SEER database and were randomly divided into a training group and a validation group at a ratio of 7 : 3. The Cox proportional hazard models were used to determine the independent factors affecting prognosis and establish a nomogram prognostic model to predict the survival rates for patients with EHE. The accuracy and discriminative ability of the model were measured using the concordance index, receiver operating characteristic curves, and calibration curves. The clinical applicability and application value of the model were evaluated by decision curve analysis. Results. The overall age-adjusted incidence of EHE was 0.31 patients per 1,000,000 individuals, with a statistically significant difference per year. Overall survival at 1, 5, and 10 years for all patients was 76.5%, 57.4%, and 48.2%, respectively. Multivariate Cox regression analysis identified age, tumour stage, degree of tissue differentiation, surgical treatment, chemotherapy, and radiotherapy as independent factors affecting prognosis ( $P<0.05$ ). The C-index values for our nomogram model of training group and validation group were 0.752 and 0.753, respectively. The calibration curve was in good agreement with the actual observation results, suggesting that the prediction model has good accuracy. The decision curve analysis indicated a relatively large net benefit. Conclusions. The nomogram model may play an important role in predicting the survival rate for EHE patients, with good concordance and accuracy, and can be applied in clinical practice.

Analysis of prognostic factors of undifferentiated pleomorphic sarcoma and construction and validation of a prediction nomogram based on SEER database

Background

Undifferentiated pleomorphic sarcoma (UPS) is considered one of the most common types of soft tissue sarcoma (STS). Current studies have shown that the prognosis of UPS is related to some of its clinical characteristics, but no survival prediction model for the overall survival (OS) of UPS patients has been reported. The purpose of this study is to construct and validate a nomogram for predicting OS in UPS patients at 3, 5 years after the diagnosis.

Methods

According to the inclusion and exclusion criteria, 1079 patients with UPS were screened from the SEER database and randomly divided into the training cohort (n = 755) and the validation cohort (n = 324). Patient demographic and clinicopathological characteristics were first described, and the correlation between the two groups was compared, using the Kaplan–Meier method and Cox regression analysis to determine independent prognostic factors. Based on the identified independent prognostic factors, a nomogram for OS in UPS patients was established using R language. The nomogram’s performance was then validated using multiple indicators, including the area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA).

Results

Both the C-index of the OS nomogram in the training cohort and the validation cohort were greater than 0 .75, and both the values of AUC were greater than 0.78. These four values were higher than their corresponding values in the TNM staging system, respectively. The calibration curves of the Nomogram prediction model and the TNM staging system were well fitted with the 45° line. Decision curve analysis showed that both the nomogram model and the TNM staging system had clinical net benefits over a wide range of threshold probabilities, and the nomogram had higher clinical net benefits than the TNM staging system as a whole.

Conclusion

With good discrimination, accuracy, and clinical practicability, the nomogram can individualize the prediction of 3-year and 5-year OS in patients with UPS, which can provide a reference for clinicians and patients to make better clinical decisions.

Undifferentiated Pleomorphic Sarcoma of the Duodenal Papilla: A Rare Case and Worth Discussing History

Background

Undifferentiated pleomorphic sarcoma (UPS) is a malignant tumor that originates in the mesenchymal tissue and is common in the extremities and retroperitoneum. Primary UPS of the duodenal papilla is rare and a distinct clinical entity.

Case presentation

In this report, a 48-year-old Chinese man was admitted to our hospital with symptoms of melena. The patient underwent choledochectomy and choledochaljejunostomy for obstructive jaundice 8 years before admission. Endoscopic examination after admission confirmed a mass located at the duodenal papilla. Then, the duodenal papilla and tumor resection were performed, and the histopathology report confirmed the diagnosis of UPS. The patient refused further treatment and died 2 months later due to local recurrence and intrahepatic metastasis.

Conclusions

It is rare that the mass in the duodenal papilla is diagnosed as UPS. The unpredicted behavior of these tumors warrants a careful plan considering their indolent nature and possible recurrence and metastasis. The prognosis was poor despite the early complete resection.

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed.

Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established.

Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan–Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/).

Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.