Abstract A019: Clinical characteristics and outcomes of primary cardiac leiomyosarcoma cases from 2015-2025: A literature review
Bibliographic record
Abstract
Abstract Background: Primary cardiac leiomyosarcoma (PCLMS) is an exceptionally rare and aggressive malignant tumor, representing less than 0.25% of all cardiac tumors. Its nonspecific symptoms, high misdiagnosis rate, and lack of standardized treatment complicate early detection and management, often resulting in poor prognoses and high recurrence rates. Objective: This literature review aims to consolidate and analyze global PCLMS cases reported between 2015 and 2025 to better characterize clinical features, diagnostic strategies, therapeutic approaches, and patient outcomes. Methods: A systematic search of PubMed, Scopus, and Google Scholar was conducted using the term “primary cardiac leiomyosarcoma*.” Inclusion criteria limited the review to human cases from 2015–2025 confirmed via histopathology. After screening 269 articles and removing duplicates, 46 unique case reports met the eligibility criteria and were analyzed descriptively. Results: The average patient age was 46 years, with a female predominance (65.2%). The left atrium was the most common tumor site. Nearly half of cases (43.5%) were initially misdiagnosed, most commonly as myxoma. All patients were symptomatic, with dyspnea, chest pain, and cough being most frequent. Biopsy confirmed diagnosis in 95.6% of cases. Multimodal imaging, particularly echocardiography, CT, and MRI, was widely used. Surgery was the primary treatment; however, recurrence (28.2%), metastasis (21.7%), and mortality (32.6%) remained high. Adjuvant chemotherapy (32.6%) and radiotherapy (23.9%) were inconsistently applied, with limited survival benefit. The longest recorded survival was nine years post-resection. Conclusion: PCLMS remains a diagnostic and therapeutic challenge due to its rarity, nonspecific presentation, and limited treatment consensus. This review underscores the urgent need for standardized diagnostic protocols, multimodal therapies, genetic profiling, and the establishment of international PCLMS registries to guide evidence-based care and improve long-term outcomes. Citation Format: Taylor C.S. Bailey. Clinical characteristics and outcomes of primary cardiac leiomyosarcoma cases from 2015-2025: A literature review [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr A019.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".