| Literature DB >> 35186755 |
Xiaoshun Shi1, Xiguang Liu1, Xiaoying Dong1, Hua Wu1, Kaican Cai1.
Abstract
Describing the changes in surgical procedures and factors affecting the surgical outcome of patients who have undergone complete resection of giant mediastinal tumors (GMTs, diameter ≥ 10 centimeters) could improve preoperative decision-making and prognostic evaluations. We accessed data from three sources, which are case reports on surgical treatment of GMTs from PubMed, Web of Science, and EMBASE until June 1, 2019; patients with resected GMT from the Surveillance, Epidemiology, and End Results (SEER) database; and retrospective review of medical records in our institution from 2000 to 2019. The worldwide distribution, clinicopathological characteristics, symptom profile, prognosis of patients with GMT resection, and nomogram for surgical outcome prediction are reported. A total of 242 rare GMT cases from four continents (Asia, North America, South America, and Europe) were included. The median age of the patients was 40 (IQR: 27, range: 13-83) years, and the male-to-female ratio was 1.57:1. Dyspnea, shortness of breath, cough, and chest pain or discomfort were the major symptoms at presentation. The prognosis of benign and low-grade malignant GMTs was superior to that of high-grade malignant GMTs. Tumor malignancy played the most critical role in predicting postoperative survival, followed by longest tumor diameter and a posterior mediastinum location. The findings of this study suggest that the number of successful GMT surgeries has increased over the last decade and describe clinical features of GMTs. Physicians should prioritize tumor malignancy as a leading factor in predicting outcome rather than tumor size.Entities:
Keywords: giant mediastinal tumor; global survey; mediastinal malignancy; risk prediction model; surgical therapy
Year: 2022 PMID: 35186755 PMCID: PMC8854276 DOI: 10.3389/fonc.2022.820720
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Clinical features of the resected GMTs by cohort.
| Literature (N = 92) | Nanfang (N = 69) | SEER (N = 81) | Overall (N = 242) | P value* | |
|---|---|---|---|---|---|
|
| |||||
| Mean (SD) | 44.7 (16.6) | 42.2 (18.1) | 38.8 (18.0) | 42.0 (17.6) | |
| Median [Min, Max] | 44.0 [15.0, 82.0] | 44.0 [13.0, 78.0] | 32.0 [18.0, 83.0] | 40.0 [13.0, 83.0] | |
|
| |||||
| Female | 41.0 (44.6%) | 38.0 (55.1%) | 15.0 (18.5%) | 94.0 (38.8%) | |
| Male | 51.0 (55.4%) | 31.0 (44.9%) | 66.0 (81.5%) | 148 (61.2%) | < 0.01 |
|
| |||||
| Anterior | 47.0 (51.1%) | 35.0 (50.7%) | 47.0 (58.0%) | 129 (53.3%) | |
| Middle | 16.0 (17.4%) | 21.0 (30.4%) | 28.0 (34.6%) | 65.0 (26.9%) | < 0.01 |
| Posterior | 29.0 (31.5%) | 13.0 (18.8%) | 6.00 (7.4%) | 48.0 (19.8%) | |
|
| |||||
| Benign | 55.0 (59.8%) | 29.0 (42.0%) | 0 (0%) | 84.0 (34.7%) | |
| Malignant | 37.0 (40.2%) | 40.0 (58.0%) | 81.0 (100%) | 158 (65.3%) | < 0.01 |
*P-values were calculated using Chi-square test for categorical variables to compare the difference between groups.
Figure 1Overview of GMT resections from 1988 to 2019 in three cohorts. GMT distribution in countries around the world (A), in reports from 1988 to 2019 (B), by age (C), by sex (D), by mediastinum location (E), by surgical approach (F), and by pathological diagnosis (G).
Figure 2Heatmap of GMT symptoms in the Nanfang and literature cohorts.
Figure 3Survival of patients with GMTs who underwent complete resection. Recurrence-free survival in patients with GMTs who underwent complete resection: benign GMTs versus malignant GMTs (A), and benign GMTs versus low-grade malignant GMTs (B). Overall survival in patients with GMTs >10 cm who underwent complete resection: benign GMTs versus malignant GMTs (C), and among benign GMTs and low-grade malignant and malignant GMTs (D).
Figure 4Predictive model for postoperative survival in patients with GMTs who underwent complete resection. (A) Postoperative prognostic nomogram for patients with resected GMTs. (B) Calibration curves for predicting 5-year and 10-year survival. (C) Evaluation of the prediction model by decision-curve analysis.