| Literature DB >> 32858763 |
Petra Schödel1,2, Stephanie T Jünger3,4, Maike Wittersheim5, Hans Christian Reinhardt6,4,7,8, Nils-Ole Schmidt1, Roland Goldbrunner3,4, Martin Proescholdt1,2, Stefan Grau3,4.
Abstract
BACKGROUND: Brain metastases (BM) frequently cause focal neurological deficits leading to a reduced Karnofsky performance score (KPS). Since KPS is routinely used to guide the choice of adjuvant therapy, we hypothesized that improving KPS by surgical resection may improve the chance for adjuvant treatment and ultimately result in better survival. We therefore analyzed the course of a large cohort undergoing resection of symptomatic brain metastases in the context of further treatment and clinical outcome. PATIENTS AND METHODS: In a bi-centric retrospective analysis we retrieved baseline, clinical, and treatment-related parameters of patients operated on BM between 2010 and 2019. Survival was calculated using Kaplan-Meier estimates; prognostic factors for survival were analyzed by Log-rank test and Cox proportional hazards.Entities:
Mesh:
Year: 2020 PMID: 32858763 PMCID: PMC7571801 DOI: 10.1002/cam4.3402
Source DB: PubMed Journal: Cancer Med ISSN: 2045-7634 Impact factor: 4.452
Demographics and baseline characteristics
| Parameter | Value |
|---|---|
|
| 61 (19‐87) |
|
| 371 (49.5) |
| Primary [n; (%)] | |
|
| 318 (42.4) |
|
| 114 (15.2) |
|
| 116 (15.5) |
|
| 72 (9.6) |
|
| 24 (3.2) |
|
| 33 (4.4) |
|
| 73 (9.7) |
|
|
281 (37.5) |
|
| 1‐34 |
|
| 462 (61.6) |
|
| 185 (24.7) |
|
| 103 (13.7) |
|
| |
|
| 208 (27.7) |
|
| 84 (11.2) |
|
| 81 (10.8) |
|
| 66 (8.8) |
|
| 196 (26.1) |
|
| 115 (15.3) |
| Neurological deficits [%] | |
|
| 13.5 |
|
| 11.3 |
|
| 21.2 |
|
| 9.3 |
|
| 23.2 |
|
| 32.7 |
| Postoperative Treatment [n;(%)] | |
|
| 613 (81.7) |
|
| 398 (53.1) |
|
| |
|
| 42.9 |
|
| 57.1 |
Figure 1(A‐C), Descriptive account of clinical parameters before and after resection of BM. (A) distribution of KPS, (B) classification of neurological symptom burden according to NPS (C) allocation of patients to RPA classes pre‐ and postoperatively
Clinical scores and prognostic group allocation
|
| Before surgery | After surgery |
|
|---|---|---|---|
|
| .0001 | ||
|
| 261 (34.8) | 433 (57.7) | |
|
| 289 (38.5) | 191 (25.5) | |
|
| 110 (14.7) | 90 (12.0) | |
|
| 77 (10.3) | 25 (3.3) | |
|
| 13 (1.7) | 11 (1.5) | |
|
| 80 (10‐100) | 90 (0‐100) | <.0001 |
|
| <.0001 | ||
|
| 139 (18.5) | 145 (19.3) | |
|
| 472 (62.9) | 526 (70.1) | |
|
| 139 (18.5) | 79 (10.5) |
Figure 2Survival estimates of the patient cohort after allocation to (A) RPA classes, (B) GPA scoring groups and (C) for patients with a preoperative RPA class III stratified according to the postoperative allocations into better classes or still class III
Analysis of prognostic factors in uni‐ and multivariate analysis
| Parameter |
Univariate (log rank) [ |
Multivariate (Cox regression) [HR 95%CI; |
|---|---|---|
|
| .01 | n.s. |
| Primary tumor | .167 | |
| Controlled systemic status | .001 | 0.67 0.55‐0.82 <.0001 |
|
Timing (synchronous vs metachronous) | .235 | |
| KPS ≥ 70 preoperative | .001 | 0.53 0.38‐0.71 <.0001 |
| KPS ≥ 70 postoperative | <.0001 | |
| Surgical complications | .015 | n.s. |
| BM count | ||
| single vs oligo | .348 | |
| single vs multiple | .009 | |
| oligo vs multiple | .081 | |
| single/oligo vs multiple | .001 | 0.63 0.50‐0.80 <.0001 |
| Postoperative radio‐therapy | .001 | 0.65 0.52‐0.82 <.0001 |
| Systemic treatment after BM resection | <.0001 |
0.55 0.45‐0.68 <.0001 |
| Extent of resection | .184 |
Figure 3Survival curves demonstrating the impact of prognostic factors from Cox proportional hazards model. These factors were KPS > 70, BM count <4, controlled systemic disease status and postoperative radio‐ and systemic therapy. Curves illustrate survival estimates in regard of the accumulation of these factors