Abstract 1361P
Background
The existing prognostic scores for lung cancer patients with brain metastases (BM) have certain limitations and have not been solely designed for lung cancer patients eligible for central nervous system (CNS) stereotactic radiosurgery (SRS). This study aims to develop a novel prognostic index for this population.
Methods
This retrospective cohort study originated from a database of 673 cancer patients with BM. Eventually, the study population comprised 431 patients who received SRS and were treated for lung cancer at Karolinska University Hospital, Stockholm, Sweden from 2009 to 2020 (all comers from the Stockholm region). Demographics and clinicopathological parameters were extracted from electronic medical records (EMRs). We collected all data necessary in order to calculate the Score Index for Radiosurgery (SIR), the Graded Prognostic Assessment (GPA), and the Basic Score for BM (BSBM). Predictors of overall survival (OS) were identified and incorporated into our novel prognostic score using a penalised Cox regression model. The internal validation of our results was performed using the Efron-Gong optimism bootstrap analysis. The predictive performances of the aforementioned prognostic indices were compared to the performance of the newly introduced prognostic score.
Results
A new prognostic index, termed SRS-Brain Prognostic Index (SRS-BPI), was developed, which included nine variables: age, Eastern Cooperative Oncology Group Performance Status (ECOG PS), histology, molecular status, BM volume, neurological symptoms, BM at diagnosis, extracranial metastases, and extracranial disease control. The in-sample C-index for 1-year survival was 0.689. The optimism-corrected C-index was 0.67 indicating minor overfitting. A nearly optimal calibration was achieved with a low mean absolute error for 1-year OS of 0.04. The SRS-BPI provided a continuous risk prediction and demonstrated superior discrimination compared to GPA, SIR, and BSBM.
Conclusions
We suggest the SRS-BPI score as a new prognostic tool for enhanced decision-making for lung cancer patients with BM eligible for CNS SRS.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
1) European Society for Medical Oncology (ESMO), 2) Hellenic Society of Medical Oncology (HeSMO) and 3) Region Stockholm.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
1352P - Real-world second-line outcomes of NSCLC patients receiving first-line chemotherapy plus immunotherapy
Presenter: Marco Russano
Session: Poster session 06
1353P - Efficacy and safety of docetaxel in combination with nintedanib or ramucirumab following immune checkpoint-Inhibitor treatment in patients with non-small cell lung cancer
Presenter: Konstantinos Ferentinos
Session: Poster session 06
1354P - Retrospective audit to determine the effect of antibiotic therapy on immune checkpoint inhibitor efficacy in stage IV NSCLC
Presenter: Deevyashali Parekh
Session: Poster session 06
1356P - Transcriptomic inflammatory profiling of non-small cell lung cancer: Insights from a 7-gene expression analysis
Presenter: Elba Marin
Session: Poster session 06
1357P - Multicenter phase II study of cisplatin and gemcitabine plus necitumumab in patients with unresectable, advanced lung squamous cell carcinoma who have progressed on or after initial treatment with immune checkpoint inhibitors plus platinum-based chemotherapy: WJOG14120L NESSIE study
Presenter: Hiroshige Yoshioka
Session: Poster session 06
1358P - Plinabulin/docetaxel versus docetaxel in survival benefits of 2L/3L EGFR wild-type NSCLC after platinum regimens (DUBLIN-3): A randomized phase III trial
Presenter: Trevor Feinstein
Session: Poster session 06
1359P - Lack of Abscopal effect and radiotherapy-induced lymphocyte depletion in advanced non-small cell lung cancer (NSCLC) patients treated with atezolizumab and radiotherapy
Presenter: Alexander Meisel
Session: Poster session 06
1362P - Evaluation of imaging-based prognostication (IPRO) for advanced non-small cell lung cancer (aNSCLC) using deep learning applied to computed tomography (CT)
Presenter: Omar Khan
Session: Poster session 06