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Matthias Guckenberger
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Current role and future of thoracic radiotherapy (ID 17)
- Event: ELCC 2019
- Type: Specialty session
- Track:
- Presentations: 1
- Moderators:
- Coordinates: 4/11/2019, 11:00 - 12:30, Room C
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Early stage NSCLC (ID 43)
11:00 - 12:30 | Presenting Author(s): Matthias Guckenberger
- Abstract
- Presentation
Abstract not provided
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Lunch & Poster Display session (ID 58)
- Event: ELCC 2019
- Type: Poster Display session
- Track:
- Presentations: 2
- Moderators:
- Coordinates: 4/11/2019, 12:30 - 13:00, Hall 1
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173P - Stereotactic radiotherapy concurrent to immune or targeted therapy for oligometastatic NSCLC: Clinical scenarios affecting survival (ID 193)
12:30 - 13:00 | Author(s): Matthias Guckenberger
- Abstract
Background
Oligometastatic NSCLC pts. may benefit from a more aggressive treatment approach. However, the concept of “oligometastasis” is complex: e.g. limited progression or resistance of disease to systemic treatment. This study evaluated the outcome of stereotactic radiotherapy (SRT) to oligoprogressive or oligoresistant NSCLC in pts. receiving concurrent immuno- or targeted therapy.
a9ded1e5ce5d75814730bb4caaf49419 Methods
The international register study (TOaSTT) collected data on metastatic NSCLC pts. receiving SRT concurrent (≤30d) to immuno- or targeted therapy. Pts. were grouped in: SRT of ≤ 5 metastases without additional metastases (oligoprogression), SRT of ≤ 5 progressive metastases with controlled disease of all other metastases (oligopersistent), and SRT of ≤ 5 metastases with otherwise mixed response/ uncontrolled disease. OS, PFS, LC and time to systemic therapy-switch after SRT were analyzed using Kaplan-Meier survival curves and log rank testing. Toxicity was scored using CTCAE.
20c51b5f4e9aeb5334c90ff072e6f928 Results
SRT of 192 lesions in 108 pts. was performed between 7/2009 - 5/2018. Median age was 63y (range 33-80). Driver mutations were: EGFR 41%, ALK 14%, other 21%, unknown/no 24%. Median FU was 18.7 (range 1-102) mo. 90% were ECOG 0-1. Median 1 (range 1-5) metastasis was treated. Targeted therapies were started before SRT in 69%, during SRT in 8%, and after SRT in 23%. 60% received an ALK- or EGFR-TKI, 31% PD-L1/PD-1 inhibitors, 8% bevacizumab. Oligoprogressive and oligopersistent pts showed improved OS compared to advanced metastatic disease (p = 0.008) (Fig.1). PFS was best in oligoprogressive patients; median 20.1 vs 7 and 4.4 mo. (p = 0.006). LC was median 21.0, 12.0 und 9.0mo: no sign. difference between groups. After 1y, 86%, 47% and 39% continued the same immuno- or targeted therapy as before SRT. Severe acute toxicity were observed in 7%, late toxicity in 4%.
fd69c5cf902969e6fb71d043085ddee6 Conclusions
An excellent survival with limited toxicity was observed when definitive SRT to a limited number of metastases was combined with targeted- or immunotherapy in oligoprogressive and oligopersistent NSCLC patients. SRT of metastatic sites allowed continuation of targeted-, or immunotherapy in many patients. These observations need to be further evaluated in prospective trials.
b651e8a99c4375feb982b7c2cad376e9 Legal entity responsible for the study
Matthias Guckenberger.
213f68309caaa4ccc14d5f99789640ad Funding
Has not received any funding.
682889d0a1d3b50267a69346a750433d Disclosure
All authors have declared no conflicts of interest.
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56P - CT image standardization is superior to larger but heterogeneous data for robust radiomic models (ID 433)
12:30 - 13:00 | Author(s): Matthias Guckenberger
- Abstract
Background
Radiomics is a promising tool for identification of new prognostic biomarkers. Radiomic models are often based on single-institution data however multi-centric data, highly heterogeneous due to different scanning protocols, reflect better the clinical reality. Robustness studies are crucial to find features robust to e.g. scanner settings. We study if a CT radiomics overall survival (OS) model trained on multi-centric data with robust feature pre-selection can achieve a similar performance as a model on standardized data.
a9ded1e5ce5d75814730bb4caaf49419 Methods
Pre-treatment CT data from 121 IIIA/N2 NSCLC patients from a prospective multi-centric randomized trial (SAKK 16/00, neoadj. chemo- or radiochemotherapy prior to surgery) were used to calculate 1404 radiomic features on the primary tumor. Two OS radiomic models were trained on (1) a sub-cohort with standardized imaging protocol (native CT, standard kernel, n = 84) and on (2) the entire heterogeneous cohort but with robust radiomic feature pre-selection. Robust features were extracted from four robustness studies (contrast, convolution kernel, motion, delineation) using the intra-class correlation coefficient (> 0.9 considered stable). Seperately for each model, principal component (PC) analysis was performed and PCs describing in total 95% data variance were selected. The feature with highest correlation to the PCs were used for the multivariate Cox model with backward selection. Model performances were quantified using Concordance Index (CI), verified with 10-fold cross-validation and compared using bootstrap with resampling.
20c51b5f4e9aeb5334c90ff072e6f928 Results
Robustness studies revealed 113 stable features. The convolution kernel was the largest influence on the feature stability. Final OS model on the entire heterogeneous data consisted of four and on standardized data of six features (all identified as unstable). The model on standardized data showed significant better prognostic performance compared to the model with robust feature pre-selection based on the entire heterogeneous data (CI = 0.64 and 0.61, p < 0.05, resp.).
fd69c5cf902969e6fb71d043085ddee6 Conclusions
For our prognostic NSCLC radiomic models image protocol standardization appears superior to using larger but heterogeneous imaging data combined with robust feature pre-selection.
b651e8a99c4375feb982b7c2cad376e9 Legal entity responsible for the study
The authors.
213f68309caaa4ccc14d5f99789640ad Funding
Swiss National Science Foundation (SNSF) Swiss Group for Clinical Cancer Research SAKK.
682889d0a1d3b50267a69346a750433d Disclosure
M. Guckenberger: Board member: European Society for Therapeutic Radiation Oncology (ESTRO). All other authors have declared no conflicts of interest.
cffcb1a185b2d7d5c44e9dc785b6bb25
