Abstract 5645
Background
Radiation induced CT changes may be apparent following completion of TRT. We sought to quantify differences in radiation-associated densities on serial CT scans of patients with ES-SCLC treated with TRT alone versus TRT followed by combined IPI and NIVO.
Methods
Between 2016 and 2018, patients at a single institution with ES-SCLC who achieved stable disease or better following initial treatment with platinum doublet chemotherapy were offered TRT and prescribed a total dose of 30Gy in 10 fractions targeting initially involved thoracic tumor sites. Combined IPI 3mg/kg and NIVO 1mg/kg was administered every 3 weeks for up to 4 doses. We evaluated an irradiated region of interest (ROI) within the lungs and a volume of lung (outside the Planning Target Volume) receiving > 20 Gy. Within the ROI, we calculated the Hounsfield unit (HU) mean for each patient prior to therapy and at subsequent follow-up CT thorax at least 60 days and closest to 120 days after commencing TRT. To quantify CT density change, we measured the difference in HU mean within the irradiated ROI before and after treatment.
Results
Seventeen patients enrolled on NCT03043599 received TRT followed by combined IPI/NIVO. Two additional patients received the same treatment off protocol. Eleven patients received TRT alone (no IPI/NIVO). The average increase in HU mean within 20Gy irradiated ROI before and after treatment was 9% (max 59%, min -19%) across the study cohort (n = 30). CTCAE grade 3 or higher pulmonary toxicity (N = 8 of 30) was significantly associated with increased CT density change within the ROI (mean 28% vs 2%, p = 0.001). Treatment with TRT and IPI/NIVO (N = 19 of 30) demonstrated a trend towards increased mean CT density change within the ROI compared to patients treated with TRT alone (mean 13% vs. 0%, p = 0.1).
Conclusions
Quantifying CT density change within irradiated lung parenchyma may offer a novel approach to predict radiation associated pulmonary toxicities. Measuring density changes across patient cohorts receiving TRT with novel systemic therapies may help to identify combined treatment strategies likely to be associated with diminished risk of toxicity.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Bristol-Myers Squibb.
Disclosure
S. Kim: Research grant / Funding (institution): Bristol-Myers Squibb. S.A. Rosenberg: Advisory / Consultancy: Novocure. J.E. Gray: Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca; Honoraria (self), Advisory / Consultancy: Celgene; Honoraria (self), Advisory / Consultancy: Takeda; Honoraria (self), Advisory / Consultancy: Janssen; Honoraria (self), Advisory / Consultancy, Research grant / Funding (institution): Genentech; Honoraria (self), Advisory / Consultancy: Eli Lilly; Honoraria (self), Advisory / Consultancy: Triptych Health Partners. S.J. Antonia: Advisory / Consultancy: Bristol-Myers Squibb; Advisory / Consultancy, Research grant / Funding (institution): Novartis; Advisory / Consultancy: Merck; Advisory / Consultancy: CBMG; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy: AstraZeneca/MedImmune; Advisory / Consultancy: Memgen; Advisory / Consultancy: FLX Bio; Advisory / Consultancy: Nektar; Advisory / Consultancy: Venn. B. Perez: Advisory / Consultancy: Bristol-Myers Squibb; Advisory / Consultancy: AstraZeneca. All other authors have declared no conflicts of interest.
Resources from the same session
2436 - Development and Validation of an RNA-Seq Assay for Gene Fusions Detection in Formalin-Fixed Paraffin-Embedded Samples
Presenter: Hua Dong
Session: Poster Display session 3
Resources:
Abstract
5271 - A Pilot Study to Implement an Artificial Intelligence (AI) System for Gastrointestinal Cancer Clinical Trial Matching
Presenter: Zhaohui Jin
Session: Poster Display session 3
Resources:
Abstract
4787 - A Blinded Comparison of Patient Treatments to Therapeutic Options Presented by an Artificial Intelligence-based Clinical Decision-support system
Presenter: Suthida Suwanvecho
Session: Poster Display session 3
Resources:
Abstract
5744 - OncOS: scalable and accurate next-generation sequencing analytics for precision oncology and personalized patient care
Presenter: Joe Thompson
Session: Poster Display session 3
Resources:
Abstract
3752 - The association between wearable device physical activity metrics and performance status in oncology: a systematic review
Presenter: Milan Kos
Session: Poster Display session 3
Resources:
Abstract
5820 - SomaticNET: neural network evaluation of somatic mutations in cancer
Presenter: Geoffroy Dubourg-Felonneau
Session: Poster Display session 3
Resources:
Abstract
4771 - Is there a role for Next-generation sequencing (NGS) profiling on metastatic non-colorectal gastrointestinal carcinomas (MNCGIC) in developing countries? A single center experience.
Presenter: Mauricio Ribeiro
Session: Poster Display session 3
Resources:
Abstract
1209 - Metastatic Cancer Whole-Exome Sequencing in daily practice
Presenter: Manon Réda
Session: Poster Display session 3
Resources:
Abstract
5702 - Genomic-Guided Individualized Precision Therapy in Refractory Metastatic Solid Tumor Patients with Extensively Poor Performance Status: A Chinese single institutional prospective observational real-world study
Presenter: Haitao Wang
Session: Poster Display session 3
Resources:
Abstract
4021 - Prospective pathological experience with research biopsies in the context of clinical trials at Vall d’Hebron Institute of Oncology
Presenter: Paolo Nuciforo
Session: Poster Display session 3
Resources:
Abstract