Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Simon A. Keek, Esma Kayan, Avishek Chatterjee, José S.A. Belderbos, Gerben Bootsma, Ben van den Borne, Anne Marie C. Dingemans, Hester A. Gietema, Harry J.M. Groen, Judith Herder, Cordula Pitz, John Praag, Dirk De Ruysscher, Janna Schoenmaekers, Hans J.M. Smit, Jos Stigt, Marcel Westenend, Haiyan Zeng, Henry C. Woodruff, Philippe LambinLizza Hendriks*

*Corresponding author for this work

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Introduction: Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC. Methods: Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [18F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC. Results: In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively). Conclusion: CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.

Original languageEnglish
JournalTherapeutic Advances in Medical Oncology
Early online date22 Aug 2022
Publication statusPublished - 2022

Bibliographical note

Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a Lung Foundation grant, n° Authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015 n° 694812 - Hypoximmuno), ERC-2020-PoC: 957565-AUTO.DISTINCT. Authors also acknowledge financial support from SME Phase II (RAIL n°673780), the European Union’s Horizon 2020 research and innovation programme under grant agreement: ImmunoSABR n° 733008, MSCA-ITN-PREDICT n° 766276, CHAIMELEON n° 952172, EuCanImage n° 952103, Scholarship of China Scholarship Council (Grant No. : CSC 201909370087).

Publisher Copyright: © The Author(s), 2022.


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