Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy

Background Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the local failure (LF) risk persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. Methods Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients (two centers); external test cohort: 99 patients (five centers)). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameters previously determined by internal 5-fold cross-validation and tested on the external test set. Results The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. Conclusions A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy. Key points Importance of the Study Local failure after treatment of brain metastases has a severe impact on patients, often resulting in additional therapy and loss of quality of life. This multicenter study investigated the possibility of predicting local failure of brain metastases after surgical resection and stereotactic radiotherapy using radiomic features extracted from the contrast-enhancing metastases and the surrounding FLAIR-hyperintense edema. By interpreting this as a survival task rather than a classification task, we were able to predict the freedom from failure probability at different time points and appropriately account for the censoring present in clinical time-to-event data. We found that synergistically combining clinical and imaging data performed better than either alone in the multicenter external test cohort, highlighting the potential of multimodal data analysis in this challenging task. Our results could improve the management of patients with brain metastases by tailoring follow-up and therapy to their individual risk of local failure. ### Competing Interest Statement AW: Consultant: Gilead and Hologic Medicor GmbH; Honoraria: ACCURAY International, Universitätsklinikum Leipzig AöR, and Sanofi-Aventis GmbH; Board: IKF GmbH am Krankenhaus Nordwest BeM: Grants: BrainLab, Zeiss, Ulrich, and Spineart; Royalties: Medacta and Spineart; Consultant and Honoraria: Medacta, Brainlab, and Zeiss; Stock: Sonovum MG: President-Elect of ESTRO NA: Independent Contractor: SAKK - Swiss Association for Clinical Cancer Research; Board: AstraZeneca; Research funding: ViewRay Inc.; Stock: Moderna Inc. and Idorsia AG; Chair: EORTC and Global Harmonization Group SR: Honoraria: Brainlab OB: Grants: European Union's Horizon 2020 research and innovation programme; Board: working groups for Stereotactic Radiotherapy of the German Radiation Oncology and Medical Physics Societies, Section Editor of Strahlentherapie und Onkologie Journal ALG: Research funding: Novocure SEC: Honoraria and travel expenses: Roche, Bristol-Myers Squibb, Brainlab, AstraZeneca, Accuray, Dr. Sennewald, Daiichi Sankyo, Elekta, Medac, Icotec AG, HMG Systems Engineering, and Carl Zeiss Meditec AG DB: Honoraria and travel expenses: Novocure The remaining authors have no potential conflicts of interest to disclose. ### Funding Statement This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Project number 504320104 - PE 3303/1-1 (JCP), WI 4936/4-1 (BW), RU 1738/5-1 (DR)). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was obtained at each institution (main approval at the Technical University of Munich: 119/19 S-SR). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes