Dataset Information

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases.

ABSTRACT: The diagnosis of brain metastasis (BM) is commonly observed in non-small cell lung cancer (NSCLC) with poor outcomes. Accordingly, developing an approach to early predict BM response to Gamma Knife radiosurgery (GKRS) may benefit the patient treatment and monitoring. A total of 237 NSCLC patients with BMs (for survival prediction) and 256 patients with 976 BMs (for prediction of local tumor control) treated with GKRS were retrospectively analyzed. All the survival data were recorded without censoring, and the status of local tumor control was determined by comparing the last MRI follow-up in patients' lives with the pre-GKRS MRI. Overall 1763 radiomic features were extracted from pre-radiosurgical magnetic resonance images. Three prediction models were constructed, using (1) clinical data, (2) radiomic features, and (3) clinical and radiomic features. Support vector machines with a 30% hold-out validation approach were constructed. For treatment outcome predictions, the models derived from both the clinical and radiomics data achieved the best results. For local tumor control, the combined model achieved an area under the curve (AUC) of 0.95, an accuracy of 90%, a sensitivity of 91%, and a specificity of 89%. For patient survival, the combined model achieved an AUC of 0.81, an accuracy of 77%, a sensitivity of 78%, and a specificity of 80%. The pre-radiosurgical radiomics data enhanced the performance of local tumor control and survival prediction models in NSCLC patients with BMs treated with GRKS. An outcome prediction model based on radiomics combined with clinical features may guide therapy in these patients.

SUBMITTER: Liao CY

PROVIDER: S-EPMC8392266 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeWhile deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.MethodsWe collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-centric dose emulation (3); validation of the envisaged plan employing a precise dosimeter in conjunction with a linear accelerator. Dose prediction paradigms were established by engineering and refining two three-dimensional UNet architectures (UNet and AttUNet). Input parameters encompassed computed tomography scans from clinical plans and demarcations of the focal point alongside organs at potential risk (OARs); the ensuing output manifested as a 3D dose matrix tailored for each case under scrutiny.ResultsDose estimations rendered by both models mirrored the manual plans and adhered to clinical stipulations. As projected by the dual models, the apex and average doses for OARs did not deviate appreciably from those delineated in the manual plan (P-value≥0.05). AttUNet showed promising results compared to the foundational UNet. Predicted doses showcased a pronounced dose gradient, with peak concentrations localized within the target vicinity. The executable plans conformed to clinical dosimetric benchmarks and aligned with their associated verification assessments (100% gamma approval rate at 3 mm/3%).ConclusionThis study demonstrates an automated planning technique for fixed-field IMRT-based SRS for brain metastases. The envisaged plans met clinical requirements, were reproducible across centers, and achievable in deliveries. This represents progress toward automated paradigms for this specific scenario.

Project description:PurposeThe virtual cone has been previously introduced as a novel technique for generating small, spherical dose distributions using a high-definition multileaf collimator (MLC) for functional radiosurgery applications. There has been no reported investigation into adapting this technique to a standard MLC for the treatment of solitary intracranial metastases as an alternative to physical stereotactic cones. This study characterizes the virtual cone technique adapted to a standard 5 mm leaf-width MLC (VCSD ).MethodsVCSD dose distributions using MLC leaf gaps of 2-5 mm were generated and isodose sphericity metrics, peak dose gradients, optimal normalization ranges, and achievable field widths were compared to those of 5.0-12.5 mm diameter physical cones. Target sizes feasible to treat were identified and planned for comparison against established techniques using Paddick conformity index (PCI) and dose volume metrics. End-to-end validation of the VCSD technique was performed.ResultsVCSD and physical cones sphericity metrics agree within 3.5% and VCSD plans achieved a dose gradient of 21.3% mm-1 , comparable to 10.0-12.5 mm diameter physical cones. Normalization within the 50%-77% range preserves the optimal dose gradient within 2%⋅mm-1 and enables the treatment of 5-11 mm diameter planning target volumes (PTVs). Mean PCI for virtual and physical cones was 0.957 and 0.949, which compared favorably against conformal arc and VMAT (0.899 and 0.926). VCSD outperformed conformal arc and VMAT for all dose volume metrics, and the mean 50% dose volume differed from physical cones by < 0.5cc for PTVs as small as 5 mm. Validation measurements showed 100% of points passing a 2% / 0.5 mm gamma test for all plans.ConclusionsThe VCSD technique efficiently generates spherical dose distributions for the treatment of small brain metastases. Characteristics of the VCSD dose distributions are sufficiently comparable to those of physical cones to support VCSD as an alternative for the treatment of spherical PTVs as small as 5 mm in diameter.

Dataset Information

Enhancement of Radiosurgical Treatment Outcome Prediction Using MRI Radiomics in Patients with Non-Small Cell Lung Cancer Brain Metastases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets