Background and purpose: Neoplastic intracerebral hemorrhage (ICH) may be incorrectly identified as non‐neoplastic ICH on imaging. Relative perihematomal edema (relPHE) on computed tomography (CT) has been proposed as a marker to discriminate neoplastic from non‐neoplastic ICH but has not been externally validated. The purpose of this study was to evaluate the discriminatory power of relPHE in an independent cohort.Methods: A total of 291 patients with acute ICH on CT and follow‐up magnetic resonance imaging (MRI) were included in this single‐center retrospective study. ICH subjects were dichotomized into non‐neoplastic or neoplastic ICH based on the diagnosis on the follow‐up MRI. ICH and PHE volumes and density values were derived from semi‐manually segmented CT scans. Calculated PHE characteristics for discriminating neoplastic ICH were evaluated using receiver‐operating characteristic (ROC) curves. ROC curve‐associated cut‐offs were calculated and compared between the initial and the validation cohort.Results: A total of 116 patients (39.86%) with neoplastic ICH and 175 (60.14%) with non‐neoplastic ICH were included. Median PHE volumes, relPHE, and relPHE adjusted for hematoma density were significantly higher in subjects with neoplastic ICH (all p values 0.70 for relPHE and >0.01 for adjusted relPHE.Conclusions: Relative perihematomal edema and adjusted relPHE accurately discriminated neoplastic from non‐neoplastic ICH on CT imaging in an external patient cohort. These results confirmed the findings of the initial study and may improve clinical decision making.

Intracerebral hemorrhage (ICH) associated with primary and metastatic brain tumors presents a significant challenge in neuro-oncology due to the substantial risk of complications

Purpose To develop and evaluate an automated segmentation and classification tool for the discrimination of neoplastic and non-neoplastic intracerebral hemorrhage (ICH) on admission Computed Tomography (CT) utilizing images containing hemorrhage and perihematomal edema. Materials and Methods The models were developed and evaluated using a retrospective dataset of patients who presented with acute ICH of unknown cause upon admission, using CT scans obtained from a single institution between January 2016 and May 2020 for both training and testing. Etiology of ICH were binarized into non-neoplastic and neoplastic ICH according to follow-up MRI results based on the ATOMIC ICH classification. Masks for ICH and PHE were manually segmented. Two separate models were trained: 1) An nnU-Net segmentation model 2) A ResNet-34 classification model. An end-to-end tool was evaluated by concatenating the two models which allowed the segmentation model to preprocess the images for the classification model. Performance enhancement was assessed by fine-tuning the model on a randomly selected, small subset of the external cohort. To assess the model’s generalizability, the performance was additionally validated on an external dataset. Evaluation metrics were accuracy (Acc), area under the curve (AUC) and corresponding sensitivities and specificities. Results A total of 291 patients were included of whom 116 (39.86%) presented with neoplastic and 175 (60.14%) with non-neoplastic ICH. The end-to-end classification tool achieved an Acc of 86% and an AUC of 85% with a sensitivity and specificity of 80% and 93% in the test set. On the external validation cohort (n=58), the classification pipeline achieved an AUC of 68% and Acc of 66% (sensitivity 64%; specificity 67%). Fine-tuning on a selected small subset of the external cohort enhanced performance, achieving an AUC and accuracy of 70% (sensitivity 70%; specificity 71%). Conclusion An end-to-end classification tool achieved a high diagnostic performance and generalizability in classifying neoplastic from non-neoplastic ICH on CT, suggesting a robust framework for a potential clinical implementation as a decision-aided tool in early ICH management.