Deep learning for brain metastasis detection and segmentation in longitudinal MRI data

Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as false positive metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Our proposed VSS loss improves the sensitivity of brain metastasis detection for DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the false positive metastases are reduced in the high sensitivity model and the precision reaches 99.6% for the high specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high sensitivity and high specificity models, on average only 1.5 false positive metastases per patient needs further check, while the majority of true positive metastases are confirmed. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice.Comment: Implementation is available to public at https://github.com/YixingHuang/DeepMedicPlu

Clinical Severity Predicts Time to Hospital Admission in Patients with Spontaneous Intracerebral Hemorrhage

Background: In this study we analyzed whether demographic, clinical and neuroradiological parameters are associated with time to hospital admission in patients with spontaneous intracerebral hemorrhage (ICH). We a priori hypothesized that the earlier a patient was admitted to hospital, the worse the clinical status would be. Methods: Demographic, clinical and neuroradiological parameters of consecutive patients with spontaneous ICH directly admitted to 2 neurological university departments were subjected to correlation, trichotomization and logistic regression analyses for prediction of (i) early hospital admission, and (ii) favorable clinical presentation at admission [dichotomized Glasgow Coma Scale (GCS) score 6 9]. Results: We analyzed 157 patients with a median age of 66 (39–93) years. Patient trichotomization according to the GCS revealed a significant difference (p ! 0.001) between all groups with regard to the time from symptom onset to hospital admission: patients with a GCS score of 3–5 were admitted after 105 (40–300) min (mean: 113 8 53), those with a GCS score of 6–9 after 180 (45–420) min (mean: 184 8 95) and those with a GCS score of 10–15 after 300 (60–1,560) min (mean: 324 8 367). There were significant correlations between (i) hematoma volume and GCS (r = –0.632; p ! 0.001); (ii) time to admission and GCS (r = 0.596; p ! 0.001), and (iii) Graeb scores for intraventricular hemorrhage and hematoma volume (r = 0.348; p ! 0.001). In the multivariate regression model for prediction of time until hospital admission, presence of intraventricular hemorrhage and the GCS score on admission were significant. In the multivariate regression model for prediction of a GCS score of 6 9 on admission, hematoma volume and time until hospital admission were significant parameters. Conclusions: Clinically more severely affected patients were admitted to hospital earlier. This highlights the importance of most rapid diagnosis of ICH. Efforts should be made to get less severely affected patients admitted earlier as they might be ideal candidates for emerging innovative treatments