Prediction Of Tissue Damage Research Articles

Prediction of Tissue Damage Using a User-Independent Machine Learning Algorithm vs. Tmax Threshold Maps

(1) Background: To test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER) to predict final lesion volumes in an independent dataset in patients with acute stroke; (2) Methods: Tissue-at-risk prediction was performed in 31 stroke patients presenting with a proximal middle cerebral artery occlusion. FDA-cleared perfusion software using the AHA recommendation for the Tmax threshold delay was tested against a prediction algorithm trained on an independent perfusion software using artificial intelligence (FASTER). Following our endovascular strategy to consequently achieve TICI 3 outcome, we compared patients with complete reperfusion (TICI 3) vs. no reperfusion (TICI 0) after mechanical thrombectomy. Final infarct volume was determined on a routine follow-up MRI or CT at 90 days after the stroke; (3) Results: Compared to the reference standard (infarct volume after 90 days), the decision forest algorithm overestimated the final infarct volume in patients without reperfusion. Underestimation was observed if patients were completely reperfused. In cases where the FDA-cleared segmentation was not interpretable due to improper definitions of the arterial input function, the decision forest provided reliable results; (4) Conclusions: The prediction accuracy of automated tissue estimation depends on (i) success of reperfusion, (ii) infarct size, and (iii) software-related factors introduced by the training sample. A principal advantage of machine learning algorithms is their improved robustness to artifacts in comparison to solely threshold-based model-dependent software. Validation on independent datasets remains a crucial condition for clinical implementations of decision support systems in stroke imaging.

Fully automated stroke tissue estimation using random forest classifiers (FASTER)

Several clinical trials have recently proven the efficacy of mechanical thrombectomy for treating ischemic stroke, within a six-hour window for therapy. To move beyond treatment windows and toward personalized risk assessment, it is essential to accurately identify the extent of tissue-at-risk (“penumbra”). We introduce a fully automated method to estimate the penumbra volume using multimodal MRI (diffusion-weighted imaging, a T2w- and T1w contrast-enhanced sequence, and dynamic susceptibility contrast perfusion MRI). The method estimates tissue-at-risk by predicting tissue damage in the case of both persistent occlusion and of complete recanalization. When applied to 19 test cases with a thrombolysis in cerebral infarction grading of 1–2a, mean overestimation of final lesion volume was 30 ml, compared with 121 ml for manually corrected thresholding. Predicted tissue-at-risk volume was positively correlated with final lesion volume (p < 0.05). We conclude that prediction of tissue damage in the event of either persistent occlusion or immediate and complete recanalization, from spatial features derived from MRI, provides a substantial improvement beyond predefined thresholds. It may serve as an alternative method for identifying tissue-at-risk that may aid in treatment selection in ischemic stroke.

Real-time control of focused ultrasound heating based on rapid MR thermometry.

Real-time control of the heating procedure is essential for hyperthermia applications of focused ultrasound (FUS). The objective of this study is to demonstrate the feasibility of MRI-controlled FUS. An automatic control system was developed using a dedicated interface between the MR system control computer and the FUS wave generator. Two algorithms were used to regulate FUS power to maintain the focal point temperature at a desired level. Automatic control of FUS power level was demonstrated ex vivo at three target temperature levels (increase of 5 degrees C, 10 degrees C, and 30 degrees C above room temperature) during 30-minute hyperthermic periods. Preliminary in vivo results on rat leg muscle confirm that necrosis estimate, calculated on-line during FUS sonication, allows prediction of tissue damage. CONCLUSIONS. The feasibility of fully automatic FUS control based on MRI thermometry has been demonstrated.

Tissue damage at the injection site after intramuscular injection of drugs

Tissue damage at the injection site has been described after intramuscular injection of antibiotics, chemotherapeutics, vitamins, lidocaine, diazepam and digoxin in dogs, swine, cows, sheep and hens. The damage caused by substances commonly used in drug vehicles has also been examined and compared with the effects of saline and distilled, sterile water. After intramuscular injection of saline or sterile water little or no tissue reaction has been observed, while vehicles containing glycerol formal or propylene glycol caused damage to muscle tissue. The investigations show that both the drug itself and the vehicles may cause tissue damage. In the development of drug preparations for intramuscular administration, which cause minimal tissue damage, it is therefore important to consider all the components of the preparation. From the point of view of international collaboration it will be of great importance that standard methods are generally accepted for the evaluation of new drugs and for the estimation and prediction of tissue damage after their intramuscular injection.