Background Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborious and time‐consuming when contoured manually. Artificial intelligence (AI)‐based auto‐segmentation has the potential to significantly accelerate the radiation therapy treatment planning process; however, the accuracy of auto‐segmentation needs to be validated before its full clinical adoption. Purpose A commercial AI‐based contouring model was trained to provide segmentation of the prostate and surrounding OARs. The segmented structures were input to a commercial auto‐planning module for automated prostate treatment planning. This study comprehensively evaluates the performance of this contouring model in the automated prostate treatment planning process. Methods and materials A 3D U‐Net‐based model (INTContour, Carina AI) was trained and validated on 84 computed tomography (CT) scans and tested on an additional 23 CT scans from patients treated in our local institution. Prostate and OARs contours generated by the AI model (AI contour) were geometrically evaluated against reference contours. The prostate contours were further evaluated against AI, reference, and two additional observer contours for comparison using inter‐observer variation (IOV) and 3D boundaries discrepancy analyses. A blinded evaluation was introduced to assess subjectively the clinical acceptability of the AI contours. Finally, treatment plans were created from an automated prostate planning workflow using the AI contours and were evaluated for their clinical acceptability following the Radiation Therapy Oncology Group‐0815 protocol. Results The AI contours demonstrated good geometric accuracy on OARs and prostate contours, with average Dice similarity coefficients (DSC) for bladder, rectum, femoral heads, seminal vesicles, and penile bulb of 0.93, 0.85, 0.96, 0.72, and 0.53, respectively. The DSC, 95% directed Hausdorff distance (HD95), and mean surface distance for the prostate were 0.83 ± 0.05, 6.07 ± 1.87 mm, and 2.07 ± 0.73 mm, respectively. No significant differences were found when comparing with IOV. In the double‐blinded evaluation, 95.7% of the AI contours were scored as either “perfect” (34.8%) or “acceptable” (60.9%), while only one case (4.3%) was scored as “unacceptable with minor changes required.” In total, 69.6% of the AI contours were considered equal to or better than the reference contours by an independent radiation oncologist. Automated treatment plans created from the AI contours produced similar and clinically acceptable dosimetric distributions as those from plans created from reference contours. Conclusions The investigated AI‐based commercial model for prostate segmentation demonstrated good performance in clinical practice. Using this model, the implementation of an automated prostate treatment planning process is clinically feasible.
Background: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors.Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. Purpose: The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. Methods and materials: DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. Results: The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. Conclusions: The proposed ML-MF approach,which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.
Background: Extracorporeal membrane oxygenation with CPR (eCPR) or therapeutic hypothermia (TH) seems to be a very effective CPR strategy to save patients with cardiac arrest (CA). Furthermore, the subsequent post-CA neurologic outcomes have become the focus. Therefore, there is an urgent need to find a way to improve survival and neurologic outcomes for CA.Objective: We conducted this meta-analysis to find a more suitable CPR strategy for patients with CA.Method: We searched four online databases (PubMed, Embase, CENTRAL, and Web of Science). From an initial 1,436 articles, 23 studies were eligible into this meta-analysis, including a total of 2,035 patients.Results: eCPR combined with TH significantly improved the short-term (at discharge or 28 days) survival [OR = 2.27, 95% CIs (1.60–3.23), p < 0.00001] and neurologic outcomes [OR = 2.60, 95% CIs (1.92–3.52), p < 0.00001). At 3 months of follow-up, the results of survival [OR = 3.36, 95% CIs (1.65–6.85), p < 0.0008] and favorable neurologic outcomes [OR = 3.02, 95% CIs (1.38–6.63), p < 0.006] were the same as above. Furthermore, there was no difference in any bleeding needed intervention [OR = 1.33, 95% CIs (0.09–1.96), p = 0.16] between two groups.Conclusions: From this meta-analysis, we found that eCPR combined with TH might be a more suitable CPR strategy for patients with CA in improving survival and neurologic outcomes, and eCPR with TH did not increase the risk of bleeding. Furthermore, single-arm meta-analyses showed a plausible way of temperature and occasion of TH.
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