Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

OBJECTIVE: To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume and organ at risk in high-dose-rate brachytherapy for cervical cancer patients. Approach. We used 73 computed tomography (CT) and 62 magnetic resonance imaging (MRI) scans from 135 (103 for training, 16 for validation, and 16 for testing) cervical cancer patients across two hospitals for HRCTV and OAR segmentation. A novel comparison of the deep learning neural networks 3D Prompt-ResUNet, nnUNet, and SAM-Med3D was applied for the segmentation. Evaluation was conducted in two parts: geometric and clinical assessments. Quantitative metrics included the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95%), Jaccard index (JI), and Matthews correlation coefficient (MCC). Clinical evaluation involved interobserver comparison, 4-grade expert scoring, and a double-blinded Turing test. Main results. The Prompt-ResUNet model performed most similarly to experienced radiation oncologists, outperforming less experienced ones. During testing, the DSC, HD95% (mm), JI, and MCC value (mean ± SD) for HRCTV were 0.92±0.03, 2.91 ± 0.69, 0.85± 0.04, and 0.92 ± 0.02, respectively. For the bladder, these values were 0.93 ± 0.05, 3.07 ± 1.05, 0.87 ± 0.08, and 0.93 ± 0.05, respectively. For the rectum, they were 0.87 ± 0.03, 3.54 ± 1.46, 0.78 ± 0.05, and 0.87 ± 0.03, respectively. For the sigmoid, they were 0.76 ± 0.11, 7.54 ± 5.54, 0.63 ± 0.14, and 0.78 ± 0.09, respectively. The Prompt-ResUNet achieved a clinical viability score of at least 2 in all evaluation cases (100%) for both HRCTV and bladder and exceeded the 30% positive rate benchmark for all evaluated structures in the Turing test. Significance. The Prompt-ResUNet architecture demonstrated high consistency with ground truth (GT) in autosegmentation of HRCTV and OARs, reducing interobserver variability and shortening treatment times. .

The efficacy of pericapsular nerve group block for reducing pain and opioid consumption after total hip arthroplasty: a systematic review and meta-analysis.

BACKGROUND: Pericapsular nerve group block (PENG) is an emerging regional anesthesia technique for hip surgery. However, its efficacy in total hip arthroplasty (THA) isn't well defined. We perform this meta-analysis aiming to assess the effect of Pericapsular nerve group block on pain control and morphine consumption in patients with total hip arthroplasty. METHODS: We searched four electronic databases (Pubmed, Embase, Cochrane Library, and Web of Science dated from 2018 to October 2023) for published eligible randomized controlled trials (RCTs) comparing PENG with placebo (no block/sham block) after THA. The outcome measurements consisted of pain score, opioid consumption, Time to first opioid, and postoperative complications. All data analyses were performed using STATA 12.0. RESULTS: Five RCTs comprising 808 participants were included. Our meta-analysis showed that there were significant differences between two groups in terms of pain score in PACU (WMD = - 0.598, 95% CI [- 0.886, - 0.310], P < 0.001), pain score at 6 h (WMD = - 0.614, 95% CI [- 0.835, - 0.392], P < 0.001) and time to first opioid (WMD = 5.214, 95% CI [4.545, 5.883], P < 0.001). However, no significant differences were revealed from the pain score at 24 h after THA (WMD = - 0.924, 95% CI [- 1.929, 0.081], P = 0.072). Meanwhile, the meta-analysis indicated that PENG significantly reduced 24-h opioid consumption (WMD = - 6.168, 95% CI [- 6.667, - 5.668], P < 0.001) and 48-h opioid consumption (WMD = - 7.171, 95% CI [- 8.994, - 5.348], P < 0.001). CONCLUSION: Pericapsular nerve group block was effective for pain control up to postoperative 6 h and extending the time to the first opioid after THA. Moreover, it reduced postoperative opioid consumption when compared with a placebo group. Due to the high heterogeneity of the pain score after 24 h and the low-quality evidence, more high-quality RCTs are required to draw a definitive conclusion about pain control.

A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.

PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ at risk (OAR) in high-dose-rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC). METHODS AND MATERIALS: On two experimental batches, a total of 321 computed tomography (CT) scans were obtained for HR CTV segmentation from 321 patients with EC, and 125 CT scans for OARs segmentation from 125 patients. The numbers of training/validation/test were 257/32/32 and 87/13/25 for HR CTV and OARs respectively. A novel comparison of the deep learning neural network 3D Prompt-nnUnet and 3D nnUnet was applied for HR CTV and OARs segmentation. Three-fold cross validation and several quantitative metrics were employed, including Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of Hausdorff distance (HD95%), and intersection over union (IoU). RESULTS: The Prompt-nnUnet included two forms of parameters Predict-Prompt (PP) and Label-Prompt (LP), with the LP performing most similarly to the experienced radiation oncologist and outperforming the less experienced ones. During the testing phase, the mean DSC values for the LP were 0.96 ± 0.02, 0.91 ± 0.02, and 0.83 ± 0.07 for HR CTV, rectum and urethra, respectively. The mean HD values (mm) were 2.73 ± 0.95, 8.18 ± 4.84, and 2.11 ± 0.50, respectively. The mean HD95% values (mm) were 1.66 ± 1.11, 3.07 ± 0.94, and 1.35 ± 0.55, respectively. The mean IoUs were 0.92 ± 0.04, 0.84 ± 0.03, and 0.71 ± 0.09, respectively. A delineation time < 2.35 s per structure in the new model was observed, which was available to save clinician time. CONCLUSION: The Prompt-nnUnet architecture, particularly the LP, was highly consistent with ground truth (GT) in HR CTV or OAR autosegmentation, reducing interobserver variability and shortening treatment time.