Peripheral zone thickness in preoperative MRI is predictive of Trifecta achievement after Holmium laser enucleation of the prostate (HoLEP).

PURPOSE: To investigate various anatomical features of the prostate using preoperative MRI and patients' clinical factors to identify predictors of successful Holmium:YAG laser enucleation of the prostate (HoLEP). METHODS: 71 patients who had received HoLEP and undergone a 3.0-T prostate MRI scan within 6 months before surgery were retrospectively enrolled. MRI features (e.g., total prostate and transitional zone volume, peripheral zone thickness [PZT], BPH patterns, prostatic urethral angle, intravesical prostatic protrusion, etc.) and clinical data (e.g., age, body mass index, surgical technique, etc.) were analyzed using univariable and multivariable logistic regression to identify predictors of successful HoLEP. Successful HoLEP was defined as achieving the Trifecta, characterized by the contemporary absence of postoperative complications within 3 months, a 3-month postoperative maximum flow rate (Qmax) > 15 mL/s, and no urinary incontinence at 3 months postoperatively. RESULTS: Trifecta achievement at 3 months post-surgery was observed in 37 (52%) patients. Patients with Trifecta achievement exhibited a lower preoperative IPSS-quality of life score (QoL) (4.1 vs. 4.5, P = 0.016) and a thinner preoperative peripheral zone thickness (PZT) on MRI (7.9 vs.10.3 mm, P < 0.001). In the multivariable regression analysis, a preoperative IPSS-QoL score < 5 (OR 3.98; 95% CI, 1.21-13.07; P = 0.017) and PZT < 9 mm (OR 11.51; 95% CI, 3.51-37.74; P < 0.001) were significant predictors of Trifecta achievement after HoLEP. CONCLUSIONS: Alongside the preoperative QoL score, PZT measurement in prostate MRI can serve as an objective predictor of successful HoLEP. Our results underscore an additional utility of prostate MRI beyond its role in excluding concurrent prostate cancer.

Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study.

BACKGROUND: Few studies have evaluated the use of automated artificial intelligence (AI)-based pain recognition in postoperative settings or the correlation with pain intensity. In this study, various machine learning (ML)-based models using facial expressions, the analgesia nociception index (ANI), and vital signs were developed to predict postoperative pain intensity, and their performances for predicting severe postoperative pain were compared. METHODS: In total, 155 facial expressions from patients who underwent gastrectomy were recorded postoperatively; one blinded anesthesiologist simultaneously recorded the ANI score, vital signs, and patient self-assessed pain intensity based on the 11-point numerical rating scale (NRS). The ML models' area under the receiver operating characteristic curves (AUROCs) were calculated and compared using DeLong's test. RESULTS: ML models were constructed using facial expressions, ANI, vital signs, and different combinations of the three datasets. The ML model constructed using facial expressions best predicted an NRS ≥ 7 (AUROC 0.93) followed by the ML model combining facial expressions and vital signs (AUROC 0.84) in the test-set. ML models constructed using combined physiological signals (vital signs, ANI) performed better than models based on individual parameters for predicting NRS ≥ 7, although the AUROCs were inferior to those of the ML model based on facial expressions (all P < 0.050). Among these parameters, absolute and relative ANI had the worst AUROCs (0.69 and 0.68, respectively) for predicting NRS ≥ 7. CONCLUSIONS: The ML model constructed using facial expressions best predicted severe postoperative pain (NRS ≥ 7) and outperformed models constructed from physiological signals.

Mesial temporal atrophy in preoperative MRI rather than steep Trendelenburg position is associated with postoperative delirium in patients undergoing a major urologic surgery.

PURPOSE: To investigate whether steep Trendelenburg in a major urologic surgery is associated with postoperative delirium, and to examine other potential clinical and radiologic factors predictive of postoperative delirium. METHODS: 182 patients who received a major urologic surgery and underwent a 3.0-T brain MRI scan within 1 year prior to the date of surgery were retrospectively enrolled. Preoperative brain MRIs were used to analyze features related to small vessel disease burden and mesial temporal atrophy. Presence of a significant mesial temporal atrophy was defined as Scheltens' scale ≥ 2. Patients' clinico-demographic data and MRI features were used to identify significant predictors of postoperative delirium using the logistic regression analysis. Independent predictors found significant in the univariate analysis were further evaluated in the multivariate analysis. RESULTS: Incidence of postoperative delirium was 6.0%. Patients with postoperative delirium had lower body mass index (21.3 vs. 25.0 kg/m2, P = 0.003), prolonged duration of anesthesia (362.7 vs. 224.7 min, P < 0.001) and surgery (302.2 vs. 174.5 min, P < 0.001), and had more significant mesial temporal atrophy (64% vs. 30%, P = 0.046). In the univariate analysis, female sex, type of surgery (radical prostatectomy over cystectomy), prolonged duration of anesthesia (≥ 6 h), and presence of a significant mesial temporal atrophy were significant predictors (all P-values < 0.050), but only the presence of significant mesial temporal atrophy was significant in the multivariate analysis [odds ratio (OR), 3.69; 95% CI 0.99-13.75; P = 0.046]. CONCLUSION: Steep Trendelenburg was not associated with postoperative delirium. Significant mesial temporal atrophy (Scheltens' scale ≥ 2) in preoperative brain MRI was predictive of postoperative delirium. TRIAL REGISTRATION: Not applicable.

Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study.

PURPOSE: This study aimed to assess whether an artificial intelligence model based on facial expressions can accurately predict significant postoperative pain. METHODS: A total of 155 facial expressions from patients who underwent gastric cancer surgery were analyzed to extract facial action units (AUs), gaze, landmarks, and positions. These features were used to construct various machine learning (ML) models, designed to predict significant postoperative pain intensity (NRS ≥ 7) from less significant pain (NRS < 7). Significant AUs predictive of NRS ≥ 7 were determined and compared to AUs known to be associated with pain in awake patients. The area under the receiver operating characteristic curves (AUROCs) of the ML models was calculated and compared using DeLong's test. RESULTS: AU17 (chin raising) and AU20 (lip stretching) were found to be associated with NRS ≥ 7 (both P ≤ 0.004). AUs known to be associated with pain in awake patients did not show an association with pain in postoperative patients. An ML model based on AU17 and AU20 demonstrated an AUROC of 0.62 for NRS ≥ 7, which was inferior to a model based on all AUs (AUROC = 0.81, P = 0.006). Among facial features, head position and facial landmarks proved to be better predictors of NRS ≥ 7 (AUROC, 0.85-0.96) than AUs. A merged ML model that utilized gaze and eye landmarks, as well as head position and facial landmarks, exhibited the best performance (AUROC, 0.90) in predicting significant postoperative pain. CONCLUSION: ML models using facial expressions can accurately predict the presence of significant postoperative pain and have the potential to screen patients in need of rescue analgesia. TRIAL REGISTRATION NUMBER: This study was registered at ClinicalTrials.gov (NCT05477303; date: June 17, 2022).

A deep learning algorithm to quantify AVF stenosis and predict 6-month primary patency: a pilot study.

Background: A deep convolutional neural network (DCNN) model that predicts the degree of arteriovenous fistula (AVF) stenosis and 6-month primary patency (PP) based on AVF shunt sounds was developed, and was compared with various machine learning (ML) models trained on patients' clinical data. Methods: Forty dysfunctional AVF patients were recruited prospectively, and AVF shunt sounds were recorded before and after percutaneous transluminal angioplasty using a wireless stethoscope. The audio files were converted to melspectrograms to predict the degree of AVF stenosis and 6-month PP. The diagnostic performance of the melspectrogram-based DCNN model (ResNet50) was compared with that of other ML models [i.e. logistic regression (LR), decision tree (DT) and support vector machine (SVM)], as well as the DCNN model (ResNet50) trained on patients' clinical data. Results: Melspectrograms qualitatively reflected the degree of AVF stenosis by exhibiting a greater amplitude at mid-to-high frequency in the systolic phase with a more severe degree of stenosis, corresponding to a high-pitched bruit. The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis. In predicting the 6-month PP, the area under the receiver operating characteristic curve of the melspectrogram-based DCNN model (ResNet50) (≥0.870) outperformed that of various ML models based on clinical data (LR, 0.783; DT, 0.766; SVM, 0.733) and that of the spiral-matrix DCNN model (0.828). Conclusion: The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis and outperformed ML-based clinical models in predicting 6-month PP.

LI-RADS Category on MRI Is Associated With Recurrence of Intrahepatic Cholangiocarcinoma After Surgery: A Multicenter Study.

BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology and interpretation of liver imaging. The association between the LI-RADS category and tumor recurrence in patients with intrahepatic cholangiocarcinomas (iCCAs) has not yet been evaluated in a multicenter study. PURPOSE: To retrospectively investigate the preoperative clinical and imaging features associated with recurrence-free survival (RFS) after curative resection of iCCAs and to identify the role of the LI-RADS category in at-risk patients. STUDY TYPE: Retrospective, multicenter. SUBJECTS: A total of 113 patients (mean age: 61.1 years; 74 men, 39 women) who underwent preoperative contrast-enhanced MRI and curative surgical resection for a single treatment-naive iCCA between 2008 and 2021. FILED STRENGTH/SEQUENCE: A 3 T dual gradient-echo T1 WI with in- and opposed-phase, turbo spin-echo T2 WI, diffusion-weighted echo-planar images, and three-dimensional gradient-echo T1 WI before and after administration of contrast agent. ASSESSMENT: MR imaging features were evaluated and assigned for each lesion using LI-RADS version 2018. RFS was calculated from the date of surgery to tumor recurrence or the last imaging date without evidence of recurrence. Factors affecting RFS were evaluated using clinical and imaging features. STATISTICAL TESTS: Cox proportional hazards model, Kaplan-Meier method, and log-rank test. A P-value of <0.05 was considered statistically significant. RESULTS: A total of 93 (82.3%) were categorized as LR-M and 20 (17.7%) were categorized as LR-4 or 5. In the multivariable analysis, LR-M category (hazard ratio [HR], 8.035; 95% confidence interval [CI], 1.096-58.931) and a tumor size >3 cm on MRI (HR, 2.690; 95% CI, 1.319-5.487) were independent factors for poor RFS. The 5-year RFS rate was significantly higher in patients with iCCA categorized as LR-4 or 5 than in those categorized as LR-M (94.4% vs. 51.9%, respectively). DATA CONCLUSION: Patients with iCCA categorized as LR-4 or 5 may have a better RFS than those categorized as LR-M. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Feasibility of Deep Learning-Based Analysis of Auscultation for Screening Significant Stenosis of Native Arteriovenous Fistula for Hemodialysis Requiring Angioplasty.

OBJECTIVE: To investigate the feasibility of using a deep learning-based analysis of auscultation data to predict significant stenosis of arteriovenous fistulas (AVF) in patients undergoing hemodialysis requiring percutaneous transluminal angioplasty (PTA). MATERIALS AND METHODS: Forty patients (24 male and 16 female; median age, 62.5 years) with dysfunctional native AVF were prospectively recruited. Digital sounds from the AVF shunt were recorded using a wireless electronic stethoscope before (pre-PTA) and after PTA (post-PTA), and the audio files were subsequently converted to mel spectrograms, which were used to construct various deep convolutional neural network (DCNN) models (DenseNet201, EfficientNetB5, and ResNet50). The performance of these models for diagnosing ≥ 50% AVF stenosis was assessed and compared. The ground truth for the presence of ≥ 50% AVF stenosis was obtained using digital subtraction angiography. Gradient-weighted class activation mapping (Grad-CAM) was used to produce visual explanations for DCNN model decisions. RESULTS: Eighty audio files were obtained from the 40 recruited patients and pooled for the study. Mel spectrograms of "pre-PTA" shunt sounds showed patterns corresponding to abnormal high-pitched bruits with systolic accentuation observed in patients with stenotic AVF. The ResNet50 and EfficientNetB5 models yielded an area under the receiver operating characteristic curve of 0.99 and 0.98, respectively, at optimized epochs for predicting ≥ 50% AVF stenosis. However, Grad-CAM heatmaps revealed that only ResNet50 highlighted areas relevant to AVF stenosis in the mel spectrogram. CONCLUSION: Mel spectrogram-based DCNN models, particularly ResNet50, successfully predicted the presence of significant AVF stenosis requiring PTA in this feasibility study and may potentially be used in AVF surveillance.

LI-RADS Version 2018 Treatment Response Algorithm: Diagnostic Performance after Transarterial Radioembolization for Hepatocellular Carcinoma.

OBJECTIVE: To assess the diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 treatment response algorithm (TRA) for the evaluation of hepatocellular carcinoma (HCC) treated with transarterial radioembolization. MATERIALS AND METHODS: This retrospective study included patients who underwent transarterial radioembolization for HCC followed by hepatic surgery between January 2011 and December 2019. The resected lesions were determined to have either complete (100%) or incomplete (< 100%) necrosis based on histopathology. Three radiologists independently reviewed the CT or MR images of pre- and post-treatment lesions and assigned categories based on the LI-RADS version 2018 and the TRA, respectively. Diagnostic performances of LI-RADS treatment response (LR-TR) viable and nonviable categories were assessed for each reader, using histopathology from hepatic surgeries as a reference standard. Inter-reader agreements were evaluated using Fleiss κ. RESULTS: A total of 27 patients (mean age ± standard deviation, 55.9 ± 9.1 years; 24 male) with 34 lesions (15 with complete necrosis and 19 with incomplete necrosis on histopathology) were included. To predict complete necrosis, the LR-TR nonviable category had a sensitivity of 73.3-80.0% and a specificity of 78.9-89.5%. For predicting incomplete necrosis, the LR-TR viable category had a sensitivity of 73.7-79.0% and a specificity of 93.3-100%. Five (14.7%) of 34 treated lesions were categorized as LR-TR equivocal by consensus, with two of the five lesions demonstrating incomplete necrosis. Inter-reader agreement for the LR-TR category was 0.81 (95% confidence interval: 0.66-0.96). CONCLUSION: The LI-RADS version 2018 TRA can be used to predict the histopathologic viability of HCCs treated with transarterial radioembolization.

Clinicopathologic and MRI features of combined hepatocellular-cholangiocarcinoma in patients with or without cirrhosis.

BACKGROUND AND AIMS: Differences in combined hepatocellular-cholangiocarcinomas (cHCC-CCAs) arising in high-risk patients with or without liver cirrhosis have not been elucidated. This study aimed to compare the clinicopathologic and imaging characteristics of cHCC-CCAs in patients with or without cirrhosis and to determine the prognostic factors for recurrence-free survival (RFS) after curative resections of single cHCC-CCAs. METHODS: This retrospective study included 113 patients with surgically resected single cHCC-CCAs who underwent preoperative magnetic resonance imaging from January 2008 to December 2019 at two tertiary referral centres. Clinical, pathologic and imaging features of tumours were compared in high-risk patients with or without cirrhosis. Imaging features were assessed using the Liver Imaging Reporting and Data System (LI-RADS) version 2018. RFS and associated factors were evaluated using Cox proportional hazards regression analysis, Kaplan-Meier analysis and log-rank test. RESULTS: cHCC-CCAs arising from cirrhotic livers had a smaller mean tumour size (2.9 cm vs. 4.5 cm; P < .001) and were more frequently categorized as LR-5 or 4 (41.2% vs. 20.0%; P = .024) than those arising from non-cirrhotic livers. In multivariable analysis, a tumour size of > 3 cm (hazard ratio [HR], 2.081; 95% confidence interval [CI], 1.180-3.668; P = .011) and the LR-M category (HR, 2.302; 95% CI, 1.198-4.424; P = .012) were independent predictors associated with worse RFS. CONCLUSIONS: The tumour size and distribution of LI-RADS categories of cHCC-CCAs differed in high-risk patients with or without cirrhosis. And LR-M category was a worse prognosis predictor after curative resections than LR-5 or 4 category.

Three-dimensional Cardiomyocytes Structure Revealed By Diffusion Tensor Imaging and Its Validation Using a Tissue-Clearing Technique.

We characterized the microstructural response of the myocardium to cardiovascular disease using diffusion tensor imaging (DTI) and performed histological validation by intact, un-sectioned, three-dimensional (3D) histology using a tissue-clearing technique. The approach was validated in normal (n = 7) and ischemic (n = 8) heart failure model mice. Whole heart fiber tracking using DTI in fixed ex-vivo mouse hearts was performed, and the hearts were processed with the tissue-clearing technique. Cardiomyocytes orientation was quantified on both DTI and 3D histology. Helix angle (HA) and global HA transmurality (HAT) were calculated, and the DTI findings were confirmed with 3D histology. Global HAT was significantly reduced in the ischemic group (DTI: 0.79 ± 0.13°/% transmural depth [TD] and 3D histology: 0.84 ± 0.26°/%TD) compared with controls (DTI: 1.31 ± 0.20°/%TD and 3D histology: 1.36 ± 0.27°/%TD, all p < 0.001). On direct comparison of DTI with 3D histology for the quantitative assessment of cardiomyocytes orientation, significant correlations were observed in both per-sample (R2 = 0.803) and per-segment analyses (R2 = 0.872). We demonstrated the capability and accuracy of DTI for mapping cardiomyocytes orientation by comparison with the intact 3D histology acquired by tissue-clearing technique. DTI is a promising tool for the noninvasive characterization of cardiomyocytes architecture.