MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

Nacubactam is a novel bicyclic non-β-lactam β-lactamase inhibitor that acts as a penicillin-binding protein (PBP-2) active antibacterial, almost exclusively cleared by renal excretion. This study assessed the effect of renal function and hemodialysis on nacubactam pharmacokinetics following a single intravenous (IV) 1 g infusion. Part 1 included three subjects with normal renal function, and six each with mild, moderate, or severe renal impairment; Part 2 included eight subjects with end-stage renal disease undergoing hemodialysis. We observed a strong linear relationship between renal function and both nacubactam total clearance and renal clearance. Compared with subjects with normal renal function, mean nacubactam total clearance was 49%, 73%, and 85% lower among subjects with mild, moderate, and severe renal impairment, respectively, and renal clearance was 42%, 70%, and 90% lower. Nacubactam systemic exposure was correspondingly greater in subjects with impaired renal function. Compared with the normal renal function group, AUC0-inf was 2-fold, 3.7-fold, and 6.6-fold higher in the mild, moderate, and severe impairment groups, respectively. Similar results were observed across four different methods of estimating renal clearance. Nacubactam was cleared by hemodialysis, with more than 60% of a single dose eliminated during a 4-h dialysis session. Renal impairment had no apparent effect on the safety profile of nacubactam, and a single 1 g dose was well tolerated in all groups. These data can be used to underwrite nacubactam dosing recommendations for patients with impaired renal function, including those receiving renal replacement therapy.CLINICAL TRIALSThis study is registered with ClinicalTrials.gov as NCT02975388.

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

655 Background: Pancreatic neuroendocrine tumors (PNETs) have varying biologic behavior based on factors including tumor grade. Many prognostic factors can only be determined post-pancreatectomy; thus, are unable to guide clinical decision making preoperatively. This is particularly relevant in small, non-functional (NF)-PNETs. Imaging analysis with radiomics may be able to elucidate biologic data such as tumor grade, without the need for tissue sampling. Our study aims to create an automatic pipeline from segmentation to radiomics-signature building to preoperatively predict tumor grade. Methods: Patients that underwent resection between 2003-21 with adequate preoperative arterial phase CT scans were divided into training and validation cohorts. In the training cohort, the pancreas and tumor region were manually segmented and used to train an auto-segmentation model. The validation cohort then underwent automatic segmentation. A total of 255 radiomics features were extracted from the tumor region. Correlated features were removed, and the minimum redundancy maximum relevance (mRMR) method was used to select the final feature set. These features were used to train a Support Vector Machine (SVM)-based classifier through the training cohort to predict grade, which was dichotomized into grade I versus II/III. The radiomic based prediction model was then evaluated in the automatically segmented validation cohort. Results: A total of 186 patients were identified and divided into training (n = 140) and validation (n = 46) cohorts. Median age was 57 (27, 85) and 52% were male. 113 (62%) and 70 (38%) patients were grades I and II/III, respectively. Median tumor size was 24 mm (6, 200) and 43 (26%) patients had positive nodal disease. The auto-segmentation model was able to accurately segment the tumor region in 33 (72%) patients of the validation cohort. In the training cohort (n = 140), the radiomics-signature produced an area under the curve (AUC) of 0.87 (0.81, 0.93). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.94 (0.9, 0.98), 0.74 (0.66, 0.81), 0.72 (0.65, 0.80) and 0.97 (0.94, 1.00) respectively. In the auto-segmented validation cohort (n = 33), the radiomics model produced an AUC of 0.75 (0.61, 0.90). Sensitivity, specificity, PPV and NPV were 0.88 (0.77, 0.99), 0.62 (0.46, 0.79), 0.71 (0.56, 0.87) and 0.83 (0.71, 0.96), respectively. Conclusions: The proposed auto-segmentation model was able to automatically identify tumor regions with a high degree of accuracy. Similarly, the subsequent radiomics-signature was also able to strongly discern PNET grade. This automatic pipeline bypasses manual segmentation and could help incorporate a radiomics-signature into preoperative clinical decision-making for PNETs.

Individualized Prediction and Risk Factors of Recurrence in Chinese Patients with Sebaceous Carcinoma: A Multicenter Study of 418 Patients

Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. Methods The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. Results For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214–0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). Conclusion We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

In this paper, design and implementation the feature extraction method of Speeded-Up Robust Features (SURF) and Support Vector Machine (SVM) classification method into the traffic signs recognition application. The output of this application is the meaning of the traffic sign with two languages, indonesia and english. In the SURF method, the smallest large number of keypoints will affect the accuracy level to recognize a image. Based on the results, accuracy of this traffic signs detection has a high accuracy rate of 96%, when taking this image right in the green box displayed on the smartphone screen and taken when the brightness level of the light on 4106 lux up to 10896 lux.

Discriminating minimal residual disease status in multiple myeloma based on MRI: utility of radiomics and comparison of machine-learning methods

MP77-06 MRI-BASED MACHINE LEARNING RADIOMICS FOR PREOPERATIVE PREDICTION OF HER2 STATUS IN UROTHELIAL BLADDER CARCINOMA

Rebleeding following endoscopic treatment in patients with cirrhosis is a serious life-threatening complication. In the present study, a novel, reliable and non-invasive score for prediction of rebleeding following endoscopic therapy for esophagogastric variceal bleeding (EGVB) was developed. The present retrospective study recruited cirrhotic patients with EGVB (n=596) who underwent endoscopic therapy. Patients hospitalized from January 2015 to January 2020 were grouped into a training (n=437) cohort to develop the new score and those hospitalized from February 2020 to February 2022 were grouped into a validation (n=159) cohort to validate the score. The international normalized ratio (INR) and albumin-bilirubin (ALBI) grade were used to develop the INR-ALBI (IALBI) score to predict risk of rebleeding. In the training cohort, the prognostic performance of the IALBI score and other ALBI-associated scores (modified ALBI, platelet-ALBI and ALBI-fibrosis-4) at 1, 3 and 12 months was assessed using receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. At each time point, most areas under the ROC curve of IALBI were higher than those of other ALBI-associated scores, particularly for prediction of early rebleeding. At 1 month, the rebleeding rates of patients with IALBI grade 2 and 3 were ~10.0- and 19.5-times higher than those of patients with grade 1, respectively. The negative predictive value (NPV) of IALBI for the training and validation cohort at 1 month was 100.0 and 97.8%, respectively. For viral and non-viral patients in the training cohort, IALBI showed good predictive ability and NPV for early rebleeding. The IALBI grading system successfully assessed rebleeding, particularly early rebleeding, in cirrhotic patients with EGVB following endoscopic therapy IALBI grade 1, predicted low risk of rebleeding and may not require endoscopic treatment again in the short-term.

Optimization of risk stratification is important for facilitating prognoses and therapeutic decisions regarding diffuse large B-cell lymphoma (DLBCL). However, a simple and applicable prognostic tool is lacking for individuals with human immunodeficiency virus (HIV)-related DLBCL in the era of combined antiretroviral therapy (cART). This retrospective multicenter observational study included 147 HIV-related DLBCL patients with histologically confirmed DLBCL from 2013 to 2020. The total group was divided into training (n = 78) and validation (n = 69) cohorts to derive the best prognostic score. Clinicopathological and characteristic biomarkers correlated with clinical outcomes were analyzed. Age, Ann Arbor stage, lactate dehydrogenase (LDH) ratio, bulky disease, and red blood cell distribution width (RDW) ratio retained robust independent correlations with overall survival (OS) in multivariate analysis. A new and practical prognostic model was generated and externally validated, classifying patients into three categories with significantly different survival rates. Moreover, the new index outperformed the International Prognostic Index (IPI) score (area under the curve values of 0.94 vs. 0.81 in the training cohort and 0.85 vs. 0.74 in the validation cohort, C-indices of 0.80 vs. 0.70 in the training cohort and 0.74 vs. 0.70 in the validation cohort, and integrated discrimination improvement values of 0.203 in the training cohort and 0.175 in the validation cohort) and was better at defining intermediate- and high-risk groups. The calibration curves performed satisfactorily for predicting 3-year OS in the training and validation cohorts. We developed and validated a simple and feasible prognostic model for patients with HIV-related DLBCL that had more discriminative and predictive accuracy than the IPI score for risk stratification and individualized treatment in the cART era.

BackgroundMost hepatocellular carcinoma (HCC) patients’ liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase)/lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC.MethodsA total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan–Meier method.ResultsAccording to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with alpha-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI 1.21–2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI 1.35–2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis.ConclusionsThe elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

BackgroundDistal cholangiocarcinoma (dCCA), originating from the common bile duct, is greatly associated with a dismal prognosis. A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. In this study, we explored and compared several novel machine learning models that might lead to an improvement in prediction accuracy and treatment options for patients with dCCA.MethodsIn this study, 169 patients with dCCA were recruited and randomly divided into the training cohort (n = 118) and the validation cohort (n = 51), and their medical records were reviewed, including survival outcomes, laboratory values, treatment strategies, pathological results, and demographic information. Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). We measured and compared the performance of models using the receiver operating characteristic (ROC) curve, integrated Brier score (IBS), and concordance index (C-index) following cross-validation. The machine learning model with the best performance was screened out and compared with the TNM Classification using ROC, IBS, and C-index. Finally, patients were stratified based on the model with the best performance to assess whether they benefited from postoperative chemotherapy through the log-rank test.ResultsAmong medical features, five variables, including tumor differentiation, T-stage, lymph node metastasis (LNM), albumin-to-fibrinogen ratio (AFR), and carbohydrate antigen 19-9 (CA19-9), were used to develop machine learning models. In the training cohort and the validation cohort, C-index achieved 0.763 vs. 0.686 (SVM), 0.749 vs. 0.692 (SurvivalTree), 0.747 vs. 0.690 (Coxboost), 0.745 vs. 0.690 (RSF), 0.746 vs. 0.711 (DeepSurv), and 0.724 vs. 0.701 (CoxPH), respectively. The DeepSurv model (0.823 vs. 0.754) had the highest mean area under the ROC curve (AUC) than other models, including SVM (0.819 vs. 0.736), SurvivalTree (0.814 vs. 0.737), Coxboost (0.816 vs. 0.734), RSF (0.813 vs. 0.730), and CoxPH (0.788 vs. 0.753). The IBS of the DeepSurv model (0.132 vs. 0.147) was lower than that of SurvivalTree (0.135 vs. 0.236), Coxboost (0.141 vs. 0.207), RSF (0.140 vs. 0.225), and CoxPH (0.145 vs. 0.196). Results of the calibration chart and decision curve analysis (DCA) also demonstrated that DeepSurv had a satisfactory predictive performance. In addition, the performance of the DeepSurv model was better than that of the TNM Classification in C-index, mean AUC, and IBS (0.746 vs. 0.598, 0.823 vs. 0.613, and 0.132 vs. 0.186, respectively) in the training cohort. Patients were stratified and divided into high- and low-risk groups based on the DeepSurv model. In the training cohort, patients in the high-risk group would not benefit from postoperative chemotherapy (p = 0.519). In the low-risk group, patients receiving postoperative chemotherapy might have a better prognosis (p = 0.035).ConclusionsIn this study, the DeepSurv model was good at predicting prognosis and risk stratification to guide treatment options. AFR level might be a potential prognostic factor for dCCA. For the low-risk group in the DeepSurv model, patients might benefit from postoperative chemotherapy.

Background: The clinical significance of Albumin-to-Alkaline Phosphatase Ratio (AAPR) has been discussed in hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). The aim of this study is to clarify the prognostic value of AAPR in patients with combined hepatocellular and cholangiocarcinoma (cHCC-CCA).Methods: A total of 267 patients pathologically diagnosed as Allen type C cHCC-CCA in our institution were retrospectively enrolled and randomly divided into the training (N=187) cohort and validation (N=80) cohort. The prognostic value of AAPR was evaluated and validated. An AAPR-based nomogram was constructed and its prediction performance was assessed.Results: We identified 0.43 as the optimal threshold value of AAPR by the X-tile software. In the training cohort, the median overall survival (OS) of patients with AAPR < 0.43 was significant shorter than that of those with AAPR ≥ 0.43(15.8 months vs 35 months, respectively, P < 0.001). Univariate and multivariate analyses demonstrated that AAPR was a strong indicator of OS. The concordance index (C-index), receiver operating characteristic (ROC) curves, likelihood ratio tests (LAT), Akaike information criteria (AIC) and decision curve analysis (DCA) demonstrated that AAPR outperformed the Child-Pugh (CP) grade and albumin-bilirubin (ALBI) grade in predicting OS. These findings were further verified in the validation cohort. The AAPR-based nomogram achieved C-index values of 0.76 (95%CI: 0.71-0.81) in the training cohort and 0.69 (95%CI: 0.60-0.78) in the validation cohort, which presented significant superiority to TNM stage.Conclusions: Preoperative AAPR is an independent prognostic predictor in cHCC-CCA. The AAPR-based nomogram contributes to personalized prognosis prediction and clinical decision making for cHCC-CCA.

Magnetic Resonance Imaging-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

106 Prediction of postoperative day 1 hemoglobin level after cesarean delivery