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Background: Lymph node metastasis (LNM) is an important prognostic factor for cervical cancer (CC) and determines the treatment strategy. Hematological indicators have been reported as being useful biomarkers for the prognosis of a variety of cancers. This study aimed to evaluate the feasibility of machine learning models characterized by preoperative hematological indicators to predict the LNM status of CC patients before surgery. Methods: The clinical data of 236 patients with pathologically confirmed CC were retrospectively analyzed at the Gynecology Oncology Department of the First Affiliated Hospital of Bengbu Medical University from November 2020 to August 2022. The least absolute shrinkage and selection operator (LASSO) was used to select 21 features from 35 hematological indicators and for the construction of 6 machine learning predictive models, including Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), and Logistic Regression (LR), as well as Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost). Evaluation metrics of predictive models included the area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1-score. Results: RF has the best overall predictive performance for ten-fold cross-validation in the training set. The specific performance indicators of RF were AUC (0.910, 95% confidence interval [CI]: 0.820--1.000), accuracy (0.831, 95% CI: 0.702--0.960), specificity (0.835, 95% CI: 0.708--0.962), sensitivity (0.831, 95% CI: 0.702--0.960), and F1-score (0.829, 95% CI: 0.696--0.962). RF had the highest AUC in the testing set (AUC = 0.854). Conclusion: RF based on preoperative hematological indicators that are easily available in clinical practice showed superior performance in the preoperative prediction of CC LNM. However, investigations on larger external cohorts of patients are required for further validation of our findings. [ABSTRACT FROM AUTHOR]

Background: Distinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma. Methods: Approximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA). Results: Ultimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p < 0.05). The AUC values of the radiomics model were 0.85 (95% CI, 0.76 to 0.95) in the training sets, and the corresponding AUC values in the validation sets were 0.82 (95% CI, 0.65 to 0.98), which were far superior to the clinical model AUC values of 0.68 (95% CI, 0.58 to 0.79) in the training sets and 0.72 (95% CI, 0.57 to 0.87) in the validation sets. The nomogram demonstrated good performance in the preoperative distinction of chondrosarcoma. The DCA analysis revealed that the nomogram model had a markedly higher clinical usefulness in predicting chondrosarcoma grading preoperatively than either the rad-score or clinical model alone. Conclusion: The nomogram based on MRI radiomics combined with optimal independent factors had better performance for the preoperative differentiation between low-grade and high-grade chondrosarcoma and has potential as a noninvasive preoperative tool for personalizing clinical plans. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the application value of multiparametric MRI in evaluating the expression status of human epithelial growth factor receptor 2 (HER2) in bladder cancer (BCa). Methods: From April 2021 to July 2023, preoperative imaging manifestations of 90 patients with pathologically confirmed BCa were retrospectively collected and analyzed. All patients underwent multiparametric MRI including synthetic MRI, DWI, from which the T1, T2, proton density (PD) and apparent diffusion coefficient (ADC) values were obtained. The clinical and imaging characteristics as well as quantitative parameters (T1, T2, PD and ADC values) between HER2-positive and -negative BCa were compared using student t test and chi-square test. The diagnostic efficacy of parameters in predicting HER2 expression status was evaluated by calculating the area under ROC curve (AUC). Results: In total, 76 patients (mean age, 63.59 years ± 12.84 [SD]; 55 men) were included: 51 with HER2-negative and 25 with HER2-positive BCa. HER2-positive group demonstrated significantly higher ADC, T1, and T2 values than HER2-negative group (all P < 0.05). The combination of ADC values and tumor grade yielded the best diagnostic performance in evaluating HER2 expression level with an AUC of 0.864. Conclusion: The multiparametric MR characterization can accurately evaluate the HER2 expression status in BCa, which may further guide the determination of individualized anti-HER2 targeted therapy strategies. [ABSTRACT FROM AUTHOR]

This study aims to review the current literature on methods of preoperative prediction of pituitary adenoma consistency. Pituitary adenoma consistency may be a limiting factor for successful surgical removal of tumors. Efforts have been made to investigate the possibility of an accurate assessment of the preoperative consistency to allow for safer and more effective surgery planning. We searched major scientific databases and systematically analyzed the results. A total of 54 relevant articles were identified and selected for inclusion. These studies evaluated methods based on either MRI intensity, enhancement, radiomics, MR elastometry, or CT evaluation. The results of these studies varied widely. Most studies used the average intensity of either T2WI or ADC maps. Firm tumors appeared hyperintense on T2WI, although only 55% of the studies reported statistically significant results. There are mixed reports on ADC values in firm tumors with findings of increased values (28%), decreased values (22%), or no correlation (50%). Multiple contrast enhancement-based methods showed good results in distinguishing between soft and firm tumors. There were mixed reports on the utility of MR elastography. Attempts to develop radiomics and machine learning-based models have achieved high accuracy and AUC values; however, they are prone to overfitting and need further validation. Multiple methods of preoperative consistency assessment have been studied. None demonstrated sufficient accuracy and reliability in clinical use. Further efforts are needed to enable reliable surgical planning. [ABSTRACT FROM AUTHOR]

The preoperative diagnosis of ovarian cancer (OC) was developed based on risk factor groups using secondary data. Binary and multiple logistic regression and its operating characteristic curve were used to analyze the data of risk factor groups for tumor markers, complete blood count (CBC), and liver function tests (LFT), respectively, and to explore potential predictors for each risk factor group. The data of 202 patients with ovarian cancer were analyzed in this research. As the tumor markers group, menopausal status, human epididymal protein 4, and cancer antigen 19-9 were included as the derivation of the preoperative diagnosis index. For the CBC group, menopausal status, lymphocyte count, and basophil cell ratio were used as predictors. Menopausal status, albumin, alkaline phosphatase, and indirect bilirubin were used as predictors for the LFT group. The area under the receiver operating characteristic curve (AUROC) for tumor markers, CBC, and LFT were 0.89 (95% CI, 0.845–0.935; sensitivity = 0.776, specificity = 0.919), 0.813 (95% CI, 0.755–0.871; sensitivity = 0.741, specificity = 0.767), and 0.81 (95% CI, 0.751–0.868; sensitivity = 0.664, specificity = 0.837), respectively. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the preoperative prediction of large-number central lymph node metastasis (CLNM) in single thyroid papillary carcinoma (PTC) with negative clinical lymph nodes. A total of 634 patients with clinically lymph node-negative single PTC who underwent thyroidectomy and central lymph node dissection at the First Affiliated Hospital of Anhui Medical University and the Nanchong Central Hospital between September 2018 and September 2021 were analyzed retrospectively. According to the CLNM status, the patients were divided into two groups: small-number (≤5 metastatic lymph nodes) and large-number (>5 metastatic lymph nodes). Univariate and multivariate analyses were used to determine the independent predictors of large-number CLNM. Simultaneously, a nomogram based on risk factors was established to predict large-number CLNM. The incidence of large-number CLNM was 7.7%. Univariate and multivariate analyses showed that age, tumor size, and calcification were independent risk factors for predicting large-number CLNM. The combination of the three independent predictors achieved an AUC of 0.806. Based on the identified risk factors that can predict large-number CLNM, a nomogram was developed. The analysis of the calibration map showed that the nomogram had good performance and clinical application. In patients with single PTC with negative clinical lymph nodes large-number CLNM is related to age, size, and calcification in patients with a single PTC with negative clinical lymph nodes. Surgeons and radiologists should pay more attention to patients with these risk factors. A nomogram can help guide the surgical decision for PTC. [ABSTRACT FROM AUTHOR]

Background The conventional surgical treatment for cholelithiasis is laparoscopic cholecystectomy (LC), although some patients still require conversion to open cholecystectomy, primarily due to technical challenges. Risk factor prediction before surgery aids in determining intraoperative challenges. There are several rating systems that can be used to anticipate intraoperative challenges during LC. However, a trustworthy and consistent scoring and prediction system must be developed. Aim and objectives to reduce complications from LC and establish a score system to anticipate difficult LC before surgery. Patient and methods This observational cohort research, which involved 50 patients with calculous cholecystitis, was conducted at the General Surgery Department of the Assiut University Hospitals. One day before to surgery, all patients undergoing elective LC underwent scoring procedures. The intraoperative activities were all documented. Every patient got the usual postoperative treatment and monitoring. Result The preoperative score and LC results were significantly correlated. Conclusion The improvement of patient counselling, surgical planning, and postoperative expectations is made possible by identifying preoperative risk factors that indicate difficult LC. These variables also assist the surgeon in LC difficulty prediction and in maintaining a lower threshold for conversion under tough intraoperative situations. [ABSTRACT FROM AUTHOR]

Due to the fixed bony structure of the pelvis, the pelvic operation space is limited, complicating the surgical operation of rectal cancer, especially middle and low rectal cancer. The closer the tumor is to the anal verge, the smaller the operative field and operating space, the longer the operative time, and the greater the incidence of intraoperative side injuries and postoperative complications. To date, there is still no clear definition of a difficult pelvis that affects the surgical operation of rectal cancer. Few related research reports exist in the literature, and views on this aspect are not the same between countries. Therefore, it is particularly important to predict the difficulty of rectal cancer surgery in a certain way before surgery and to select the surgical method most suitable for each case during the treatment of rectal cancer. [ABSTRACT FROM AUTHOR]

Background: Preoperative prediction of postoperative tumor progression of intracranial grade II–III hemangiopericytoma is the basis for clinical treatment decisions. Purpose: To use preoperative magnetic resonance imaging (MRI) semantic features for predicting postoperative tumor progression in patients with intracranial grade II–III solitary fibrous tumor/hemangiopericytoma (SFT/HPC). Material and Methods: We retrospectively analyzed the preoperative MRI data of 42 patients with intracranial grade II–III SFT/HPC, as confirmed by surgical resection and pathology in our hospital from October 2010 to October 2017, who were followed up for evaluation of recurrence, metastasis, or death. We applied strict inclusion and exclusion criteria and finally included 37 patients. The follow-up time was in the range of 8–120 months (mean = 57.1 months). Results: Single-factor survival analysis revealed that tumor grade (log-rank, P = 0.024), broad-based tumor attachment to the dura mater (log-rank, P = 0.009), a blurred tumor-brain interface (log-rank, P = 0.008), skull invasion (log-rank, P = 0.002), and the absence of postoperative radiotherapy (log-rank, P = 0.006) predicted postoperative intracranial SFT/HPC progression. Multivariate survival analysis revealed that tumor grade (P = 0.009; hazard ratio [HR] = 11.42; 95% confidence interval [CI] = 1.832–71.150), skull invasion (P = 0.014; HR = 5.72; 95% CI = 1.421–22.984), and the absence of postoperative radiotherapy (P = 0.001; HR = 0.05; 95% CI = 0.008–0.315) were independent predictors of postoperative intracranial SFT/HPC progression. Conclusion: Broad-based tumor attachment to the dura mater, skull invasion, and blurring of the tumor–brain interface can predict postoperative intracranial SFT/HPC progression. [ABSTRACT FROM AUTHOR]

Background: Microvascular invasion (MVI) is important in early recurrence and leads to poor overall survival (OS) in hepatocellular carcinoma (HCC). A number of studies have reported independent risk factors for MVI. In this retrospective study, we designed to develop a preoperative model for predicting the presence of MVI in HCC patients to help surgeons in their surgical decision-making and improve patient management. Patients and Methods: We developed a predictive model based on a nomogram in a training cohort of 225 HCC patients. We analyzed patients' clinical information, laboratory examinations, and imaging features from contrast-enhanced CT. Mann–Whitney U test and multiple logistic regression analysis were used to confirm independent risk factors and develop the predictive model. Internal and external validation was performed on 75 and 77 HCC patients, respectively. Moreover, the diagnostic performance of our model was evaluated using receiver operating characteristic (ROC) curves. Results: In the training cohort, maximum tumor diameter (> 50 mm), tumor margin, direct bilirubin (> 2.7 µmol/L), and AFP (> 360.7 ng/mL) were confirmed as independent risk factors for MVI. In the internal and external validation cohort, the developed nomogram model demonstrated good diagnostic ability for MVI with an area under the curve (AUC) of 0.723 and 0.829, respectively. Conclusion: Based on routine clinical examinations, which may be helpful for clinical decision-making, we have developed a nomogram model that can successfully assess the risk of MVI in HCC patients preoperatively. When predicting HCC patients with a high risk of MVI, the surgeons may perform an anatomical or wide-margin hepatectomy on the patient. [ABSTRACT FROM AUTHOR]

This study aims to review the current literature on methods of preoperative prediction of pituitary adenoma consistency. Pituitary adenoma consistency may be a limiting factor for successful surgical removal of tumors. Efforts have been made to investigate the possibility of an accurate assessment of the preoperative consistency to allow for safer and more effective surgery planning. We searched major scientific databases and systematically analyzed the results. A total of 54 relevant articles were identified and selected for inclusion. These studies evaluated methods based on either MRI intensity, enhancement, radiomics, MR elastometry, or CT evaluation. The results of these studies varied widely. Most studies used the average intensity of either T2WI or ADC maps. Firm tumors appeared hyperintense on T2WI, although only 55% of the studies reported statistically significant results. There are mixed reports on ADC values in firm tumors with findings of increased values (28%), decreased values (22%), or no correlation (50%). Multiple contrast enhancement-based methods showed good results in distinguishing between soft and firm tumors. There were mixed reports on the utility of MR elastography. Attempts to develop radiomics and machine learning-based models have achieved high accuracy and AUC values; however, they are prone to overfitting and need further validation. Multiple methods of preoperative consistency assessment have been studied. None demonstrated sufficient accuracy and reliability in clinical use. Further efforts are needed to enable reliable surgical planning. [ABSTRACT FROM AUTHOR]

Background: Preoperative prediction of severe cholecystitis (SC), including acute gangrenous cholecystitis (AGC) and acute purulent cholecystitis (APC), as opposed to acute exacerbation of chronic cholecystitis (ACC), is of great significance, as SC is associated with high mortality rate.Methods: In this study, we retrospectively investigated medical records of 114 cholecystitis patients, treated in Shanghai No. 6 People's Hospital from February 2009 to July 2020. Gallbladder wall thickness (GBWT), indexes of blood routine examination, including white blood cell (WBC), alkaline phosphatase (ALP), the percentage of neutrophil, alanine transaminase (ALT), aspartate aminotransferase (AST), fibrinogen (FIB), gamma-glutamyl transferase, prothrombin time and total bilirubin were evaluated. One-way analysis of variance (ANOVA) was used to evaluate significant differences between a certain kind of SC and ACC to select a prediction index for each kind of SC. Receiver operating characteristic (ROC) curve analysis was conducted to identify the prediction effectiveness of these indexes and their optimal cut-off values.Results: Higher WBC and lower ALP were associated with AGC diagnosis (P < 0.05). Higher percentage of neutrophils was indicative of APC and AGC, while higher GBWT was significantly associated with APC diagnosis (P < 0.05) The optimal cut-off values for these indexes were established at 11.1*109/L (OR: 5.333, 95% CI 2.576-10.68, P < 0.0001, sensitivity: 72.73%, specificity: 66.67%), 79.75% (OR: 5.735, 95% CI 2.749-12.05, P < 0.0001, sensitivity: 77.92%, specificity: 61.9%) and 5.5 mm (OR: 22, 95% CI 4.757-83.42, P < 0.0001, sensitivity: 78.57%, specificity: 85.71%), respectively.Conclusion: We established a predictive model for the differentiations of APC and AGC from ACC using clinical indexes, such as GBWT, the percentage of neutrophil and WBC, and determined cut-off values for these indexes based on ROC curves. Index values exceeding these cut-off values will allow to diagnose patients as APC and AGC, as opposed to a diagnosis of ACC. [ABSTRACT FROM AUTHOR]

Purpose: To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC). Methods: 184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell’s concordance index (C-index) and decision curve analysis (DCA). The Kaplan Meier (K-M) method was used for OS analysis. Results: Univariate and multivariate analysis indicated that Rad – score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort. Conclusion: We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future. [ABSTRACT FROM AUTHOR]

Background: Peritoneal sarcomatosis (PS) could occur in patients with retroperitoneal sarcomas (RPS). This study aimed to expand the understanding of PS on its characteristics and prognostic role, and develop a nomogram to predict its occurrence preoperatively. Methods: Data of 211 consecutive patients with RPS who underwent surgical treatment between 2011 and 2019 was retrospectively reviewed. First, the clinicopathological characteristics of PS were summarized and analyzed. Second, the disease-specific survival (DSS) and recurrence-free survival (RFS) of patients were analyzed to evaluate the prognostic role of PS. Third, preoperative imaging, nearly the only way to detect PS preoperatively, was combined with other screened risk factors to develop a nomogram. The performance of the nomogram was assessed. Results: Among the 211 patients, 49 (23.2%) patients had PS with an incidence of 13.0% in the primary patients and 35.4% in the recurrent patients. The highest incidence of PS occurred in dedifferentiated liposarcoma (25.3%) and undifferentiated pleomorphic sarcoma (25.0%). The diagnostic sensitivity of the preoperative imaging was 71.4% and its specificity was 92.6%. The maximum standardized uptake value (SUVmax) was elevated in patients with PS (P<0.001). IHC staining for liposarcoma revealed that the expression of VEGFR-2 was significantly higher in the PS group than that in the non-PS group (P = 0.008). Survival analysis (n =196) showed significantly worse DSS in the PS group than in non-PS group (median: 16.0 months vs. not reached, P < 0.001). In addition, PS was proven as one of the most significant prognostic predictors of both DSS and RFS by random survival forest algorithm. A nomogram to predict PS status was developed based on preoperative imaging combined with four risk factors including the presentation status (primary vs. recurrent), ascites, SUVmax, and tumor size. The nomogram significantly improved the diagnostic sensitivity compared to preoperative imaging alone (44/49, 89.8% vs. 35/49, 71.4%). The C-statistics of the nomogram was 0.932, and similar C-statistics (0.886) was achieved at internal cross-validation. Conclusion: PS is a significant prognostic indicator for RPS, and it occurs more often in recurrent RPS and in RPS with higher malignant tendency. The proposed nomogram is effective to predict PS preoperatively. [ABSTRACT FROM AUTHOR]

Background: Lymph node metastasis (LNM) is considered as an adverse prognostic indicator for cancer patients. Preoperative knowledge of LNM is valuable for pretreatment decision making. Here, we sought to develop and validate an LNM signature for preoperative prediction of LNM in patients with head and neck squamous cell carcinoma (HNSCC). Methods: By studying single cell RNA‐sequencing data (scRNA‐seq), differentially expressed mRNA were selected and analyzed through univariate logistic regression and least absolute shrinkage and selection operator (LASSO) to identify an LNM signature. Multivariate logistic regression was utilized to establish an LNM nomogram incorporating LNM signature and T‐classification. Results: The LNM signature was significantly associated with lymph node status and prognosis. The LNM signature and LNM nomogram displayed a robust predictive effect. Conclusion: Our study reveals that LNM signature is a powerful biomarker for preoperative prediction of LNM in patients with HNSCC, which may be effective to realize individualized outcome prediction. [ABSTRACT FROM AUTHOR]

Background: This study aimed to determine preoperative predictors for sagittal imbalance in kyphosis secondary to ankylosing spondylitis (AS) after one-level three-column osteotomy.Methods: A total of 55 patients with AS who underwent one-level three-column osteotomy were enrolled. The patients were divided into two groups according to sagittal vertical axis (SVA) value at the final follow-up (group A: SVA > 5 cm; group B: SVA ≤ 5 cm). The radiographic measures included global kyphosis, lumbar lordosis (LL), pelvic tilt (PT), pelvic incidence (PI), sacral slope, T1 pelvic angle (TPA), SVA, osteotomized vertebral angle and PI and LL mismatch (PI - LL). Postoperative clinical outcomes were evaluated using Scoliosis Research Society-22 questionnaire (SRS-22) and Oswestry Disability Index (ODI).Results: Fifty-five AS patients had an average follow-up of 30.6 ± 10.2 months (range 24-84 months). Group A had larger preoperative and postoperative LL, PT, PI - LL, TPA and SVA values compared with group B (P < 0.05), and no significant differences were found in ODI and SRS-22 scores between the two groups (P > 0.05). Preoperative LL, PT, PI - LL, TPA, and SVA values were positively correlated with the follow-up SVA value (P < 0.05). Among them, TPA > 40.9°, PI - LL > 32.5° and SVA > 13.7 cm were the top three predictors with the best accuracy to predict sagittal imbalance. Immediate postoperative SVA value of ≤ 7.4 cm was a key factor in reducing the risk of sagittal imbalance during follow-up.Conclusions: Preoperative TPA > 40.9°, PI - LL > 32.5° and SVA > 13.7 cm could predict sagittal imbalance in AS kyphosis after one-level three-column osteotomy, and additional osteotomies were recommended for this condition. Immediate postoperative SVA ≤ 7.4 cm was an optimal indicator for preventing sagittal imbalance.Level Of Evidence: IV. [ABSTRACT FROM AUTHOR]

Background: Infiltration is important for the surgical planning and prognosis of pituitary adenomas. Differences in preoperative diagnosis have been noted. The aim of this article is to assess the accuracy of machine learning analysis of texture-derived parameters of pituitary adenoma obtained from preoperative MRI for the prediction of high infiltration. Methods: A total of 196 pituitary adenoma patients (training set: n = 176; validation set: n = 20) were enrolled in this retrospective study. In total, 4120 quantitative imaging features were extracted from CE-T1 MR images. To select the most informative features, the least absolute shrinkage and selection operator (LASSO) and variance threshold method were performed. The linear support vector machine (SVM) was used to fit the predictive model based on infiltration features. Furthermore, the receiver operating characteristic curve (ROC) was generated, and the diagnostic performance of the model was evaluated by calculating the area under the curve (AUC), accuracy, precision, recall, and F1 value. Results: A variance threshold of 0.85 was used to exclude 16 features with small differences using the LASSO algorithm, and 19 optimal features were finally selected. The SVM models for predicting high infiltration yielded an AUC of 0.86 (sensitivity: 0.81, specificity 0.79) in the training set and 0.73 (sensitivity: 0.87, specificity: 0.80) in the validation set. The four evaluation indicators of the predictive model achieved good diagnostic capabilities in the training set (accuracy: 0.80, precision: 0.82, recall: 0.81, F1 score: 0.81) and independent verification set (accuracy: 0.85, precision: 0.93, recall: 0.87, F1 score: 0.90). Conclusions: The radiomics model developed in this study demonstrates efficacy for the prediction of pituitary adenoma infiltration. This model could potentially aid neurosurgeons in the preoperative prediction of infiltration in PAs and contribute to the selection of ideal surgical strategies. [ABSTRACT FROM AUTHOR]

Treatment of urolithiasis, a benign disease, requires high efficacy and safety. Endoscopic treatment of impacted ureteral stones remains a challenging procedure for urologists, despite recent remarkable advances in surgical technology in treatment of urolithiasis. The success rate of endoscopic treatment in patients with impacted stones is reported to be lower than that in patients with nonimpacted stones. Moreover, the presence of stone impaction is associated with high rates of intraoperative and postoperative complications. The best management for patients with impacted ureteral stones should therefore be devised based on the latest knowledge and techniques. The present review focuses on the preoperative prediction of stone impaction, the safest and most effective endoscopic surgical procedures, and the most appropriate management for postoperative ureteral strictures. We overview comprehensive endoscopic management for impacted ureteral stones based on literature review and expert opinions. [ABSTRACT FROM AUTHOR]

Background: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management. Purpose: To develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists. Study Type: Retrospective study of eight clinical centers. Subjects: Between January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159). Field Strength/Sequence: Three axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2‐weighted imaging with fat saturation (T2WI FS), echo planar diffusion‐weighted imaging, and 2D volumetric interpolated breath‐hold examination of contrast‐enhanced T1‐weighted imaging (CE‐T1WI) with FS. Assessment: Three monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE‐T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience. Statistical Tests: We used DeLong test, chi‐square test, Mann–Whitney U‐test, and t‐test, with significance level of 0.05. Results: Both EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795–0.915) and 0.884 (95% CI, 0.831–0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797). Data Conclusion: The developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments. Level of Evidence: 3 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Purpose: We aimed to develop and to validate a novel nomogram based on inflammatory markers to preoperatively predict microvascular invasion (MVI) in patients with solitary primary hepatocellular carcinoma (HCC). Patients and Methods: Data from 658 patients with solitary primary HCC who underwent hepatectomy at the First Affiliated Hospital of Zhengzhou University from June 2018 to October 2021 were retrospectively analyzed. Patients were divided into training (n=441) and validation (n=217) cohorts according to surgical data. Independent risk factors for MVI were identified via univariate and multivariate logistic regression analyses in the training cohort. A novel nomogram was developed based on the independent risk factors identified. Its accuracy was evaluated using a calibration curve and concordance index (C-index). The predictive value was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: Preoperative alpha-fetoprotein > 969 μg/L (P< 0.001), tumor size (P=0.002), neutrophil > 1.8× 109/L (P=0.002), gamma-glutamyl transpeptidase-to-platelet ratio (GPR) > 0.32 (P=0.001), aspartate aminotransferase-to-platelet ratio (APR) > 0.18 (P< 0.001), gamma-glutamyl transpeptidase-to-albumin ratio (GAR) > 2.30 (P=0.001), and gamma-glutamyl transpeptidase-to-lymphocyte ratio > 29.58 (P< 0.001) were identified as preoperative independent risk factors for MVI and were used to establish the nomogram. The C-index of the training and validation cohorts were 0.788 (95% confidence interval [CI]: 0.744– 0.831) and 0.735 (95% CI: 0.668– 0.802), respectively. The calibration curve analysis revealed that the standard curve fit well with the predicted curve. ROC curve analysis demonstrated high efficiency of the nomogram. DCA verified that the nomogram had notable clinical value. Conclusion: Preoperative GPR > 0.32, APR > 0.18, and GAR > 2.30 were independent risk factors for MVI in patients with solitary primary HCC, suggesting their utility as preoperative predictors of MVI. The novel nomogram developed and validated in this study may aid in determining optimal therapeutic approaches for patients with solitary HCC at risk for MVI. [ABSTRACT FROM AUTHOR]

Background: No existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing's disease (CD) after transsphenoidal surgery. Purpose: The aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery. Methods: A total of 419 patients with CD from Peking Union Medical College Hospital were enrolled between January 2014 and August 2020. The EMR of the patients were embedded and transformed into low-dimensional dense vectors that can be included in four ML-based models together with structured features. The area under the curve (AUC) of receiver operating characteristic curves was used to evaluate the performance of the models. Results: The overall remission rate of the 419 patients was 75.7%. From the results of logistic multivariate analysis, operation (p < 0.001), invasion of cavernous sinus from MRI (p = 0.046), and ACTH (p = 0.024) were strongly correlated with IR. The AUC values for the four ML-based models ranged from 0.686 to 0.793. The highest AUC value (0.793) was for logistic regression when 11 structured features and "individual conclusions of the case by doctor" were included. Conclusion: An ML-based model was developed using both structured and unstructured features (after being processed using a word embedding method) as input to preoperatively predict postoperative IR. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC). Methods: This retrospective study enrolled 254 patients with pathologically confirmed RC between December 2017 and December 2019. Patients were divided into a training set (n = 203) and a validation set (n = 51). A large number of radiomics features were extracted from the portal venous phase images of CT. After selecting features with L1-based method, we established Rad-score by using the logistic regression analysis. Furthermore, a combined model incorporating Rad-score and clinical factors was developed and visualized as the nomogram. The models were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). Results: One hundred and seventeen of 254 patients were eventually found to be TDs+. Rad-score and clinical factors including carbohydrate antigen (CA) 19-9, CT-reported T stage (cT), and CT-reported peritumoral nodules (+/-) were significantly different between the TDs+ and TDs- groups (all P < 0.001). These factors were all included in the combined model by the logistic regression analysis (odds ratio = 2.378 for Rad-score, 2.253 for CA19-9, 2.281 for cT, and 4.485 for peritumoral nodules). This model showed good performance to predict TDs in the training and validation cohorts (AUC = 0.830 and 0.832, respectively). Furthermore, the combined model outperformed the clinical model incorporating CA19-9, cT, and peritumoral nodules (+/-) in both training and validation cohorts for predicting TDs preoperatively (AUC = 0.773 and 0.718, P = 0.008 and 0.039). Conclusions: The combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients. [ABSTRACT FROM AUTHOR]

Objectives: This study sought to develop a multiparametric MRI radiomics-based machine learning model for the preoperative prediction of clinical success for high-intensity-focused ultrasound (HIFU) ablation of uterine leiomyomas. Methods: One hundred and thirty patients who received HIFU ablation therapy for uterine leiomyomas were enrolled in this retrospective study. Radiomics features were extracted from T2-weighted (T2WI) image and ADC map derived from diffusion-weighted imaging (DWI). Three feature selection algorithms including least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF algorithm were used to select radiomics features, respectively, which were fed into four machine learning classifiers including k-nearest neighbors (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) for the construction of outcome prediction models before HIFU treatment. The performance, predication ability, and clinical usefulness of these models were verified and evaluated using receiver operating characteristics (ROC), calibration, and decision curve analyses. Results: The radiomics analysis provided an effective preoperative prediction for HIFU ablation of uterine leiomyomas. Using SVM with ReliefF algorithm, the multiparametric MRI radiomics model showed the favorable performance with average accuracy of 0.849, sensitivity of 0.814, specificity of 0.896, positive predictive value (PPV) of 0.903, negative predictive value (NPV) of 0.823, and the area under the ROC curve (AUC) of 0.887 (95% CI = 0.848–0.939) in fivefold cross-validation, followed by RF with ReliefF. Calibration and decision curve analyses confirmed the potential of model in predication ability and clinical usefulness. Conclusions: The radiomics-based machine learning model can predict preoperatively HIFU ablation response for the patients with uterine leiomyomas and contribute to determining individual treatment strategies. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To develop and validate a radiomics model based on contrast-enhanced spectral mammography (CESM), and preoperatively discriminate low-grade (grade I/II) and high-grade (grade III) invasive breast cancer. METHOD: A total of 205 patients with CESM examination and pathologically confirmed invasive breast cancer were retrospectively enrolled. We randomly divided patients into two independent sets namely, training set (164 patients) and test set (41 patients) with a ratio of 8:2. Radiomics features were extracted from the low-energy and subtracted images. The least absolute shrinkage and selection operator (LASSO) logistic regression were established for feature selection, which were then utilized to construct three classification models namely, low energy, subtracted images and their combined model to discriminate high- and low-grade invasive breast cancer. Receiver operator characteristic (ROC) curves were used to confirm performance of three models in training set. The clinical usefulness was evaluated by using decision curve analysis (DCA). An independent test set was used to confirm the discriminatory power of the models. To test robustness of the result, we used 100 times LGOCV (leave group out cross validation) to validate three models. RESULTS: From initial radiomics feature pool, 17 and 11 features were selected for low-energy image and subtracted image, respectively. The combined model using 28 features showed the best performance for preoperatively evaluating the histologic grade of invasive breast cancer, with an area under the curve, AUC = 0.88, and 95%confidence interval [CI] 0.85 to 0.92 in the training set and AUC = 0.80 (95%CI 0.67 to 0.92) in the test set. The mean AUC of LGOCV is 0.82. CONCLUSIONS: CESM-based radiomics model is a non-invasive predictive tool that demonstrates good application prospects in preoperatively predicting histological grade of invasive breast cancer. [ABSTRACT FROM AUTHOR]

Background: Parathyroid carcinoma (PC) requires preoperative prediction for appropriate surgical management. Differentiation from symptomatic primary hyperparathyroidism (sPHPT) cohort is difficult. Methods: Patients with sPHPT from a tertiary‐care center, Western India, including Cohort‐A (n = 19 [10/M; 9/F]) with PC and Cohort‐B (n = 93 [33/M; 60/F] with benign parathyroid lesions) were compared to derive predictors for differential diagnosis. Results: There were no differences in clinical or biochemical parameters between the two cohorts. Comparison of CECT parameters showed that irregular shape, tumor heterogeneity, infiltration, short/long‐axis ratio >0.76, and long‐diameter >30 mm had high negative‐predictive value and intratumoral calcification had 100% positive‐predictive value to diagnose PC; whereas there were no differences in contrast‐enhancement patterns. Long diameter, short/long‐axis ratio, and heterogeneity were significant predictors on multivariate analysis. Conclusion: It is difficult to predict diagnosis of PC in an Indian sPHPT cohort based on clinical and biochemical parameters, whereas CECT parathyroid‐based parameters can aid in diagnosis. [ABSTRACT FROM AUTHOR]

Background/purpose: Overt hepatic encephalopathy (HE) risk should be preoperatively predicted to identify patients suitable for curative transjugular intrahepatic portosystemic shunt (TIPS) instead of palliative treatments. Methods: A total of 185 patients who underwent TIPS procedure were randomised (130 in the training dataset and 55 in the validation dataset). Clinical factors and imaging characteristics were assessed. Three different models were established by logistic regression analyses based on clinical factors (ModelC), imaging characteristics (ModelI), and a combination of both (ModelCI). Their discrimination, calibration, and decision curves were compared, to identify the best model. Subgroup analysis was performed for the best model. Results: ModelCI, which contained two clinical factors and two imaging characteristics, was identified as the best model. The areas under the curve of ModelC, ModelI, and ModelCI were 0.870, 0.963, and 0.978 for the training dataset and 0.831, 0.971, and 0.969 for the validation dataset. The combined model outperformed the clinical and imaging models in terms of calibration and decision curves. The performance of ModelCI was not influenced by total bilirubin, Child–Pugh stages, model of end-stage liver disease score, or ammonia. The subgroup with a risk score ≥ 0.88 exhibited a higher proportion of overt HE (training dataset: 13.3% vs. 97.4%, p < 0.001; validation dataset: 0.0% vs. 87.5%, p < 0.001). Conclusion: Our combination model can successfully predict the risk of overt HE post-TIPS. For the low-risk subgroup, TIPS can be performed safely; however, for the high-risk subgroup, it should be considered more carefully. [ABSTRACT FROM AUTHOR]

Purpose: Microdissection testicular sperm extraction (micro-TESE) could retrieve sperm from the testicles to help the non-obstructive azoospermia (NOA) patients to get their biological children, but also would cause damage to the testicles. Therefore, it is necessary to preoperatively predict the micro-TESE outcome in NOA patients. For this purpose, we aim to develop a model based on extracellular vesicles' (EVs) piRNAs (EV-piRNAs) in seminal plasma. Methods: To identify EV-piRNAs that were associated with spermatogenic ability, small RNA-seq was performed between the NOA group (n = 8) and normal group (n = 8). Validation of EV-piRNA expression in seminal plasma EVs and testicles tissues was used to select EV-piRNAs for the model. Candidate EV-piRNAs were further selected by LASSO regression analysis. Binary logistic regression analysis was used for the models' calculation formula. ROC analysis and Hosmer–Lemeshow test was used to assess the models' performance in the training (n = 20) and validation (n = 25) cohorts. Results: We identified 8 EV-piRNAs which were associated with spermatogenic ability. Two EV-piRNAs (pir-60351 and pir-61927) were selected by LASSO regression analysis. Finally, we developed a favorable model based on the expression of pir-61927 with good discrimination wherein the AUC was 0.82 (95% CI: 0.63~1.00, p = 0.016) in the training cohort and 0.83 (95% CI: 0.66~1.00, p = 0.005) in the validation cohort, as well as good calibration. Conclusions: A favorable model based on the expression of pir-61927 in seminal plasma EVs was established to predict the micro-TESE outcome in NOA patients. [ABSTRACT FROM AUTHOR]

Background: To investigate characteristic clinical and imaging features and establish a scoring system for preoperative prediction of malignancy in the bulging duodenal papilla. Methods: A total of 147 patients with bulging duodenal papilla (Benign enlargement n = 67; malignant enlargement n = 80) from our hospital between 2010 and 2020 were retrospectively analyzed. We investigated meaningful clinical and CT imaging features and established the score model through logistic regression and weighted. The calibration test, the ROC, AUC, and cut-off points were performed in score model. The model was also divided into three score ranges for convenient clinical evaluation. Results: Three clinical and CT imaging features were finally included in the score model including direct bilirubin (DBil) increase >7 umol/L (3 points), pancreatic duct (PD) dilation >5 mm (2 points), and irregular shape (2 points). The AUCs of the primary predictive model and score model were 0.896 (95% CI, 0.835–0.940) and 0.896 (95% CI, 0.835–0.940), respectively. This scoring system presented with a sensitivity of 78.8% and a specificity of 88.1% when using 2.5 points as cutoff value. Three score ranges were also proposed for convenient clinical use as follows: 0–2 points; 3–4 points; 5–7 points. The number of patients with malignant duodenal papillary enlargement increased with the increasing scores. Conclusions: We proposed a convenient scoring system to preoperative predict malignancy in the bulging duodenal papilla. [ABSTRACT FROM AUTHOR]

Background: There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing's disease (CD). Purpose: Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. Methods: A total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC. Results: The overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. Conclusion: We developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD. [ABSTRACT FROM AUTHOR]

Introduction: Traditional classification that divided gliomas into glioblastoma multiformes (GBM) and lower grade gliomas (LGG) based on pathological morphology has been challenged over the past decade by improvements in molecular stratification, however, the reproducibility and diagnostic accuracy of glioma classification still remains poor. This study aimed to establish and validate a novel nomogram for the preoperative diagnosis of GBM by using integrated data combined with feasible baseline characteristics and preoperative tests. Material and method: The models were established in a primary cohort that included 259 glioma patients who had undergone surgical resection and were pathologically diagnosed from March 2014 to May 2016 in the First Affiliated Hospital of Xi'an Jiaotong University. The preoperative data were used to construct three models by the best subset regression, the forward stepwise regression, and the least absolute shrinkage and selection operator, and to furthermore establish the nomogram among those models. The assessment of nomogram was carried out by the discrimination and calibration in internal cohorts and external cohorts. Results and discussion: Out of all three models, model 2 contained eight clinical-related variables, which exhibited the minimum Akaike Information Criterion (173.71) and maximum concordance index (0.894). Compared with the other two models, the integrated discrimination index for model 2 was significantly improved, indicating that the nomogram obtained from model 2 was the most appropriate model. Likewise, the nomogram showed great calibration and significant clinical benefit according to calibration curves and the decision curve analysis. Conclusion: In conclusion, our study showed a novel preoperative model that incorporated clinically relevant variables and imaging features with laboratory data that could be used for preoperative prediction in glioma patients, thus providing more reliable evidence for surgical decision-making. [ABSTRACT FROM AUTHOR]

Background: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.Purpose: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.Study Type: Retrospective study of eight clinical centers.Population: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).Field Strength/sequence: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI.Assessment: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.Statistical Tests: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).Results: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts.Data Conclusion: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.Level Of Evidence: Level 4.Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904. [ABSTRACT FROM AUTHOR]

Background: The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients. Methods: This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram. Results: Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively. Conclusions: Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection.Purpose: To explore the use of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)-based radiomics for preoperative prediction of LVI in invasive breast cancer.Study Type: Prospective.Population: Ninety training cohort patients (22 LVI-positive and 68 LVI-negative) and 59 validation cohort patients (22 LVI-positive and 37 LVI-negative) were enrolled.Field Strength/sequence: 1.5 T and 3.0 T, T1 -weighted DCE-MRI.Assessment: Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE-MRI. A radiomics signature was constructed in the training cohort with 10-fold cross-validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort.Statistical Tests: Mann-Whitney U-test, chi-square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA).Results: Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone.Data Conclusion: The DCE-MRI-based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery.Level Of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847-857. [ABSTRACT FROM AUTHOR]

Objective: To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients.Methods: Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram.Results: The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79).Conclusions: We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients.Key Points: • ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis. [ABSTRACT FROM AUTHOR]

Purpose: Infected nonunion after open reduction internal fixation (ORIF) is a serious complication. The aim of this study was to evaluate the usefulness of serum D-dimer for preoperative diagnosis of infected nonunion. Patients and methods: Patients undergoing debridement and external fixation for infected nonunion (n=32) and replacement of internal fixation due to aseptic failure (n=34) were enrolled and compared in this retrospective study. The optimum cutoff value of D-dimer for identification of infected nonunion was determined by calculating the Youden J statistic. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of four preoperative laboratory parameters—serum D-dimer level, white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP)—for diagnosis of infected nonunion were compared. Results: Serum D-dimer level was significantly higher in patients with infected nonunion than in patients with aseptic nonunion: 2.62 mg/mL (range, 0.13–11.90 mg/mL) vs 0.35 mg/mL (range, 0.07–6.46 mg/mL; p<0.001). WBC count, CRP, and ESR demonstrated sensitivity of 12.5% (95% CI: 4.08–29.93), 40.6% (95% CI: 24.22–59.21), and 56.3% (95% CI: 37.88–73.16), respectively, and specificity of 94.1% (95% CI: 78.94–98.97), 88.2% (95% CI: 71.61–96.16), and 85.3% (95% CI: 68.17–94.46), respectively. Using the Youden index, 1.70 mg/mL was determined as the optimal threshold value for serum D-dimer for the diagnosis of infected nonunion. The sensitivity and specificity of serum D-dimer (>1.70 mg/mL) were 75.0% (95% CI: 56.25–87.87) and 91.2% (95% CI: 75.19–97.69). Conclusions: Serum D-dimer level may be useful for preoperative prediction of infected nonunion in patients after ORIF. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to assess the heterogeneity of tumor enhancement using fractal analysis on contrast-enhanced computed tomography (CE-CT) for predicting malignant potential of gastrointestinal stromal tumor (GIST).Methods: We retrospectively identified 64 patients (36 M/28 W; median age: 65) with GISTs who received CE-CT and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) followed by curative surgery. Fractal analysis was applied to CE-CT image, and fractal dimension (FD) was measured. Diagnostic value of FD for malignant potential of GIST was compared with that of FDG-PET using the risk classification and Ki67 index.Results: 14 patients were categorized as the high risk, and 50 patients were as the very low, low or intermediate risk. FD of high-risk group was significantly higher than that of the other-risk group (p < 0.05). The areas under the ROC curves of FD and SUVmax for prediction of high-risk group were 0.82 and 0.93 (accuracy: 84.4% and 98.5%). FD showed a significant positive correlation with Ki67 index (p = 0.01).Conclusion: Diagnostic value of CT fractal analysis for prediction of high-risk GIST is comparable with FDG-PET. In terms of cost and availability, fractal analysis has a potential to be an optimal preoperative biomarker. [ABSTRACT FROM AUTHOR]

Objectives: To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T2-weighted fat suppression (T2-FS) and diffusion-weighted MRI (DWI).Methods: We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T2-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1-10) based on different combination of image features using stepwise forward method.Results: For T2-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T2-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.Conclusions: Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.Key Points: • SLN biopsy to access breast cancer metastasis has multiple complications. • Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. • We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively. [ABSTRACT FROM AUTHOR]

Purpose Laparoscopic cholecystectomy (LC) has become the treatment of choice for cholelithiasis. Still some patients required conversion to open cholecystectomy (OC). Our aim was to develop a standardized Ultrasound based scoring system for preoperative prediction of difficult LC. Methods and materials Ultrasound findings of 300 patients who underwent LC were reviewed retrospectively. Four parameters (time taken, biliary leakage, duct or arterial injury, and conversion) were analyzed to classify LC as easy or difficult. The following ultrasound findings were analyzed: GB wall thickness, pericholecystic collection, distended GB, impacted stones, multiple stones, CBD diameter and liver size. Out of seven parameters, four were statistically significant in our study. A score of 2 was assigned for the presence of each significant finding and a score of 1 was assigned for the remaining parameters to a total score of 11. A cut-off value of 5 was taken to predict easy and difficult LC. Results 66 out of 83 cases of difficult LC and 199 out of 217 cases of easy LC were correctly predicted on the basis of scoring system. A score of >5 had sensitivity 80.7% and specificity 91.7% for correctly identifying difficult LC. Prediction came true in 78.8% difficult and 92.6% easy cases. US findings of GB wall thickness, distended GB, impacted stones and dilated CBD were found statistically significant. Conclusion This indigenous scoring system is effective in predicting conversion risk of LC to OC. Patients having high risk may be informed and scheduled appropriately and decision to convert to OC in case of anticipated difficulty may be taken earlier. [ABSTRACT FROM AUTHOR]

Background Cytoreduction is important as a survival predictor in advanced ovarian cancer. Purpose To determine the prediction of suboptimal resection (SOR) in advanced ovarian cancer based on clinical and computed tomography (CT) parameters. Material and Methods Between 2007 and 2015, 327 consecutive patients with FIGO stage III-IV ovarian cancer and preoperative CT were included. During 2007-2012, patients were assigned to a derivation dataset ( n = 220) and the others were assigned to a validation dataset ( n = 107). Clinical parameters were reviewed and two radiologists assessed the presence or absence of tabulated parameters on CT images. Logistic regression analyses based on area under the receiver-operating characteristic curve (AUROC) were performed to identify variables predicting SOR, and generated simple score using Cox proportional hazards model. Results There was no statistical difference in patients' characteristics in both datasets, except for residual disease ( P = 0.001). Optimal resection improved from 45.0% (99/220) in the derivation dataset to 64.4% (69/107) in the validation dataset. Logistic regression identified that Eastern Cooperative Oncology Group-performance status (ECOG-PS 2), involvements of peritoneum, diaphragm, bowel mesentery and suprarenal lymph nodes, and pleural effusion were independent variables of SOR. Overall AUROC for score predicting SOR was 0.761 with sensitivity, specificity, and positive and negative predictive values of 70.6%, 73.2%, 68.7%, and 91.9%, respectively. In the derivation dataset, AUROC was 0.792, with sensitivity of 71.4% and specificity of 74.3%, and AUROC of 0.758 with sensitivity of 69.2% and specificity of 72.8% in the validation dataset. Conclusion CT may be a useful preoperative predictor of SOR in advanced ovarian cancer. [ABSTRACT FROM AUTHOR]

Background/Objective No previous study has quantitatively investigated the degree of enhancement of pancreatic neuroendocrine tumors (pNETs) using a routine preoperative modality. The aim of this study was to evaluate the contrast-enhancement ratio (CER) of pNETs using multiphase enhanced CT and to assess the impact of the CER on disease recurrence after surgery. Methods A retrospective study was performed using data from 47 consecutive patients with pNETs who had undergone curative surgery. The CER of the tumor was calculated by dividing the CT attenuation value obtained during the maximum-enhanced phase by that obtained during the pre-enhanced phase. A region of interest was placed in the largest tumor dimension plane so as to cover as much surface of the tumor as possible while avoiding adjacent normal structures, calcification, and necrotic areas of the tumor. Results During a median follow-up period of 51 months (range, 1–132 months), a total of 4 patients (8.5%) developed disease recurrence. The median CER value was significantly lower for the patients with recurrence than for the patients without recurrence (2.9 vs. 4.3, P = 0.013). Univariate analyses showed that a CER ≤3.2 was significantly associated with disease recurrence ( P < 0.001). All the patients with disease recurrence had tumors that were both large (>20 mm) and weakly enhanced (CER ≤ 3.2), whereas no recurrences were observed even in patients with tumors >20 mm when the CER was greater than 3.2. Conclusions CER might be a useful predictor of disease recurrence in patients with pNETs. [ABSTRACT FROM AUTHOR]

Background: The relationship between pathological factors and lymph node metastasis of pathological stage early gastric cancer has been extensively investigated. By contrast, the relationship between preoperative factors and lymph node metastasis of clinical stage early gastric cancer has not been investigated. The present study was to investigate discrepancies between preoperative and postoperative values.Methods: From January 2011 to December 2013, 1042 patients with clinical stage early gastric cancer who underwent gastrectomy with lymphadenectomy were enrolled. Preoperative and postoperative values were collected for subsequent analysis. Receiver operating characteristics curves were computed using independent predictive factors.Results: Several discrepancies were observed between preoperative and postoperative values, including existence of ulcer, gross type, and histology (all McNemar p-values were <0.001). Multivariate analyses identified the following independent predictive factors for lymph node metastasis: postoperative values including age (p = 0.002), tumor size (p < 0.001), and tumor depth (p < 0.001); preoperative values including age (p = 0.017), existence of ulcer (p = 0.037), tumor size (p = 0.009), and prediction of the presence of lymph node metastasis in computed tomography scans (p = 0.002). These postoperative and preoperative independent predictive factors produced areas under the receiver operating characteristics curves of 0.824 and 0.660, respectively.Conclusions: Surgeons need to be aware of limitations in preoperative predictions of the presence of lymph node metastasis for clinical stage early gastric cancer. [ABSTRACT FROM AUTHOR]

Objective To investigate preoperative predictors of ureteral involvement of bladder malignancy and to develop a novel preoperative model for the prediction of ureteral involvement in bladder cancer patients undergoing radical cystectomy. Methods This study included 197 consecutive bladder cancer patients treated with radical cystectomy. The correlations of preoperative factors with ureteral involvement were analyzed by univariate analysis with Pearson's χ2-test and multivariate logistic regression analysis with a stepwise selection procedure. Results Positive ureteral involvement was observed in 38 (19.3%) patients. Tumor location (involvement of the vesical trigone), clinical T stage (≥ cT3) and the number of tumors (≥3), but not sex, tumor grade and histological features determined by transurethral resection of bladder tumor, tumor size, shape of tumor, concomitant presence of carcinoma in situ, preoperative intravesical therapy, number of transurethral resection of bladder tumor procedures or the presence of hydronephrosis were significantly associated with ureteral involvement in the univariate analysis. Multivariate logistic regression analysis confirmed that the aforementioned three significant factors identified in the univariate analysis were significant independent predictors of ureteral involvement. The probability of ureteral involvement estimated by a combination of these three parameters was well correlated with the real incidence ( R = 0.904, P = 0.0021). Conclusions Tumor location (involvement of vesical trigone), clinical T stage (≥ cT3) and the number of tumors (≥3) are significant independent preoperative predictors of ureteral involvement of malignancy in bladder cancer patients undergoing radical cystectomy. Our predictive model might be useful for preoperative prediction of ureteral tumor involvement. [ABSTRACT FROM AUTHOR]

Background: A small but significant percentage of patients are considered failures after laparoscopic Nissen fundoplication (LNF). We sought to identify preoperative predictors of failure in a cohort of patients who underwent LNF more than 10 years ago.Methods: Of 312 consecutive patients undergoing primary LNF between 1992 and 1995, recent follow-up was obtained from 166 patients at a mean of 11.0 +/- 1.2 years. Eight additional patients who underwent reoperation were lost to follow-up but are included. Failure is broadly defined as any reoperation, lack of satisfaction, or any severe symptoms at follow-up. Potential predictors evaluated included sex, age, body-mass index (BMI), response to acid reducing medications (ARM), psychiatric history, typical versus atypical symptoms, manometry, esophageal pH, and others. Logistic regression was used to assess significance of predictors in univariate analysis.Results: Of 174 known outcomes, 131 were classified as successful (75.3%), while 43 were failures (24.7%): 26 reoperations, 13 unsatisfied, and 13 with severe symptoms. Response and lack of response to ARM were associated with 77.1% and 56.0% success rates respectively (P = 0.035). Eighty five percent of patients with typical symptoms had a successful outcome, compared to only 41% with atypical symptoms (P < 0.001). Preoperative morbid obesity (BMI > 35 kg/m2) was associated with failure (P = 0.036), while obesity (BMI 30-34.9 kg/m2) was not. A history of psychiatric illness trended toward significance (P = 0.06).Conclusions: In a cohort with 11 years follow-up after LNF, factors predictive of a successful outcome include preoperative response to ARM, typical symptoms, and BMI < 35 kg/m2. Patients with atypical symptoms, no response to ARM, or morbid obesity should be informed of their higher risk of failure. Some patients in these groups do have successful outcomes, and further research may clarify which of these patients can benefit from LNF. [ABSTRACT FROM AUTHOR]