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Anastomotic leakage in visceral surgery is associated with a large number of known and also unknown or even unmeasurable parameters. Furthermore, the associations between the individual factors are intertwined and complex. According to current data a preoperative prediction is not reliably possible and should be distinguished from intraoperative or postoperative prediction models. Most studies on this topic do not exceed an area under the curve (AUC) of 0.70. A thorough understanding of statistics and prediction models is necessary to correctly interpret the published works. Due to the relatively low incidence rate of anastomotic leakage from a statistical point of view, large datasets are required for adequate prediction. Multimodal data and complex algorithms can potentially handle big data more accurately and improve predictability; however, these models have so far not been applied in the clinical routine. [ABSTRACT FROM AUTHOR]

Abstract 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

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]

Abstract 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 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 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

PurposeThe purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC).MethodsA total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves.ResultsMean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P 0.05).ConclusionThe clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

Abstract 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). Preoperative LL, PT, PI − LL, TPA, and SVA values were positively correlated with the follow-up SVA value (P 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 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.

Objective: Parathyroid carcinoma is a rare tumor among parathyroid tumors. Aspiration cytology and needle biopsy are generally not recommended for diagnostic purposes because they cause dissemination. Therefore, it is commonly diagnosed by postoperative histopathological examination. In this study, we investigated whether preoperative inflammatory markers can be used as predictors of cancer in patients with primary hyperparathyroidism. Design: This was a retrospective study. Methods: Thirty-six cases of parathyroid carcinoma and 50 cases of parathyroid adenoma (PA) operated with the diagnosis of primary hyperparathyroidism and confirmed histopathologically at Ito Hospital were included in this study. Preoperative clinical characteristics and inflammatory markers (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio (LMR)) were compared and their values in preoperative prediction were evaluated and analyzed. Results: Preoperative intact-parathyroid hormone (P = 0.0003), serum calcium (P = 0.0048), and tumor diameter (P = 0.0002) were significantly higher in parathyroid carcinoma than in PA. LMR showed a significant decrease in parathyroid carcinoma (P = 0.0062). In multivariate analysis, LMR and tumor length diameter were independent predictors. In the receiver operating characteristics analysis, the cut-off values for LMR and tumor length diameter were 4.85 and 28.0 mm, respectively, for parathyroid cancer prediction. When the two extracted factors were stratified by the number of factors held, the predictive ability improved as the number of factors increased. Conclusion: In the preoperative evaluation, a combination of tumor length diameter of more than 28 mm and LMR of less than 4.85 was considered to have a high probability of cancer.

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]

Abstract Purpose The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasis in breast cancer patients. Procedures A total of 432 patients with breast cancer were enrolled in this retrospective study and divided into a training cohort (n = 296) and a validation cohort (n = 136). Radiomics features were extracted from the second phase of dynamic contrast enhanced (DCE) MRI images. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen optimal features and construct a radiomics signature in the training cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on the radiomics signature and clinical factors. The predictive performance of the nomogram was quantified with respect to discrimination and calibration, which was further evaluated in the independent validation cohort. Results Fourteen ALN metastasis-related features were selected to construct the radiomics signature, with an area under the curve (AUC) of 0.847 and 0.805 in the training and validation cohorts, respectively. The nomogram was established by incorporating the histological grade, multifocality, MRI report lymph node status and radiomics signature and showed good calibration and excellent performance for ALN detection (AUC of 0.907 and 0.874 in the training and validation cohorts, respectively). The decision curve, which demonstrated the radiomics nomogram, displayed promising clinical utility. Conclusions The radiomics nomogram can be used as a noninvasive and reliable tool to assist clinicians in accurately predicting ALN metastasis in breast cancer preoperatively.

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.

Yufei Gu, Fengyu Zheng, Yingxuan Zhang, Shishi Qiao Department of Hepatobiliary Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, People’s Republic of ChinaCorrespondence: Shishi Qiao, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Zhengzhou University, No. 50 Jianshe East Road, Erqi District, Zhengzhou City, Henan Province, People’s Republic of China, Tel +86 18595811956, Email qiaoshishiv@163.comPurpose: 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.Keywords: hepatocellular carcinoma, microvascular invasion, preoperative prediction, nomogram

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]

• Preoperative prediction of lymph node status has important guiding significance for surgical methods. • Models for predicting lymph node positivity was constructed based on the basic patient data and imaging data readily available to clinicians. • All models were validated for their clinical value by survival analysis. • The results of this study will provide a reference for accurate lymph node dissection during lung cancer surgery. To develop and validate nomograms for preoperative prediction of precision lymph node (LN) dissection in lung cancer. The prediction models of each group LNs (LNx) were developed in a primary cohort that consisted of 1380 patients with clinicopathologically confirmed lung cancer. Clinical characteristics and CT reports were extracted. Patients with LNx dissection were divided into training cohort and testing cohort. Nomograms were built through univariate and multivariate regression analysis in the training cohort and internally verified in the testing cohort. The accuracy of the models was verified by constructing survival analysis in patients without LNx dissection. Due to the lack of sufficient patients for LN1, 8, 13, a total of 10 nomograms were constructed in this study, including LN-2 ∼ 7, 9 ∼ 12. According to the nomogram of each group LN, the most common independent risk factors predicting LN status were CT-reported lymphadenectasis, tumor diameter and location, and the others include age, gender, and whether there were multiple nodules, etc. All models showed good discrimination, with the average C-index of 0.738 in the training cohort and 0.707 in the testing cohort. Survival analysis in patients without LNx dissection all showed the high accuracy of each nomogram to predict LN metastasis status and TNM staging. We constructed nomograms to predict the metastasis status of each group of lymph nodes based on clinical characteristics and CT reports. Surgeons can accurately determine the extent of lymph node dissection in patients with lung cancer based on our nomogram models before surgery. [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]

Anastomotic leakage in visceral surgery is associated with a large number of known and also unknown or even unmeasurable parameters. Furthermore, the associations between the individual factors are intertwined and complex. According to current data a preoperative prediction is not reliably possible and should be distinguished from intraoperative or postoperative prediction models. Most studies on this topic do not exceed an area under the curve (AUC) of 0.70. A thorough understanding of statistics and prediction models is necessary to correctly interpret the published works. Due to the relatively low incidence rate of anastomotic leakage from a statistical point of view, large datasets are required for adequate prediction. Multimodal data and complex algorithms can potentially handle big data more accurately and improve predictability; however, these models have so far not been applied in the clinical routine., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Objective To evaluate the predictive value of vesical imaging-reporting and data system (VI-RADS) based on multi-parameter magnetic resonance imaging (MRI) for muscle invasive bladder cancer. Methods Clinical data of 278 patients undergoing transurethral resection of bladder tumor (TURBT) or radical cystectomy in our hospital from January 2017 to March 2021 were collected in this study. After VI-RADS score was assessed by 2 radiologists independently, Kappa test was used for the consistency of the obtained scores. Chi-square test was employed to analyze the consistency of VI-RADS score with pathological results. Receiver operating characteristic (ROC) curve was drawn to analyze the efficacy of VI-RADS scores in the diagnosis of muscle invasive bladder cancer. The consistency of VI-RADS score with pathological Results of tissues obtained by different surgical Methods was analyzed with Chi-square test. Results The VI-RADS scores of the 2 radiologists were consistent with each other (coincidence rate: 79.86%, kappa value: 0.750 8, P < 0.001). ROC curve analysis showed that the area under curve (AUC), Yodon index, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.774, 54.78%, 96.16%, 58.62%, 58.14% and 96.23%, respectively, when predicting muscular invasion with VI-RADS score ≥3 as the threshold. The study also found that the coincidence rate between the pathological Results of radical cystectomy and VI-RADS score was 92.85%, which was significantly higher than that of TURBT (79.81%, P < 0.05). Conclusion VI-RADS score based on preoperative multi-parameter MRI has good consistency and stability, and is highly consistent with the Results of postoperative pathology. VI-RADS score ≥3 has predictive diagnostic value for muscular-invasive bladder cancer and can guide the selection of clinical surgical methods.

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]

To develop and validate a cross-modality tensor fusion (CMTF) model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study included 174 HCC patients (47 MVI-positive and 127 MVI-negative) confirmed by postoperative pathology. The synthetic minority over-sampling technique was used to augment MVI-positive samples. The amplified dataset of 254 samples (127 MVI-positive and 127 MVI-negative) was randomly divided into training and test cohorts in a 7:3 ratio. Radiomics features were respectively extracted from arterial phase, delayed phase, diffusion-weighted imaging, and fat-suppressed T2-weighted imaging. The least absolute shrinkage and selection operator was used for feature selection. Univariate and multivariate logistic regression analyses were employed to identify clinical and radiological independent predictors. The selected multi-modality MRI radiomics features, clinical and radiological characteristics were used to construct the CMTF model, single modality (SM) model, early fusion (EF) model. The CMTF model demonstrated superior performance in predicting MVI compared to the SM and EF models. When integrating four MRI modalities, the CMTF model achieved a high area under the curve (AUC) with 95 % confidence interval (95 % CI) of 0.894 (0.820–0.968). Additionally, incorporating clinical and radiological characteristics further enhanced the predictive performance of CMTF model, the AUC (95 % CI) value increased to 0.945 (0.892–0.998). The CMTF model showed promising performance in preoperative MVI prediction, providing a more effective non-invasive detection tool for HCC patients. [ABSTRACT FROM AUTHOR]

BackgroundThe risk factors of lymph node metastasis (LNM) in gastric cancer (GC) remain controversial. We aimed to identify risk factors of LNM in GC and construct a predictive model.MethodsA total of 1,337 resectable GC patients who underwent radical D2 lymphadenectomy at the first affiliated Hospital of Anhui Medical University from January 2011 to January 2014 were retrospectively analyzed and randomly divided into training and validation cohorts (n = 1,003 and n = 334, respectively) in a 3:1 ratio. Collecting indicators include age, gender, body mass index (BMI), tumor location, pathology, histological grade, tumor size, preoperative neutrophils to lymphocytes ratio (NLR), platelets to lymphocytes ratio (PLR), fibrinogen to albumin ratio (FAR), carcinoembryonic antigen (CEA), cancer antigen19-9 (CA19-9) and lymph nodes status. Significant risk factors were identified through univariate and multivariate logistic regression analysis, which were then included and presented as a nomogram. The performance of the model was assessed with receiver operating characteristic curves (ROC curves), calibration plots, and Decision curve analysis (DCA), and the risk groups were divided into low-and high-risk groups according to the cutoff value which was determined by the ROC curve.ResultsBMI, histological grade, tumor size, CEA, and CA19-9 were enrolled in the model as independent risk factors of LNM. The model showed good resolution, with a C-index of 0.716 and 0.727 in the training and validation cohort, respectively, and good calibration. The cutoff value for predicted probability is 0.594, the proportion of patients with LNM in the high-risk group was significantly higher than that in the low-risk group. Decision curve analysis also indicated that the model had a good positive net gain.ConclusionsThe nomogram-based prediction model developed in this study is stable with good resolution, reliability, and net gain. It can be used by clinicians to assess preoperative lymph node metastasis and risk stratification to develop individualized treatment plans.

Purpose: It is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions. Methods: A total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts. Results: ADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p

Abstract 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.

IntroductionCurrently, the commonly used surgical methods for cervical lesions include loop electrosurgical excision procedure (LEEP) and cold knife conization (CKC). However, the positive rate of surgical margins after LEEP is relatively high, which leads to disease recurrence and places further demand on clinical treatment. This study investigated factors related to positive margins after LEEP and established a scoring system to enhance preoperative risk assessment and surgical selection.Materials and MethodsA retrospective analysis of the clinical data of 411 patients undergoing LEEP surgery for cervical lesions in the First Affiliated Hospital of University of Science and Technology of China (USTC), from January 2016 to March 2021, was performed. Cases were divided into a negative margin group (349 cases) and a positive margin group according to postoperative pathology. In the positive group (62 cases), single-factor and multi-factor analyses screened influencing factors; a logistic and additive scoring system was established; furthermore, a ROC curve was used to evaluate scoring effectiveness.ResultsThe positive rate of resection margins after LEEP was 15.1%. Univariate analysis indicated a relationship to patient age, menopause, preoperative ThinPrep Cytology Test (TCT) results, lesion quadrant number under colposcopy, cervical biopsy, and the result of endocervical curettage (ECC). Multivariate analysis showed that age >35 y, menopause, preoperative TCT being high-grade squamous intraepithelial lesion (HSIL), four quadrants being involved under colposcopy, and ECC being HSIL were all independent influencing factors of positive margins after LEEP (P < 0.05). These were included with the above factors to establish a logistic and additive scoring system. When the logistic score was 17, the sensitivity and specificity of predicting positive margins after LEEP were 80.6 and 61.6%, respectively. When the additive score was 6, the sensitivity and specificity were 74.2 and 66.2%, respectively. Both scoring systems had good predictability (area under the curve AUC >0.75).ConclusionsThis study quantified factors influencing positive margins after LEEP and established a scoring system for evaluating patients before surgery to provide a basis for individualized treatment and selection of surgical methods.

Objective: The objective of this study was to provide a convenient preoperative prediction of the risk of early postoperative mortality., Materials and Methods: This retrospective study included patients who underwent surgery for spinal metastasis at our hospital between 2009 and 2021. Preoperative blood test data of all patients were collected, and the survival time was calculated by dividing the blood data. A multivariate analysis was conducted using a Cox proportional hazards model to identify prognostic factors., Results: The study population included 83 patients (average: 64.5 years), 22 of whom died within 3 months. The most common lesion was the thoracic spine, and incomplete paralysis was observed in 57 patients. The surgical methods included posterior implant fixation (n = 17), posterior decompression (n = 31), and posterior decompression with fixation (n = 35). In the univariate analysis, the presence of abnormal values was significantly associated with postoperative survival in six preoperative blood collection items (hemoglobin, C-reactive protein, albumin, white blood cell, gamma-glutamyl transpeptidase, and lactate dehydrogenase). In a multivariate analysis, four test items (hemoglobin, C-reactive protein, white blood cell, and lactate dehydrogenase) were identified as independent prognostic factors.Comparing cases with ≥3 abnormal values among the above four items (high-risk group; n = 23) and those with ≤2 (low-risk group; n = 60), there was a significant difference in survival time. In addition, it was possible to predict cases of early death within 3 months after surgery with 73% sensitivity and 89% specificity., Conclusions: The study showed that four preoperative blood test abnormalities (hemoglobin, C-reactive protein white blood cell, and lactate dehydrogenase) indicated the possibility of early death within 3 months after surgery., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhao, Wang, Sun, Wang, Shi, Liu and Zhang.)

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]

BackgroundNo 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.PurposeThe 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.MethodsA 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.ResultsThe 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.ConclusionAn 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.

ObjectivesThis 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.MethodsOne 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.ResultsThe 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.ConclusionsThe radiomics-based machine learning model can predict preoperatively HIFU ablation response for the patients with uterine leiomyomas and contribute to determining individual treatment strategies.

ObjectiveTo develop and validate a computed tomography (CT)-based radiomics model for predicting tumor deposits (TDs) preoperatively in patients with rectal cancer (RC).MethodsThis 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).ResultsOne 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).ConclusionsThe combined model incorporating Rad-score and clinical factors could provide a preoperative prediction of TDs and help clinicians guide individualized treatment for RC patients.

Purpose: Radiomic models have been demonstrated to have acceptable discrimination capability for detecting lymph node metastasis (LNM). We aimed to develop a computed tomography–based radiomic model and validate its usefulness in the prediction of normal-sized LNM at node level in cervical cancer. Methods: A total of 273 LNs of 219 patients from 10 centers were evaluated in this study. We randomly divided the LNs from the 2 centers with the largest number of LNs into the training and internal validation cohorts, and the rest as the external validation cohort. Radiomic features were extracted from the arterial and venous phase images. We trained an artificial neural network (ANN) to develop two single-phase models. A radiomic model reflecting the features of two-phase images was also built for directly predicting LNM in cervical cancer. Moreover, four state-of-the-art methods were used for comparison. The performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). Results: Among the models we built, the models combining the features of two phases surpassed the single-phase models, and the models generated by ANN had better performance than the others. We found that the radiomic model achieved the highest AUCs of 0.912 and 0.859 in the training and internal validation cohorts, respectively. In the external validation cohort, the AUC of the radiomic model was 0.800. Conclusion: We constructed a radiomic model that exhibited great ability in the prediction of LNM. The application of the model could optimize clinical staging and decision-making.

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]