Development and validation of a nomogram for predicting operating time in laparoscopic anterior resection of rectal cancer.

Aims: The goal of this study is to create and verify a nomogram estimate operating time in rectal cancer (RC) patients based on clinicopathological factors and MRI/CT measurements before surgery. Materials and Methods: The nomogram was developed in a cohort of patients who underwent laparoscopic anterior resection (L-AR) for RC. The clinicopathological and pelvis parameters were collected. Risk factors for a long operating time were determined by univariate and multivariate logistic regression analyses, and a nomogram was established with independent risk factors. The performance of the nomogram was evaluated. An independent cohort of consecutive patients served as the validation dataset. Results: The development group recruited 159 RC patients, while 54 patients were enrolled in the validation group. Independent risk factors identified in multivariate analysis were a distance from the anal verge <5 cm (P = 0.024), the transverse diameter of the pelvic inlet (P < 0.001), mesorectal fat area (P = 0.017), and visceral fat area (P < 0.001). Then, a nomogram was built based on these four independent risk factors. The C-indexes of the nomogram in the development and validation group were 0.886 and 0.855, respectively. And values of AUC were the same with C-indexes in both groups. Besides, the calibration plots showed satisfactory consistency between actual observation and nomogram-predicted probabilities of long operating time. Conclusions: A nomogram for predicting the risk of long operating duration in L-AR of RC was developed. And the nomogram displayed a good prediction effect and can be utilized as a tool for evaluating operating time preoperatively.

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules.

Objective: To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs). Materials and methods: Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created. Results: Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful. Conclusion: Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

Longitudinal variation of endolymphatic hydrops in patients with Ménière's disease.

Background: The relationships of endolymphatic hydrops (EH) and perilymphatic enhancement (PE) with Ménière's disease (MD) remains unclear. This study aimed to describe the dynamic variation of EH and PE for MD patients over 2 hospitalizations by applying magnetic resonance imaging (MRI) to further clarify the relationships of EH and PE with MD. Methods: A total of 77 MD patients who underwent inner ear MRI after intravenous administration of gadolinium and pure-tone average (PTA) testing during a first and second hospitalization were included. The degree of EH and PE were evaluated via MRI, and the duration and frequency of vertigo attacks and PTA were collected and recorded. The PTA, EH, and PE for the 2 hospitalizations were compared, and the relationships of EH and cochlear PE with the MD stage were investigated. Results: There was no difference between the 2 hospitalizations for duration of vertigo attacks or frequency of vertigo attacks. However, there were significant differences in PTA (Z=-3.02, P=0.003). Additionally, the cochlear and vestibular EH in the asymptomatic ear at the second hospitalization was significantly worse than that of the first hospitalization (Z=-2.33 and -2.49, P=0.020 and 0.013, respectively), while there were no differences in EH and PE in the affected ear (all P>0.05). Moreover, the degree of cochlear and vestibular EH was correlated with MD stage (both P<0.01). Conclusions: Although EH and PE in the affected ear were unchanged over 2 hospitalizations, an underlying EH in the asymptomatic ear and hearing loss in the affected ear for MD patients developed longitudinally with the duration of disease, and EH varied with the natural course of MD whereas PE did not. Therefore, EH instead of PE is necessary but insufficient to cause the clinical symptoms of MD.

Radiomics features from perihematomal edema for prediction of prognosis in the patients with basal ganglia hemorrhage.

Objective: We developed and validated a clinical-radiomics nomogram to predict the prognosis of basal ganglia hemorrhage patients. Methods: Retrospective analyses were conducted in 197 patients with basal ganglia hemorrhage (training cohort: n = 136, test cohort: n = 61) who were admitted to The First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) and underwent computed tomography (CT) scan. According to different prognoses, patients with basal ganglia hemorrhage were divided into two groups. Independent clinical risk factors were derived with univariate and multivariate regression analysis. Radiomics signatures were obtained using least absolute shrinkage and selection operator. A radiomics score (Rad-score) was generated by 12 radiomics signatures of perihematomal edema (PHE) from CT images that were correlated with the prognosis of basal ganglia hemorrhage patients. A clinical-radiomics nomogram was conducted by combing the Rad-score and clinical risk factors using logistic regression analysis. The prediction performance of the nomogram was tested in the training cohort and verified in the test cohort. Results: The clinical model conducted by four clinical risk factors and 12 radiomcis features were used to establish the Rad-score. The clinical-radiomics nomogram outperformed the clinical model in the training cohort [area under the curve (AUC), 0.92 vs. 0.85] and the test cohort (AUC, 0.91 vs 0.85). The clinical-radiomics nomogram showed good calibration and clinical benefit in both the training and test cohorts. Conclusion: Radiomics features of PHE in patients with basal ganglia hemorrhage could contribute to the outcome prediction. The clinical-radiomics nomogram may help first-line clinicians to make individual clinical treatment decisions for patients with basal ganglia hemorrhage.

Predicting tumor deposits in patients with rectal cancer: Using the models of multiple mathematical parameters derived from diffusion-weighted imaging.

PURPOSE: Using mono-exponential, bi-exponential, and stretched-exponential models of multi-b-value diffusion-weighted imaging (DWI) to predict tumor depositions (TDs) in patients with rectal cancer (RC). MATERIAL AND METHODS: This retrospective study, between January 2018 and November 2021, enrolled 30 TDs-positive and 38 TDs-negative of patients with rectal cancer. The mathematical parameters including ADC from mono-exponential model, D, D* and f from bi-exponential model, and DDC and α from stretched-exponential model, clinical factors (such as age, gender, pathological stage, etc.) and image features (such as length, thickness, location, etc.) from tumor characteristics were obtained to identify the two groups of patients. The results were evaluated by the receiver operating characteristic curve (ROC) analysis and area under the ROC curve (AUC). Multivariate binary logistic regression analysis was conducted to determine the independent risk factors. RESULTS: The D* and α values, pt. stage, tumor location, mesorecta fascia (MRF) / peritoneum status and percentage of rectal wall circumference invaded (PCI) were significantly different between the TDs-positive and TDs-negative groups (P < 0.001, P < 0.001, P = 0.029, P = 0.008, P < 0.001 and P = 0.002, respectively), with the AUC were 0.838, 0.901, 0.618, 0.698 0.694 and 0.758, respectively. The D* and α values were proved to be independent risk factors after multivariate binary logistic regression analysis (p = 0.022 and 0.004, respectively). The AUC of the model consisting of the D* and α values was 0.913 (95 % CI 0.820 âˆ¼ 0.968).The combined model constructed by D*, α and tumor location demonstrated superior diagnostic performance, with the AUC, sensitivity, specificity, and accuracy of 0.947 (95 % confidence interval, CI, 0.865-0.987), 0.900, 0.868 and 0.853, respectively. CONCLUSION: Multiple mathematical parameters can be used as preoperative auxiliary diagnostic tools to predict TDs of RC. The combined model constructed by D*, α and tumor location show better diagnostic performance for TDs.

Development and validation of a high-resolution T2WI-based radiomic signature for the diagnosis of lymph node status within the mesorectum in rectal cancer.

Purpose: The aim of this study was to explore the feasibility of a high-resolution T2-weighted imaging (HR-T2WI)-based radiomics prediction model for diagnosing metastatic lymph nodes (LNs) within the mesorectum in rectal cancer. Method: A total of 604 LNs (306 metastatic and 298 non-metastatic) from 166 patients were obtained. All patients underwent HR-T2WI examination and total mesorectal excision (TME) surgery. Four kinds of segmentation methods were used to select region of interest (ROI), including method 1 along the border of LNs; method 2 along the expanded border of LNs with an additional 2-3 mm; method 3 covering the border of LNs only; and method 4, a circle region only within LNs. A total of 1,409 features were extracted for each method. Variance threshold method, Select K Best, and Lasso algorithm were used to reduce the dimension. All LNs were divided into training and test sets. Fivefold cross-validation was used to build the logistic model, which was evaluated by the receiver operating characteristic (ROC) with four indicators, including area under the curve (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). Three radiologists with different working experience in diagnosing rectal diseases assessed LN metastasis respectively. The diagnostic efficiencies with each of four segmentation methods and three radiologists were compared to each other. Results: For the test set, the AUCs of four segmentation methods were 0.820, 0.799, 0.764, and 0.741; the ACCs were 0.725, 0.704, 0.709, and 0.670; the SEs were 0.756, 0.634, 0.700, and 0.589; and the SPs were 0.696, 0.772, 0.717, and 0.750, respectively. There was no statistically significant difference in AUC between the four methods (p > 0.05). Method 1 had the highest values of AUC, ACC, and SE. For three radiologists, the overall diagnostic efficiency was moderate. The corresponding AUCs were 0.604, 0.634, and 0.671; the ACCs were 0.601, 0.632, and 0.667; the SEs were 0.366, 0.552, and 0.392; and the SPs were 0.842, 0.715, and 0.950, respectively. Conclusions: The proposed HR-T2WI-based radiomic signature exhibited a robust performance on predicting mesorectal LN status and could potentially be used for clinicians in order to determine the status of metastatic LNs in rectal cancer patients.

Intravoxel incoherent motion DWI with different mathematical models in predicting rectal adenoma with and without canceration.

PURPOSE: To evaluate the clinical value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) with mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models for predicting rectal adenomas with canceration. MATERIAL AND METHODS: Sixty patients with postoperative pathology-confirmed rectal adenoma (n = 31) and adenoma with canceration (n = 29) were enrolled and underwent IVIM-DWI scanning. The ME-derived apparent diffusion coefficient (ADC), BE-derived true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), SE-derived distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured. The differences in each parameter between adenoma and canceration were compared. Multivariate binary logistic regression analysis was used to establish models for predicting rectal adenomas with canceration. Receiver operating characteristic curve analysis was applied to evaluate diagnostic performances of each model in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). RESULTS: The AUCs of ADC, D, D*, f, DDC and α were 0.851 (95 % confidence interval, CI, 0.735-0.930), 0.895 (95 % CI, 0.789-0.960), 0.720 (95 % CI, 0.589-0.828), 0.791 (95 % CI, 0.667-0.886), 0.841 (95 % CI, 0.724-0.923) and 0.738 (95 % CI, 0.608-0.834), respectively. The AUCs of BE and SE models were 0.927 (95 % CI, 0.829-0.978) and 0.874 (95 % CI, 0.763-0.946), respectively. The AUC, sensitivity, specificity, and accuracy of the derived four values (ADC, D, f, and DDC) from the combination of three models were 0.950, 96.6 % (95 % CI, 95.3-97.6 %), 80.6 % (95 % CI, 78.0-82.9 %), and 88.3 % (95 % CI, 86.2-90.2 %), respectively. CONCLUSION: ADC can easily and effectively predict rectal adenomas with canceration. The BE model has a better combination of sensitivity and specificity for the diagnosis of rectal adenoma canceration.

Association of plasma neutrophil gelatinase-associated lipocalin and thoracic aorta calcification in maintenance hemodialysis patients with and without diabetes.

BACKGROUND: Neutrophil gelatinase-associated lipocalin (NGAL) is not only a bone-derived factor involved in metabolism, but also a biomarker of kidney disease and cardiovascular pathophysiology. We conducted this cross-sectional observational study to explore relationships between plasma NGAL and thoracic aorta calcification (TAC) in maintenance hemodialysis (MHD) patients with and without diabetes. METHODS: Plasma NGAL was measured by ELISA, TAC was evaluated via computed tomography scan using a 3D quantification method or chest radiography aortic arch calcification score. Spearman correlation, Logistic regression and Partial correlation analysis were used to describe the correlations between NGAL and TAC. RESULTS: Plasma NGAL levels were lower in MHD patients with diabetes compared to those without diabetes (49.33(42.37, 55.48) vs 56.78(44.37, 674.13) ng/mL, P = 0.026). In MHD patients without diabetes, lg (NGAL) was positively correlated with ARC value(R = 0.612, P = 0.003) analyzed by Spearman correlation; for partial correlation analysis, lg (NGAL) was positively correlated with ARC value, after adjusting for age and sex (R = 0.550, P = 0.015), adjusting for age, sex and CHD (R = 0.565, P = 0.015), adjusting for age, sex, CHD and Alb (R = 0.536, P = 0.027), or adjusting for age, sex, CHD, Alb, and dialyzer membrane (polysulfone) (R = 0.590, P = 0.016); however, when adjusting for age, sex, CHD, Alb and Ca, the correlation between lg (NGAL) and ARC value disappeared. Positive correlation were found between NGAL and Ca (R = 0.644, P < 0.001), Ca and ACR (R = 0.534, P = 0.013) in Spearman coefficient analysis. CONCLUSION: There were positive correlations among plasma NGAL, serum Ca and ARC in MHD patients without diabetes; which suggests that NGAL is possibly a participant in cardiovascular calcification, in non-diabetic MHD.

Eustachian tube teratoma: A case report.

BACKGROUND: Mature teratoma composed of all three basic germ cell layers of the head and neck is a rare disease. Teratomas involving the temporal bone are particularly scarce. CASE SUMMARY: A 48-year-old male patient with a history of chronic otitis of the left ear from infancy, for which he had been operated on twice, was referred to our hospital for chronic otitis, cholesteatoma and a middle ear mass. Computed tomography (CT) scan and magnetic resonance imaging (MRI) revealed a eustachian tube teratoma, in which the anterior lower part and posterior upper part were connected by a thin membranaceous tissue. The mass was removed completely under general anesthesia by mastoidectomy. As of last follow-up (2 years post-surgery), the disease had not relapsed. CONCLUSION: Pre-operative CT and MRI are necessary for eustachian tube teratoma. Complete surgical resection provided excellent prognosis.

Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration.

OBJECTIVES: To propose multiparametric MRI-based machine learning models and assess their ability to preoperatively predict rectal adenoma with canceration. MATERIALS AND METHODS: A total of 53 patients with postoperative pathology confirming rectal adenoma (n = 29) and adenoma with canceration (n = 24) were enrolled in this retrospective study. All patients were divided into a training cohort (n = 42) and a test cohort (n = 11). All patients underwent preoperative pelvic MR examination, including high-resolution T2-weighted imaging (HR-T2WI) and diffusion-weighted imaging (DWI). A total of 1396 radiomics features were extracted from the HR-T2WI and DWI sequences, respectively. The least absolute shrinkage and selection operator (LASSO) was utilized for feature selection from the radiomics feature sets from the HR-T2WI and DWI sequences and from the combined feature set with 2792 radiomics features incorporating two sequences. Five-fold cross-validation and two machine learning algorithms (logistic regression, LR; support vector machine, SVM) were utilized for model construction in the training cohort. The diagnostic performance of the models was evaluated by sensitivity, specificity and area under the curve (AUC) and compared with the Delong's test. RESULTS: Ten, 8, and 25 optimal features were selected from 1396 HR-T2WI, 1396 DWI and 2792 combined features, respectively. Three group models were constructed using the selected features from HR-T2WI (ModelT2), DWI (ModelDWI) and the two sequences combined (Modelcombined). Modelcombined showed better prediction performance than ModelT2 and ModelDWI. In Modelcombined, there was no significant difference between the LR and SVM algorithms (p = 0.4795), with AUCs in the test cohort of 0.867 and 0.900, respectively. CONCLUSIONS: Multiparametric MRI-based machine learning models have the potential to predict rectal adenoma with canceration. Compared with ModelT2 and ModelDWI, Modelcombined showed the best performance. Moreover, both LR and SVM have equal excellent performance for model construction.

The Effect of Rectal Distention on the Intravoxel Incoherent Motion Parameters: Using Sonography Transmission Gel.

OBJECTIVE: To compare the intravoxel incoherent motion (IVIM) parameters of rectal tumors before and after lumen distension obtained with sonography transmission gel. METHODS: Twenty-five patients were enrolled. The multiple b values of IVIM including 0, 20, 50, 100, 150, 200, 400, 600, 800, 1000, 1500, and 2000 s/mm. Two blinded readers have drawn the region of interests and calculated the D, D*, and f values. Interobserver variability between the 2 readers was measured by intraclass correlation coefficients and Altman-Bland plots. The intergroup differences of the average values were compared with the paired sample t test. RESULTS: After distention, the interrater agreement of the D* value increased obviously (from 0.547 to 0.692) and that of the D and f values increased slightly (from 0.731 and 0.618 to 0.807 and 0.666). The difference in the D value had statistical significance (P = 0.0043). CONCLUSIONS: Intraluminal distension can increase the repeatability of IVIM parameters and the value of IVIM.

A Model Using Texture Features to Differentiate the Nature of Thyroid Nodules on Sonography.

OBJECTIVES: To evaluate the use of texture-based gray-level co-occurrence matrix (GLCM) features extracted from thyroid sonograms in building prediction models to determine the nature of thyroid nodules. METHODS: A GLCM was used to extract the texture features of 155 sonograms of thyroid nodules (76 benign and 79 malignant). The GLCM features included energy, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, information measures of correlation, and maximal correlation coefficient. The texture features extracted by the GLCM were used to build 6 different statistical models, including support vector machine, random tree, random forest, boost, logistic, and artificial neural network models. The models' performances were evaluated by 10-fold cross-validation combining a receiver operating characteristic curve, indices of accuracy, true-positive rate, false-positive rate, sensitivity, specificity, precision, recall, F-measure, and area under the receiver operating characteristic curve. External validation was used to examine the stability of the model that showed the best performance. RESULTS: The logistic model showed the best performance, according to 10-fold cross-validation, among the 6 models, with the highest area under the curve (0.84), accuracy (78.5%), true-positive rate (0.785), sensitivity (0.789), specificity (0.785), precision (0.789), recall (0.785), and F-measure (0.784), as well as the lowest false-positive rate (0.215). The external validation results showed that the logistic model was stable. CONCLUSIONS: Gray-level co-occurrence matrix texture features extracted from sonograms of thyroid nodules coupled with a logistic model are useful for differentiating between benign and malignant thyroid nodules.