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The tower foundation is an important component of the tower grounding device. The conditions in permafrost areas are relatively harsh, and damage to the foundation can affect the grounding performance of the grounding device. However, the damage characteristics of permafrost to the tower foundation in permafrost areas are not yet clear, and effective protection of the foundation cannot be achieved. Based on this, this article constructs a simulated frozen soil experimental platform through experiments, replacing the tower foundation with concrete specimens. The electrical and mechanical properties after freeze-thaw were measured using the quadrupole method and resonance method, respectively. The self performance of the tower foundation under different moisture content freeze-thaw conditions was studied, and the performance change law of the tower foundation after freeze-thaw was obtained to explore the damage of frozen soil to the tower foundation in frozen soil areas. It can be considered that the frozen soil in permafrost areas does not cause significant damage to the tower foundation itself in a limited number of years.

BACKGROUND: Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier‐transform mid‐infrared (FT‐MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT‐MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk. RESULTS: We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high‐content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy. CONCLUSION: This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT‐MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Accurate prediction of bone metastasis-free survival (BMFS) after complete surgical resection in patients with non-small cell lung cancer (NSCLC) may facilitate appropriate follow-up planning. The aim of this study was to establish and validate a preoperative CT-based deep learning (DL) signature to predict BMFS in NSCLC patients. We performed a retrospective analysis of 1547 NSCLC patients who underwent complete surgical resection, followed by at least 36 months of monitoring at two hospitals. We constructed a DL signature from multiparametric CT images using 3D convolutional neural networks, and we integrated this signature with clinical-imaging factors to establish a deep learning clinical-imaging signature (DLCS). We evaluated performance using Harrell's concordance index (C-index) and the time-dependent receiver operating characteristic. We also assessed the risk of bone metastasis (BM) in NSCLC patients at different clinical stages using DLCS. The DL signature successfully predicted BM, with C-indexes of 0.799 and 0.818 for the validation cohorts. DLCS outperformed the DL signature with corresponding C-indexes of 0.806 and 0.834. Ranges for area under the curve at 1, 2, and 3 years were 0.820–0.865 for internal and 0.860–0.884 for external validation cohorts. Furthermore, DLCS successfully stratified patients with different clinical stages of NSCLC as high- and low-risk groups for BM (p < 0.05). CT-based DL can predict BMFS in NSCLC patients undergoing complete surgical resection, and may assist in the assessment of BM risk for patients at different clinical stages. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, deep learning combined with udder ultrasonography of buffalo was used for the detection of mastitis for the first time, with the aim of establishing an accurate, rapid, and inexpensive method to detect buffalo mastitis instead of routine laboratory examination. This method provides a basis for mastitis detection in buffaloes mostly raised by small farmers and has the opportunity to be used in a variety of dairy animals in the future. Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 105 cells/mL and 4 × 105 cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network's performance. The results showed that, when the SCC threshold was 2 × 105 cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 105 cells/mL than when the SCC threshold was 2 × 105 cells/mL. Therefore, when SCC ≥ 4 × 105 cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries. [ABSTRACT FROM AUTHOR]

Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10–90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety. [ABSTRACT FROM AUTHOR]

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms. [ABSTRACT FROM AUTHOR]

Background: Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. Purpose: To establish an MRI‐based radiomics nomogram for assessing relapse risk in patients with SNMT. Study Type: Retrospective. Population: A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). Field Strength/Sequence: A 1.5‐T and 3‐T, fat‐suppressed fast spin echo (FSE) T2‐weighted imaging (FS‐T2WI), FSE T1‐weighted imaging (T1WI), and FSE contrast‐enhanced T1WI (T1WI + C). Assessment: Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS‐T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high‐dimensional features. The clinical model was built based on clinical and MRI features. The Ki‐67‐based and tumor‐node‐metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse‐free survival analysis was used among 143 patients. Statistical Tests: The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan–Meier curve, log‐rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. Results: The T1 + C‐based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS‐T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki‐67‐based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. Data Conclusion: A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki‐67‐based model, and TNM model. Evidence Level: 3. Technical Efficacy: Stage 5. [ABSTRACT FROM AUTHOR]

Colostrum is a vital performance for buffaloes and potentially functional foods in the future. Therefore, this study aimed to evaluate the difference between the parity of buffalo colostrum and mature milk. Twenty pregnant buffaloes (primiparous = 10; multiparous = 10) were assigned to the same diet prepartum and milking routine postpartum. Calves were separated from the dams immediately after birth and colostrum was harvested within 2 h, whilst mature milk was harvested at 7 days postpartum. The colostrum was analyzed for immunoglobulin G and milk composition as the mature milk. The results showed that there was a higher level of protein, solid not fat, and milk urea nitrogen (p < 0.05), with a tendency for higher total solids (p = 0.08) in primiparous buffaloes' colostrum compared with multiparous. No parity effect was observed in colostrum immunoglobulin G, fat, lactose, and yields of colostrum and composition (p > 0.05). There was no difference in mature milk composition and yield by parity affected (p > 0.05). Compared with mature milk composition, colostrum had a higher content protein, total solids, solid not fat, and milk urea nitrogen (p < 0.05); however, fat and lactose were lower than that of mature milk (p < 0.05). For minerals, multiparous buffaloes' colostrum had a higher concentration of Fe (p = 0.05), while the mature milk had higher concentrations of K and P compared with primiparous. Buffalo colostrum had higher concentrations of Na, Mg, Co, Fe, and K with a lower concentration of Ca relative to mature milk (p < 0.05). It was observed that parity affected colostrum characteristics rather than mature milk and caused subtle variations in minerals in colostrum and mature milk of buffaloes. As lactation proceeded, both milk composition and minerals in the milk changed drastically. [ABSTRACT FROM AUTHOR]

Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow's milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis. [ABSTRACT FROM AUTHOR]

This research aimed to evaluate the effects of Capsicum oleoresin (CAP) supplementation on rumen fermentation in vivo and In vitro, and lactation performance in buffaloes. In the experiment in vitro, 2 × 5 factorial design was carried out according to two temperatures (normal temperature: 39 °C; hyperthermal temperature: 42 °C) and five CAP concentrations (0 mg/L; 2 mg/L; 20 mg/L; 200 mg/L; 2000 mg/L). In the experiment in vivo, four multiparous mid-lactating Mediterranean buffaloes (body weight: 640.08 ± 17.90 kg) were randomly allocated to four treatments according to 4 × 4 Latin square design for CAP supplementation in four dosages (0 mg/kg, 10 mg/kg, 20 mg/kg, or 40 mg/kg of dry matter). The experiment's results In vitro showed that hyperthermal temperature affected all fermentation characteristics measured in this research. CAP decreased the pH, short-chain fatty acids concentration, and percentages of propionate, butyrate, isobutyrate, valerate, and caproate, while increasing the percentage of acetate and the ratio of acetate to propionate at normal temperature (p ≤ 0.05). In the experiment in vivo, CAP decreased the percentage of propionate and quadratically affected acetate percentage in rumen fluid (p ≤ 0.05). CAP reduced rectal temperature and respiratory rates (p ≤ 0.05) and tended to increase dry matter intake quadratically (p ≤ 0.10). For lactation performance, CAP increased milk yield and milk lactose yield (p ≤ 0.05), and tended to increase milk protein yield (p ≤ 0.10). In conclusion, CAP modified rumen fermentation characteristics in vivo and In vitro and had beneficial effects on lactation performance in buffaloes during summer. [ABSTRACT FROM AUTHOR]

The objective of the study was to compare and reveal differences in basic chemical parameters, fatty acids, amino acids, and lipid quality indices of crossbred buffalo (swamp x river type) milk produced in summer and winter. The buffalo milk samples were collected in summer (Jul–Aug) and winter (Dec–Jan) from Hubei province, China. The samples were detected by using CombiFoss apparatus, gas chromatography, and an automated specialized amino acid analyzer. The results showed that the basic chemical parameters, fatty acid profiles, lipid quality indices, and amino acid profiles of crossbred buffalo milk differed between summer and winter. Specifically, summer buffalo milk exhibited a higher content of MUFA (monounsaturated fatty acids) and PUFA (polyunsaturated fatty acids) than winter buffalo milk. Summer buffalo milk had a lower content of major SFA (saturated fatty acids), a higher content of ω-3 and DFA (hypocholesterolemic fatty acids), a lower ω-6/ω-3 ratio, a higher value of 3 unsaturated fatty acid indices (C14, C16, C18), and a lower value of IA (index of atherogenicity) and IT (index of thrombogenicity) than winter buffalo milk. Additionally, 17 amino acids, including 8 EAA (essential amino acids) and 9 NEAA (non-essential amino acids) were higher in summer buffalo milk. These results indicated that summer buffalo milk was more health-beneficial than winter buffalo milk. Therefore, summer buffalo milk might be a desirable diet option for human nutrition and health. Our findings provide valuable information for the research and development of buffalo dairy products in China or other Asian countries. [ABSTRACT FROM AUTHOR]

Background: Renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) have a common cellular origin and different clinical management and prognosis. Purpose: To explore the utility of computed tomography (CT) in the differentiation of RO and chRCC. Material and Methods: Twenty-five patients with RO and 73 patients with chRCC presenting with the central scar were included retrospectively. Two experienced radiologists independently reviewed the CT imaging features, including location, tumor size, relative density ratio, segmental enhancement inversion (SEI), necrosis, and perirenal fascia thickening, among others. Interclass correlation coefficient (ICC, for continuous variables) or Kappa coefficient test (for categorical variables) was used to determine intra-observer and inter-observer bias between the two radiologists. Results: The inter- and intra-reader reproducibility of the other CT imaging parameters were nearly perfect (>0.81) except for the measurements of fat (0.662). RO differed from chRCC in the cortical or medullary side (P = 0.005), relative density ratio (P = 0.020), SEI (P < 0.001), and necrosis (P = 0.045). The logistic regression model showed that location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were highly predictive of RO. The combined indicators from logistic regression model were used for ROC analysis. The area under the ROC curve was 0.923 (P < 0.001). The sensitivity and specificity of the four factors combined for diagnosing RO were 88% and 86.3%, respectively. The correlation coefficient between necrosis and tumor size in all tumors including both of RO and chRCC was 0.584, indicating a positive correlation (P < 0.001). Conclusion: The CT imaging features of location (right kidney), hypo-density on non-enhanced CT, SEI, and perirenal fascia thickening were valuable indicators in distinguishing RO from chRCC. [ABSTRACT FROM AUTHOR]

Pre-operative differentiation between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is critical due to their different clinical behavior and different clinical treatment decisions. The aim of this study was to develop and validate a CT-based radiomics nomogram for the pre-operative differentiation of RO from chRCC. A total of 141 patients (84 in training data set and 57 in external validation data set) with ROs (n = 47) or chRCCs (n = 94) were included. Radiomics features were extracted from tri-phasic enhanced-CT images. A clinical model was developed based on significant patient characteristics and CT imaging features. A radiomics signature model was developed and a radiomics score (Rad-score) was calculated. A radiomics nomogram model incorporating the Rad-score and independent clinical factors was developed by multivariate logistic regression analysis. The diagnostic performance was evaluated and validated in three models using ROC curves. Twelve features from CT images were selected to develop the radiomics signature. The radiomics nomogram combining a clinical factor (segmental enhancement inversion) and radiomics signature showed an AUC value of 0.988 in the validation set. Decision curve analysis revealed that the diagnostic performance of the radiomics nomogram was better than the clinical model and the radiomics signature. The radiomics nomogram combining clinical factors and radiomics signature performed well for distinguishing RO from chRCC. Differential diagnosis between renal oncocytoma (RO) and chromophobe renal cell carcinoma (chRCC) is rather difficult by conventional imaging modalities when a central scar was present. A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of RO from chRCC with improved diagnostic efficacy. The CT-based radiomics nomogram might spare unnecessary surgery for RO. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation. Materials and Methods: One hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination. Results: Rad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808–0.940 vs. 0.803; 95% CI: 0.705–0.899, P < 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800–0.921 vs. 0.846; 95% CI: 0.777–0.915, P < 0.05). Conclusions: The radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment. [ABSTRACT FROM AUTHOR]

We described the perioperative management of a child patient with central core disease for bronchoscopy with bronchoalveolar lavage. It is safe to avoid triggering agents (volatile anesthetics and succinylcholine) probably in preventing this appearance of malignant hyperthermia (MH). It is important to recognize potential complications and know how to prevent and manage them in patients with this condition. A 5-year-old boy (weight: 8.8 kg; height: 63 cm) presented to the pediatric department after five days of intermittent fever (highest body temperature is 39.3 °C) and cough, and aggravation 1 day, meanwhile he had phlegm in throat but he couldn't cough out. The child was found to have motor retardation at his one-month-old physical examination, then genetic analysis showed central core disease. Bronchoscopy with bronchoalveolar lavage was performed for better treatment under the premise of symptomatic treatment. The patients with central core disease are particularly to develop malignant hyperthermia, so adequate precautions are in place to prevent and treat MH before anesthetic induction. The anesthesiologists need to make adequate preoperative anesthesia management strategies to ensure the safety of the child with central core disease for bronchoscopy with bronchoalveolar lavage. The child was discharged from the hospital one week after anti-inflammatory and anti-asthmatic treatment. We summarized the anesthetic precautions and management in patients with central core disease, meanwhile we offered some suggestions about anesthetic focus on bronchoscopy with bronchoalveolar lavage. • Good perioperative management of bronchoscopy in a child patient with central core disease is necessary for good prognosis. • We summarized the anesthetic precautions and management in patients with central core disease. • We offered some suggestions about anesthetic focus on bronchoscopy with bronchoalveolar lavage. [ABSTRACT FROM AUTHOR]

Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well‐recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had ≤5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI‐based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI‐based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment. What's new? Liver metastasis is a major cause of death in patients with colorectal cancer. Tumor regression grade (TRG) is a good way to assess tumor response and predict patient survival, and is typically measured by pathological evaluation. Here, the authors tested a deep learning algorithm that uses MRI data to obtain TRG in patients with colorectal liver metastasis who were undergoing preoperative chemotherapy. The deep learning model achieved diagnostic accuracy of 87.5%, a big improvement over the previous method, the RECIST criteria (57.8%). This system could help clinicians provide appropriate personalized information to patients about treatment response and prognosis. [ABSTRACT FROM AUTHOR]

Objective: To investigate the diagnostic value of digital tomosynthesis in urolithiasis compared with multidetector computed tomography (MDCT). Subjects and Methods: This study received the approval of institutional review board and obtained the informed consents of all subjects. Fifty patients with acute renal colic and hematuria from February 2014 to February 2015 were enrolled. They underwent both MDCT and digital tomosynthesis. Images were interpreted by two experienced radiologists, and inter-reader agreement was assessed. The diagnostic performance of urinary stones with digital tomosynthesis and MDCT was compared. The entrance surface dose (ESD) in MDCT and digital tomosynthesis for patients was measured and compared. Results: No significant difference was found between digital tomosynthesis and MDCT in the diagnosis of urinary stones (X2 = 1.357, p > 0.05). The ESD in digital tomosynthesis was lower than that in both routine-dose and low-dose MDCT for patients (p < 0.05). Conclusions: Digital tomosynthesis has great diagnostic value in urolithiasis. Compared with MDCT, digital tomosynthesis can significantly decrease ESD for patients and financial loan with a similar detection rate. [ABSTRACT FROM AUTHOR]