Quantitative Imaging in Medicine and Surgery最新文献

Background: The preoperative prediction of the pathological nuclear grade of clear cell renal cell carcinoma (CCRCC) is crucial for clinical decision making. However, radiomics features from one or two computed tomography (CT) phases are required to predict the CCRCC grade, which reduces the predictive performance and generalizability of this method. We aimed to develop and externally validate a multiparameter CT radiomics-based model for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of CCRCC.

Methods: A total of 500 CCRCC patients at The First, Second, and Yongchuan Hospitals of Chongqing Medical University between January 2016 and May 2022 were retrospectively enrolled in this study. The patients were divided into the training set (n=268), internal testing set (n=115), and two external testing sets (testing set 1, n=62; testing set 2, n=55). Radiomics features were extracted from multi-phase CT images, and radiomics signatures (RSs) were created by least absolute shrinkage and selection operator (LASSO) regression. In addition, a clinical model was developed. A combined model was also established that integrated the RSs with the clinical factors, and was visualized via a nomogram. The performance of the established model was assessed using area under the curve (AUC) values, a calibration curve analysis, and a decision curve analysis (DCA).

Results: Among the four RSs and the clinical model, the RS-Triphasic had the best predictive performance with AUCs of 0.88 [95% confidence interval (CI): 0.85-0.91] and 0.84 (95% CI: 0.74-0.95) in the training and testing sets, respectively, and 0.82 (95% CI: 0.72-0.93) and 0.82 (95% CI: 0.71-0.93) in external testing sets 1 and 2. Integrating the RS-Triphasic, RS-corticomedullary phase (CMP), RS-nephrographic phase (NP), RS-non-contrast phase (NCP) with the clinical risk factors, a combined model was established with AUCs of 0.92 (95% CI: 0.89-0.94), 0.86 (95% CI: 0.76-0.95), 0.84 (95% CI: 0.73-0.95), and 0.82 (95% CI: 0.70-0.94) for the training, internal testing, and external testing sets 1 and 2, respectively. The DCA indicated that the nomogram had a greater overall net benefit than the clinical and radiomics models.

Conclusions: The multiparameter CT RS fusion-based model had high accuracy in differentiating between high- and low-grade CCRCC preoperatively. Thus, it has great potential as a useful tool for personalized treatment planning and clinical decision making for CCRCC patients.

Background: Fatty kidney disease is linked to renal function damage, but there is no noninvasive tool for monitoring renal fat accumulation. This study aimed to explore the repeatability of the iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification (IDEAL-IQ) sequence imaging in quantifying renal fat deposition and to assess the differences observed in patients with type 2 diabetes mellitus (T2DM).

Methods: A total of 26 healthy participants underwent two IDEAL-IQ scans without repositioning, and the repeatability of the imaging technique was assessed with Bland-Altman analysis. Additionally, 96 patients with T2DM underwent a single IDEAL-IQ scan for the examination of renal fat deposition. The patients with T2DM were classified into three groups based on their estimated glomerular filtration rate (eGFR). One-way analysis of variance was used to analyze the differences of renal fat depositions between the groups. Receiver operating characteristic curve analysis was used to assess the diagnostic performance of IDEAL-IQ.

Results: Bland-Altman analyses showed narrower limits of agreement and a significant correlation (r=0.81; P<0.05) between the two IDEAL-IQ scans. Statistically significant differences between the healthy volunteers and patients with T2DM, diabetic kidney disease (DKD) I-II, and or DKD III-IV were found in renal parenchymal proton-density fat fraction (PDFF) values (P<0.001). Renal parenchymal PDFF was negatively correlated with eGFR (r=-0.437; P<0.001) and positive correlated with serum creatinine level (µmol/L) (r=0.421; P<0.001). The area under the curve of IDEAL-IQ in discriminating between the healthy volunteers and patients with T2DM was 0.857. For discriminating T2DM from DKD I-II and DKD III-IV, the IDEAL-IQ had an area under the curve of 0.689 and 0.823, respectively.

Conclusions: IDEAL-IQ is a promising and reproducible technique for the assessment of renal fat deposition and identification of risk of DKD in patients with T2DM. Moreover, IDEAL-IQ imaging is expected to improve the sensitivity and specificity of early renal function damage and staging assessment of patients with T2DM.

Background: Diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) is a valuable tool for detecting acute brain lesions but does suffer from image distortions. Multishot echo-planar imaging (msEPI) is a technique for reducing such distortions. This study aimed to compare the image quality and diagnostic efficacy of ssEPI- and msEPI-DWI at 5.0 T for brain disease detection.

Methods: This study retrospectively reviewed images of 107 consecutive patients with suspected brain diseases who underwent ssEPI- and msEPI-DWI at 5.0 T at the First Affiliated Hospital of University of Science and Technology of China from August 2023 to September 2023. Two radiologists independently graded image quality and measured the image distortion. Signal-to-noise ratio, contrast-to-noise ratio, and apparent diffusion coefficient (ADC) were calculated and compared between ssEPI- and msEPI-DWI. Image quality scores were compared using the Wilcoxon test and other continuous variables by the paired t-test. The diagnostic accuracy of ADC values in distinguishing lesions from normal-appearing tissues was measured with the area under the curve (AUC).

Results: Image quality evaluation and distortion analysis revealed that msEPI-DWI significantly outperformed ssEPI-DWI (two-sided P<0.001). No significant difference was observed in signal-to-noise ratio, contrast-to-noise ratio, or ADC values between msEPI- and ssEPI-DWI (two-sided P≥0.601). The ADC values of msEPI- and ssEPI-DWI showed strong correlations for both lesions (r=0.97) and contralateral normal tissues (r=0.91) (two-sided P<0.001). Compared to those of the contralateral white matter, ADC values of low-grade gliomas (LGGs) were significantly higher [ssEPI-DWI: 1,119.9±273.1 vs. 805.1±73.9; msEPI-DWI: 1,196.2±355.6 vs. 757.3±98.0 (unit: ×10^-6 mm²/s)], while the ADC values of acute cerebral infarction (ACI) lesions were significantly lower [ssEPI-DWI: 603.9±273.2 vs. 888.9±212.0; msEPI-DWI: 538.0±281.2 vs. 905.0±188.9 (unit: ×10^-6 mm²/s)] (two-sided P≤0.003). The AUCs for detecting LGGs were excellent for both ssEPI-DWI [AUC =0.934; 95% confidence interval (CI): 0.84-1.00] and msEPI-DWI (AUC =0.944; 95% CI: 0.86-1.00) (two-sided P<0.001; two-sided DeLong test: P=0.833).

Conclusions: As compared to ssEPI-DWI, msEPI-DWI, when performed at 5.0 T, demonstrated superior image quality and less anatomical distortion in a wide spectrum of brain diseases and showed promising diagnostic performance for LGGs and ACI. In the future, msEPI-DWI at 5.0 T could become clinically routine in the diagnosis and grading of brain disorders.

Background: Kirsten rat sarcoma (KRAS) mutation drives resistance to anti-epidermal growth factor receptor (anti-EGFR)-targeted therapies in rectal cancer. Amide proton transfer-weighted magnetic resonance imaging (APTw MRI) might be a supplement to the evaluation of KRAS mutation because the APTw value can reflect mobile cellular protein content in vivo. This study aimed to determine whether APTw MRI could predict KRAS mutation in rectal cancer and compare this technique with diffusion-weighted imaging (DWI).

Methods: This retrospective study reviewed 153 consecutive patients with rectal cancer from April 2019 to June 2021 in our hospital. Among them, a total of 55 patients who did not undergo neoadjuvant chemoradiotherapy and underwent preoperative APTw MRI, DWI, and postoperative KRAS tests were included in this study. In two-dimensional APTw images, two radiologists manually delineated three regions of interest (ROIs) along tumor contour in the largest slice and the adjacent two slices of tumor respectively. The mean APTw value within a ROI was calculated, and the values of three ROIs were averaged for each patient. In consecutive DWI images, two radiologists depicted the ROIs of the whole lesion, and the mean apparent diffusion coefficient (ADC) was generated. The intraclass correlation coefficient (ICC), Shapiro-Wilk test and Student's t-test were used for statistical analyses. Receiver operating characteristic (ROC) curves were constructed for APTw and ADC values respectively, and the area under the curve (AUC) was used to evaluate the diagnostic performance for the prediction of KRAS mutation.

Results: Among these 55 patients, KRAS mutation occurred in 21 patients. The ICCs of two independent raters for APTw and ADC values were 0.937 [95% confidence interval (CI), 0.914-0.953] and 0.976 (95% CI, 0.959-0.986), respectively. ADC values did not show a statistically significant difference between the KRAS-mutant group and the wild type (WT) group (P=0.733). KRAS-mutant tumors exhibited a higher APTw value than WT tumors in patients with rectal non-mucinous adenocarcinoma (3.324%±0.685% vs. 2.230%±0.833%, P<0.001). The AUC of the APTw value was 0.827 (95% CI, 0.701-0.916), with a cutoff value of 2.4% (sensitivity, 95.2%; specificity, 55.9%).

Conclusions: DWI cannot differentiate mutant KRAS genes from WT genes in patients with rectal cancer, but APTw MRI has potential for evaluating KRAS mutation in rectal cancer. The APTw value had moderate diagnostic performance in the prediction of KRAS mutation with a high sensitivity but a low specificity. APTw MRI might be a promising supplement to KRAS genomic analysis in rectal cancer patients.

Background: Glioblastoma (GBM) exhibits diffuse and invasive growth patterns, with a 5-year overall survival (OS) rate of 5-10%. In addition, approximately 40 percent of GBMs are localized in the frontal lobe, a region closely linked to essential life functions including cognition, so that it cannot be completely eradicated through surgical intervention, leading to very poor prognosis. Postoperative therapy is an essential treatment modality. The aim of this study is to explain the possible role of radiation therapy (RT) in the treatment of frontal GBM, providing more evidence for clinical application.

Methods: In the study, patient information pertaining to frontal GBM patients was collected from the Surveillance, Epidemiology, and End Results (SEER) database for the period 2000 to 2018 with 9,904 patients deemed appropriate for inclusion in this study. A 1:2 propensity score matching analysis was conducted to balance the non-radiotherapy and radiotherapy group. This study is a retrospective study.

Results: Before matching, the median OS, tumor specific survival (TSS) and hazard ratio (HR) were 3 months, 3 months and 4.408 [95% confidence interval (CI): 3.762-4.535, P<0.001] in the non-RT group compared to those of 13 months, 14 months and 2.463 (95% CI: 2.247-2.936, P<0.001) in the RT group. After matching, the median OS, TSS and HR were 3 months, 4 months and 1.433 (95% CI: 1.387-1.692, P<0.001) in the non-RT group compared to those of 8 months, 8 months and 1.427 (95% CI: 1.374-1.682, P<0.001) in the RT group.

Conclusions: Radiotherapy is an important local therapy, which can significantly improve the tumor-specific survival and OS of frontal GBM patients. With the arrival of the era of precision radiotherapy, the continuous progress of radiotherapy technology may bring more benefits to frontal GBM patients.

Background: Aortic dissection is a life-threatening clinical emergency, but it is often missed and misdiagnosed due to the limitations of diagnostic technology. In this study, we developed a deep learning-based algorithm for identifying the true and false lumens in the aorta on non-contrast-enhanced computed tomography (NCE-CT) scans and to ascertain the presence of aortic dissection. Additionally, we compared the diagnostic performance of this algorithm with that of radiologists in detecting aortic dissection.

Methods: We included 320 patients with suspected acute aortic syndrome from three centers (Beijing Anzhen Hospital Affiliated to Capital Medical University, Fujian Provincial Hospital, and Xiangya Hospital of Central South University) between May 2020 and May 2022 in this retrospective study. All patients underwent simultaneous NCE-CT and contrast-enhanced CT (CE-CT). The cohort comprised 160 patients with aortic dissection and 160 without aortic dissection. A deep learning algorithm, three-dimensional (3D) full-resolution U-Net, was continuously trained and refined to segment the true and false lumens of the aorta to determine the presence of aortic dissection. The algorithm's efficacy in detecting dissections was evaluated using the receiver operating characteristic (ROC) curve, including the area under the curve (AUC), sensitivity, and specificity. Furthermore, a comparative analysis of the diagnostic capabilities between our algorithm and three radiologists was conducted.

Results: In diagnosing aortic dissection using NCE-CT images, the developed algorithm demonstrated an accuracy of 93.8% [95% confidence interval (CI): 89.8-98.3%], a sensitivity of 91.6% (95% CI: 86.7-95.8%), and a specificity of 95.6% (95% CI: 91.2-99.3%). In contrast, the radiologists achieved an accuracy of 88.8% (95% CI: 83.5-94.1%), a sensitivity of 90.6% (95% CI: 83.5-94.1%), and a specificity of 94.1% (95% CI: 72.9-97.6%). There was no significant difference between the algorithm's performance and radiologists' mean performance in accuracy, sensitivity, or specificity (P>0.05).

Conclusions: The algorithm proficiently segments the true and false lumens in aortic NCE-CT images, exhibiting diagnostic capabilities comparable to those of radiologists in detecting aortic dissection. This suggests that the algorithm could reduce misdiagnoses in clinical practice, thereby enhancing patient care.