Purpose To develop and validate a CT-based radiomics model for differentiating primary gastric lymphoma (PGL) from Borrmann type IV gastric cancer (GC). Materials and methods A total of 136 patients with pathologically confirmed PGL (n = 56) and Borrmann type IV GC (n = 80) were retrospectively enrolled between January 2016 and May 2022. The cohort was randomly partitioned into a training set (n = 95) and a testing set (n = 41) at a 7:3 ratio. Radiomics features were extracted from unenhanced, arterial, venous, double-phase (arterial + venous), and three-phase (unenhanced + arterial + venous) CT images. After feature selection using the Least Absolute Shrinkage and Selection Operator, radiomics models were constructed via logistic regression. A clinical-radiomics model was developed through multivariate analysis. The models were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves with the Hosmer-Lemeshow test, and decision Curve Analysis (DCA) for clinical net benefit. Results Clinical model comprised of high-enhanced serosa sign, normalized CT value on venous phase, and perigastric fat infiltration showed good performance with AUCs of 0.902 (training set) and 0.878 (testing set). Among the radiomics models, the three-phase model outperformed others (AUC: 0.871 training, 0.865 testing). The clinical-radiomics combined model further improved discriminatory performance, achieving AUCs of 0.960 and 0.932 in the training and testing sets, respectively. DCA confirmed that the combined model provided the highest clinical net benefit. Conclusion Clinical-radiomics model incorporating three-phase radiomics signatures and CT findings achieved satisfactory performance for differentiating PGL from Borrmann type IV GC, serving as a reliable non-invasive tool for clinical decision-making.

Background Lymph node metastasis (LNM) is a poor prognostic predictor and is highly correlated with local recurrence in rectal cancer patients. Objective To investigate the value of radiomics from dual-energy CT-derived iodine maps for the preoperative prediction of LNM in rectal cancer patients. Methods A total of 176 patients were enrolled in this study (training group, n = 123; validation group, n = 53). A radiomic signature was constructed via support vector machine (SVM) modeling. Seven models, including a clinical feature model (Model 1), an arterial model (Model 2), a venous model (Model 3), an arterial-venous model (Model 4), an arterial–clinical model (Model 5), a venous-clinical model (Model 6) and an arterial–venous–clinical model (Model 7), were established via logistic regression modeling. Diagnostic performance was assessed via receiver operating characteristic (ROC) curves. Results Tumor location and carcinoembryonic antigen levels were used to construct Model 1 (training group, AUC [area under the ROC curve] = 0.721, 95% CI [confidence intervals], 0.630–0.813; validation group, AUC = 0.729, 95% CI, 0.593–0.865). Model 6 and Model 7 further improved the discriminatory performance in the training (AUC = 0.850 and 0.869, 95% CI, 0.782–0.919 and 0.807–0.932, respectively; p = 0.250) and validation groups (AUC = 0.780 and 0.716, 95% CI, 0.653–0.906 and 0.576–0.856, respectively; p = 0.115). Moreover, decision curve analysis revealed a greater net benefit with Model 6. Conclusions The combination of radiomic features based on dual-energy CT-derived iodine maps and clinical features provides better diagnostic performance for predicting LNM in rectal cancer patients.

To develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM). A total of 1160 patients were collected from three hospitals. A vision transformer network-based DL_3D model was trained, and its performance in differentiating LUAD from TBM was evaluated using validation and external test sets. The performance of the DL_3D model was compared with that of two-dimensional features (DL_2D), radiomics, and six radiologists. Diagnostic performance was assessed using the area under the receiver operating characteristic curves (AUCs) analysis. The study included 840 patients in the training set (mean age, 54.8 years [range, 19–86 years]; 514 men), 210 patients in the validation set (mean age, 54.3 years [range, 18–86 years]; 128 men), and 110 patients in the external test set (mean age, 54.7 years [range, 22–88 years]; 51 men). In both the validation and external test sets, DL_3D exhibited excellent diagnostic performance (AUCs, 0.895 and 0.913, respectively). In the test set, the DL_3D model showed better performance (AUC, 0.913; 95

To determine whether quantitative parameters derived from dual-energy CT (DECT) could predict prognosis in patients with resectable rectal cancer (RC). One hundred and thirty-four patients (recurrence/distant metastasis group, n = 36; non-metastasis/non-recurrence group, n = 98) with RC who underwent radical resection and DECT were retrospectively included. DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), electron density (Rho), effective atomic number (Zeff), dual-energy index (DEI), the slope of the spectral Hounsfield unit curve (λHU) on arterial and venous phase images. Univariate and multivariate Cox proportional hazards models were employed to identify independent risk factors of prognosis. The area under the receiver operating characteristic curve (AUC) was used to assess the performance. Disease-free survival (DFS) curves were constructed using the Kaplan–Meier method. Patients in the metastasis/recurrence group had higher Rho in arterial phase (A-Rho), NIC in venous phase (V-NIC), Rho in venous phase (V-Rho), Zeff in venous phase (V-Zeff), λHU in venous phase (V-λHU), pT stage, pN stage, serum carcinoembryonic antigen (CEA), carbohydrate antigen-199 levels and more frequent in extramural venous invasion than those in non-metastasis/non-recurrence group (all p < 0.05). V-NIC, V-λHU, and CEA were independent risk factors of recurrence/distant metastasis (all p < 0.05). The AUC of combined indicator integrating three independent risk factors achieved the best diagnostic performance (AUC = 0.900). In stratified survival analysis, patients with high V-NIC, V-λHU, and CEA had lower 3-year DFS than those with low V-NIC, V-λHU, and CEA. Combining V-NIC, V-λHU, and CEA could be used to noninvasively predict prognosis in resectable RC. Question TNM staging fails to accurately prognosticate; can quantitative parameters derived from dual-energy CT predict prognosis in patients with resectable rectal cancer? Findings Normalized iodine concentration (V-NIC) and the slope of the spectral Hounsfield unit curve in venous phase (V-λHU), and carcinoembryonic antigen (CEA) are independent risk factors for recurrence/metastasis. Clinical relevance The combined indicator integrating V-NIC, V-λHU, and CEA could predict 3-year disease-free survival in patients with resectable rectal cancer and could aid in postoperative survival risk stratification to guide personalized treatment.

To determine whether intratumoral and peritumoral radiomics derived from dual-phase contrast-enhanced CT imaging could predict lymph node metastasis (LNM) in gastric cancer. Patients with gastric cancer from January 2017 to January 2022 were retrospectively collected and were randomly divided into training cohort (n = 287) and test cohort (n = 121) with a ratio of 7: 3. Clinical features and traditional radiological features were analyzed to construct clinical model. Radiomics features based on intratumoral (ITV) and peritumoral volumetric (PTV) regions of the tumor were extracted and screened to construct radiomics models. Clinical-radiomics combined model was constructed by the most predictive radiomics features and clinical independent predictors. The correlation between LNM predicted by the best model and 2-year disease-free survival (DFS) was evaluated by the Kaplan-Meier analysis. CT-LNM and CT-T stage were independent predictors of LNM. Compared with other radiomics models, ITV + PTV on atrial and venous phase (ITV + PTV-AP + VP) radiomics model presented moderate AUCs of 0.679 and 0.670 in the training cohort and validation cohort, respectively. Among the models, clinical-radiomics combined model achieved the highest AUC of 0.894 and 0.872 in the training and test cohorts, and 0.744 and 0.784 in the T1-2 and T3-4 subgroups, respectively. Clinical-radiomics combined model based LNM could stratify patients into high-risk and low-risk groups, and 2-year DFS of high-risk group was significantly lower than that of low-risk group (p < 0.001). Clinical-radiomics combined model integrating CT-LNM, CT-T stage, and ITV-PTV-AP + VP radiomics features could predict LNM, and this combined model based LNM was associated with 2-year DFS.

To analyze the correlation between the main perfusion parameters of the left ventricle and various physiological and coronary artery disease (CAD) risk factors or comorbidities using dynamic stress computed tomography myocardial perfusion imaging (CT-MPI) in patients without obstructive coronary stenosis. This retrospective analysis included 119 patients without obstructive coronary artery stenosis in computed tomography angiography (CTA), and without perfusion defects in CT-MPI. Patients were categorized into groups based on the presence or absence of physiological and CAD risk factors or comorbidities. The global myocardial blood flow (MBF), myocardial blood volume (MBV), and perfused capillary blood volume (PCBV) of the left ventricle were compared between groups, and correlations with continuous variables were analyzed. Multivariate linear regression was used to identify independent factors. Perfusion parameters were higher (MBF, 149.41 ± 26.38 vs. 159.20 ± 21.31 ml/100 ml/min, MBV, 17.09 ± 2.37 vs.18.84 ± 1.89, and PCBV, 9.82 ± 2.21 vs. 11.47 ± 1.79 ml/100 ml [all P < 0.05]) in female patients than in male patients. Hypertension and overweight/obesity resulted in lower perfusion parameters (hypertension vs. normotension: MBF, 148.09 ± 21.15 vs. 161.47 ± 25.13 ml/100 ml/min, PCBV, 10.25 ± 2.23 vs. 11.22 ± 1.96 ml/100 ml; overweight/obesity vs. none: MBF, 148.82 ± 20.98 vs. 159.51 ± 25.44 ml/100 ml/min, PCBV, 10.20 ± 1.93 vs. 11.15 ± 2.22 ml/100 ml [all P < 0.05]). Body surface area (BSA), body mass index, stress heart rate (HR), incremental HR, coronary total plaque volume, and stress systolic blood pressure were significantly correlated with perfusion parameters (all P < 0.05). Stress HR, BSA, and hypertension were independent predictors of MBF, stress HR and sex were independent predictors of MBV, and stress HR and BSA were independent predictors of PCBV. Dynamic stress CT-MPI myocardial perfusion is affected by stress HR, sex, and BSA, and can identify early perfusion distribution in hypertension and obesity/overweight.

Objective:This study aimed to investigate how dynamic contrast-enhanced CT imaging signs correlate with the differentiation grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to assess their predictive value for MVI when combined with clinical characteristics. Methods:We conducted a retrospective analysis of clinical data from 232 patients diagnosed with HCC at our hospital between 2021 and 2022. All patients underwent preoperative enhanced CT scans, laboratory tests, and postoperative pathological examinations. Among the 232 patients, 89 were identified as MVI-positive and 143 as MVI-negative. Regarding tumor differentiation, 56 patients were well-differentiated, 145 moderately, and 31 poorly. Multivariate logistic regression analysis was employed to establish a prediction model for variables showing significant differences. Additionally, the diagnostic performance of various indicators were evaluated using ROC analysis. Results:Among the qualitative data, significant differences (P<0.05) were observed between the MVI-positive and MVI-negative groups in 5 items such as peritumoral enhancement. In terms of quantitative data, the MVI-positive group exhibited higher maximum tumor length, AST, ALT, AFP levels and the ALBI score (P<0.05). Conversely, CT values in the arterial phase (AP), portal venous phase (PVP), and PT levels were lower in the MVI-positive group (P<0.05). Multivariate Logistic regression analysis identified ALBI score, PT level, CT value in PVP, and tumor capsule as independent risk factors for MVI occurrence (AUC: 0.71, 0.58, 0.66, and 0.60). The combined diagnostic AUC value was 0.82 (95% CI: 0.76-0.87). Significant differences were found among different differentiation grade groups in 10 items such as non-smooth tumor margin (P<0.05). Conclusion:Preoperative dynamic contrast-enhanced CT examination in patients with HCC can be utilized to predict the presence of MVI. When combined with clinical characteristics, these imaging signs demonstrate good predictive performance for MVI status. Furthermore, this approach has significant implications for determining the differentiation grade of tumors.

Nuclear medicine is an interdisciplinary field that integrates basic science with clinical medicine. The traditional classroom teaching model lacks interactive and efficient teaching methods and does not adequately address the learning needs and educational goals associated with standardized training for residents. The teaching model that combines Small Private Online Courses (SPOCs) with a flipped classroom approach is more aligned with the demands of real-life scenarios and workplace requirements, thereby assisting students in developing comprehensive literacy and practical problem-solving skills. However, this innovative teaching model has yet to be implemented in Nuclear medicine courses. This study aimed to explore whether the post-training competence for residents can be improved based on this new teaching model. A total of 103 first-year residents from Sichuan Provincial People’s Hospital were randomly assigned to either an experimental group (n = 52) or a control group (n = 51) between July 2019 and June 2023. The experimental group utilized a SPOC and flipped classroom-blended teaching model, while the control group received traditional lecture-based learning (LBL). We assessed the theoretical evaluation scores and questionnaire responses from both groups to determine the effectiveness of the new pedagogical approach. Residents in the experimental group demonstrated a superior understanding of nuclear medicine content compared to those in the control group, achieving higher scores on pre-class assessments, after-class tests, and final exams (P < 0.01). A majority of the residents in the experimental group expressed that the innovative teaching model, which integrated SPOC and a flipped classroom approach, significantly enhanced their motivation and contributed to the development of their ‘professional skills,’ ‘patient care,’ ‘interaction and teamwork,’ ‘teaching proficiency,’ and ‘learning capabilities’. The teaching satisfaction survey indicated that the experimental group reported significantly higher levels of ‘overall satisfaction,’ as well as greater satisfaction with ' teaching methodologies ' and ' fulfillment of targeted clinical skills,’ compared to the control group (P < 0.01). The SPOC and flipped classroom teaching model is better than traditional LBL in enriching residents’ professional knowledge and cultivating their post-training competence. It can effectively promote educational quality, improve residents’ learning, and enhance their satisfaction.

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P=0.001), accuracy (93.4% vs. 77.8%, P=0.001), specificity (87.3% vs. 63.5%, P=0.002), PPV (88.1% vs. 70.5%, P=0.010), and NPV (100% vs. 90.9%, P=0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P=0.011) and accuracy (83.6% vs. 71.3%, P=0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 +/- 0.53 vs. 1.47 +/- 0.58 mGy, P<0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM.

IntroductionStress dynamic computed tomography myocardial perfusion imaging (CT-MPI) is an accurate quantitative method for diagnosing myocardial ischemia in coronary artery disease (CAD). However, its clinical application has been limited, partly due to the varied cutoff values for absolute myocardial blood flow (MBFa) and the uncertain value of the relative myocardial blood flow ratio (MBF-ratio). This study aimed to compare the diagnostic efficacy of and investigate the optimal cutoff values for MBFa and the MBF-ratio in CT-MPI for diagnosing myocardial ischemia in patients with hemodynamically significant CAD.MethodsPatients with suspected or known hemodynamically significant CAD who underwent CT-MPI + CT angiography and invasive coronary angiography (ICA)/fractional flow reserve (FFR) between October 2020 and December 2023 were retrospectively evaluated. ICA ≥80% or FFR ≤0.8 were set as the diagnostic standards for functional ischemia. The patients and vessels were categorized into ischemic and non-ischemic groups, and differences in MBFa and the MBF-ratio were compared between the groups. The area under the curve (AUC) and optimal cutoff values were calculated. Diagnostic efficacy parameters, such as sensitivity, specificity, and accuracy, were also compared. In addition, a consistency test was performed.ResultsA total of 46 patients (mean age: 65.37 ± 8.25 years; 120 vessels) were evaluated. Hemodynamically significant stenosis was detected in 30/46 patients (48%) and 81/120 vessels (67.5%). The MBFa and MBF-ratio values were significantly lower in the ischemic than in the non-ischemic group; in the per-vessel analysis, the MBFa values were 73 vs. 128 (P < 0.001) and the MBF-ratio values were 0.781 vs. 0.856 (P < 0.001), respectively. The optimal cutoff values for MBFa and the MBF-ratio were 117.71 and 0.67, respectively. MBFa demonstrated a sensitivity, specificity, accuracy, AUC, positive predictive value, negative predictive value, and kappa value of 97.44%, 74.07%, 81.66%, 0.936 [95% confidence interval (CI): 0.876–0.973, P < 0.001], 63.33%, 98.36%, and 0.631 (95% CI: 0.500–0.762), respectively. The corresponding values for the MBF-ratio were 92.31%, 85.19%, 87.5%, 0.962 (95% CI: 0.911–0.989, P < 0.001), 75%, 95.83%, and 0.731 (95% CI: 0.606–0.857, P < 0.001), with no significant difference (P = 0.1225).ConclusionBoth MBFa and the MBF-ratio exhibit excellent diagnostic performance for myocardial ischemia in patients with hemodynamically significant CAD. The MBF-ratio is more robust than MBFa for interpreting CT-MPI findings in clinical practice, which is useful for radiologists and clinicians implementing CT-MPI.

Background:Noninvasively detecting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients before targeted therapy remains a challenge. This study aimed to develop a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model to predict EGFR mutation status using computed tomography (CT) images. Methods:We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models. Results:Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661-0.732; test set, AUC =68.4%, 95% CI, 0.609-0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812-0.873; test set, AUC =72.4%, 95% CI, 0.653-0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920-0.961; test set, AUC =94.7%, 95% CI, 0.894-0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942-0.971; test set, AUC =87.4%, 95% CI, 0.820-0.924), the CNN model had better stability. Conclusions:The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.

RATIONALE AND OBJECTIVES:This study aimed to non-invasively predict epidermal growth factor receptor (EGFR) mutation status in patients with lung adenocarcinoma using multi-phase computed tomography (CT) radiomics features. MATERIALS AND METHODS:A total of 424 patients with lung adenocarcinoma were recruited from two hospitals who underwent preoperative non-enhanced CT (NE-CT) and enhanced CT (including arterial phase CT [AP-CT], and venous phase CT [VP-CT]). Patients were divided into training (n = 297) and external validation (n = 127) cohorts according to hospital. Radiomics features were extracted from the NE-CT, AP-CT, and VP-CT images, respectively. The Wilcoxon test, correlation analysis, and simulated annealing were used for feature screening. A clinical model and eight radiomics models were established. Furthermore, a clinical-radiomics model was constructed by incorporating multi-phase CT features and clinical risk factors. Receiver operating characteristic curves were used to evaluate the predictive performance of the models. RESULTS:The predictive performance of multi-phase CT radiomics model (AUC of 0.925 [95% CI, 0.879-0.971] in the validation cohort) was higher than that of NE-CT, AP-CT, VP-CT, and clinical models (AUCs of 0.860 [95% CI,0.794-0.927], 0.792 [95% CI, 0.713-0.871], 0.753 [95% CI, 0.669-0.838], and 0.706 [95% CI, 0.620-0.791] in the validation cohort, respectively) (all P < 0.05). The predictive performance of the clinical-radiomics model (AUC of 0.927 [95% CI, 0.882-0.971] in the validation cohort) was comparable to that of multi-phase CT radiomics model (P > 0.05). CONCLUSION:Our multi-phase CT radiomics model showed good performance in identifying the EGFR mutation status in patients with lung adenocarcinoma, which may assist personalized treatment decisions.

Objective To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer. Methods One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS). Results Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233–5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247–5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710). Conclusion The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS. Critical relevance statement A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer. Key points • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS. Graphical Abstract

目的 探讨双能量CT的不同定量参数在直肠癌患者术前评估中的诊断价值.方法 回顾性搜集 118例直肠癌患者,术前均行双能量CT扫描.根据病理,分为无淋巴结转移组和有淋巴结转移组、癌结节组和无癌结节组、脉管神经侵犯组和无脉管神经侵犯组.比较不同分组在动静脉期的对比剂含量、碘浓度(IC)、标准化碘浓度(nIC)、电子云密度(Rho)、原子序数(Z)、双能量指数(DEI)、能谱曲线斜率(K)的差异.绘制受试者工作特征(ROC)曲线,比较曲线下面积(AUC),明确诊断效能.结果 定量参数的组间一致性为0.803～0.928.无淋巴结转移组的静脉期nIC明显低于有淋巴结转移组(P<0.001,AUC =0.771).无癌结节组的静脉期Rho高于癌结节组(P =0.037,AUC =0.635).无脉管神经侵犯组的动脉期Rho高于脉管神经侵犯组(P =0.025,AUC =0.648).结论 双能量CT的不同定量参数可用于评估直肠癌患者术前的淋巴结状态、癌结节和脉管神经侵犯情况,为直肠癌患者精准化治疗提供有效依据.

目的 探讨不同ADC值在局部进展期直肠癌(LARC)患者新辅助放化疗(nCRT)后淋巴结转移诊断价值.方法 回顾性纳入65例LARC患者,根据术后病理淋巴结状态,分为有淋巴结转移组和无淋巴结转移组.两名不同年资的影像诊断医师采用容积感兴趣区(ROI)勾画法分别测得nCRT前后ADC的最大值、最小值、均值、差值(最大值与最小值之差),并计算nCRT前后ADC最大值差(治疗后ADC最大值与治疗前ADC最大值之差)、最小值差(治疗后ADC最小值与治疗前ADC最小值之差)、均值差(治疗后ADC均值与治疗前ADC均值之差)以及最大值变化率(ADC最大值差/治疗前ADC最大值)、最小值变化率(ADC最小值差/治疗前ADC最小值)、均值变化率(ADC均值差/治疗前ADC均值).分析上述不同的ADC值在nCRT后有无淋巴结转移中的差异,绘制受试者工作特征曲线(ROC)明确各ADC值预测直肠癌nCRT后淋巴结转移的诊断效能.结果 14个不同ADC参数值的组间一致性较好,组内相关系数(ICC)值为0.408～0.886.nCRT后ADC最大值、ADC最小值、ADC均值、治疗前后ADC最大值差、治疗前后ADC最小值差、nCRT前后ADC均值差、ADC最大值变化率、ADC最小值变化率和ADC均值变化率与nCRT后淋巴结转移有相关性(r=-0.272～-0.434).nCRT后ADC最大值、ADC最小值、ADC均值、治疗前后ADC最大值差、治疗前后ADC最小值差、nCRT前后ADC均值差、ADC最大值变化率、ADC最小值变化率和ADC均值变化率在nCRT后有淋巴结转移组和无淋巴结转移组中差异具有统计学意义(P=0.001～0.03).其中nCRT后ADC均值、ADC均值差在区分有无淋巴结转移的诊断效能最好,曲线下面积(AUC)分别为0.740、0.753.结论 不同ADC值在一定程度上反映直肠癌nCRT后淋巴结状态,测量nCRT后直肠肿瘤ADC均值对nCRT后淋巴结状态进行评估更加方便、准确.

To develop and validate clinical-radiomics models for predicting lymph node metastasis following neoadjuvant chemoradiation therapy in locally advanced rectal cancer .83 patients were retrospectively enrolled.pre-,post- and delta radiomics signatures of T2WI and ADC images were constructed by support vector machine model. These models were applied to predict LNM and 5-year disease-free survival. The clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training cohort (AUC=0.895) and validation cohort (AUC=0.900). In ypT0-T2 stage, this model could predict 5-year RFS. Clinical-deltaADC radiomics combined model has good performance to predict LNM after nCRT and helped identify patients with poor prognosis.

To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer. A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)–based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively. Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk. Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery. • Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer. • The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD. • The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.

Background: Readout-segmented echo-planar imaging (rs-EPI) with simultaneous multislice (SMS) technology has been successfully applied to tumor research in many organs, but no feasibility study in rectal cancer has been reported, and the optimal acceleration of SMS with rs-EPI in rectal cancer has not been well determined yet. Objective: To investigate the feasibility of SMS rs-EPI of rectal cancer with different acceleration factors (AFs) and its influence on image quality, acquisition time and apparent diffusion coefficients (ADCs) in comparison to conventional sequences. Methods: All patients underwent rs-EPI and SMS rs-EPI with AFs of 2 and 3 (2 × SMS rs-EPI and 3 × SMS rs-EPI, respectively) using a 3T scanner. Acquisition times of the three rs-EPI sequences were measured. Image qualitative parameters (5-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion, and apparent diffusion coefficient (ADC) values of the three sequences were compared. Results: A total of eighty-three patients were enrolled in our study. rs-EPI and 2 × SMS rs-EPI offered equivalently high overall image quality with a scan time reduction to nearly half (rs-EPI: 137 s, 2 × SM rs-EPI: 60 s). 3 × SMS rs-EPI showed significantly poorer image quality (p < 0.05). ADC values were significantly lower in 3 × SMS rs-EPI compared to rs-EPI in rectal tumors and normal tissue (tumor tissue: rs-EPI 1.19 ± 0.21 × 10−3 mm2/s, 3 × SMS rs-EPI 1.10 ± 0.26 × 10−3 mm2/s, p < 0.001; normal tissue: rs-EPI 1.68 ± 0.13 × 10−3 mm2/s, 3 × SMS rs-EPI 1.54 ± 0.20 × 10−3 mm2/s, p < 0.001). Conclusions: SMS rs-EPI using an AF of 2 is feasible for rectal MRI resulting in substantial reductions in acquisition time while maintaining diagnostic image quality and similar ADC values to those of rs-EPI when the slice distance and number of shots are the same among three rs-EPI sequences.

Purpose:This study aimed to investigate clinical features and computed tomography (CT) manifestations of rifampicin primary drug-resistant pulmonary tuberculosis in Liangshan Yi Autonomous Prefecture.Patients and Methods:A total of 100 inpatients with confirmed primary rifampicin-resistant pulmonary tuberculosis were recruited from January 2020 to December 2022 at an infectious disease hospital located in the Liangshan Yi Autonomous Prefecture. Additionally, 100 inpatients with confirmed drug-susceptible pulmonary tuberculosis during the same period were matched to the rifampicin-resistant group based on gender, age, and ethnicity. The clinical characteristics of the two groups were recorded separately. Furthermore, the CT manifestations in these patients were independently analyzed by three radiologists.Results:The results showed that comorbid diabetes mellitus was more prevalent in the drug-resistant tuberculosis (DR-TB) group than in the drug-susceptible tuberculosis (DS-TB) group (9% vs 0%, p=0.0032). In terms of imaging presentation, DR-TB patients exhibited a higher frequency of calcifications (55% vs 35.00%, p=0.0068), greater median number of cavities (5 vs 2, p=0.0027), and larger maximum cavity diameter (52.08±25.55 mm vs 42.72±17.48 mm, p=0.0097). Additionally, bilateral involvement was more common in DR-TB patients at the site of the lesion (89% vs 76%, p=0.0246), with a higher prevalence in the right middle (82% vs 68%, p=0.0332), right lower (82% vs 68%, p=0.0332), left upper (91% vs 77%, p=0.0113), and left lower lobes (92% vs 66%, p<0.0001). Conversely, the involvement of only one lobe was less frequent in patients with DR-TB than in those with DS-TB (4% vs 13%, p=0.0398), whereas the involvement of all five lobes was more common (68% vs 51%, p=0.0209).Conclusion:Patients with DR-TB exhibit a higher prevalence of severe imaging manifestations, highlighting the importance of CT in the early detection and diagnosis of DR-TB.

Background Diaphragmatic dysfunction is known to be associated with difficulties weaning from invasive mechanical ventilation and is related to worse patient outcomes yet our understanding of how to prevent diaphragmatic dysfunction remains incomplete. We examined potentially modifiable risk factors for diaphragmatic dysfunction and attempted to estimate benefits attributable to altering these modifiable risk factors. Methods This prospective multicenter observational study was undertaken in the general ICUs of two tertiary care teaching hospitals. Critically ill adults expected to receive invasive mechanical ventilation for at least 48 h were enrolled. Diaphragm function was assessed by ultrasound each study day, with dysfunction defined as thickening fraction less than 20%. Results From January to December 2019, 856 patients were screened and 126 patients were enrolled. Overall, 40.5% (51/126) of patients experienced diaphragmatic dysfunction during invasive mechanical ventilation. Patients with diaphragmatic dysfunction were more likely to develop ventilator associated pneumonia (risk difference [RD] + 12.9%, 95% Confidence Interval [CI] 1.4 to 24.4%, P = 0.028), were more likely to experience extubation failure (RD + 8.5%, 95% CI 0.4 to 16.6%, P = 0.039) and required a longer duration of invasive mechanical ventilation (RD + 1.3 days, 95% CI 0.1 to 2.5 days, P = 0.035). They also required a longer hospital stay (RD + 1.2 days, 95% CI 0.04 to 2.4 days, P = 0.041) and were more likely to die before hospital discharge (RD + 18.1%, 95% CI 3.7 to 32.5%, P = 0.014). Multivariable analysis considered the impact of age, sex, pre-existing nutritional status, caloric intake, amino acid intake, acute disease severity, modes of mechanical ventilation, measures of respiratory status, sedation, pain control and baseline diaphragm thickness. Only SOFA score ( P = 0.008) and early amino acid intake ( P = 0.001) remained significant independent risk factors for the onset of diaphragmatic dysfunction. Causal path modeling suggested early amino acid intake may significantly reduce diaphragmatic dysfunction (RRR 29%, 95% CI 10% to 48%, P = 0.003) and may also reduce mortality (RRR 49%, 95% CI 25% to 73%, P < 0.0001). Conclusions Amino acid intake during the first 24 h of ICU stay may represent an important, modifiable risk factor for diaphragmatic dysfunction and may have a direct causal effect on mortality. We recommend additional research on this topic.