BackgroundMagnetic resonance imaging (MRI) is increasingly used to evaluate axillary lymph node (ALN) status in breast cancer. However, the correlation between MRI features of the primary tumor and the ALN metastasis (ALNM) burden remains poorly understood. This study aimed to develop a non-invasive MRI-based model to preoperatively distinguish between low (≤2 nodes) and high (>2 nodes) ALNM burden in T1 and T2 stage breast cancer.MethodsThis retrospective single-center study included 185 patients, categorized by ALNM burden [≤ 2 nodes (n = 149) or >2 nodes (n = 36)]. The kinetic and radiomic features were extracted from the segmented whole tumor on dynamic contrast-enhanced MRI (DCE-MRI). A forward-stepwise feature selection method was employed based on the ANOVA F-score from the training cohort. Features were added according to F-values and logistical regression model was built iteratively. The final model, trained on the entire training set, was evaluated on the independent test cohort.ResultsThe model incorporated five kinetic and three radiomic features, demonstrating moderate predictive performance. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.705 in the test cohort. It showed a sensitivity of 72.7% and a specificity of 77.8%. The negative predictive value (NPV) was 92.1%.ConclusionThe kinetic and radiomic features from DCE-MRI showed potential for predicting ALNM burden (≤2 or > 2 nodes) in T1 and T2 stage breast cancer. The high NPV particularly supported their utility as a non-invasive tool to identify candidates for less invasive axillary procedures.

To explore the application value of three-dimensional breath-hold gradient spin echo sequence (3D BH-GRASE) in MRCP. We conducted MRCP imaging on 56 patients with pancreatic and biliary diseases via both 3D BH-GRASE and 3D NT-TSE. We compared and statistically analysed the acquisition time, signal-to-noise ratio (SNR), contrast ratio (CR), contrast-to-noise ratio (CNR), and image quality between the two techniques. The mean image acquisition time of 3D BH-GRASE was 16.4 s, which was significantly shorter than the (238.12 ± 43.85) seconds required for 3D NT-TSE (p < 0.05). Compared with3D NT-TSE, 3D BH-GRASE achieved superior scores in overall image quality, artifacts, and visualization of the common bile duct, hepatic duct, and gallbladder/cystic duct (p < 0.05), but not in the left/right hepatic ducts. In contrast, 3D BH-GRASE was significantly inferior to 3D NT-TSE in visualizing the left and right secondary hepatic ducts (p < 0.05), although not for the pancreatic duct. The image quality scores of the 3D NT-TSE group combined with the 3D BH-GRASE group were significantly greater than those of the individual sequences (p < 0.05). 3D BH-GRASE addresses image quality concerns arising from motion artefacts in 3D NT-TSE and serves as a valuable supplementary imaging modality.

ObjectiveTo explore the value of dual-accelerated simultaneous multi-slice (SMS) imaging in diffusion tensor imaging (DTI) of glioma.MethodsThirty-four patients with glioma who underwent magnetic resonance imaging (MRI) in our hospital from January 2022 to March 2023 were randomly selected. The results of dual-accelerated SMS-DTI and conventional DTI were retrospectively analyzed. All patients were scanned using a uMR790 3.0T MRI scanner, and the scanning technicians followed a predefined sequence to ensure consistency in scan parameters. The images were subjectively evaluated using a Likert 5-point scoring system. Objective evaluation was performed by measuring the required values of the images with b-value = 1000 s/mm2, primarily measuring the signal intensity in the tumor region and the contralateral normal brain white matter region. The standard deviation values were used to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the same encoding direction as the background noise. The number of generated fiber pathways, fractional anisotropy (FA), and mean diffusivity (MD) were measured and analyzed using post-processing software. The relative FA (rFA) and relative MD (rMD) were calculated.ResultsThe results of conventional DTI and SMS-accelerated DTI were compared. In terms of subjective evaluation, including overall image quality, tumor edge clarity, and magnetic sensitivity artifacts, both techniques showed no significant differences, indicating comparable diagnostic performance in anatomical visualization. In terms of objective evaluation and quantitative parameter measurement, there were statistically significant differences in SNR and CNR values, with slightly lower values in the dual-accelerated SMS-DTI compared with conventional DTI, a significant reduction in scanning time can be achieved through a slight loss in image quality. The number of fiber pathways and the rFA and rMD values did not show typical differences between the two techniques. The correlation between these measures was highly similar, with no significant differences observed.ConclusionThe application of dual-accelerated simultaneous multi-slice imaging in DTI of glioma is feasible.

Background Non-invasive modalities for assessing axillary lymph node (ALN) are needed in clinical practice. Purpose To investigate the suspicious ALN on unenhanced T2-weighted (T2W) imaging and intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) for predicting ALN metastases (ALNM) in patients with T1-T2 stage breast cancer and clinically negative ALN. Material and Methods Two radiologists identified the most suspicious ALN or the largest ALN in negative axilla by T2W imaging features, including short axis (Size-S), long axis (Size-L)/S ratio, fatty hilum, margin, and signal intensity on T2W imaging. The IVIM parameters of these selected ALNs were also obtained. The Mann-Whitney U test or t-test was used to compare the metastatic and non-metastatic ALN groups. Finally, logistic regression analysis with T2W imaging and IVIM features for predicting ALNM was conducted. Results This study included 49 patients with metastatic ALNs and 50 patients with non-metastatic ALNs. Using the above conventional features on T2W imaging, the sensitivity and specificity in predicting ALNM were not high. Compared with non-metastatic ALNs, metastatic ALNs had lower pseudo-diffusion coefficient (D*) (P = 0.043). Logistic regression analysis showed that the most useful features for predicting ALNM were signal intensity and D*. The sensitivity and specificity predicting ALNM that satisfied abnormal signal intensity and lower D* were 73.5% and 84%, respectively. Conclusions The abnormal signal intensity on T2W imaging and one IVIM feature (D*) were significantly associated with ALNM, with sensitivity of 73.5% and specificity of 84%.

目的 探讨基于药代动力学动态增强MRI(dynamic contrast-enhanced MRI,DCE-MRI)的全肿瘤影像组学特征对三阴型乳腺癌的诊断价值.材料与方法 回顾性分析85例治疗前行DCE-MRI扫描的乳腺癌患者,Luminal型39例、人表皮生长因子受体2(human epidermal growth factor receptor,HER-2)过表达型16例、三阴(triple negative,TN)型30例.提取全肿瘤药代动力学及增强图像的影像组学特征.采用Spearman相关分析及最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)筛选最优影像组学特征,并构建Logistic模型,对TN型与Luminal型、TN型与HER-2过表达型、TN型与非TN型进行鉴别,并绘制受试者工作特征曲线,计算AUC.利用五折交叉验证法验证预测性能.结果 TN型和Luminal型预测模型共筛选出6个重要特征,鉴别准确度和AUC分别为0.783、0.865.TN型和HER-2过表达型预测模型筛选出14个重要特征,鉴别准确度和AUC分别为0.870、0.923.TN型和非TN型预测模型共筛选17个重要特征,鉴别准确度和AUC分别为0.847、0.913.结论 基于药代动力学DCE-MRI的全肿瘤影像组学特征有利于鉴别三阴型与其他分子分型的乳腺癌.

OBJECTIVE:To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.MATERIALS AND METHODS:A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.RESULTS:The 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).CONCLUSION:Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.

目的 研究乳腺癌腋窝淋巴结的动态对比增强磁共振成像(DCE-MRI)影像组学特征对诊断其转移状态的价值.方法 回顾性选取67例经术后病理确诊乳腺癌且经腋窝淋巴结清扫存在3枚及以上腋窝淋巴结转移患者.每个患者选取乳腺癌同侧的最大可评价腋窝淋巴结作为转移性淋巴结组,选取同一患者对侧的可评价最大淋巴结作为非转移性淋巴结对照组.将所有MRI原始数据输入Omni-Kinetics后处理软件,经手动3D分割淋巴结,并基于药物动力学模型对分割的病灶进行全体素分析,自动生成全淋巴结内的药物动力学参数(Ktrans,Kep,Vp)和每个参数对应的直方图分布特征(共22个特征),以及增强后第一期的增强纹理特征(共75个特征).采用配对非参数检验比较转移淋巴结组和对照组间各影像组学特征差异.采用拉索回归分析筛选对转移性淋巴结诊断最有价值的特征,使用线性判别分析和留一交叉验证法分析这些影像组学特征对转移性淋巴结的判别诊断能力,采用受试者曲线分析评价判别模型的诊断效能.结果 配对非参数检验结果显示,转移性淋巴结组与对照组间,分别有6个(6/22)药物动力学参数及直方图特征和53个(53/75)增强后动态纹理特征存在统计学差异(P＜0.05).经过拉索回归分析结果显示,排列前4位的转移性淋巴结组和对照组间有差异的特征都是增强纹理特征.线性判别分析结果显示,基于腋窝淋巴结的影像组学特征对转移性腋窝淋巴结的判别准确性达到90％(60/67),对应的曲线下面积达到0.987(P=0.000).结论 乳腺癌腋窝淋巴结基于药物动力学模型DCE-MRI上的影像组学特征对诊断其转移状态具有很好的价值.

Purpose To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. Methods This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive ( n = 93) and negative ( n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong’s test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the “Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis” (TRIPOD) statement. Results The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717–0.849) and 0.680 (95% CI, 0.604–0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737–0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783–0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). Conclusion FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.