0引言 据中国国家癌症中心发布的数据,2022年全国原发性肝癌发病人数36.77万,占全球病例的42.5％,位列各种癌症新发患者数第4位、发病率位列第5位;2022年因原发性肝癌死亡人数31.65万,死亡人数和病死率均位列第2位;人群肝癌5年相对生存率为14.4％,诊断时为中期和晚期比例达50％以上[1-3].
Objective:To investigate the application value of fractional-order calculus(FROC)model diffusion weighted imaging(DWI)combined with simultaneous multi-slice(SMS)acquisition technology in the differentiation between benign and malignant breast lesions.Materials and Methods:A total of 124 patients(with 141 lesions)who underwent breast MRI scan at our hospital from January 2021 to December 2022 were retrospectively analyzed.All patients underwent DWI scanning with two sets of multiple b values(14 b values,the highest b value was 3 000 s/mm2)using a 3.0 T MRI system.One group underwent conventional single-shot echo planar imaging(SSEPI-DWI),while the other group underwent SMS-SSEPI-DWI.Independent sample t test or Mann-Whitney U test was used to compare the image quality scores,FROC model parameters(D,β,μ)and apparent diffusion coefficient(ADC)value between benign and malignant groups.The receiver operating characteristic curve was used to evaluate the diagnostic efficacy of each parameter.Bland-Altman plots were used to assess the agreement between the two DWI-derived parameters.Results:The ADC,D and β values of malignant breast lesions were significantly lower than those of benign breast lesions(P＜0.05),while the p value of malignant breast lesions was notably higher than that of benign breast lesions(P＜0.05).Among the SSEP-DWI and SMS-SSEP-DWI sequences,the D value had the largest area under the curve,the p value demonstrated the highest diagnostic sensitivity,and the D value displayed the highest specificity.Bland-Altman plot indicated unbiased and substantial agreement between the corresponding parameter values derived from the two DWI sequences were unbiased and had good agreement.Conclusions:The FROC model based on SMS-SSEPI DWI can provide high-quality images and lesion characteristic parameter values with shorten scan time.Compared with SSEPI-DWI.It has a comparable diagnostic performance in distinguishing between benign and malignant breast lesions.Particularly.The D and p value show better diagnostic performance.
Objective:To explore the application value of deep learning reconstruction(DLR)technology in enhancing the image quality and reducing the scan time of fast-spin echo proton density weighted imaging(FSE-PD)in MRI of the temporomandibular joint(TMJ).Materials and Methods:Recruit 40 healthy volunteers and undergo MRI scans of the TMJ.Each healthy volunteer underwent conventional FSE-PD MRI scans and accelerated FSE-PD scans using DLR,the original accelerated FSE-PD images without DLR were simultaneously preserved.Two radiologists qualitatively and quantitatively evaluated the image quality of the three FSE-PD image sets,individually.Qualitative assessments utilized a Likert scale(5-point)for subjective scoring of anatomical structure clarity and overall image quality.Quantitative assessments utilized signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)for objective evaluation of image quality.One-way ANOVA and Kruskal-Wallis test were used to compare the differences in subjective scores and objective indicators among the three groups.The intra-class correlation coefficient(ICC)was used to evaluate the consistency of the subjective scores of the two radiologists.Results:Compared to the conventional FSE-PD group,the DLR-accelerated FSE-PD group demonstrated a 67％reduction in scan time.The two radiologists exhibited good consistency in subjective scores for anatomical structure clarity and overall image quality(ICC of 0.80 and 0.78,respectively).There were significant differences in anatomical clarity and overall image quality scores among the conventional FSE-PD group,accelerated FSE-PD group,and DLR-accelerated FSE-PD group(P＜0.05).The differences in SNR and CNR among the three FSE-PD groups were statistically significant(P＜0.05).Qualitative and quantitative evaluation results for the DLR-accelerated FSE-PD group were both significantly superior to the conventional FSE-PD group.Conclusions:DLR technology could shorten the scanning time of conventional FSE-PD MRI of the TMJ,enhance image quality,and help patients complete the examination faster.
一、概述 根据中国国家癌症中心发布的数据,2022年全国原发性肝癌发病人数36.77万,位列各种癌症新发病人数第4位(肺、结直肠、甲状腺、肝),发病率位列第5位(肺、女性乳腺、甲状腺、结直肠、肝);2022年因原发性肝癌死亡人数31.65万,死亡人数和死亡率均位列第2位(肺、肝)[1-2].原发性肝癌主要包括肝细胞癌(hepatocellular carcinoma,HCC)、肝内胆管癌(intrahepatic cholangiocarcinoma,ICC)和混合型肝细胞癌-胆管癌(combined hepatocellular-cholangiocarcinoma,cHCC-CCA)3 种不同病理学类型,三者在发病机制、生物学行为、病理组织学、治疗方法以及预后等方面差异较大,其中HCC占75％～85％、ICC占10％～15％[3-4].本指南中的"肝癌"仅指HCC.
BackgroundThis study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).MethodsPatients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA).ResultsA total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively.ConclusionsThe pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.
