Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer...Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.展开更多
BACKGROUND Colorectal cancer is a malignancy with a high risk of lymph node metastasis and poor prognosis,and thus requires an accurate diagnosis.AIM To assess the diagnostic value of combined magnetic resonance T2-we...BACKGROUND Colorectal cancer is a malignancy with a high risk of lymph node metastasis and poor prognosis,and thus requires an accurate diagnosis.AIM To assess the diagnostic value of combined magnetic resonance T2-weighted imaging(T2WI)and diffusion-weighted imaging(DWI)in colorectal cancer.METHODS We included 120 patients with suspected colorectal cancer who underwent magnetic resonance imaging.Surgical pathology was used as the gold standard for comparison.Combined T2WI and DWI showed higher diagnostic efficacy than either of the two methods used individually.RESULTS The combined method achieved 94.74%sensitivity,95.45%specificity,95.00%accuracy,94.74%positive predictive value,and 95.45%negative predictive value in qualitative diagnosis.It showed 94.44%sensitivity,95.00%specificity,94.74%accuracy,94.44%positive predictive value,and 95.00%negative predictive value in clinical staging.Finally,it showed 94.74%sensitivity,94.59%specificity,94.74%accuracy,94.74%positive predictive value,and 94.59%negative predictive value in diagnosing lymph node metastasis.These results were highly consistent with that of the gold standard.CONCLUSION This study combined T2WI and DWI for accurate diagnosis of colorectal cancer,aiding clinical staging and lymph node metastasis assessment.This approach is promising for clinical application.展开更多
BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr...BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.展开更多
目的探讨距骨骨软骨损伤(osteochondral lesions of the talus,OLT)软骨基质金属蛋白酶-1(matrix metalloproteinase-1,MMP-1)的表达与磁共振T2值之间的关系及其在距骨骨软骨损伤的机制。材料与方法收集30例踝关节距骨骨软骨损伤行微创...目的探讨距骨骨软骨损伤(osteochondral lesions of the talus,OLT)软骨基质金属蛋白酶-1(matrix metalloproteinase-1,MMP-1)的表达与磁共振T2值之间的关系及其在距骨骨软骨损伤的机制。材料与方法收集30例踝关节距骨骨软骨损伤行微创治疗术患者及30名健康志愿者,均行3.0 T常规踝关节磁共振成像(magnetic resonance imaging,MRI)及T2-mapping功能成像,测量距骨软骨T2值。术后离体距骨骨软骨行苏木精-伊红染色(hematoxylin-eosin staining,HE)和Western Blot法分析距骨骨软骨MMP-1的表达,统计学分析距骨骨软骨T2值与MMP-1蛋白表达的相关性。结果距骨骨软骨损伤以内中侧最为多见(磁共振6分区法),距骨骨软骨T2值随软骨损伤Mankin分级明显增大(P<0.05)。软骨MMP-1蛋白表达高级别与低级别差异明显(P<0.05)。软骨T2值与软骨MMP-1表达呈线性相关趋势,Pearson分析距骨骨软骨损伤T2值增加与MMP-1蛋白表达增加呈直线性正相关(r=0.821,P<0.0001)。结论距骨骨软骨损伤以内中侧最为多见,距骨骨软骨磁共振T2值随软骨损伤分级逐级递增;距骨骨软骨损伤磁共振T2值与MMP-1蛋白表达呈正相关,距骨骨软骨磁共振T2-mapping功能成像T2值定量分析可在体、无创性预测不同级别软骨损伤MMP-1蛋白的表达含量,为临床治疗OLT提供影像学定量依据。展开更多
文摘Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
文摘BACKGROUND Colorectal cancer is a malignancy with a high risk of lymph node metastasis and poor prognosis,and thus requires an accurate diagnosis.AIM To assess the diagnostic value of combined magnetic resonance T2-weighted imaging(T2WI)and diffusion-weighted imaging(DWI)in colorectal cancer.METHODS We included 120 patients with suspected colorectal cancer who underwent magnetic resonance imaging.Surgical pathology was used as the gold standard for comparison.Combined T2WI and DWI showed higher diagnostic efficacy than either of the two methods used individually.RESULTS The combined method achieved 94.74%sensitivity,95.45%specificity,95.00%accuracy,94.74%positive predictive value,and 95.45%negative predictive value in qualitative diagnosis.It showed 94.44%sensitivity,95.00%specificity,94.74%accuracy,94.44%positive predictive value,and 95.00%negative predictive value in clinical staging.Finally,it showed 94.74%sensitivity,94.59%specificity,94.74%accuracy,94.74%positive predictive value,and 94.59%negative predictive value in diagnosing lymph node metastasis.These results were highly consistent with that of the gold standard.CONCLUSION This study combined T2WI and DWI for accurate diagnosis of colorectal cancer,aiding clinical staging and lymph node metastasis assessment.This approach is promising for clinical application.
基金the Fujian Province Clinical Key Specialty Construction Project,No.2022884Quanzhou Science and Technology Plan Project,No.2021N034S+1 种基金The Youth Research Project of Fujian Provincial Health Commission,No.2022QNA067Malignant Tumor Clinical Medicine Research Center,No.2020N090s.
文摘BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.
