BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive mod...BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.展开更多
BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients accor...BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis.However,the preoperative evaluation of PNI status is still challenging.AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019.These patients were classified as the training cohort(n=242)and validation cohort(n=61)at a ratio of 8:2.A large number of intraand peritumoral radiomics features were extracted from portal venous phase images of computed tomography(CT).After deleting redundant features,we tested different feature selection(n=6)and machine-learning(n=14)methods to form 84 classifiers.The best performing classifier was then selected to establish Rad-score.Finally,the clinicoradiological model(combined model)was developed by combining Rad-score with clinical factors.These models for predicting PNI were compared using receiver operating characteristic curve(ROC)analysis and area under the ROC curve(AUC).RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNIpositive.Clinical factors including CT-reported T stage(cT),N stage(cN),and carcinoembryonic antigen(CEA)level were independent risk factors for predicting PNI preoperatively.We established Rad-score by logistic regression analysis after selecting features with the L1-based method.The combined model was developed by combining Rad-score with cT,cN,and CEA.The combined model showed good performance to predict PNI status,with an AUC of 0.828[95%confidence interval(CI):0.774-0.873]in the training cohort and 0.801(95%CI:0.679-0.892)in the validation cohort.For comparison of the models,the combined model achieved a higher AUC than the clinical model(cT+cN+CEA)achieved(P<0.001 in the training cohort,and P=0.045 in the validation cohort).CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.展开更多
Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features ext...Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.展开更多
背景与目的囊腔型肺癌作为一种特殊类型的肺癌逐步得到人们的关注,其最常见的病理类型为腺癌。囊腔型肺腺癌的浸润性对诊疗方案的选择和预后至关重要。本研究旨在分析囊腔型肺腺癌临床多特征,探讨其浸润性的独立危险因素并建立风险预测...背景与目的囊腔型肺癌作为一种特殊类型的肺癌逐步得到人们的关注,其最常见的病理类型为腺癌。囊腔型肺腺癌的浸润性对诊疗方案的选择和预后至关重要。本研究旨在分析囊腔型肺腺癌临床多特征,探讨其浸润性的独立危险因素并建立风险预测模型。方法回顾性分析2021年1月至2022年7月于南京医科大学第一附属医院胸外科行手术治疗的129例囊腔型肺腺癌患者,根据病理结果分成浸润前组:非典型腺瘤样增生(atypical adenomatous hyperplasia,AAH)、原位腺癌(adenocarcinoma in situ,AIS)、微浸润型腺癌(minimally invasive adenocarcinoma,MIA)与浸润组:浸润性腺癌(invasive adenocarcinoma,IAC)。其中浸润前组47例,男性19例,女性28例,平均年龄(51.23±14.96)岁;浸润组82例,男性60例,女性22例,平均年龄(61.27±11.74)岁。收集两组病例多组临床特征,采用单因素分析、LASSO回归、多因素Logistic回归分析得出囊腔型肺腺癌浸润性的独立危险因素,建立浸润性风险预测模型。结果单因素分析显示年龄、性别、吸烟史、肺气肿、神经元特异性烯醇化酶(neuron-specific enolase,NSE)、囊腔数、病灶直径、囊腔直径、结节直径、实性成分直径、囊壁结节、囊壁光滑程度、囊腔形状、分叶征、短毛刺征、胸膜牵拉、血管穿行与支气管穿行在囊腔型肺腺癌浸润前组与浸润组间存在统计学差异(P<0.05)。上述变量经LASSO回归降维处理,进一步筛选出的变量包括:年龄、性别、吸烟史、NSE、囊腔数、病灶直径、囊腔直径、囊壁结节、囊壁光滑程度与分叶征,并纳入多因素Logistic回归分析,发现囊壁结节(P=0.035)与分叶征(P=0.001)是囊腔型肺腺癌浸润性的独立危险因素(P<0.05)。建立预测模型如下:P=e^x/(1+e^x),x=-7.927+1.476*囊壁结节+2.407*分叶征,曲线下面积(area under the curve,AUC)为0.950。结论囊壁结节及分叶征为囊腔型肺腺癌浸润性的独立危险因素,对囊腔型肺腺癌的浸润性预测具有一定的指导意义。展开更多
背景与目的肺浸润性黏液腺癌(invasive mucinous adenocarcinoma of the lung,IMA)是肺腺癌中一种少见且特殊的类型,该类肿瘤的特点往往是少有淋巴结转移,因此对于该类肿瘤的预后评估依靠现有的肿瘤原发灶-淋巴结-转移(tumor-node-metas...背景与目的肺浸润性黏液腺癌(invasive mucinous adenocarcinoma of the lung,IMA)是肺腺癌中一种少见且特殊的类型,该类肿瘤的特点往往是少有淋巴结转移,因此对于该类肿瘤的预后评估依靠现有的肿瘤原发灶-淋巴结-转移(tumor-node-metastasis,TNM)分期存在困难。本研究的目的是构建列线图来预测术后淋巴结阴性的IMA患者的预后。方法根据纳入标准和排除标准,回顾性分析2012年7月至2017年5月宁波大学附属李惠利医院(训练队列,n=78)和宁波市第二医院(验证队列,n=66)胸外科收治的术后病理为淋巴结阴性的IMA患者的资料,分析训练队列的临床病理特征的预后价值并建立预后预测模型,并对模型性能进行评价,最后将验证队列的数据代入进行外部验证。结果单因素分析显示肺炎型、较大的肿块、包含黏液和非黏液成分的混合型、较高的总分期是5年无进展生存期(progression-free survival,PFS)及总生存期(overall survival,OS)的影响因素。多因素分析进一步表明,影像学分型、肿块大小、黏液成分是5年PFS及OS的独立预后因素。5年PFS率和OS率分别为62.82%和75.64%,亚组的生存分析显示,肺炎型和包含黏液和非黏液成分的混合型IMA患者的5年PFS及OS分别明显低于孤立型和纯黏液型IMA患者。5年PFS和OS的Harrell’s C指数分别为0.815(95%CI:0.741-0.889)和0.767(95%CI:0.669-0.865),这两个模型的校准曲线及决策曲线分析(decision curve analysis,DCA)在两个队列中显示出良好的预测性能。结论本次基于临床病理特征构建的列线图在一定程度上可以作为IMA切除术后淋巴结阴性患者的一种有效预后预测工具。展开更多
基金Supported by the National Natural Science Foundation of China Youth Training Project,No.2021GZR003and Medical-engineering Interdisciplinary Research Youth Training Project,No.2022YGJC001.
文摘BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.
基金This study was reviewed and approved by the Ethics Committee of West China Hospital of Sichuan University(Approved No.1159).
文摘BACKGROUND Perineural invasion(PNI),as a key pathological feature of tumor spread,has emerged as an independent prognostic factor in patients with rectal cancer(RC).The preoperative stratification of RC patients according to PNI status is beneficial for individualized treatment and improved prognosis.However,the preoperative evaluation of PNI status is still challenging.AIM To establish a radiomics model for evaluating PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 303 RC patients in a single institution from March 2018 to October 2019.These patients were classified as the training cohort(n=242)and validation cohort(n=61)at a ratio of 8:2.A large number of intraand peritumoral radiomics features were extracted from portal venous phase images of computed tomography(CT).After deleting redundant features,we tested different feature selection(n=6)and machine-learning(n=14)methods to form 84 classifiers.The best performing classifier was then selected to establish Rad-score.Finally,the clinicoradiological model(combined model)was developed by combining Rad-score with clinical factors.These models for predicting PNI were compared using receiver operating characteristic curve(ROC)analysis and area under the ROC curve(AUC).RESULTS One hundred and forty-four of the 303 patients were eventually found to be PNIpositive.Clinical factors including CT-reported T stage(cT),N stage(cN),and carcinoembryonic antigen(CEA)level were independent risk factors for predicting PNI preoperatively.We established Rad-score by logistic regression analysis after selecting features with the L1-based method.The combined model was developed by combining Rad-score with cT,cN,and CEA.The combined model showed good performance to predict PNI status,with an AUC of 0.828[95%confidence interval(CI):0.774-0.873]in the training cohort and 0.801(95%CI:0.679-0.892)in the validation cohort.For comparison of the models,the combined model achieved a higher AUC than the clinical model(cT+cN+CEA)achieved(P<0.001 in the training cohort,and P=0.045 in the validation cohort).CONCLUSION The combined model incorporating Rad-score and clinical factors can provide an individualized evaluation of PNI status and help clinicians guide individualized treatment of RC patients.
基金supported by the National Key Research and Development Program of China (No. 2017YFC1309100)the National Natural Scientific Foundation of China (No. 81771912, 81701782 and 81601469)
文摘Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.
文摘背景与目的囊腔型肺癌作为一种特殊类型的肺癌逐步得到人们的关注,其最常见的病理类型为腺癌。囊腔型肺腺癌的浸润性对诊疗方案的选择和预后至关重要。本研究旨在分析囊腔型肺腺癌临床多特征,探讨其浸润性的独立危险因素并建立风险预测模型。方法回顾性分析2021年1月至2022年7月于南京医科大学第一附属医院胸外科行手术治疗的129例囊腔型肺腺癌患者,根据病理结果分成浸润前组:非典型腺瘤样增生(atypical adenomatous hyperplasia,AAH)、原位腺癌(adenocarcinoma in situ,AIS)、微浸润型腺癌(minimally invasive adenocarcinoma,MIA)与浸润组:浸润性腺癌(invasive adenocarcinoma,IAC)。其中浸润前组47例,男性19例,女性28例,平均年龄(51.23±14.96)岁;浸润组82例,男性60例,女性22例,平均年龄(61.27±11.74)岁。收集两组病例多组临床特征,采用单因素分析、LASSO回归、多因素Logistic回归分析得出囊腔型肺腺癌浸润性的独立危险因素,建立浸润性风险预测模型。结果单因素分析显示年龄、性别、吸烟史、肺气肿、神经元特异性烯醇化酶(neuron-specific enolase,NSE)、囊腔数、病灶直径、囊腔直径、结节直径、实性成分直径、囊壁结节、囊壁光滑程度、囊腔形状、分叶征、短毛刺征、胸膜牵拉、血管穿行与支气管穿行在囊腔型肺腺癌浸润前组与浸润组间存在统计学差异(P<0.05)。上述变量经LASSO回归降维处理,进一步筛选出的变量包括:年龄、性别、吸烟史、NSE、囊腔数、病灶直径、囊腔直径、囊壁结节、囊壁光滑程度与分叶征,并纳入多因素Logistic回归分析,发现囊壁结节(P=0.035)与分叶征(P=0.001)是囊腔型肺腺癌浸润性的独立危险因素(P<0.05)。建立预测模型如下:P=e^x/(1+e^x),x=-7.927+1.476*囊壁结节+2.407*分叶征,曲线下面积(area under the curve,AUC)为0.950。结论囊壁结节及分叶征为囊腔型肺腺癌浸润性的独立危险因素,对囊腔型肺腺癌的浸润性预测具有一定的指导意义。
