A new hydrothermal field(Tianshi)was discovered on the rift valley wall through plume anomaly surveys and geological work conducted in 2012 and 2018 between 2°35′N and 2°43′N of the slow-spreading Carlsber...A new hydrothermal field(Tianshi)was discovered on the rift valley wall through plume anomaly surveys and geological work conducted in 2012 and 2018 between 2°35′N and 2°43′N of the slow-spreading Carlsberg Ridge(CR).Here,the results of two expeditions conducted to detect and characterize the new hydrothermal field are reported.Mineralogical and geochemical data,as well as 14 C ages of a sediment core collected near the field are presented to reveal the hydrothermal history.Results show that the Tianshi field is a basalt-hosted hydrothermal system.Geochemical data of the sediments collected near the field indicate a strong hydrothermal contribution,and hydrothermal Fe and Cu fluxes range from 30 to 155 mg/(cm^(2)·ka)and 0.59 to 11.49 mg/(cm^(2)·ka),respectively.Temporal variations in the fluxes of hydrothermal Fe indicate that there have been at least three amplified hydrothermal venting events(H 1,H 2,and H 3)in the Tianshi field over the last 35.2 ka,in 28.6-35.2 ka BP,22.0-27.6 ka BP,and 1.2-11.4 ka BP,respectively.Hydrothermal event H 2 was driven by an increased magmatic production associated with sea level fall during the Last Glacial Maximum,while event H 3 was promoted by tectonic activity associated with a rapid sea level rise.This study further verified the role of sea level change in modulating hydrothermal activity on mid-ocean ridges.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
BACKGROUND Neoadjuvant chemotherapy is currently recommended as preoperative treatment for locally advanced rectal cancer(LARC);however,evaluation of treatment response to neoadjuvant chemotherapy is still challenging...BACKGROUND Neoadjuvant chemotherapy is currently recommended as preoperative treatment for locally advanced rectal cancer(LARC);however,evaluation of treatment response to neoadjuvant chemotherapy is still challenging.AIM To create a multi-modal radiomics model to assess therapeutic response after neoadjuvant chemotherapy for LARC.METHODS This retrospective study consecutively included 118 patients with LARC who underwent both computed tomography(CT)and magnetic resonance imaging(MRI)before neoadjuvant chemotherapy between October 2016 and June 2019.Histopathological findings were used as the reference standard for pathological response.Patients were randomly divided into a training set(n=70)and a validation set(n=48).The performance of different models based on CT and MRI,including apparent diffusion coefficient(ADC),dynamic contrast enhanced T1 images(DCE-T1),high resolution T2-weighted imaging(HR-T2WI),and imaging features,was assessed by using the receiver operating characteristic curve analysis.This was demonstrated as area under the curve(AUC)and accuracy(ACC).Calibration plots with Hosmer-Lemeshow tests were used to investigate the agreement and performance characteristics of the nomogram.RESULTS Eighty out of 118 patients(68%)achieved a pathological response.For an individual radiomics model,HR-T2WI performed better(AUC=0.859,ACC=0.896)than CT(AUC=0.766,ACC=0.792),DCE-T1(AUC=0.812,ACC=0.854),and ADC(AUC=0.828,ACC=0.833)in the validation set.The imaging performance for extramural venous invasion detection was relatively low in both the training(AUC=0.73,ACC=0.714)and validation(AUC=0.578,ACC=0.583)sets.The multi-modal radiomics model reached an AUC of 0.925 and ACC of 0.886 in the training set,and an AUC of 0.93 and ACC of 0.875 in the validation set.For the clinical radiomics nomogram,good agreement was found between the nomogram prediction and actual observation.CONCLUSION A multi-modal nomogram using traditional imaging features and radiomics of preoperative CT and MRI adds accuracy to the prediction of treatment outcome,and thus contributes to the personalized selection of neoadjuvant chemotherapy for LARC.展开更多
BACKGROUND Rectal cancer(RC)patient stratification by different factors may yield variable results.Therefore,more efficient prognostic biomarkers are needed for improved risk stratification,personalized treatment,and ...BACKGROUND Rectal cancer(RC)patient stratification by different factors may yield variable results.Therefore,more efficient prognostic biomarkers are needed for improved risk stratification,personalized treatment,and prognostication of RC patients.AIM To build a novel model for predicting the presence of distant metastases and 3-year overall survival(OS)in RC patients.METHODS This was a retrospective analysis of 148 patients(76 males and 72 females)with RC treated with curative resection,without neoadjuvant or postoperative chemoradiotherapy,between October 2012 and December 2015.These patients were allocated to a training or validation set,with a ratio of 7:3.Radiomic features were extracted from portal venous phase computed tomography(CT)images of RC.The least absolute shrinkage and selection operator regression analysis was used for feature selection.Multivariate logistic regression analysis was used to develop the radiomics signature(Rad-score)and the clinicoradiologic risk model(the combined model).Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC.The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis.RESULTS A total of 51(34.5%)patients had distant metastases,while 26(17.6%)patients died,and 122(82.4%)patients lived at least 3 years post-surgery.The values of both the Rad-score(consisted of three selected features)and the combined model were significantly different between the distant metastasis group and the nonmetastasis group(0.46±0.21 vs 0.32±0.24 for the Rad-score,and 0.60±0.23 vs 0.28±0.26 for the combined model;P<0.001 for both models).Predictors contained in the combined model included the Rad-score,pathological N-stage,and T-stage.The addition of histologic grade to the model failed to show incremental prognostic value.The combined model showed good discrimination,with areas under the curve of 0.842 and 0.802 for the training set and validation set,respectively.For the survival analysis,the combined model was associated with an improved OS in the whole cohort and the respective subgroups.CONCLUSION This study presents a clinicoradiologic risk model,visualized in a nomogram,that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.展开更多
基金Supported by the National Natural Science Foundation of China(No.41976075)the National Key Research and Development Program of China(No.2021YFF0501302)+1 种基金the Fundamental Research Funds for National Non-profit Institute Grant(No.JG 2103)the China Ocean Mineral Resources R&D Association Project(No.DY135-S 2-1-03)。
文摘A new hydrothermal field(Tianshi)was discovered on the rift valley wall through plume anomaly surveys and geological work conducted in 2012 and 2018 between 2°35′N and 2°43′N of the slow-spreading Carlsberg Ridge(CR).Here,the results of two expeditions conducted to detect and characterize the new hydrothermal field are reported.Mineralogical and geochemical data,as well as 14 C ages of a sediment core collected near the field are presented to reveal the hydrothermal history.Results show that the Tianshi field is a basalt-hosted hydrothermal system.Geochemical data of the sediments collected near the field indicate a strong hydrothermal contribution,and hydrothermal Fe and Cu fluxes range from 30 to 155 mg/(cm^(2)·ka)and 0.59 to 11.49 mg/(cm^(2)·ka),respectively.Temporal variations in the fluxes of hydrothermal Fe indicate that there have been at least three amplified hydrothermal venting events(H 1,H 2,and H 3)in the Tianshi field over the last 35.2 ka,in 28.6-35.2 ka BP,22.0-27.6 ka BP,and 1.2-11.4 ka BP,respectively.Hydrothermal event H 2 was driven by an increased magmatic production associated with sea level fall during the Last Glacial Maximum,while event H 3 was promoted by tectonic activity associated with a rapid sea level rise.This study further verified the role of sea level change in modulating hydrothermal activity on mid-ocean ridges.
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 Research Grant of National Nature Science Foundation of China,No.81971571Multimodal MR Imaging and Radiomics of Rectal Cancer,Science and Technology Department of Sichuan Province,No.2019YFS0431Sichuan University Training Program of Innovation and Entrepreneurship for Undergraduates,No.C2019104739.
文摘BACKGROUND Neoadjuvant chemotherapy is currently recommended as preoperative treatment for locally advanced rectal cancer(LARC);however,evaluation of treatment response to neoadjuvant chemotherapy is still challenging.AIM To create a multi-modal radiomics model to assess therapeutic response after neoadjuvant chemotherapy for LARC.METHODS This retrospective study consecutively included 118 patients with LARC who underwent both computed tomography(CT)and magnetic resonance imaging(MRI)before neoadjuvant chemotherapy between October 2016 and June 2019.Histopathological findings were used as the reference standard for pathological response.Patients were randomly divided into a training set(n=70)and a validation set(n=48).The performance of different models based on CT and MRI,including apparent diffusion coefficient(ADC),dynamic contrast enhanced T1 images(DCE-T1),high resolution T2-weighted imaging(HR-T2WI),and imaging features,was assessed by using the receiver operating characteristic curve analysis.This was demonstrated as area under the curve(AUC)and accuracy(ACC).Calibration plots with Hosmer-Lemeshow tests were used to investigate the agreement and performance characteristics of the nomogram.RESULTS Eighty out of 118 patients(68%)achieved a pathological response.For an individual radiomics model,HR-T2WI performed better(AUC=0.859,ACC=0.896)than CT(AUC=0.766,ACC=0.792),DCE-T1(AUC=0.812,ACC=0.854),and ADC(AUC=0.828,ACC=0.833)in the validation set.The imaging performance for extramural venous invasion detection was relatively low in both the training(AUC=0.73,ACC=0.714)and validation(AUC=0.578,ACC=0.583)sets.The multi-modal radiomics model reached an AUC of 0.925 and ACC of 0.886 in the training set,and an AUC of 0.93 and ACC of 0.875 in the validation set.For the clinical radiomics nomogram,good agreement was found between the nomogram prediction and actual observation.CONCLUSION A multi-modal nomogram using traditional imaging features and radiomics of preoperative CT and MRI adds accuracy to the prediction of treatment outcome,and thus contributes to the personalized selection of neoadjuvant chemotherapy for LARC.
文摘BACKGROUND Rectal cancer(RC)patient stratification by different factors may yield variable results.Therefore,more efficient prognostic biomarkers are needed for improved risk stratification,personalized treatment,and prognostication of RC patients.AIM To build a novel model for predicting the presence of distant metastases and 3-year overall survival(OS)in RC patients.METHODS This was a retrospective analysis of 148 patients(76 males and 72 females)with RC treated with curative resection,without neoadjuvant or postoperative chemoradiotherapy,between October 2012 and December 2015.These patients were allocated to a training or validation set,with a ratio of 7:3.Radiomic features were extracted from portal venous phase computed tomography(CT)images of RC.The least absolute shrinkage and selection operator regression analysis was used for feature selection.Multivariate logistic regression analysis was used to develop the radiomics signature(Rad-score)and the clinicoradiologic risk model(the combined model).Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC.The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis.RESULTS A total of 51(34.5%)patients had distant metastases,while 26(17.6%)patients died,and 122(82.4%)patients lived at least 3 years post-surgery.The values of both the Rad-score(consisted of three selected features)and the combined model were significantly different between the distant metastasis group and the nonmetastasis group(0.46±0.21 vs 0.32±0.24 for the Rad-score,and 0.60±0.23 vs 0.28±0.26 for the combined model;P<0.001 for both models).Predictors contained in the combined model included the Rad-score,pathological N-stage,and T-stage.The addition of histologic grade to the model failed to show incremental prognostic value.The combined model showed good discrimination,with areas under the curve of 0.842 and 0.802 for the training set and validation set,respectively.For the survival analysis,the combined model was associated with an improved OS in the whole cohort and the respective subgroups.CONCLUSION This study presents a clinicoradiologic risk model,visualized in a nomogram,that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.
