Cocamidopropyl hydroxyl sulfobetaine(CHSB)is one of the most promising foaming agents for high-salinity reservoirs because the salt in place facilitates its foam stability,even with salinity as high as 2×10^(5)mg...Cocamidopropyl hydroxyl sulfobetaine(CHSB)is one of the most promising foaming agents for high-salinity reservoirs because the salt in place facilitates its foam stability,even with salinity as high as 2×10^(5)mg/L.However,the synergistic effects between CHSB and salt have not been fully understood.This study utilized bulk foam tests and thin-film interferometry to comprehensively investigate the macroscopic and microscopic decay processes of CHSB foams with NaCl concentrations ranging from 2.3×10^(4)to 2.1×10^(5)mg/L.We focused on the dilatational viscoelasticity and dynamic thin-film thickness to elucidate the high-salinity-enhanced foam stability.The increase in dilatational viscoelasticity and supramolecular oscillating structural force(Π_(OS))with salinity dominated the superior stability of CHSB foam.With increasing salinity,more CHSB molecules accumulated on the surface with a lower diffusion rate,leading to high dilatational moduli and surface elasticity,thus decelerating coarsening and coalescence.Meanwhile,the number density of micelles in the thin film increased with salinity,resulting in increasedΠOS.Consequently,the energy barrier for stepwise thinning intensified,and the thin-film drainage slowed.This work conduces to understand the mechanisms behind the pronounced stability of betaine foam and can promote the widespread application of foam in harsh reservoirs.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 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
BACKGROUND Tumor deposits(TDs)are not equivalent to lymph node(LN)metastasis(LNM)but have become independent adverse prognostic factors in patients with rectal cancer(RC).Although preoperatively differentiating TDs an...BACKGROUND Tumor deposits(TDs)are not equivalent to lymph node(LN)metastasis(LNM)but have become independent adverse prognostic factors in patients with rectal cancer(RC).Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses,it is a challenging task.AIM To establish a computed tomography(CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC.METHODS This study retrospectively enrolled 219 patients with RC[TDs+LNM-(n=89);LNM+TDs-(n=115);TDs+LNM+(n=15)]from a single center between September 2016 and September 2021.Single-positive patients(i.e.,TDs+LNM-and LNM+TDs-)were classified into the training(n=163)and validation(n=41)sets.We extracted numerous features from the enhanced CT(region 1:The main tumor;region 2:The largest peritumoral nodule).After deleting redundant features,three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model(Rad-score).After validating Rad-score,its performance was further evaluated in the field of diagnosing double-positive patients(i.e.,TDs+LNM+)by outlining all peritumoral nodules with diameter(short-axis)>3 mm.RESULTS Rad-score 1(radiomics signature of the main tumor)had an area under the curve(AUC)of 0.768 on the training dataset and 0.700 on the validation dataset.Rad-score 2(radiomics signature of the largest peritumoral nodule)had a higher AUC(training set:0.940;validation set:0.918)than Radscore 1.Clinical factors,including age,gender,location of RC,tumor markers,and radiological features of the largest peritumoral nodule,were excluded by logistic regression.Thus,the combined model was comprised of Rad-scores of 1 and 2.Considering that the combined model had similar AUCs with Rad-score 2(P=0.134 in the training set and 0.594 in the validation set),Rad-score 2 was used as the final model.For the diagnosis of double-positive patients in the mixed group[TDs+LNM+(n=15);single-positive(n=15)],Rad-score 2 demonstrated moderate performance(sensitivity,73.3%;specificity,66.6%;and accuracy,70.0%).CONCLUSION Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.展开更多
基金The authors would like to be grateful for the financial support of National Natural Science Foundation of China(No.51904256).
文摘Cocamidopropyl hydroxyl sulfobetaine(CHSB)is one of the most promising foaming agents for high-salinity reservoirs because the salt in place facilitates its foam stability,even with salinity as high as 2×10^(5)mg/L.However,the synergistic effects between CHSB and salt have not been fully understood.This study utilized bulk foam tests and thin-film interferometry to comprehensively investigate the macroscopic and microscopic decay processes of CHSB foams with NaCl concentrations ranging from 2.3×10^(4)to 2.1×10^(5)mg/L.We focused on the dilatational viscoelasticity and dynamic thin-film thickness to elucidate the high-salinity-enhanced foam stability.The increase in dilatational viscoelasticity and supramolecular oscillating structural force(Π_(OS))with salinity dominated the superior stability of CHSB foam.With increasing salinity,more CHSB molecules accumulated on the surface with a lower diffusion rate,leading to high dilatational moduli and surface elasticity,thus decelerating coarsening and coalescence.Meanwhile,the number density of micelles in the thin film increased with salinity,resulting in increasedΠOS.Consequently,the energy barrier for stepwise thinning intensified,and the thin-film drainage slowed.This work conduces to understand the mechanisms behind the pronounced stability of betaine foam and can promote the widespread application of foam in harsh reservoirs.
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.
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.
文摘BACKGROUND Tumor deposits(TDs)are not equivalent to lymph node(LN)metastasis(LNM)but have become independent adverse prognostic factors in patients with rectal cancer(RC).Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses,it is a challenging task.AIM To establish a computed tomography(CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC.METHODS This study retrospectively enrolled 219 patients with RC[TDs+LNM-(n=89);LNM+TDs-(n=115);TDs+LNM+(n=15)]from a single center between September 2016 and September 2021.Single-positive patients(i.e.,TDs+LNM-and LNM+TDs-)were classified into the training(n=163)and validation(n=41)sets.We extracted numerous features from the enhanced CT(region 1:The main tumor;region 2:The largest peritumoral nodule).After deleting redundant features,three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model(Rad-score).After validating Rad-score,its performance was further evaluated in the field of diagnosing double-positive patients(i.e.,TDs+LNM+)by outlining all peritumoral nodules with diameter(short-axis)>3 mm.RESULTS Rad-score 1(radiomics signature of the main tumor)had an area under the curve(AUC)of 0.768 on the training dataset and 0.700 on the validation dataset.Rad-score 2(radiomics signature of the largest peritumoral nodule)had a higher AUC(training set:0.940;validation set:0.918)than Radscore 1.Clinical factors,including age,gender,location of RC,tumor markers,and radiological features of the largest peritumoral nodule,were excluded by logistic regression.Thus,the combined model was comprised of Rad-scores of 1 and 2.Considering that the combined model had similar AUCs with Rad-score 2(P=0.134 in the training set and 0.594 in the validation set),Rad-score 2 was used as the final model.For the diagnosis of double-positive patients in the mixed group[TDs+LNM+(n=15);single-positive(n=15)],Rad-score 2 demonstrated moderate performance(sensitivity,73.3%;specificity,66.6%;and accuracy,70.0%).CONCLUSION Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.
