BACKGROUND Advanced esophageal squamous cell carcinoma(ESCC)has an extremely poor prognosis.Preoperative chemoradiotherapy(CRT)can significantly prolong survival,especially in those who achieve pathological complete r...BACKGROUND Advanced esophageal squamous cell carcinoma(ESCC)has an extremely poor prognosis.Preoperative chemoradiotherapy(CRT)can significantly prolong survival,especially in those who achieve pathological complete response(pCR).However,the pretherapeutic prediction of pCR remains challenging.AIM To predict pCR and survival in ESCC patients undergoing CRT using an artificial intelligence(AI)-based diffusion-weighted magnetic resonance imaging(DWI-MRI)radiomics model.METHODS We retrospectively analyzed 70 patients with ESCC who underwent curative surgery following CRT.For each patient,pre-treatment tumors were semi-automatically segmented in three dimensions from DWI-MRI images(b=0,1000 second/mm^(2)),and a total of 76 radiomics features were extracted from each segmented tumor.Using these features as explanatory variables and pCR as the objective variable,machine learning models for predicting pCR were developed using AutoGluon,an automated machine learning library,and validated by stratified double cross-validation.RESULTS pCR was achieved in 15 patients(21.4%).Apparent diffusion coefficient skewness demonstrated the highest predictive performance[area under the curve(AUC)=0.77].Gray-level co-occurrence matrix(GLCM)entropy(b=1000 second/mm²)was an independent prognostic factor for relapse-free survival(RFS)(hazard ratio=0.32,P=0.009).In Kaplan-Meier analysis,patients with high GLCM entropy showed significantly better RFS(P<0.001,log-rank).The best-performing machine learning model achieved an AUC of 0.85.The predicted pCR-positive group showed significantly better RFS than the predicted pCR-negative group(P=0.007,log-rank).CONCLUSION AI-based radiomics analysis of DWI-MRI images in ESCC has the potential to accurately predict the effect of CRT before treatment and contribute to constructing optimal treatment strategies.展开更多
Esophageal cancer is known as one of the malignant cancers with poor prognosis.To improve the outcome,combined multimodality treatment is attempted.On the other hand,advances in genomics and other“omic”technologies ...Esophageal cancer is known as one of the malignant cancers with poor prognosis.To improve the outcome,combined multimodality treatment is attempted.On the other hand,advances in genomics and other“omic”technologies are paving way to the patient-oriented treatment called“personalized”or“precision”medicine.Recent advancements of imaging techniques such as functional imaging make it possible to use imaging features as biomarker for diagnosis,treatment response,and prognosis in cancer treatment.In this review,we will discuss how we can use imaging derived tumor features as biomarker for the treatment of esophageal cancer.展开更多
Neovascularization was reported to arise early in the adenoma-carcinoma sequence in colorectal cancer(CRC),and the importance of angiogenesis in cancer progression has been established.Computed tomography(CT)perfusion...Neovascularization was reported to arise early in the adenoma-carcinoma sequence in colorectal cancer(CRC),and the importance of angiogenesis in cancer progression has been established.Computed tomography(CT)perfusion(CTP)based on high temporal resolution CT images enables evaluation of hemodynamics of tissue in vivo by modeling tracer kinetics.CTP has been reported to characterize tumor angiogenesis,and to be a sensitive marker for predicting recurrence or survival in CRC.In this review,we will discuss the biomarker value of CTP in the management of CRC patients.展开更多
文摘BACKGROUND Advanced esophageal squamous cell carcinoma(ESCC)has an extremely poor prognosis.Preoperative chemoradiotherapy(CRT)can significantly prolong survival,especially in those who achieve pathological complete response(pCR).However,the pretherapeutic prediction of pCR remains challenging.AIM To predict pCR and survival in ESCC patients undergoing CRT using an artificial intelligence(AI)-based diffusion-weighted magnetic resonance imaging(DWI-MRI)radiomics model.METHODS We retrospectively analyzed 70 patients with ESCC who underwent curative surgery following CRT.For each patient,pre-treatment tumors were semi-automatically segmented in three dimensions from DWI-MRI images(b=0,1000 second/mm^(2)),and a total of 76 radiomics features were extracted from each segmented tumor.Using these features as explanatory variables and pCR as the objective variable,machine learning models for predicting pCR were developed using AutoGluon,an automated machine learning library,and validated by stratified double cross-validation.RESULTS pCR was achieved in 15 patients(21.4%).Apparent diffusion coefficient skewness demonstrated the highest predictive performance[area under the curve(AUC)=0.77].Gray-level co-occurrence matrix(GLCM)entropy(b=1000 second/mm²)was an independent prognostic factor for relapse-free survival(RFS)(hazard ratio=0.32,P=0.009).In Kaplan-Meier analysis,patients with high GLCM entropy showed significantly better RFS(P<0.001,log-rank).The best-performing machine learning model achieved an AUC of 0.85.The predicted pCR-positive group showed significantly better RFS than the predicted pCR-negative group(P=0.007,log-rank).CONCLUSION AI-based radiomics analysis of DWI-MRI images in ESCC has the potential to accurately predict the effect of CRT before treatment and contribute to constructing optimal treatment strategies.
文摘Esophageal cancer is known as one of the malignant cancers with poor prognosis.To improve the outcome,combined multimodality treatment is attempted.On the other hand,advances in genomics and other“omic”technologies are paving way to the patient-oriented treatment called“personalized”or“precision”medicine.Recent advancements of imaging techniques such as functional imaging make it possible to use imaging features as biomarker for diagnosis,treatment response,and prognosis in cancer treatment.In this review,we will discuss how we can use imaging derived tumor features as biomarker for the treatment of esophageal cancer.
文摘Neovascularization was reported to arise early in the adenoma-carcinoma sequence in colorectal cancer(CRC),and the importance of angiogenesis in cancer progression has been established.Computed tomography(CT)perfusion(CTP)based on high temporal resolution CT images enables evaluation of hemodynamics of tissue in vivo by modeling tracer kinetics.CTP has been reported to characterize tumor angiogenesis,and to be a sensitive marker for predicting recurrence or survival in CRC.In this review,we will discuss the biomarker value of CTP in the management of CRC patients.
