Digestive and Liver Disease, Год журнала: 2023, Номер 55(7), С. 833 - 847
Опубликована: Янв. 13, 2023
Язык: Английский
Digestive and Liver Disease, Год журнала: 2023, Номер 55(7), С. 833 - 847
Опубликована: Янв. 13, 2023
Язык: Английский
BMC Cancer, Год журнала: 2022, Номер 22(1)
Опубликована: Ноя. 29, 2022
Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data preoperative differential common tumors pancreas.Clinical CT 193 patients with PCN were collected this study. Among these patients, 99 pathologically diagnosed serous cystadenoma (SCA), 55 mucinous (MCA) 39 intraductal papillary (IPMN). The regions interest (ROIs) obtained based on manual image segmentation slices. radiomics-DL constructed support vector machines (SVMs). Moreover, fusion clinical radiological features, best combined feature set was according Akaike information criterion (AIC) analysis. Then fused model logistic regression.For SCA diagnosis, performed an average area under curve (AUC) 0.916. It had a including position, polycystic features (≥6), wall calcification, duct dilatation score. For MCA IPMN AUC 0.973 age, communication score.The radiomics, images have favorable diagnostic performance SCA, IPMN. These findings may be beneficial exploration individualized management strategies.
Язык: Английский
Процитировано
24Cancers, Год журнала: 2023, Номер 15(5), С. 1538 - 1538
Опубликована: Фев. 28, 2023
In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types DL based only on venous phase (VP) contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in study. All CECT collected, the randomly divided into training validation cohorts at a ratio 4:1. We proposed novel transformer-based end-to-end model, named MVI-TR, which is supervised method. MVI-TR can capture features automatically from radiomics perform assessments. addition, popular self-supervised method, contrastive widely used residual networks (ResNets family) fair comparisons. With an accuracy 99.1%, precision 99.3%, area under curve (AUC) 0.98, recalling rate 98.8%, F1-score 99.1% cohort, achieved superior outcomes. Additionally, cohort's best (97.2%), (97.3%), AUC (0.935), (93.1%), (95.2%). outperformed other predicting showed great predictive value HCC patients.
Язык: Английский
Процитировано
17European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2023, Номер 50(8), С. 2501 - 2513
Опубликована: Март 16, 2023
Язык: Английский
Процитировано
17Hepatobiliary & pancreatic diseases international, Год журнала: 2023, Номер 22(4), С. 346 - 351
Опубликована: Март 25, 2023
Язык: Английский
Процитировано
15Abdominal Radiology, Год журнала: 2024, Номер 49(5), С. 1397 - 1410
Опубликована: Март 3, 2024
Язык: Английский
Процитировано
6Frontiers in Oncology, Год журнала: 2021, Номер 11
Опубликована: Апрель 29, 2021
Patients with small hepatocellular carcinoma (HCC) (3 cm) still have a poor prognosis. The purpose of this study was to develop radiomics nomogram preoperatively predict early recurrence (ER) (2 years) HCC.The population included 111 patients HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and 2018 were followed for at least 2 years. Radiomic features extracted from the entire tumor by using MaZda software. absolute shrinkage selection operator (LASS0) method applied feature selection, signature construction. A rad-score then calculated. Multivariable logistic regression analysis used establish prediction model including independent clinical risk factors, radiologic rad-score, which ultimately presented as nomogram. predictive ability evaluated area under receiver operating characteristic (ROC) curve internal validation performed via bootstrap resampling 5-fold cross-validation method.A total 53 (53/111, 47.7%) had confirmed ER according final outcomes. In univariate analysis, cirrhosis hepatitis B infection (P=0.015 0.083, respectively), hepatobiliary phase hypointensity (P=0.089), Child-Pugh score (P=0.083), preoperative platelet count (P=0.003), (P<0.001) correlated ER. However, after multivariate only predictors in model. ROC (AUC) 0.981 (95% CI: 0.957, 1.00), while AUC verified is 0.980 0.962, indicating goodness-of-fit model.The containing factors can be quantitative tool individual probability HCC.
Язык: Английский
Процитировано
29Frontiers in Oncology, Год журнала: 2022, Номер 12
Опубликована: Фев. 24, 2022
The presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting non-deep learning algorithms (NDLAs) deep (DLAs), has been widely used for MVI prediction medical imaging.To assess the diagnostic accuracy AI non-invasive, preoperative based on imaging data.Original studies reporting quantitative data were identified databases PubMed, Embase, Web Science. quality included was assessed using Quality Assessment Diagnostic Accuracy Studies 2 (QUADAS-2) scale. pooled sensitivity, specificity, positive likelihood ratio (PLR), negative (NLR) calculated a random-effects model 95% CIs. A summary receiver operating characteristic curve area under (AUC) generated to models. In group, we further performed meta-regression subgroup analyses identify source heterogeneity.Data from 16 4,759 cases available meta-analysis. Four models, 12 two compared efficiency types. For predictive performance PLR, NLR, AUC values 0.84 [0.75-0.90], [0.77-0.89], 5.14 [3.53-7.48], 0.2 [0.12-0.31], 0.90 [0.87-0.93]; they 0.77 [0.71-0.82], [0.73-0.80], 3.30 [2.83-3.84], 0.30 [0.24-0.38], 0.82 [0.79-0.85], respectively. Subgroup showed significant difference between single tumor multiple AUC.This meta-analysis demonstrates high methods status their promising potential clinical decision-making. Deep models perform better than terms prediction, methodology, cost-effectiveness.https://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID=260891, ID:CRD42021260891.
Язык: Английский
Процитировано
22Cancers, Год журнала: 2022, Номер 14(7), С. 1816 - 1816
Опубликована: Апрель 3, 2022
Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses radiomic analysis. Furthermore, current MVI studies have not been designed small HCC nodules, which plethora treatments exists. This study aimed to identify predictors in nodules ≤3.0 cm by analysing the zone transition (ZOT), crossing tumour and peritumour, automatically detected face uncertainties radiologist's segmentation. The considered 117 patients imaged contrast-enhanced computed tomography; 78 were finally enrolled Radiomic features extracted from ZOT, using an adaptive procedure based on local image contrast variations. After data oversampling, support vector machine classifier was developed validated. Classifier performance assessed receiver operating characteristic (ROC) curve analysis related metrics. original 89 (32 MVI+ 57 MVI-) became 169 (62 107 oversampling. Of four within signature, three ZOT heterogeneity measures regarding both arterial venous phases. On test set (19MVI+ 33MVI-), predicts with area under 0.86 (95%CI (0.70-0.93), p∼10-5), sensitivity = 79% specificity 82%. showed negative positive predictive values 87% 71%, respectively. highest diagnostic literature, disclosing role predicting status.
Язык: Английский
Процитировано
22Cancer Medicine, Год журнала: 2024, Номер 13(5)
Опубликована: Март 1, 2024
Microvascular invasion (MVI) is an independent prognostic factor that associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach relatively subjective, time-consuming, heterogeneous in diagnosis MVI. The aim this study was to develop a deep-learning model could significantly improve efficiency accuracy MVI diagnosis.
Язык: Английский
Процитировано
5World Journal of Gastroenterology, Год журнала: 2021, Номер 27(32), С. 5341 - 5350
Опубликована: Авг. 24, 2021
Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC challenging because atypical imaging manifestations and diversity focal lesions. Artificial intelligence (AI), such as machine learning (ML) deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI used throughout entire radiomics process plays a critical role multiple fields medicine. This review summarizes applications various aspects preoperative HCC, including segmentation, differential diagnosis, prediction histopathology, early detection recurrence after curative treatment, evaluation treatment response. We also limitations previous studies discuss future directions diagnostic HCC.
Язык: Английский
Процитировано
26