General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis.

The foundation model, trained on extensive and diverse datasets, has shown strong performance across numerous downstream tasks. Nevertheless, its application in the medical domain is significantly hindered by issues such as data volume, heterogeneity, and privacy concerns. Therefore, we propose the Vision Foundation Model General Lightweight (VFMGL) framework, which facilitates the decentralized construction of expert clinical models for various medical tasks.

AI-based fingerprint index of visceral adipose tissue for the prediction of bowel damage in patients with Crohn's disease.

The fingerprint features of visceral adipose tissue (VAT) are intricately linked to bowel damage (BD) in patients with Crohn's disease (CD). We aimed to develop a VAT fingerprint index (VAT-FI) using radiomics and deep learning features extracted from computed tomography (CT) images of 1,135 CD patients across six hospitals (training cohort,  = 600; testing cohort,  = 535) for predicting BD, and to compare it with a subcutaneous adipose tissue (SAT)-FI. VAT-FI exhibited greater predictive accuracy than SAT-FI in both training (area under the receiver operating characteristic curve [AUC] = 0.

Predicting postoperative prognosis in clear cell renal cell carcinoma using a multiphase CT-based deep learning model.

Background: Some clinicopathological risk stratification systems (CRSSs) such as the leibovich score have been used to predict the postoperative prognosis of patients with clear cell renal cell carcinoma (ccRCC), but there are no reliable noninvasive preoperative indicators for predicting postoperative prognosis in clinical practice.

Purpose: To assess the value of a deep learning (DL) model based on CT images in predicting the postoperative prognosis of patients with ccRCC.

Materials And Methods: A total of 382 patients with ccRCC were retrospectively enrolled andallocated to training (n = 229) or testing (n = 153) cohorts at a 6:4 ratio.

Multibioinspired Hybrid Superwetting Surface for Efficient Fog Collection and Power Generation.

Obtaining water and renewable energy from the atmosphere provides a potential solution to the growing energy shortage. Leveraging the synergistic inspiration from desert beetles, cactus spines, and rice leaves, here, a multibioinspired hybrid wetting rod (HWR) is prepared through simple solution immersion and laser etching, which endows an efficient water collection from the atmosphere. Importantly, benefiting from the bionic asymmetric pattern design and the three-dimensional structure, the HWR possesses an omnidirectional fog collection with a rate of up to 23 g cm h.

Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.

Purpose: To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods: A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL).

Deep learning vs. robust federal learning for distinguishing adrenal metastases from benign lesions with multi-phase CT images.

Background: Differentiating adrenal adenomas from metastases poses a significant challenge, particularly in patients with a history of extra-adrenal malignancy. This study investigates the performance of three-phase computed tomography (CT) based robust federal learning algorithm and traditional deep learning for distinguishing metastases from benign adrenal lesions.

Material And Methods: This retrospective analysis includes 1187 instances who underwent three-phase CT scans between January 2008 and March 2021, comprising 720 benign lesions and 467 metastases.

Enhancing preoperative diagnosis of microvascular invasion in hepatocellular carcinoma: domain-adaptation fusion of multi-phase CT images.

Objectives: In patients with hepatocellular carcinoma (HCC), accurately predicting the preoperative microvascular invasion (MVI) status is crucial for improving survival rates. This study proposes a multi-modal domain-adaptive fusion model based on deep learning methods to predict the preoperative MVI status in HCC.

Materials And Methods: From January 2008 to May 2022, we collected 163 cases of HCC from our institution and 42 cases from another medical facility, with each case including Computed Tomography (CT) images from the pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP).

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

The prediction of patient disease risk via computed tomography (CT) images and artificial intelligence techniques shows great potential. However, training a robust artificial intelligence model typically requires large-scale data support. In practice, the collection of medical data faces obstacles related to privacy protection.

Differentiating adrenal metastases from benign lesions with multiphase CT imaging: Deep learning could play an active role in assisting radiologists.

Objectives: To develop and externally validate multiphase CT-based deep learning (DL) models for differentiating adrenal metastases from benign lesions.

Materials And Methods: This retrospective two-center study included 1146 adrenal lesions from 1059 patients who underwent multiphase CT scanning between January 2008 and March 2021. The study encompassed 564 surgically confirmed adenomas, along with 135 benign lesions and 447 metastases confirmed by observation.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

Purpose: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs).

Methods: Data from 841 patients with SPSNs from five centres were collected retrospectively.

Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies.

Objectives: To determine whether a CT-based machine learning (ML) can differentiate benign renal tumors from renal cell carcinomas (RCCs) and improve radiologists' diagnostic performance, and evaluate the impact of variable CT imaging phases, slices, tumor sizes, and region of interest (ROI) segmentation strategies.

Methods: Patients with pathologically proven RCCs and benign renal tumors from our institution between 2008 and 2020 were included as the training dataset for ML model development and internal validation (including 418 RCCs and 78 benign tumors), and patients from two independent institutions and a public database (TCIA) were included as the external dataset for individual testing (including 262 RCCs and 47 benign tumors). Features were extracted from three-phase CT images.

CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study.

Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM.

Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation.

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain.

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.

Objective: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC).

Methods: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort.

A novel index for staging hepatitis B related liver cirrhosis in patients with hepatocellular carcinoma.

Information on the stage of liver cirrhosis is essential for prognostication and decisions on surgical planning for hepatocellular carcinoma (HCC) patients. But a non-invasive liver cirrhosis staging model is still lacking. The aim of our study was to develop a non-invasive model based on routine clinical parameters to evaluate the severity of cirrhosis in hepatitis B related HCC patients.

Detecting Muscle Invasion of Bladder Cancer Using a Proposed Magnetic Resonance Imaging Strategy.

Background: Accurate evaluation of the invasion depth of tumors with a Vesical Imaging-Reporting and Data System (VI-RADS) score of 3 is difficult.

Purpose: To evaluate the diagnostic performance of a new magnetic resonance imaging (MRI) strategy based on the integration of the VI-RADS and tumor contact length (TCL) for the diagnosis of muscle-invasive bladder cancer (MIBC).

Study Type: Single center, retrospective.

Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features.

Purposes: To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices.

Materials And Methods: The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images.

Performance of Dual-Source CT in Calculi Component Analysis: A Systematic Review and Meta-Analysis of 2151 Calculi.

Objective: To evaluate the performance of dual-source computed tomography (DSCT) in the component analysis of all types of calculi by doing a systematic review and meta-analysis.

Methods: We searched MEDLINE, Embase, Scopus, and CNKI up to February 28, 2020, for in vivo studies investigating the performance of DSCT in the component analysis of calculi. We pooled the sensitivity, specificity, and areas under the summary receiver operating characteristic (AUROC) curves using a random-effect model in the meta-analysis.

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.

Purpose: To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs).

Materials And Methods: The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively.

A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma.

Purpose: To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade clear cell renal cell carcinoma (ccRCC).

Method: Patients with pathologically proven ccRCC diagnosed between October 2009 and March 2019 were assigned to training or internal test dataset, and external test dataset was acquired from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. The effects of different methodologies on the performance of DL-model, including image cropping (IC), setting the attention level, selecting model complexity (MC), and applying transfer learning (TL), were compared using repeated measures analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis.

Combination of hepatocyte fraction and diffusion-weighted imaging as a predictor in quantitative hepatic fibrosis evaluation.

Objective: To investigate the performance of the combined hepatocyte fraction (HepF) and apparent diffusion coefficient (ADC) values to stage hepatic fibrosis (HF) in patients with hepatitis B/C.

Materials And Methods: A total of 281 patients with hepatitis B/C prospectively underwent gadoxetate disodium-based T1 mapping and diffusion-weighted imaging. HepF was determined from pre and postcontrast T1 mapping with pharmacokinetics.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.

Objective: To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC).

Materials And Methods: Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT.

Whole-Lesion Computed Tomography-Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules.

Objective: The aim of this study was to investigate the differentiation of computed tomography (CT)-based entropy parameters between minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions appearing as pulmonary subsolid nodules (SSNs).

Methods: This study was approved by the institutional review board in our hospital. From July 2015 to November 2018, 186 consecutive patients with solitary peripheral pulmonary SSNs that were pathologically confirmed as pulmonary adenocarcinomas (74 MIA and 112 IAC lesions) were included and subdivided into the training data set and the validation data set.

CT and MRI imaging and interpretation of hepatic arterioportal shunts.

Hepatic arterioportal shunts (HAPS) occur due to organic or functional fistulization of blood flow between arterial hepatic vasculature and venous portal systems. It is a type of hemodynamic abnormality of the liver being observed increasingly with the use of temporal imaging modalities. HAPS occur due to other underlying hepatic abnormalities including the presence of an underlying tumor or malignancy.