Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.

Background: This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making.

Methods: A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.

Importance: Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

PLEK2 and IFI6, representing mesenchymal and immune-suppressive microenvironment, predicts resistance to neoadjuvant immunotherapy in esophageal squamous cell carcinoma.

Background: Immunotherapy has largely improved clinical outcome of patients with esophageal squamous cell carcinoma (ESCC). However, a proportion of patients still fail to benefit. Thus, biomarkers predicting therapeutic resistance and underlying mechanism needs to be investigated.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography.

Background: Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC.

MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone.

Objectives: To build radiomics based OS prediction tools for local advanced cervical cancer (LACC) patients treated by concurrent chemoradiotherapy (CCRT) alone or followed by adjuvant chemotherapy (ACT). And, to construct adjuvant chemotherapy decision aid.

Methods: 83 patients treated by ACT following CCRT and 47 patients treated by CCRT were included in the ACT cohort and non-ACT cohort.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

Background: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC.

Methods: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.

Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images.

Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies.

Background: Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging.

Purpose: To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies.

Study Type: Retrospective.

Selection Between Liver Resection Versus Transarterial Chemoembolization in Hepatocellular Carcinoma: A Multicenter Study.

Objectives: Models should be developed to assist choice between liver resection (LR) and transarterial chemoembolization (TACE) for hepatocellular carcinoma.

Methods: After separating 520 cases from 5 hospitals into training (n = 302) and validation (n = 218) data sets, we weighted the cases to control baseline difference and ensured the causal effect between treatments (LR and TACE) and estimated progression-free survival (PFS) difference. A noninvasive PFS model was constructed with clinical factors, radiological characteristics, and radiomic features.

A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery.

Objectives: To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating.

Methods: One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing.

Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.

Objectives: To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging.

Methods: A total of 194 patients with Knosp grade two and three PAs (training set: n = 97; test set: n = 97) were enrolled in this retrospective study. From CE-T1 and T2 MR images, 2553 quantitative imaging features were extracted.

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas.

Purpose: To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.

Methods: This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient.

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication.

Objectives: Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect.

Methods: We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas.

Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery.

Purpose: To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach.

Methods: We enrolled 112 patients (training set: n = 75; test set: n = 37) with complete T1-weighted magnetic resonance imaging (MRI) and contrast-enhanced T1-weighted MRI (CE-T1). A total of 1482 quantitative imaging features were extracted from T1 and CE-T1 images.

Systematic Investigation of High-Sensitivity Luminescent Sensing for Polyoxometalates and Iron(III) by MOFs Assembled with a New Resorcin[4]arene-Functionalized Tetracarboxylate.

A new family of resorcin[4]arene-based metal-organic frameworks (MOFs), namely, [Eu(HL)(DMF)(H2 O)2 ]⋅3 H2 O (1), [Tb(HL)(DMF)(H2 O)2 ] 3 H2 O (2), [Cd4 (L)2 (DMF)4 (H2 O)2 ] 3 H2 O (3) and [Zn3 (HL)2 (H2 O)2 ] 2 DMF⋅7 H2 O (4), have been constructed from a new resorcin[4]arene-functionalized tetracarboxylic acid (H4 L=2,8,14,20-tetra-ethyl-6,12,18,24-tetra-methoxy-4,10,16,22-tetra-carboxy-methoxy-calix[4]arene). Isostructural 1 and 2 exhibit charming 1D motifs built with the cup-like HL(3-) anions and rare earth cations. Compounds 3 and 4 show a unique sandwich-based 2D layer and a fascinating 3D framework, respectively.