Core-Satellite Nanoassembly Overcomes Spatial Heterogeneity of Dendric Cell Distribution in Pancreatic Tumors for Effective Chemoimmunotherapy.

Pancreatic cancer therapies such as chemotherapy and immunotherapy are hindered by the dense extracellular matrix known as physical barriers, leading to heterogeneity impeding the effective penetration of chemotherapeutic agents and activation of antitumor immune responses. To address this challenge, we developed a hybrid nanoassembly with a distinct core-satellite-like heterostructure, PLAF@P/T-PD, which is responsive to both internal pH/redox and external ultrasound stimulations. This heterostructural nanoassembly features a polymersome core encapsulating an ultrasound contrast agent perfluoropentane and a chemotherapeutic agent Taxol (PLAF@P/T) electrostatically coated with satellite-like polyplexes carrying an immune agonist dsDNA (PD), which brings about synergistic functions inside the pancreatic tumor.

Anatomic association between the gastrocolic trunk of Henle and right colic artery by high-quality CT venography.

In hepatic flexure and transverse colon cancer surgeries, mobilizing the right mesocolon and precisely dissecting the gastrocolic trunk of Henle (GTH) are crucial. Previous classifications of GTH tributaries do not guide radical right hemicolectomy due to post-procedural anatomical acquisition. This study analyzed vessel associations, including the middle colic vein (MCV) converging site, right colic artery (RCA) presence, and other GTH tributaries, using ultra-thin CT for reconstruction.

Development and validation of the MRI-based deep learning classifier for distinguishing perianal fistulizing Crohn's disease from cryptoglandular fistula: a multicenter cohort study.

Background: A singular reliable modality for early distinguishing perianal fistulizing Crohn's disease (PFCD) from cryptoglandular fistula (CGF) is currently lacking. We aimed to develop and validate an MRI-based deep learning classifier to effectively discriminate between them.

Methods: The present study retrospectively enrolled 1054 patients with PFCD or CGF from three Chinese tertiary referral hospitals between January 1, 2015, and December 31, 2021.

Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study.

Objective: To explore the sub-regional histogram features of amide proton transfer-weighted (APTw) MRI, compared with those of diffusion-weighted imaging (DWI), in predicting the tumor budding (TB) grade of rectal cancer (RC).

Materials And Methods: This study prospectively enrolled 74 patients with pathologically confirmed RC, who underwent APTw MRI before surgery from July 2022 to March 2023. Hematoxylin-eosin staining was used for TB scoring.

A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study.

Background: Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC).

Mesenteric creeping fat index defined by CT enterography is associated with early postoperative recurrence in patients with Crohn's disease.

Objectives: To investigate the value of mesenteric creeping fat index (MCFI) defined by computed-tomography enterography (CTE) in patients with Crohn's Disease (CD) for predicting early postoperative recurrence.

Methods: A total of 110 patients with CD who underwent CTE and I-stage intestinal resection surgery from December 2013 to December 2018 were enrolled. Two radiologists independently assessed CTE parameters, including MCFI, with scores ranging from 1 to 8; bowel-wall thickening, with a scale of 1 to 3; mural hyperenhancement, mural stratification, submucosal fat deposition, mesenteric fibrofatty proliferation, mesenteric hypervascularity, mesenteric fat stranding, with a scale of 0 to 2; abscess/fistula, enlarged mesenteric lymph node, abdominal and pelvic effusion, with a scale of 0 to 1.

Corrigendum: Association of CT-based delta radiomics biomarker with progression-free survival in patients with colorectal liver metastases undergo chemotherapy.

[This corrects the article DOI: 10.3389/fonc.2022.

Residual abnormalities on CTE predict adverse outcomes in Crohn's disease with endoscopic healing.

Background: Residual abnormalities on computed tomography enterography (CTE) in Crohn's disease (CD) with endoscopic healing (EH) may have prognostic implications and affect therapeutic strategy.

Methods: CD patients with EH who underwent CTE between March 2015 and June 2022 were enrolled. CTE findings of the terminal ileum and the most severe segment of colon at the time of EH were assessed respectively for each patient.

A Longitudinal MRI-Based Artificial Intelligence System to Predict Pathological Complete Response After Neoadjuvant Therapy in Rectal Cancer: A Multicenter Validation Study.

Background: Accurate prediction of response to neoadjuvant chemoradiotherapy is critical for subsequent treatment decisions for patients with locally advanced rectal cancer.

Objective: To develop and validate a deep learning model based on the comparison of paired MRI before and after neoadjuvant chemoradiotherapy to predict pathological complete response.

Design: By capturing the changes from MRI before and after neoadjuvant chemoradiotherapy in 638 patients, we trained a multitask deep learning model for response prediction (DeepRP-RC) that also allowed simultaneous segmentation.

An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers.

Computed tomography-based radiomics nomogram using machine learning for predicting 1-year surgical risk after diagnosis of Crohn's disease.

Background: Identifying patients with aggressive Crohn's disease (CD) threatened by a high risk of early onset surgery is challenging.

Purpose: We aimed to establish and validate a radiomics nomogram to predict 1-year surgical risk after the diagnosis of CD, thereby facilitating therapeutic strategies making.

Methods: Patients with CD who had undergone baseline computed tomography enterography (CTE) examination at diagnosis were recruited and randomly divided into training and test cohorts at a ratio of 7:3.

Prevalent Pseudoprogression and Pseudoresidue in Patients With Rectal Cancer Treated With Neoadjuvant Immune Checkpoint Inhibitors.

Background: Immune checkpoint inhibitor (ICI) treatment in patients with microsatellite instability-high/mismatch repair deficient (MSI-H/dMMR) tumors holds promise in reshaping organ preservation in rectal cancer. However, the benefits are accompanied by distinctive patterns of response, introducing a dilemma in the response evaluation for clinical decision-making.

Patients And Methods: Patients with locally advanced rectal cancer with MSI-H/dMMR tumors receiving neoadjuvant ICI (nICI) treatment (n=13) and matched patients receiving neoadjuvant chemoradiotherapy (nCRT; n=13) were included to compare clinical response and histopathologic features.

Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images.

Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model.

Association of CT-Based Delta Radiomics Biomarker With Progression-Free Survival in Patients With Colorectal Liver Metastases Undergo Chemotherapy.

Unlabelled: Predicting the prognosis of patients in advance is conducive to providing personalized treatment for patients. Our aim was to predict the therapeutic efficacy and progression free survival (PFS) of patients with liver metastasis of colorectal cancer according to the changes of computed tomography (CT) radiomics before and after chemotherapy.

Methods: This retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020.

Mild phototherapy mediated by manganese dioxide-loaded mesoporous polydopamine enhances immunotherapy against colorectal cancer.

One of the main challenges in applying the immune checkpoint blockade to treat colorectal cancer (CRC) is the immunosuppressive tumor microenvironment. Owing to its excellent cancer cell killing ability and immune activation, mild photothermal therapy (PTT) has shown bright promise to sensitize tumors to immune checkpoint inhibition through turning the immunologically "cold" tumors into "hot" ones. Herein, a mild photothermal effect-assisted theragnostic nanodrug (MnO@MPDA-PEG NPs) is developed by incorporating MnO into PEGylated-mesoporous polydopamine nanoparticles (MPDA-PEG NPs).

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

Objectives: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently.

A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.

Background: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.

Methods: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020.

Anatomical variations of the branches from left colic artery and middle colic artery at splenic flexure.

Background: Variations of the vasculature at splenic flexure by left colic artery (LCA) and middle colic artery (MCA) remain ambiguous.

Objectives: This study aim to investigate the anatomical variations of the branches from LCA and MCA at splenic flexure area.

Methods: Using ultra-thin CT images (0.

GSH-Responsive Metal-Organic Framework for Intratumoral Release of NO and IDO Inhibitor to Enhance Antitumor Immunotherapy.

Immunotherapy brings great benefits for tumor therapy in clinical treatments but encounters the severe challenge of low response rate mainly because of the immunosuppressive tumor microenvironment. Multifunctional nanoplatforms integrating effective drug delivery and medical imaging offer tremendous potential for cancer treatment, which may play a critical role in combinational immunotherapy to overcome the immunosuppressive microenvironment for efficient tumor therapy. Here, a nanodrug (BMS-SNAP-MOF) is prepared using glutathione (GSH)-sensitive metal-organic framework (MOF) to encapsulate an immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO) inhibitor BMS-986205, and the nitric oxide (NO) donor s-nitrosothiol groups.

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

Background: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides.

Methods: In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction.

Tumor response as defined by iRECIST in gastrointestinal malignancies treated with PD-1 and PD-L1 inhibitors and correlation with survival.

Background: Atypical tumor response patterns during immune checkpoint inhibitor therapy pose a challenge to clinicians and investigators in immuno-oncology practice. This study evaluated tumor burden dynamics to identify imaging biomarkers for treatment response and overall survival (OS) in advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors.

Methods: This retrospective study enrolled a total of 198 target lesions in 75 patients with advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors between January 2017 and March 2021.

Radiomics diagnosed histopathological growth pattern in prediction of response and 1-year progression free survival for colorectal liver metastases patients treated with bevacizumab containing chemotherapy.

Objective: To investigate the capability of a radiomics model, which was designed to identify histopathologic growth pattern (HGP) of colorectal liver metastases (CRLMs) based on contrast-enhanced multidetector computed tomography (ceMDCT), to predict early response and 1-year progression free survival (PFS) in patients treated with bevacizumab-containing chemotherapy.

Methods: Patients with unresectable CRLMs who were treated with bevacizumab-containing chemotherapy were included in this multicenter retrospective study. For each target lesion, the radiomics-diagnosed HGP (RAD_HGP) of desmoplastic (D) pattern or replacement (R) pattern was determined.

Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.

Background: Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics.

Methods: We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals.

A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.

Aims: To develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm.

Method: A total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort.