Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions.

This editorial offers commentary on the article which aimed to forecast the likelihood of short-term major postoperative complications (Clavien-Dindo grade ≥ III), including anastomotic fistula, intra-abdominal sepsis, bleeding, and intestinal obstruction within 30 days, as well as prolonged hospital stays following ileocecal resection in patients with Crohn's disease (CD). This prediction relied on a machine learning (ML) model trained on a cohort that integrated a nomogram predictive model derived from logistic regression analysis and a random forest (RF) model. Both the nomogram and RF showed good performance, with the RF model demonstrating superior predictive ability.

Machine learning-based discrimination of benign and malignant breast lesions on US: The contribution of shear-wave elastography.

Purpose: To build and validate a combined radiomics and machine learning (ML) approach using B-mode US and SWE images to differentiate benign from malignant solid breast lesions (BLs) and compare its performance with that of an expert radiologist.

Methods: Patients with at least one BI-RADS 2-6 BL who performed breast US integrated with SWE were retrospectively included. B-mode US and SWE images were manually segmented to extract radiomics features.

Fixed rate vs fixed injection duration in single-pass contrast-enhanced abdominal multi-detector CT: effects on vascular enhancement.

Objectives: To evaluate the effects on vascular enhancement of either a fixed rate (FR) or a fixed injection duration (FID) in single-pass (SP) contrast-enhanced abdominal multi-detector CT (CE-MDCT).

Methods: Ninety-nine (54 M; 45 F; aged 18-86 years) patients with nontraumatic acute abdomen underwent a SP CE-MDCT after i.v.

Current Advances in Radioactive Iodine-Refractory Differentiated Thyroid Cancer.

Background: Differentiated thyroid cancer (DTC) patients have an outstanding overall long-term survival rate, and certain subsets of DTC patients have a very high likelihood of disease recurrence. Radioactive iodine (RAI) therapy is a cornerstone in DTC management, but cancer cells can eventually develop resistance to RAI. Radioactive iodine-refractory DTC (RAIR-DTC) is a condition defined by ATA 2015 guidelines when DTC cannot concentrate RAI ab initio or loses RAI uptake ability after the initial therapy.

Training radiology residents to evaluate deep myometrial invasion in endometrial cancer patients on MRI: A learning curve study.

Purpose: To evaluate the impact of a four-month training program on radiology residents' diagnostic accuracy in assessing deep myometrial invasion (DMI) in endometrial cancer (EC) using MRI.

Method: Three radiology residents with limited EC MRI experience participated in the training program, which included conventional didactic sessions, case-centric workshops, and interactive classes. Utilizing a training dataset of 120 EC MRI scans, trainees independently assessed subsets of cases over five reading sessions.