Predicting pathological complete response following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer using merged model integrating MRI-based radiomics and deep learning data.

Background: To construct and compare merged models integrating clinical factors, MRI-based radiomics features and deep learning (DL) models for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

Methods: Totally 197 patients with LARC administered surgical resection after nCRT were assigned to cohort 1 (training and test sets); meanwhile, 52 cases were assigned to cohort 2 as a validation set. Radscore and DL models were established for predicting pCR applying pre- and post-nCRT MRI data, respectively.

Deep learning models for preoperative T-stage assessment in rectal cancer using MRI: exploring the impact of rectal filling.

Background: The objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models.

Methods: A retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans.

Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study.

Background: Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC.

Materials And Methods: Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals.

Predictive value of modified MRI-based split scar sign (mrSSS) score for pathological complete response after neoadjuvant chemoradiotherapy for patients with rectal cancer.

Purpose: To measure the diagnostic performance of modified MRI-based split scar sign (mrSSS) score for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for patients with rectal cancer.

Methods: The modified MRI-based split scar sign (mrSSS) score, which consists of T2-weighted images (T2WI)-based score and diffusion-weighted images (DWI)-based score. The sensitivity, specificity, and accuracy of modified mrSSS score, endoscopic gross type, and MRI-based tumor regression grading (mrTRG) score, in the prediction of pCR, were compared.

Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study.

Detecting mismatch-repair (MMR) status is crucial for personalized treatment strategies and prognosis in rectal cancer (RC). A preoperative, noninvasive, and cost-efficient predictive tool for MMR is critically needed. Therefore, this study developed and validated machine learning radiomics models for predicting MMR status in patients directly on preoperative MRI scans.

An MRI-based pelvimetry nomogram for predicting surgical difficulty of transabdominal resection in patients with middle and low rectal cancer.

Objective: The current work aimed to develop a nomogram comprised of MRI-based pelvimetry and clinical factors for predicting the difficulty of rectal surgery for middle and low rectal cancer (RC).

Methods: Consecutive mid to low RC cases who underwent transabdominal resection between June 2020 and August 2021 were retrospectively enrolled. Univariable and multivariable logistic regression analyses were carried out for identifying factors (clinical factors and MRI-based pelvimetry parameters) independently associated with the difficulty level of rectal surgery.

Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer?

Purpose: To determine whether rectal filling with ultrasound gel is clinically more beneficial in preoperative T staging of patients with rectal cancer (RC) using radiomics model based on magnetic resonance imaging (MRI).

Methods: A total of 94 RC patients were assigned to cohort 1 (leave-one-out cross-validation [LOO-CV] set) and 230 RC patients were assigned to cohort 2 (test set). Patients were grouped according to different pathological T stages.

Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods.

The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of patients with rectal cancer (RC). A total of 454 consecutive patients with pathologically confirmed RC who underwent preoperative MRI between January 2018 and December 2019 were retrospectively analyzed.

Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.

Background: To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of rectal cancer.

Methods: A total of 80 patients with locally advanced rectal cancer (LARC) who underwent surgical resection after nCRT were enrolled retrospectively. Rectal MR images were scanned pre- and post-nCRT.

The usefulness of b value threshold map in the evaluation of rectal adenocarcinoma.

Purpose: To investigate the usefulness of b value threshold (b) map in the evaluation of rectal adenocarcinoma by comparing it with diffusion-weighted images and ADC maps regarding lesion detection and the prediction of pathological features.

Materials And Methods: Thirty-five patients with rectal tumors were enrolled and underwent axial DWI using a 3-Tesla MRI system. Contrast-to-noise ratio (CNR) between the lesions and normal tissues were assessed on the diffusion-weighted images and b maps.

Diffusion-weighted imaging of rectal cancer on repeatability and cancer characterization: an effect of b-value distribution study.

Background: To explore the effect of b-value distributions on the repeatability and diagnostic performance of the ADC value in rectal cancer patients using multiple b-values and mono-exponential model diffusion-weighted imaging (DWI).

Methods: Thirty-two preoperative rectal cancer patients, without receiving neoadjuvant therapy, were scanned on a 3 Tesla magnetic resonance imaging scanner using DWI with 10 b-values ranging from 0 to 2000 s/mm. The apparent diffusion coefficient (ADC) value was calculated using a mono-exponential model and 31 b-value combinations consisting of 2 to 10 b-values were explored.

[Transperitoneal versus extraperitoneal robot-assisted radical prostatectomy for localized prostate cancer].

Objective: To compare the clinical effects of transperitoneal (Tp) versus extraperitoneal (Ep) robot-assisted radical prostatectomy (RARP) in the treatment of localized prostate cancer.

Methods: We searched PubMed, EMBASE, Web of Science, EBSCO, Cochrane Library, Wanfang, CNKI, and CBM for the articles comparing the clinical effect Tp-RARP with that of Ep-RARP in the treatment of localized prostate cancer published from January 2000 to November 2016. All the articles must meet the inclusion criteria, that is, dealing with at least one of the following aspects: operation time, intraoperative blood loss, postoperative catheterization time, length of bed confinement, perioperative complications, positive surgical margins, bowel-related complications, postoperative anastomotic leakage, and postoperative urinary continence.