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. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

To evaluate cosmesis, body image, pain, and quality of life (QoL) after single-port laparoscopic cholecystectomy (SPLC) versus conventional 4-port laparoscopic cholecystectomy (4PLC). The impact of SPLC on improving cosmesis, body image, pain, and QoL has not been evaluated in double-blinded randomized controlled trials (RCT). This approach therefore remains controversial. Between October 2011 and February 2014, 110 patients from 2 centers were randomly assigned to SPLC (n = 55) or 4PLC (n = 55). Primary endpoints were a validated cosmesis (3-24 points) and body image (5-20 points) score after 3 and 12 months. Secondary endpoints included operative duration, postoperative pain, complications, QoL, and length of hospital stay. Patients, physicians, and nurses were blinded until the seventh postoperative day. Demographics were equally distributed between both groups (mean age: 46 years, SD: 14, 62 females, 34 males). The SPLC-group showed superior mean cosmesis and body image compared with the 4PLC-group at 12-weeks (21 vs 16, P < 0.001 and 5 vs 6, P = 0.013, respectively) and at 1-year (24 vs 16, P < 0.001 and 5 vs 6, P < 0.017, respectively). Operation duration was longer in the SPLC-group (mean 101 vs 90 minutes, p = 0.031). Although postoperative pain was less in the SPLC-group (mean VAS 1 vs 2, p = 0.005), there were no differences in complications, and length of hospital-stay. This is the first multicenter double-blinded RCT reporting superior short- and long-term cosmetic and body image, postoperative pain, and QoL in SPLC compared with 4PLC. Although cost-effectiveness is still a subject of ongoing debate, SPLC should be offered to patients undergoing surgery for benign gallbladder disease.

Introduction Orthotopic liver transplantation (OLT) has emerged as the mainstay of treatment for end-stage liver disease. In an attempt to improve the availability of donor allografts and reduce waiting list mortality, graft acceptance criteria were extended increasingly over the decades. The use of extended criteria donor (ECD) allografts is associated with a higher incidence of primary graft non-function and/or delayed graft function. As such, several strategies have been developed aiming at reconditioning poor quality ECD liver allografts. Hypothermic oxygenated machine perfusion (HOPE) has been successfully tested in preclinical experiments and in few clinical series of donation after cardiac death OLT. Methods and analysis HOPE ECD-DBD is an investigator-initiated, open-label, phase-II, prospective multicentre randomised controlled trial on the effects of HOPE on ECD allografts in donation after brain death (DBD) OLT. Human whole organ liver grafts will be submitted to 1–2 hours of HOPE (n=23) via the portal vein before implantation and are going to be compared with a control group (n=23) of patients transplanted after conventional cold storage. Primary (peak and Δ peak alanine aminotransferase within 7 days) and secondary (aspartate aminotransferase, bilirubin and international normalised ratio, postoperative complications, early allograft dysfunction, duration of hospital and intensive care unit stay, 1-year patient and graft survival) endpoints will be analysed within a 12-month follow-up. Extent of ischaemia–reperfusion (I/R) injury will be assessed using liver tissue, perfusate, bile and serum samples taken during the perioperative phase of OLT. Ethics and dissemination The study was approved by the institutional review board of the RWTH Aachen University, Aachen, Germany (EK 049/17). The current paper represent the pre-results phase. First results are expected in 2018. Trial registration number NCT03124641.

The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting.