Background Twenty‐five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. Purpose To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. Study Type Retrospective. Subjects In all, 104 rectal cancer patients staged with MRI prior to long‐course chemoradiation followed by proctectomy; curated from three institutions. Field Strength/Sequence 1.5T–3.0T, axial higher resolution T2‐weighted turbo spin echo sequence. Assessment Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single‐slice sections of rectal tumors on processed pretreatment T2‐weighted MRI. Statistical Tests Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross‐validation). The top‐selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. Results Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold‐out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07–0.96). Data Conclusion Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. Level of Evidence 3 Technical Efficacy Stage 2

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The unique and robust methodology in this trial produced an assessment tool that was feasible for raters to use when assessing videotaped laparoscopic right hemicolectomies. The potential applications for this new tool are widespread, including both training and evaluation of competence at the attending level. See Video Abstract at http://links.lww.com/DCR/A369, http://links.lww.com/DCR/A370, http://links.lww.com/DCR/A371.

Social media is a source of news and information for an increasing portion of the general public and physicians. The recent political election was a vivid example of how social media can be used for the rapid spread of "fake news" and that posts on social media are not subject to fact-checking or editorial review. The medical field is susceptible to propagation of misinformation, with poor differentiation between authenticated and erroneous information. Due to the presence of social "bubbles," surgeons may not be aware of the misinformation that patients are reading, and thus, it may be difficult to counteract the false information that is seen by the general public. Medical professionals may also be prone to unrecognized spread of misinformation and must be diligent to ensure the information they share is accurate.