Total neoadjuvant therapy (TNT) is a novel strategy for rectal cancer that administers both (chemo)radiotherapy and systemic chemotherapy before surgery. TNT is expected to improve treatment compliance, tumor regression, organ preservation, and oncologic outcomes. Multiple TNT regimens are currently available with various combinations of the treatments including induction or consolidation chemotherapy, triplet or doublet chemotherapy, and long-course chemoradiotherapy or short-course radiotherapy. Evidence on TNT is rapidly evolving with new data on clinical trials, and no definitive consensus has been established on which regimens to use for improving outcomes. Clinicians need to understand the advantages and limitations of the available regimens for multidisciplinary decision making. This article reviews currently available evidence on TNT for rectal cancer. A decision making flow chart is provided for tailor-made use of TNT regimens based on tumor location and local and systemic risk.

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist’s skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

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PURPOSE: The TNM classification at pathological evaluation determines prognosis following cancer treatment. For rectal cancer patients undergoing neoadjuvant therapy, whether ypTNM likely confers different prognosis than pTNM. We compared survival outcomes of patients classified by ypTNM vs pTNM staging. PATIENTS AND METHODS: Patients with resected locoregional rectal cancer from the National Cancer Database who underwent upfront surgery (pTNM) and had ≥5 years follow-up (2010 to 2017) were compared with those receiving neoadjuvant therapy followed by surgery (ypTNM). To address treatment selection bias, analyses were stratified by receipt of guideline-concordant care. Overall survival was compared using Kaplan-Meier and multivariable Cox regression analyses. RESULTS: Of 50,436 patients (median age 60; 61.4% male), 11,732 (23.3%) underwent upfront surgery and 38,704 (76.7%) received neoadjuvant therapy; 78.8% of the upfront surgery group received guideline-concordant care. In analyses stratified by guideline-concordant care, overall survival was similar for stage 0-I (hazard ratio [HR] 1.05; 95% CI 0.96-1.15) but significantly worse for ypTNM vs pTNM in stage II (HR 1.50; 95% CI 1.27-1.77; 5-year OS 75.6% vs 83.2%) and stage III (HR 1.60; 95% CI 1.46-1.75; 5-year OS 67.0% vs 78.2%). Multivariable analysis confirmed ypTNM was associated with worse survival at each stage (stage 0-HR 1.14, P=.004; stage II: HR 1.49, P<.001; stage III: HR 1.60, P<.001). CONCLUSION: Stage for stage, ypTNM classification is associated with worse survival than pTNM classification, with increasing difference at higher stages. Future staging systems and management guidelines should account for these differences to optimize prognostication and stratification for clinical trials and surveillance strategies.