Total Neoadjuvant Therapy for Rectal Cancer: Which Regimens to Use?

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.

Para-aortic Lymph Node Dissection for Colorectal Cancer: Predicting Pathologic Lymph Node Positivity and Optimizing Outcomes.

INTRODUCTION: In colorectal cancer, the presence of para-aortic lymph nodes (PALN) indicates extraregional disease. Appropriately selecting patients for whom PALN dissection will provide oncologic benefit remains challenging. This study identified factors to predict survival among patients undergoing PALN dissection for colorectal cancer. METHODS: An institutional database was queried for patients who underwent curative-intent resection of clinically positive PALN for colorectal cancer between 2007 and 2020. Preoperative radiologic images were reviewed, and patients who did and did not have positive PALN on final pathology were compared. Survival analysis was performed to evaluate the impact of pathologically positive PALN on recurrence-free (RFS) and overall survival (OS). RESULTS: Of 74 patients who underwent PALN dissection, 51 had PALN metastasis at the time of primary tumor diagnosis, whereas 23 had metachronous PALN disease. Preoperative chemotherapy ± radiotherapy was given in 60 cases (81.1%), and 28 (37.8%) had pathologically positive PALN. Independent factors associated with positive PALN pathology included metachronous PALN disease and pretreatment and posttreatment radiographically abnormal PALN. On multivariable analysis, pathologically positive PALN was significantly associated with decreased RFS (hazard ratio 3.90) and OS (HR 4.49). Among patients with pathologically positive PALN, well/moderately differentiated histology was associated with better OS, and metachronous disease trended toward an association with better OS. CONCLUSIONS: Pathologically positive PALN are associated with poorer RFS and OS after PALN dissection for colorectal cancer. Clinicopathologic factors may predict pathologic PALN positivity. Curative-intent surgery may provide benefit, especially in patients with well-to-moderately differentiated primary tumors and possibly metachronous PALN disease.

Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market.

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy.

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.

Prognostic Impact and Clinicopathological Features of Multiple Colorectal Cancers and Extracolorectal Malignancies: A Nationwide Retrospective Study.

INTRODUCTION: Multiple primary malignancies (MPMs) are likely to develop in patients with colorectal cancer (CRC); however, their prognoses are unclear. This study aims to investigate the prognostic impacts and clinicopathological features of multiple CRCs and extracolorectal malignancies (EMs) with CRC. METHODS: We retrospectively evaluated a total of 22,628 patients with stage I-III CRC who underwent curative resection at 24 referral institutes in Japan between January 2004 and December 2012. MPMs were classified as synchronous CRCs (SCRCs), metachronous CRCs, synchronous EMs (SEMs), and metachronous EMs. RESULTS: The presence of SCRCs (odds ratio 1.54, p < 0.001) was independently associated with SEMs in the multivariate analyses. SEMs were the strongest poor prognostic factor for OS (hazard ratio [HR] 2.21, p < 0.001) and RFS (HR 1.69, p < 0.001) compared with age, sex, and primary T and N factors. The incidence of stomach cancer was the highest in EMs, followed by lung, breast, and prostate cancers. Multiple CRCs were evenly distributed throughout the right-side colon to the rectum. DISCUSSION/CONCLUSION: SEMs were a strong poor prognostic factor for patients with stage I-III CRC. Patients with CRC, particularly those with SCRCs, should be surveyed for SEMs, especially for stomach and lung cancers.

A diamond 14 MeV neutron energy spectrometer with high energy resolution.

A self-standing single-crystal chemical vapor deposited diamond was obtained using lift-off method. It was fabricated into a radiation detector and response function measurements for 14 MeV neutrons were taken at the fusion neutronics source. 1.5% of high energy resolution was obtained by using the (12)C(n, α)(9)Be reaction at an angle of 100° with the deuteron beam line. The intrinsic energy resolution, excluding energy spreading caused by neutron scattering, slowing in the target and circuit noises was 0.79%, which was also the best resolution of the diamond detector ever reported.

Propagation characteristics of neutrons leaking from the accelerator facilities.

In this study spatial and time distribution of neutrons leaking from Hokkaido University 45 MeV Electron Linac facility have measured and compared with the Monte Carlo simulations. The neutron transport processes inside and outside the facility building has been simulated using MCNP. The neutrons have measured by BF3 counters and 3He counters with polyethylene moderators up to the distance of 330 m from the facility. The spatial distribution of ambient dose equivalent converted from the counts has been compared with the simulation. The distribution estimated from the counts by the BF3 counter has been shown fairly good agreement with the calculation. The spatial distribution of counts obtained at the 45 MeV Electron Linac facility has been compared with that obtained at the Fusion Neutronics Source (FNS) facility of JAERI. The difference between the propagation characteristics of neutrons leaking from those facilities has been discussed.