Background A large number of patients suffer recurrence after curative resection, and mortality from colon cancer remains high. The role of systemic inflammatory response, as reflected by neutrophil-to-lymphocyte ratio (NLR), in cancer recurrence and death has been increasingly recognized. This study aimed to analyze long-term oncologic outcomes of Stage II-III colon cancer to examine the prognostic value of NLR using a propensity score analysis. Methods A total of 375 patients with colon cancer underwent radical surgery between 2000 and 2014 at Tokyo Medical University Hospital. Long-term oncologic outcomes of these patients were evaluated according to NLR values. A cut-off NLR of 3.0 was used based on receiver operating characteristic curve analysis. Primary outcomes were overall survival (OS) and relapse-free survival (RFS). An analysis of outcomes according to tumor sidedness was also performed. Results Patients with lower NLR values (“lower NLR group”) were more likely to have lymph node metastasis compared to those with higher NLR values (“higher NLR group”) before case matching. After case matching, clinical outcomes were similar between the two groups. There were no significant differences in 5-year OS and 5-year RFS rates between the two groups before case matching based on propensity scores. After case matching, 5-year OS rates were 94.5% in the lower NLR group (n = 135) and 87.0% in the higher NLR group (n = 135), showing a significant difference (p = 0.042). Five-year RFS rates were 87.8% in the lower NLR group and 77.9% in the higher NLR group, also showing a significant difference (p = 0.032). Among patients with left-sided colon cancer in the matched cohort, 5-year OS and 5-year RFS rates were 95.2 and 87.3% in the lower NLR group (n = 88), respectively, and 86.4 and 79.2% in the higher NLR group (n = 71), respectively, showing significant differences (p = 0.014 and p = 0.047, respectively). Conclusions The NLR is an important prognostic factor for advanced colon cancer, especially for left-sided colon cancer.

ISR is a feasible surgical procedure for T1-2 tumors. Patients with stage III tumors should be considered for adjuvant therapy to control distant recurrence regardless of the surgical procedure.

Background No previous study has compared the risk of surgical site infection (SSI) between intracorporeal anastomosis (IA) and extracorporeal anastomosis (EA) related to intra-abdominal infection in laparoscopic right hemicolectomy. Therefore, this study aimed to compare the risk of SSI in IA and EA in this context. Methods From July 2014 to March 2018, 101 consecutive (median age, 73 years; male, 54) patients underwent laparoscopic right hemicolectomy for colon cancer. The IA and EA groups consisted of 51 and 50 cases, respectively. After either IA or EA, lavage was performed with 100 mL of saline in the area surrounding the anastomosis, and a sample was collected for bacterial culture. The product of the virulence score and dose of bacterial contamination score called the risk of SSI score was evaluated in both groups, and short-term outcomes in both groups were analyzed retrospectively. Results No significant difference was found in patient characteristics between the 2 groups. The frequency of organ/space SSI in the IA group was significantly higher than that in the EA group (7.8% vs 0%, P = .04). The risk of SSI score was significantly higher in the IA group than in the EA group (median, 9 vs 1, P < .01). Conclusions Compared with EA, IA in laparoscopic right hemicolectomy increased organ/space SSI rates, signifying intra-abdominal infection. We strongly recommend prevention of intra-abdominal infection when performing an IA.

BackgroundIntra-abdominal desmoid tumors are usually slow growing and solitary, but multifocal desmoid tumors develop on rare occasions. Diagnosing desmoid tumors before histological examination of a surgical biopsy is often difficult. In particular, if a patient has a prior history of malignancy, it may be difficult to differentiate between these lesions and disease recurrence or metastasis.Case presentationWe present a rare case of multiple rapidly growing intra-abdominal desmoid tumors after surgical trauma, without familial adenomatous polyposis. A 51-year-old male underwent abdominal perineal resection with lateral lymph node dissection after neoadjuvant chemotherapy for lower rectal cancer. Follow-up computed tomography (CT), performed 6 months after primary surgery, showed a 20-mm solitary mass in the pelvic mesentery. Another CT scan, performed 3 months later, revealed that the mass had grown to 35 mm in size and that two new masses had formed. Based on imaging studies and his medical history, it was difficult to distinguish the desmoid tumors from recurrence of rectal cancer. Curative resection was chosen for therapeutic diagnosis. The pathological diagnosis was multiple mesenteric desmoid tumors.ConclusionsDesmoid tumors should not be excluded as a differential diagnosis for intra-abdominal masses after intra-abdominal surgery, even in cases of rapidly growing multiple masses.

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This study aimed to validate and reanalyze urinary biomarkers for detecting colorectal cancers (CRCs). We previously conducted urinary metabolomic analyses using capillary electrophoresis-mass spectrometry and found a significant difference in various metabolites, especially polyamines, between patients with CRC and healthy controls (HC). We analyzed additional samples and confirmed consistency between the newly and previously analyzed data. In total, we included 36 HC, 34 adenoma (AD), and 214 CRC samples, which were used for subsequent analyses. Among the 132 quantified metabolites, 16 exhibited consistent differences in both datasets, which included polyamines, etc. Pathway analyses of the integrated data revealed significant differences in many metabolites, such as glutamine, and metabolites of the TCA and urea cycles. The discrimination ability of the combination of multiple metabolites among the three groups was evaluated, which yielded higher sensitivity than tumor markers. The Mann–Whitney test was employed to evaluate the prognosis predictivity of the assessed metabolites and the difference between the patients with or without recurrence, which yielded 16 significantly different metabolites. Among these 16 metabolites, 11 presented significant prognosis predictivity. These data indicated the potential of metabolite-based discrimination of patients with CRC and AD from HC and prognosis predictivity of the monitored metabolites.

As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva‐based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)‐based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC ( n = 235), AD ( n = 50), and HC ( n = 2317). Data were randomly divided into training ( n = 1301) and validation datasets ( n = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross‐validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver‐operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828‐0.891) for CV and 0.870 (95% CI: 0.837‐0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851‐0.907) and 0.870 (95% CI: 0.838‐0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.