Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.

Research on the relationship between the geriatric nutritional risk index (GNRI) and postoperative complications/oncological outcomes in elderly colorectal cancer (CRC) patients is limited. This study investigated the prognostic value of the GNRI in aged CRC patients. We retrospectively analyzed 1206 consecutive CRC patients aged over 75 years who underwent curative-intent surgery from January 2008 to December 2015 and categorized them into high GNRI (≥98) and low GNRI (<98) groups according to a receiver operating characteristic (ROC) curve analysis. Uni- and multivariate logistic regression analysis were used to explore the association of the GNRI with postoperative complications. Kaplan–Meier survival analyses and the Cox proportional hazard model were used to explore the association between GNRI and survival. We discovered that GNRI is an independent risk factor for postoperative complications (HR: 1.774, p = 0.037). Surgical site infection, wound dehiscence and pneumonia were more common in patients with GNRI < 98. Survival analysis showed significantly worse overall survival and disease-free survival in the low GNRI group (both p < 0.001). In the multivariate analysis, GNRI < 98 was an independent risk factor for OS (HR: 1.329, p = 0.031) and DFS (HR: 1.312, p = 0.034). Thus, preoperative GNRI can be effectively used to predict postoperative complications and long-term survival in elderly CRC patients after curative surgery.

Colorectal cancer (CRC) represents a significant health burden worldwide, with a notable increase in early-onset colorectal cancer (EOCRC) cases, defined as those diagnosed before the age of 50 years. Using data from Taiwan's national cancer registry and a retrospective cohort from Chang Gung Memorial Hospital, this study analyzed CRC cases diagnosed between 2008 and 2019. The analysis compared the EOCRC and late-onset CRC (LOCRC) groups in terms of clinicopathological characteristics, pre-diagnostic symptoms, and survival outcomes. The analysis revealed a continuous increase in the annual incidence of EOCRC, with colon cancer and rectal cancer rising by 3.2 % and 3.3 %, respectively. Patients with EOCRC presented with more aggressive disease characteristics, such as signet-ring cell adenocarcinoma, mucinous adenocarcinoma, and poorly differentiated grade. Advanced stages at diagnosis, stages III and IV, were more common with EOCRC (62.4 %) than with LOCRC (50.3 %). Patients with EOCRC reported rectal bleeding, changes in bowel habits, and abdominal pain more frequently than those in the LOCRC group. There is a strong association between stool-related symptoms and left-sided CRC. Despite similar surgical outcomes, the 5-year cancer-specific survival rate of patients with stage IV EOCRC was significantly lower than that of patients with LOCRC (32.8 % vs. 51.9 %, p = 0.012). This study highlights a persistent rise in the incidence of EOCRC, with patients presenting with more aggressive disease and experiencing inferior survival. These findings underscore the importance of heightened awareness and early detection strategies for CRC, especially in younger populations, to improve the prognosis.

Neoadjuvant short course radiotherapy (SCRT) followed by consolidation chemotherapy (CCT) is an alternative treatment for locally advanced rectal cancer (LARC). We performed this systematic review and meta-analysis to explore the tumor response and oncological outcomes of this new approach compared to conventional chemoradiotherapy (CRT). An online search of the PubMed, Embase, and Cochrane Library databases was performed. This review included 7507 patients from 14 different cohorts. The pCR rate was higher with SCRT + CCT than that with CRT (RR: 1.60; 95% CI: 1.35–1.91; p < 0.01). SCRT + CCT provided a higher ypN0 response (RR: 1.06; 95% CI: 1.01–1.12; p = 0.02). There were no differences in R0 resection and positive CRM rates; however, more sphincter-preservation surgeries were performed in the SCRT + CCT arm (RR: 1.06; 95% CI: 1.01–1.11; p = 0.02). There was no difference in the OS and DFS between the SCRT + CCT and the CRT arms (OS: HR: 0.85, p = 0.07; DFS: HR: 0.88, p = 0.08). The compliance and toxicity were comparable between the SCRT and CRT groups. In the subgroup analysis, patients who underwent four or more cycles of CCT had better pCR and DFS events. Therefore, SCRT followed by consolidation chemotherapy might be an effective alternative treatment for LARC.

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly. In addition, the sensitivity of CT in detecting colorectal cancer is greatly dependent on the size of the tumor. Missed incidental colon cancers using CT are an emerging problem for clinicians and radiologists; consequently, the automatic localization of lesions in the CT images of unprepared bowels is needed. Therefore, this study used artificial intelligence (AI) to localize colorectal cancer in CT images. We enrolled 190 colorectal cancer patients to obtain 1558 tumor slices annotated by radiologists and colorectal surgeons. The tumor sites were double-confirmed via colonoscopy or other related examinations, including physical examination or image study, and the final tumor sites were obtained from the operation records if available. The localization and training models used were RetinaNet, YOLOv3, and YOLOv8. We achieved an F1 score of 0.97 (±0.002), a mAP of 0.984 when performing slice-wise testing, 0.83 (±0.29) sensitivity, 0.97 (±0.01) specificity, and 0.96 (±0.01) accuracy when performing patient-wise testing using our derived model YOLOv8 with hyperparameter tuning.

Inflammatory reactions play a crucial role in cancer progression and may contribute to systemic inflammation. In routine clinical practice, some inflammatory biomarkers can be utilized as valuable predictors for colorectal cancer (CRC). This study aims to determine the usefulness of a novel cancer-inflammation prognostic index (CIPI) marker derived from calculating carcinoembryonic antigen (CEA) multiplied by the neutrophil-to-lymphocyte ratio (NLR) values established for non-metastatic CRCs. Between January 1995 and December 2018, 12,092 patients were diagnosed with stage I to III primary CRC and had radical resection—they were all included in this study for further investigation. There were 5996 (49.6%) patients in the low-CIPI group and 6096 (50.4%) patients in the high-CIPI group according to the cutoff value of 8. For long-term outcomes, the high-CIPI group had a significantly higher incidence of recurrence (30.6% vs. 16.0%, p < 0.001) and worse relapse-free survival (RFS) and overall survival (OS) rates (p < 0.001). High CIPI was an independent prognostic factor for RFS and OS in univariate and multivariate analyses. This research is the first to document the independent significance of CIPI as a prognostic factor for CRC. To ensure that it works, this CIPI needs to be tested on more CRC prediction models.