The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.

Rectal cancer patients have a higher incidence of pulmonary metastases than those with colon cancer. This study aimed to examine the effects of rectal cancer level on recurrence patterns in rectal cancer patients.

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.

The quantitative relationship between serum albumin level and surgical outcomes has not been clearly established. This study included 3732 patients with colon cancer who underwent a potentially curative colectomy. Post-operative mortality and morbidity were analysed according to the patients' demographic data, pre-operative comorbidities, and tumour-related factors. Age, asthma, renal impairment, and albumin level were significantly associated with post-operative morbidity and mortality in the multivariate analyses. Logistic regression analysis revealed linear relationships of post-operative morbidity and mortality with albumin level. The morbidity and mortality rates decreased by 7.3% and 15.6%, respectively, for each 0.1 g/dL increase in albumin level. This finding remained significant in the hypoalbuminaemia subgroup but not in the normoalbuminaemia subgroup. That is, the morbidity and mortality rates significantly decreased by 8.7% and 17.7%, respectively (both P < 0.001), in the former group and decreased by 2.7% (P = 0.112) and 11.6% (P = 0.092), respectively, in the latter group. This study demonstrated that serum albumin level linearly predicted the post-operative morbidity and mortality among the colorectal cancer patients. Pre-operative serum albumin level may therefore be used as a continuous rather than a categorical marker of disease severity, especially among patients with hypoalbuminaemia.